Subjects -> INSTRUMENTS (Total: 63 journals)
Showing 1 - 16 of 16 Journals sorted by number of followers
International Journal of Remote Sensing     Hybrid Journal   (Followers: 151)
IEEE Sensors Journal     Hybrid Journal   (Followers: 112)
Remote Sensing of Environment     Hybrid Journal   (Followers: 96)
Journal of Applied Remote Sensing     Hybrid Journal   (Followers: 88)
Remote Sensing     Open Access   (Followers: 57)
Modern Instrumentation     Open Access   (Followers: 57)
International Journal of Remote Sensing Applications     Open Access   (Followers: 49)
International Journal of Instrumentation Science     Open Access   (Followers: 41)
Experimental Astronomy     Hybrid Journal   (Followers: 39)
Measurement and Control     Open Access   (Followers: 36)
Photogrammetric Engineering & Remote Sensing     Full-text available via subscription   (Followers: 33)
Journal of Instrumentation     Hybrid Journal   (Followers: 31)
Remote Sensing Science     Open Access   (Followers: 30)
Applied Mechanics Reviews     Full-text available via subscription   (Followers: 27)
Review of Scientific Instruments     Hybrid Journal   (Followers: 20)
European Journal of Remote Sensing     Open Access   (Followers: 18)
Flow Measurement and Instrumentation     Hybrid Journal   (Followers: 15)
Journal of Sensors and Sensor Systems     Open Access   (Followers: 12)
Transactions of the Institute of Measurement and Control     Hybrid Journal   (Followers: 12)
Remote Sensing Applications : Society and Environment     Full-text available via subscription   (Followers: 9)
Videoscopy     Full-text available via subscription   (Followers: 9)
International Journal of Applied Mechanics     Hybrid Journal   (Followers: 8)
Metrology and Measurement Systems     Open Access   (Followers: 8)
Science of Remote Sensing     Open Access   (Followers: 7)
Imaging & Microscopy     Hybrid Journal   (Followers: 7)
Instrumentation Science & Technology     Hybrid Journal   (Followers: 7)
Microscopy     Hybrid Journal   (Followers: 7)
International Journal of Metrology and Quality Engineering     Full-text available via subscription   (Followers: 6)
Computational Visual Media     Open Access   (Followers: 5)
Measurement : Sensors     Open Access   (Followers: 5)
PFG : Journal of Photogrammetry, Remote Sensing and Geoinformation Science     Hybrid Journal   (Followers: 5)
Optoelectronics, Instrumentation and Data Processing     Hybrid Journal   (Followers: 5)
Journal of Astronomical Instrumentation     Open Access   (Followers: 4)
IEEE Sensors Letters     Hybrid Journal   (Followers: 4)
Sensors and Materials     Open Access   (Followers: 4)
Journal of Optical Technology     Full-text available via subscription   (Followers: 4)
Journal of Medical Devices     Full-text available via subscription   (Followers: 4)
Measurement Techniques     Hybrid Journal   (Followers: 3)
Sensors International     Open Access   (Followers: 3)
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems)     Open Access   (Followers: 3)
IEEE Journal on Miniaturization for Air and Space Systems     Hybrid Journal   (Followers: 3)
Solid State Nuclear Magnetic Resonance     Hybrid Journal   (Followers: 3)
Journal of Instrumentation Technology & Innovations     Full-text available via subscription   (Followers: 2)
International Journal of Sensor Networks     Hybrid Journal   (Followers: 2)
Geoscientific Instrumentation, Methods and Data Systems     Open Access   (Followers: 2)
International Journal of Testing     Hybrid Journal   (Followers: 1)
Medical Devices & Sensors     Hybrid Journal   (Followers: 1)
Invention Disclosure     Open Access   (Followers: 1)
Journal of Research of NIST     Open Access   (Followers: 1)
Geoscientific Instrumentation, Methods and Data Systems Discussions     Open Access   (Followers: 1)
International Journal of Measurement Technologies and Instrumentation Engineering     Full-text available via subscription   (Followers: 1)
Journal of Medical Signals and Sensors     Open Access   (Followers: 1)
Instruments and Experimental Techniques     Hybrid Journal   (Followers: 1)
Journal of Vacuum Science & Technology B     Hybrid Journal   (Followers: 1)
Metrology and Instruments / Метрологія та прилади     Open Access  
Measurement Instruments for the Social Sciences     Open Access  
Труды СПИИРАН     Open Access  
Standards     Open Access  
Jurnal Informatika Upgris     Open Access  
InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan     Open Access  
Devices and Methods of Measurements     Open Access  
EPJ Techniques and Instrumentation     Open Access  
Documenta & Instrumenta - Documenta et Instrumenta     Open Access  
Similar Journals
Journal Cover
Remote Sensing
Journal Prestige (SJR): 1.386
Citation Impact (citeScore): 4
Number of Followers: 57  

  This is an Open Access Journal Open Access journal
ISSN (Print) 2072-4292
Published by MDPI Homepage  [84 journals]
  • Remote Sensing, Vol. 14, Pages 3079: The Assessment of More Suitable Image
           Spatial Resolutions for Offshore Aquaculture Areas Automatic Monitoring
           Based on Coupled NDWI and Mask R-CNN

    • Authors: Yonggui Wang, Yaxin Zhang, Yan Chen, Junjie Wang, Hui Bai, Bo Wu, Wei Li, Shouwei Li, Tianyu Zheng
      First page: 3079
      Abstract: Wide-scale automatic monitoring based on the Normalized Difference Water Index (NDWI) and Mask Region-based Convolutional Neural Network (Mask R-CNN) with remote sensing images is of great significance for the management of aquaculture areas. However, different spatial resolutions brought different cost and model performance. To find more suitable image spatial resolutions for automatic monitoring offshore aquaculture areas, seven different resolution remote sensing images in the Sandu’ao area of China, from 2 m, 4 m, to 50 m, were compared. Results showed that the remote sensing images with a resolution of 15 m and above can achieve the corresponding recognition effect when no financial issues were considered, with the F1 score of over 0.75. By establishing a cost-effectiveness evaluation formula that comprehensively considers image price and recognition effect, the best image resolution in different scenes can be found, thus providing the most appropriate data scheme for the automatic monitoring of offshore aquaculture areas.
      Citation: Remote Sensing
      PubDate: 2022-06-27
      DOI: 10.3390/rs14133079
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3080: Sentinel-2 Satellite Image
           Time-Series Land Cover Classification with Bernstein Copula Approach

    • Authors: Cristiano Tamborrino, Roberto Interdonato, Maguelonne Teisseire
      First page: 3080
      Abstract: A variety of remote sensing applications call for automatic optical classification of satellite images. Recently, satellite missions, such as Sentinel-2, allow us to capture images in real-time of the Earth’s scenario. The classification of this large amount of data requires increasingly precise and fast methods, which must take into account not only the spectral features dependence of each individual image but also that of the temporal ones. Copulas are an excellent statistical tool, able to model joint distributions between even random variables. In this paper, we propose a new approach for Satellite Image Time-Series (SITS) land cover classification, which combines the matrix factorization to reduce the dimensionality of the data and the use of copulas distribution to model the dependencies. We will show how the use of particular copulas can improve the accuracy of classification compared to the latest methodologies used for the classification task, such as those using Neural Networks. Experiments were conducted at a study site located on Reunion Island, using Sentinel-2 SITS data. Results are compared to those achieved by several approaches commonly used to address SITS-based land cover mapping and show that the use of copulas, in combination with the matrix factorization, achieved the highest classification yield compared to competing approaches.
      Citation: Remote Sensing
      PubDate: 2022-06-27
      DOI: 10.3390/rs14133080
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3081: Settlement Prediction of Reclaimed
           Coastal Airports with InSAR Observation: A Case Study of the Xiamen
           Xiang’an International Airport, China

    • Authors: Zhiqiang Xiong, Kailiang Deng, Guangcai Feng, Lu Miao, Kaifeng Li, Chulu He, Yuanrong He
      First page: 3081
      Abstract: Many coastal cities reclaim land from the sea to meet the rapidly growing demand for land caused by population growth and economic development. Settlement in reclaimed land may delay construction and even damage infrastructures, so accurately predicting the settlement over reclaimed areas is important. However, the limited settlement observation and ambiguous final settlement estimation affect accurate settlement prediction in traditional methods. This study proposes a new strategy to solve these problems by using the Multitemporal Interferometric Synthetic Aperture Radar (MT-InSAR) method and takes the Xiamen Xiang’an International Airport, built on reclaimed land, as an example. The MT-InSAR is adopted to process the Sentinel-1 images to obtain the settlement history of the study area. The results show that settlement mainly occurs in the reclaimed areas, with the maximum average settlement rate exceeding 40 mm/y. We use the statistical properties of curve fitting to choose the best curve model from several candidate curve models to predict the settlement time series. The Asaoka method is used to identify the critical state between settlement and stability. We predict the consolidation time of the whole study area and reveal that the deformation rate is positively correlated with the consolidation time. The maximum remaining settlement time is over ten years since 24 December 2019. Therefore, manual compaction operations can be carried out to speed up settlement in the areas that need a long time to consolidate. The proposed method can be used to predict the settlement of similar reclaimed areas, and the predicted results can provide a reference for engineering construction.
      Citation: Remote Sensing
      PubDate: 2022-06-27
      DOI: 10.3390/rs14133081
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3082: Influence of Terrestrial Water
           Storage on Flood Potential Index in the Yangtze River Basin, China

    • Authors: Peng Yang, Wenyu Wang, Xiaoyan Zhai, Jun Xia, Yulong Zhong, Xiangang Luo, Shengqing Zhang, Nengcheng Chen
      First page: 3082
      Abstract: In a changing environment, changes in terrestrial water storage (TWS) in basins have a significant impact on potential floods and affect flood risk assessment. Therefore, we aimed to study the impact of TWS on potential floods. In this study, we reconstructed the TWS based on precipitation and temperature, evaluated the reconstructed TWS data based on Gravity Recovery and Climate Experiment (GRACE)-TWS data, and analyzed and calculated the flood potential index (FPI) in the Yangtze River Basin (YRB). The related influencing factors were analyzed based on the Global Land Data Assimilation System (GLDAS) data and Granger’s causality test. The main conclusions are as follows: (1) although the GRACE-TWS anomaly (GRACE-TWSA) in the YRB showed an increasing trend for the averaged TWSA over all grids in the whole basin (i.e., 0.31 cm/a, p < 0.05), the variable infiltration capacity-soil moisture anomalies (VIC-SMA) showed a decreasing trend (i.e., −0.048 cm/a, p > 0.05) during April 2002–December 2019; (2) a larger relative contribution of detrended precipitation to FPI was found in the Jialingjiang River Basin (JRB), Wujiang River Basin (WRB), Dongting Lake Rivers Basin (DLRB), YinBin-Yichang reaches (YB-YC), and Yichang-Hukou reaches (YC-HK), while the contribution of detrended TWS to FPI in the Poyang Lake Rivers Basin (PLRB) was larger than that in other basins; and (3) the original and detrended soil moisture (SM) and TWS in the YRB showed a significant positive correlation (p < 0.05), while the significant effect of SM on TWS caused a change in FPI in the YRB and its sub-basins. This study is of great significance for the correct understanding of the FPI and the accurate assessment of flood risk.
      Citation: Remote Sensing
      PubDate: 2022-06-27
      DOI: 10.3390/rs14133082
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3083: Self-Supervised Denoising for Real
           Satellite Hyperspectral Imagery

    • Authors: Jinchun Qin, Hongrui Zhao, Bing Liu
      First page: 3083
      Abstract: Satellite hyperspectral remote sensing has gradually become an important means of Earth observation, but the existence of various types of noise seriously limits the application value of satellite hyperspectral images. With the continuous development of deep learning technology, breakthroughs have been made in improving hyperspectral image denoising algorithms based on supervised learning; however, these methods usually require a large number of clean/noisy training pairs, a target that is difficult to meet for real satellite hyperspectral imagery. In this paper, we propose a self-supervised learning-based algorithm, 3S-HSID, for denoising real satellite hyperspectral images without requiring external data support. The 3S-HSID framework can perform robust denoising of a single satellite hyperspectral image in all bands simultaneously. It first conducts a Bernoulli sampling of the input data, then uses the Bernoulli sampling results to construct the training pairs. Furthermore, the global spectral consistency and minimum local variance are used in the loss function to train the network. We use the training model to predict different Bernoulli sampling results, and the average of multiple predicted values is used as the denoising result. To prevent overfitting, we adopt a dropout strategy during training and testing. The results of denoising experiments on the simulated hyperspectral data show that the denoising performance of 3S-HSID is better than most state-of-the-art algorithms, especially in terms of maintaining the spectral characteristics of hyperspectral images. The denoising results for different types of real satellite hyperspectral data also demonstrate the reliability of the proposed method. The 3S-HSID framework provides a new technical means for real satellite hyperspectral image preprocessing.
      Citation: Remote Sensing
      PubDate: 2022-06-27
      DOI: 10.3390/rs14133083
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3084: A New Automatic Extraction Method for
           Glaciers on the Tibetan Plateau under Clouds, Shadows and Snow Cover

    • Authors: Mingcheng Hu, Guangsheng Zhou, Xiaomin Lv, Li Zhou, Xiaohui He, Zhihui Tian
      First page: 3084
      Abstract: Accurately assessing the dynamic changes of glaciers under the background of climate warming is of great significance for taking scientific countermeasures to cope with climate change. Aiming at the difficulties of glacier identification, such as mountain and cloud shadow, cloud cover and seasonal snow cover in high altitude areas, this paper proposes a reflectivity difference index for identifying glaciers in shadow and glacial lakes and a multi-temporal minimum band ratio index for reducing the influence of snow cover. It establishes a new large-scale glacier extraction method (so-called Double RF) based on the random forest algorithm of Google Earth Engine (GEE) and applies it to the Tibetan Plateau. The verification results based on 30% sample points show that overall accuracies of the first and second classification of 96.04% and 90.75%, respectively, and Kappa coefficients of 0.92 and 0.83, respectively. Compared with the real glacier dataset, the percentage of correctly extracted glacier area of the total area of glacier dataset (PGD) was 84.07%, and the percentage of correctly extracted glacier area of the total area of extracted glacier (PGE) was 89.06%; the harmonic mean (HM) of the two was 86.49%. The extraction results were superior to the commonly used glacier extraction methods: the band ratio method based on median composite image (Median_Band) (HM = 79.47%), the band ratio method based on minimum composite image (Min_Band) (HM = 81.19%), the normalized difference snow cover index method based on median composite image (Median_NDSI) (HM = 83.48%), the normalized difference snow cover index method based on minimum composite image (Min_NDSI) (HM = 84.08%), the random forest method based on median composite image (Median_RF) (HM = 83.87%) and the random forest method based on minimum composite image (Min_RF) (HM = 85.36%). The new glacier extraction method constructed in this study could significantly improve the identification accuracy of glaciers under the influences of shadow, snow cover, cloud cover and debris. This study provides technical support for obtaining long-term glacier distribution data on the Tibetan Plateau and revealing the impact of climate warming on glaciers on the Tibetan Plateau.
      Citation: Remote Sensing
      PubDate: 2022-06-27
      DOI: 10.3390/rs14133084
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3085: Analysis of the Optical Turbulence
           Model Using Meteorological Data

    • Authors: Manman Xu, Shiyong Shao, Ningquan Weng, Qing Liu
      First page: 3085
      Abstract: The model of atmosphere optical turbulence is important in the research field of laser atmospheric transmission, and plays a key role in astronomical site selection. In this paper, the single and overall statistical analysis between different outer scale models (HMNSP99 and the Dewan model) were conducted and the results show that the HMNSP99 model has better performance with the lowest bias, root mean square error, and center root mean square error. The results of the statistical analysis of three turbulence parameters revealed that there is a correlation between turbulence parameters and statistical operators, where statistical operators increase significantly when wind shear and temperature gradient respectively exceed 0.016 s−1, 0 K/m, and the outer scale is within 2.5 m. Furthermore, a new statistical outer-scale model, the WSTG model, is proposed and the results of statistical analysis present that the WSTG model is more reliable than the HMNSP99 model in reconstructing optical turbulence strength. These results acquired from this paper add substantially to our understanding of atmosphere optical turbulence and the conclusions can be applied to improve the performance of an adaptive optics system and astronomical site selection.
      Citation: Remote Sensing
      PubDate: 2022-06-27
      DOI: 10.3390/rs14133085
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3086: Vehicle Target Detection Network in
           SAR Images Based on Rectangle-Invariant Rotatable Convolution

    • Authors: Lu Li, Yuang Du, Lan Du
      First page: 3086
      Abstract: In recent years, convolutional neural network (CNN)-based methods have been extensively explored for synthetic aperture radar (SAR) target detection. Nevertheless, the convolutional sampling locations of CNNs cannot accurately fit vehicle targets due to the fixed sampling mechanism in the convolutional kernel. In this paper, we focus on the vehicle target detection task in SAR images and propose a novel rectangle-invariant rotatable convolution (RIRConv) to determine more accurately the convolutional sampling locations for vehicle targets. Specifically, this paper considers the shape characteristic of vehicle targets in SAR images, which always retain a rectangular shape despite having varying sizes, aspect ratios, and rotation angles. The proposed RIRConv equips three additional learnable attribute parameters, namely, width, height, and angle attributes, to adaptively adjust the sampling locations in the convolutional kernel according to the targets. In addition, the RIRConv applies a modulation mechanism to focus on the sampling locations that significantly affect the output. Finally, the RIRConv is introduced into the single-shot multibox detector (SSD) to realize SAR vehicle target detection. In this way, the feature representation capability of SSD for vehicle targets can be enhanced, thus leading to higher detection performance. Notably, the proposed RIRConv is “plug-and-play” and can also be used with other existing advanced technologies to achieve higher detection performance. The experiments based on the measured miniSAR data validate the effectiveness of the proposed method.
      Citation: Remote Sensing
      PubDate: 2022-06-27
      DOI: 10.3390/rs14133086
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3087: Fine-Grained Ship Classification by
           Combining CNN and Swin Transformer

    • Authors: Liang Huang, Fengxiang Wang, Yalun Zhang, Qingxia Xu
      First page: 3087
      Abstract: The mainstream algorithms used for ship classification and detection can be improved based on convolutional neural networks (CNNs). By analyzing the characteristics of ship images, we found that the difficulty in ship image classification lies in distinguishing ships with similar hull structures but different equipment and superstructures. To extract features such as ship superstructures, this paper introduces transformer architecture with self-attention into ship classification and detection, and a CNN and Swin transformer model (CNN-Swin model) is proposed for ship image classification and detection. The main contributions of this study are as follows: (1) The proposed approach pays attention to different scale features in ship image classification and detection, introduces a transformer architecture with self-attention into ship classification and detection for the first time, and uses a parallel network of a CNN and a transformer to extract features of images. (2) To exploit the CNN’s performance and avoid overfitting as much as possible, a multi-branch CNN-Block is designed and used to construct a CNN backbone with simplicity and accessibility to extract features. (3) The performance of the CNN-Swin model is validated on the open FGSC-23 dataset and a dataset containing typical military ship categories based on open-source images. The results show that the model achieved accuracies of 90.9% and 91.9% for the FGSC-23 dataset and the military ship dataset, respectively, outperforming the existing nine state-of-the-art approaches. (4) The good extraction effect on the ship features of the CNN-Swin model is validated as the backbone of the three state-of-the-art detection methods on the open datasets HRSC2016 and FAIR1M. The results show the great potential of the CNN-Swin backbone with self-attention in ship detection.
      Citation: Remote Sensing
      PubDate: 2022-06-27
      DOI: 10.3390/rs14133087
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3088: A Spherical
           “Earth–Ionosphere” Model for Deep Resource Exploration
           Using Artificial ELF-EM Field

    • Authors: Fanghua Zheng, Qingyun Di, Changmin Fu
      First page: 3088
      Abstract: Fully coupled lithosphere, atmosphere, and ionosphere theory has demonstrated that extremely low-frequency electromagnetic (ELF-EM) fields present a broad application prospect in deep resource exploration, but previous studies have ignored the contribution of the Earth’s curvature. This study extends the theory of ELF-EM over a stratified Earth to the case where the Earth’s curvature must be taken into account, and presents an analytical solution of the ELF-EM field excited by a grounded horizontal antenna in a spherical Earth–ionosphere model, whose theoretical approach and solution method are notably different from the flat Earth–ionosphere model. Additionally, the Earth is treated as a concentric-layered sphere rather than an ideal homogeneous sphere. We aim to investigate the effects of the Earth’s curvature on the surface field, so as to broaden the coverage of the ELF wave in resource exploration. The solution is mathematically accurate and physically reasonable, since it reflects the sphericity and radially stratified structure of the Earth. We first verify the correctness and reliability of the proposed method by comparing the results with FDTD in a full-space spherical model. Additionally, we then compared the spherical results with the conventional controlled-source electromagnetic method and flat Earth–ionosphere results. The results show that when the distance between the transmitter and the receiver is comparable to the Earth radius, the spherical model better reflects the resonance of the wave in the cavity, suggesting that the effect of the Earth’s curvature is not negligible. Then, the numerical simulations conducted to investigate the properties of the EM fields and their sensitivities to the conductivity at depth in the Earth are discussed. Finally, the EM responses of some simple electrical conductivity structures models are modeled to illustrate their prospects in future resource exploration.
      Citation: Remote Sensing
      PubDate: 2022-06-27
      DOI: 10.3390/rs14133088
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3089: An Improved Independent Parameter
           Decomposition Method for Gaofen-3 Surveying and Mapping Calibration

    • Authors: Tao Li, Jun Fan, Yanyang Liu, Ruifeng Lu, Yusheng Hou, Jing Lu
      First page: 3089
      Abstract: The Gaofen-3 (GF-3) satellite can provide digital elevation model (DEM) data from its interferogram outputs. However, the accuracy of these data cannot be ensured without applying a surveying and mapping (SAM) calibration process, thus necessitating geometric and interferometric calibration technologies. In this paper, we propose an independent parameter decomposition (IPD) method to conduct SAM calibration on GF-3 data and generate high-accuracy DEMs. We resolved the geometric parameters to improve the location accuracy and resolved the interferometric parameters to improve the height accuracy. First, we established a geometric calibration model, analyzed the Range–Doppler (RD) model and resolved the initial imaging time error as well as the initial slant range error. Then, we established a three-dimensional reconstruction (TDR) model to analyze the height error sources. Finally, the interferometric phase error and baseline vector error were precisely estimated to ensure the vertical accuracy of the interferometric results by establishing the interferometric calibration model. We then used the GF-3 interferometric data derived on the same orbit in a north–south distribution to conduct the calibration experiment. The results show that the plane positioning accuracy was 5.09 m following geometric calibration, that the vertical accuracy of the interferometric results was 4.18 m following interferometric calibration and that the average absolute elevation accuracy of the derived DEM product was better than 3.09 m when using the GF-3 SAR data, thus confirming the correctness and effectiveness of the proposed GF-3 IPD calibration method. These results provide a technical basis for SAM calibration using GF-3 interferograms at the 1:50,000 scale in China.
      Citation: Remote Sensing
      PubDate: 2022-06-27
      DOI: 10.3390/rs14133089
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3090: Evaluating the Accuracy and Spatial
           Agreement of Five Global Land Cover Datasets in the Ecologically
           Vulnerable South China Karst

    • Authors: Pengyu Liu, Jie Pei, Han Guo, Haifeng Tian, Huajun Fang, Li Wang
      First page: 3090
      Abstract: Accurate and reliable land cover information is vital for ecosystem management and regional sustainable development, especially for ecologically vulnerable areas. The South China Karst, one of the largest and most concentrated karst distribution areas globally, has been undergoing large-scale afforestation projects to combat accelerating land degradation since the turn of the new millennium. Here, we assess five recent and widely used global land cover datasets (i.e., CCI-LC, MCD12Q1, GlobeLand30, GlobCover, and CGLS-LC) for their comparative performances in land dynamics monitoring in the South China Karst during 2000–2020 based on the reference China Land Use/Cover Database. The assessment proceeded from three aspects: areal comparison, spatial agreement, and accuracy metrics. Moreover, divergent responses of overall accuracy with regard to varying terrain and geomorphic conditions have also been quantified. The results reveal that obvious discrepancies exist amongst land cover maps in both area and spatial patterns. The spatial agreement remains low in the Yunnan–Guizhou Plateau and heterogeneous mountainous karst areas. Furthermore, the overall accuracy of the five datasets ranges from 40.3% to 52.0%. The CGLS-LC dataset, with the highest accuracy, is the most accurate dataset for mountainous southern China, followed by GlobeLand30 (51.4%), CCI-LC (50.0%), MCD12Q1 (41.4%), and GlobCover (40.3%). Despite the low overall accuracy, MCD12Q1 has the best accuracy in areas with an elevation above 1200 m or a slope greater than 25°. With regard to geomorphic types, accuracy in non-karst areas is evidently higher than in karst areas. Additionally, dataset accuracy declines significantly (p < 0.05) with an increase in landscape heterogeneity in the region. These findings provide useful guidelines for future land cover mapping and dataset fusion.
      Citation: Remote Sensing
      PubDate: 2022-06-27
      DOI: 10.3390/rs14133090
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3091: A Dual Multi-Head Contextual
           Attention Network for Hyperspectral Image Classification

    • Authors: Miaomiao Liang, Qinghua He, Xiangchun Yu, Huai Wang, Zhe Meng, Licheng Jiao
      First page: 3091
      Abstract: To learn discriminative features, hyperspectral image (HSI), containing 3-D cube data, is a preferable means of capturing multi-head self-attention from both spatial and spectral domains if the burden in model optimization and computation is low. In this paper, we design a dual multi-head contextual self-attention (DMuCA) network for HSI classification with the fewest possible parameters and lower computation costs. To effectively capture rich contextual dependencies from both domains, we decouple the spatial and spectral contextual attention into two sub-blocks, SaMCA and SeMCA, where depth-wise convolution is employed to contextualize the input keys in the pure dimension. Thereafter, multi-head local attentions are implemented as group processing when the keys are alternately concatenated with the queries. In particular, in the SeMCA block, we group the spatial pixels by evenly sampling and create multi-head channel attention on each sampling set, to reduce the number of the training parameters and avoid the storage increase. In addition, the static contextual keys are fused with the dynamic attentional features in each block to strengthen the capacity of the model in data representation. Finally, the decoupled sub-blocks are weighted and summed together for 3-D attention perception of HSI. The DMuCA module is then plugged into a ResNet to perform HSI classification. Extensive experiments demonstrate that our proposed DMuCA achieves excellent results over several state-of-the-art attention mechanisms with the same backbone.
      Citation: Remote Sensing
      PubDate: 2022-06-27
      DOI: 10.3390/rs14133091
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3092: Application of LISA Gravitational
           Reference Sensor Hardware to Future Intersatellite Geodesy Missions

    • Authors: William Joseph Weber, Daniele Bortoluzzi, Paolo Bosetti, Gabriel Consolini, Rita Dolesi, Stefano Vitale
      First page: 3092
      Abstract: Like gravitational wave detection, inter-spacecraft geodesy is a measurement of gravitational tidal accelerations deforming a constellation of two or more orbiting reference test masses (TM). The LISA TM system requires TM in free fall with residual stray accelerations approaching the fm/s2/Hz1/2 level in the mHz band, as demonstrated in the LISA Pathfinder “Einstein’s geodesic explorer” mission. Current geodesy missions are limited by accelerometers with 100 pm/s2/Hz1/2 level, due to intrinsic design limitations, as well as the challenging low Earth orbit environment and operating conditions. A reduction in the TM acceleration noise could lead to an important improvement in the scientific return of future geodesy missions focusing on mass change, especially in a scenario with multiple pairs of geodesy satellites. We present here a preliminary assessment of how the LISA TM system, known as the “gravitational reference sensor” (GRS), could be adapted for use in future geodesy missions aiming at residual TM accelerations noise at the pm/s2/Hz1/2 level, addressing the major design issues and performance limitations. We find that such a performance is possible in a geodesy GRS that is simpler and smaller than that used for LISA, with a lighter, sub-kg TM and gaps reduced from 4 mm to less than 1 mm. Acceleration noise performance limitations will likely be closely tied to the required levels of applied actuation forces on the TM.
      Citation: Remote Sensing
      PubDate: 2022-06-27
      DOI: 10.3390/rs14133092
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3093: A Fast PARAFAC Algorithm for
           Parameter Estimation in Monostatic FDA-MIMO Radar

    • Authors: Wenshuai Wang, Xiang Lan, Jinmei Shi, Xianpeng Wang
      First page: 3093
      Abstract: This paper studies the joint range and angle estimation of monostatic frequency diverse array multiple-input multiple-output (FDA-MIMO) radar and proposes a joint estimation algorithm. First, the transmit direction matrix is converted into real values by unitary transformation, and the Vandermonde-like matrix structure is used to construct an augmented output that doubles the aperture of the receive array. Then the augmented output is combined into a third-order tensor. Next, the factor matrices are initially estimated. Finally, the direction matrices are estimated utilizing parallel factor (PARAFAC) decomposition, and the range and angle are calculated by employing least square fitting. As contrasted with the classic PARAFAC method, the proposed method can estimate more targets and provide better estimation performance, and requires less computational complexity. The availability and excellence of the proposed method are reflected by numerical simulations and complexity analysis.
      Citation: Remote Sensing
      PubDate: 2022-06-27
      DOI: 10.3390/rs14133093
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3094: Remote Estimation of Water Clarity
           and Suspended Particulate Matter in Qinghai Lake from 2001 to 2020 Using
           MODIS Images

    • Authors: Zhenyu Tan, Zhigang Cao, Ming Shen, Jun Chen, Qingjun Song, Hongtao Duan
      First page: 3094
      Abstract: Climate change and human activities have been heavily affecting oceanic and inland waters, and it is critical to have a comprehensive understanding of the aquatic optical properties of lakes. Since many key watercolor parameters of Qinghai Lake are not yet available , this paper aims to study the spatial and temporal variations of the water clarity (i.e., Secchi-disk depth, ZSD) and suspended particulate matter concentration (CSPM) in Qinghai Lake from 2001 to 2020 using MODIS images. First, the four atmospheric correction models, including the NIR–SWIR, MUMM, POLYMER, and C2RCC were tested. The NIR–SWIR with decent accuracy in all bands was chosen for the experiment. Then, four existing models for ZSD and six models for CSPM were evaluated. Two semi-analytical models proposed by Lee (2015) and Jiang (2021) were selected for ZSD (R2 = 0.74) and CSPM (R2 = 0.73), respectively. Finally, the distribution and variation of the ZSD and CSPM were derived over the past 20 years. Overall, the water of Qinghai Lake is quite clear: the monthly mean ZSD is 5.34 ± 1.33 m, and CSPM is 2.05 ± 1.22 mg/L. Further analytical results reveal that the ZSD and CSPM are highly correlated, and the relationship can be formulated with ZSD=8.072e−0.212CSPM (R2 = 0.65). Moreover, turbid water mainly exists along the edge of Qinghai Lake, especially on the northwestern and northeastern shores. The variation in the lakeshore exhibits some irregularity, while the main area of the lake experiences mild water quality deterioration. Statistically, 81.67% of the total area is dominated by constantly increased CSPM, and the area with decreased CSPM occupies 4.56%. There has been distinct seasonal water quality deterioration in the non-frozen period (from May to October). The water quality broadly deteriorated from 2001 to 2008. The year 2008 witnessed a sudden distinct improvement, and after that, the water quality experienced an extremely inconspicuous degradation. This study can fill the gap regarding the long-time monitoring of water clarity and total suspended matter in Qinghai Lake and is expected to provide a scientific reference for the protection and management of the lake.
      Citation: Remote Sensing
      PubDate: 2022-06-27
      DOI: 10.3390/rs14133094
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3095: The Hitchhiker’s Guide to Fused
           Twins: A Review of Access to Digital Twins In Situ in Smart Cities

    • Authors: Jascha Grübel, Tyler Thrash, Leonel Aguilar, Michal Gath-Morad, Julia Chatain, Robert W. Sumner, Christoph Hölscher, Victor R. Schinazi
      First page: 3095
      Abstract: Smart Cities already surround us, and yet they are still incomprehensibly far from directly impacting everyday life. While current Smart Cities are often inaccessible, the experience of everyday citizens may be enhanced with a combination of the emerging technologies Digital Twins (DTs) and Situated Analytics. DTs represent their Physical Twin (PT) in the real world via models, simulations, (remotely) sensed data, context awareness, and interactions. However, interaction requires appropriate interfaces to address the complexity of the city. Ultimately, leveraging the potential of Smart Cities requires going beyond assembling the DT to be comprehensive and accessible. Situated Analytics allows for the anchoring of city information in its spatial context. We advance the concept of embedding the DT into the PT through Situated Analytics to form Fused Twins (FTs). This fusion allows access to data in the location that it is generated in in an embodied context that can make the data more understandable. Prototypes of FTs are rapidly emerging from different domains, but Smart Cities represent the context with the most potential for FTs in the future. This paper reviews DTs, Situated Analytics, and Smart Cities as the foundations of FTs. Regarding DTs, we define five components (physical, data, analytical, virtual, and Connection Environments) that we relate to several cognates (i.e., similar but different terms) from existing literature. Regarding Situated Analytics, we review the effects of user embodiment on cognition and cognitive load. Finally, we classify existing partial examples of FTs from the literature and address their construction from Augmented Reality, Geographic Information Systems, Building/City Information Models, and DTs and provide an overview of future directions.
      Citation: Remote Sensing
      PubDate: 2022-06-27
      DOI: 10.3390/rs14133095
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3096: Drought Risk Evaluation in Iran by
           Using Geospatial Technologies

    • Authors: Abdolreza Ansari Amoli, Hossein Aghighi, Ernesto Lopez-Baeza
      First page: 3096
      Abstract: A drought risk map has been developed at the national scale by using remote-sensing satellite data over Iran by combining output layers resulting from three main components of a risk-evaluation procedure including Hazard Quantification (HQ), Vulnerability Assessment (VA) and Identification of Elements at Risk (IER) in a GIS environment. In this respect, Drought Severity (DS) was calculated by using the monthly Normalized Difference Vegetation Index (NDVI) (over 31 years from 1986–2016). Iran landcover classification and a slope map, population density maps, and irrigated farm percentages at the provincial scale were utilized within the drought risk evaluation (DRE) process. The final risk map reveals that the northwest of the country, with a climate similar to the central European weather conditions, is exposed to the maximum drought risk. In contrast, the areas with an arid climate, mainly located in the middle of Iran, exhibits minimum risk against drought. Based on the risk map, the southern part of the Caspian Sea shows very low drought risk due to the moderate and subtropical climate in this region. The outputs of this research will provide advice and warnings to help decision makers reduce drought risk consequences after prioritizing risk areas at the administrative scale.
      Citation: Remote Sensing
      PubDate: 2022-06-27
      DOI: 10.3390/rs14133096
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3097: Demand for Ecosystem Services Drive
           Large-Scale Shifts in Land-Use in Tropical Mountainous Watersheds Prone to

    • Authors: Francisco Javier Álvarez-Vargas, María Angélica Villa Castaño, Carla Restrepo
      First page: 3097
      Abstract: An increasing frequency of extreme atmospheric events is challenging our basic knowledge about the resilience mechanisms that mediate the response of small mountainous watersheds (SMW) to landslides, including production of water-derived ecosystem services (WES). We hypothesized that the demand for WES increases the connectivity between lowland and upland regions, and decreases the heterogeneity of SMW. Focusing on four watersheds in the Central Andes of Colombia and combining “site-specific knowledge”, historic land cover maps (1970s and 1980s), and open, analysis-ready remotely sensed data (GLAD Landsat ARD; 1990–2000), we addressed three questions. Over roughly 120 years, the site-specific data revealed an increasing demand for diverse WES, as well as variation among the watersheds in the supply of WES. At watershed-scales, variation in the water balances—a surrogate for water-derived ES flows—exhibited complex relationships with forest cover. Fractional forest cover (pi) and forest aggregation (AIi) varied between the historic and current data sets, but in general showed non-linear relationships with elevation and slope. In the current data set (1990–2000), differences in the number of significant, linear models explaining variation in pi with time, suggest that slope may play a more important role than elevation in land cover change. We found ample evidence for a combined effect of slope and elevation on the two land cover metrics, which would be consistent with strategies directed to mitigate site-specific landslide-associated risks. Overall, our work shows strong feedbacks between lowland and upland areas, raising questions about the sustainable production of WES.
      Citation: Remote Sensing
      PubDate: 2022-06-27
      DOI: 10.3390/rs14133097
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3098: Snow Cover in the Three Stable Snow
           Cover Areas of China and Spatio-Temporal Patterns of the Future

    • Authors: Yifan Zou, Peng Sun, Zice Ma, Yinfeng Lv, Qiang Zhang
      First page: 3098
      Abstract: In the context of global warming, relevant studies have shown that China will experience the largest temperature rise in the Qinghai–Tibet Plateau and northwestern regions in the future. Based on MOD10A2 and MYD10A2 snow products and snow depth data, this study analyzes the temporal and spatial evolution characteristics of the snow cover fraction, snow depth, and snow cover days in the three stable snow cover areas in China, and combines 15 modes in CMIP6 snow cover data in four different scenarios with three kinds of variables, predicting the spatiotemporal evolution pattern of snow cover in China’s three stable snow cover areas in the future. The results show that (1) the mean snow cover fraction, snow depth, and snow cover days in the snow cover area of Northern Xinjiang are all the highest. Seasonal changes in the snow cover areas of the Qinghai–Tibet Plateau are the most stable. The snow cover fraction, snow depth, and snow cover days of the three stable snow cover areas are consistent in spatial distribution. The high values are mainly distributed in the southeast and west of the Qinghai–Tibet Plateau, the south and northeast of Northern Xinjiang, and the north of the snow cover area of Northeast China. (2) The future snow changes in the three stable snow cover areas will continue to decline with the increase in development imbalance. Snow cover fraction and snow depth decrease most significantly in the Qinghai–Tibet Plateau and the snow cover days in Northern Xinjiang decrease most significantly under the SSPs585 scenario. In the future, the southeast of the Qinghai–Tibet Plateau, the northwest of Northern Xinjiang, and the north of Northeast China will be the center of snow cover reduction. (3) Under the four different scenarios, the snow cover changes in the Qinghai–Tibet Plateau and Northern Xinjiang are the most significant. Under the SSPs126 and SSPs245 scenarios, the Qinghai–Tibet Plateau snow cover has the most significant change in response. Under the SSPs370 and SSPs585 scenarios, the snow cover in Northern Xinjiang has the most significant change.
      Citation: Remote Sensing
      PubDate: 2022-06-27
      DOI: 10.3390/rs14133098
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3099: Target Height Measurement under
           Complex Multipath Interferences without Exact Knowledge on the Propagation

    • Authors: Yuan Liu, Hongwei Liu
      First page: 3099
      Abstract: This paper investigates the direction-of-arrival (DOA) estimation-based target localization problem using an array radar under complex multipath propagation scenarios. Prevalent methods may suffer from performance degradation due to the deterministic signal model mismatch, especially when the exact knowledge of a propagation environment is unavailable. To cope with this problem, we first establish an improved signal model of multipath propagation for low-angle target localization scenarios, where the dynamic nature of convoluted interferences induced by complex terrain reflections is taken into account. Subsequently, an iterative implementation-based target localization algorithm with the improved propagation model is proposed to eliminate the detrimental effect of coherent interferences on target localization performance. Compared to existing works, the proposed algorithm can maintain satisfactory estimation performance in terms of target location parameters, even in severe multipath interference conditions, where the decorrelation preprocessing and accurate knowledge about the multipath propagation environment are not required. Both simulation and experimental results demonstrate the effectiveness of the proposed propagation model and localization algorithm.
      Citation: Remote Sensing
      PubDate: 2022-06-28
      DOI: 10.3390/rs14133099
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3100: A Temporal-Reliable Method for Change
           Detection in High-Resolution Bi-Temporal Remote Sensing Images

    • Authors: Fei Pan, Zebin Wu, Xiuping Jia, Qian Liu, Yang Xu, Zhihui Wei
      First page: 3100
      Abstract: Very-high-resolution (VHR) bi-temporal images change detection (CD) is a basic remote sensing images (RSIs) processing task. Recently, deep convolutional neural networks (DCNNs) have shown great feature representation abilities in computer vision tasks and have achieved remarkable breakthroughs in automatic CD. However, a great majority of the existing fusion-based CD methods pay no attention to the definition of CD, so they can only detect one-way changes. Therefore, we propose a new temporal reliable change detection (TRCD) algorithm to solve this drawback of fusion-based methods. Specifically, a potential and effective algorithm is proposed for learning temporal-reliable features for CD, which is achieved by designing a novel objective function. Unlike the traditional CD objective function, we impose a regular term in the objective function, which aims to enforce the extracted features before and after exchanging sequences of bi-temporal images that are similar to each other. In addition, our backbone architecture is designed based on a high-resolution network. The captured features are semantically richer and more spatially precise, which can improve the performance for small region changes. Comprehensive experimental results on two public datasets demonstrate that the proposed method is more advanced than other state-of-the-art (SOTA) methods, and our proposed objective function shows great potential.
      Citation: Remote Sensing
      PubDate: 2022-06-28
      DOI: 10.3390/rs14133100
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3101: Comprehensive Risk Assessment of
           Urban Waterlogging Disaster Based on MCDA-GIS Integration: The Case Study
           of Changchun, China

    • Authors: Chenyu Duan, Jiquan Zhang, Yanan Chen, Qiuling Lang, Yichen Zhang, Chenyang Wu, Zhen Zhang
      First page: 3101
      Abstract: Urban waterlogging will harm economic development and people’s life safety; however, the waterlogging risk zoning map provides the necessary decision support for the management of urban waterlogging, urban development and urban planning. This paper proposes an urban waterlogging risk assessment method that combines multi-criteria decision analysis (MCDA) with a geographic information system (GIS). The framework of urban waterlogging risk assessment includes four main elements: hazard, exposure, vulnerability, and emergency response and recovery capability. Therefore, we selected the urban area of Changchun City, Jilin Province as the study area. The Analytic Hierarchy Process (AHP) is a generally accepted MCDA method, it is used to calculate the weight and generate a result map of hazards, exposure, vulnerability, and emergency responses and recovery capability. Based to the principle of natural disaster risk formation, a total of 18 parameters, including spatial data and attribute data, were collected in this study. The model results are compared with the recorded waterlogging points, and the results show that the model is more reliable.
      Citation: Remote Sensing
      PubDate: 2022-06-28
      DOI: 10.3390/rs14133101
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3102: High-Resolution Wide-Swath Ambiguous
           Synthetic Aperture Radar Modes for Ship Monitoring

    • Authors: Nertjana Ustalli, Michelangelo Villano
      First page: 3102
      Abstract: This paper proposes two high-resolution, wide-swath synthetic aperture radar (SAR) acquisition modes for ship monitoring that tolerate ambiguities and do not require digital beamforming. Both modes, referred to as the low pulse repetition frequency (PRF) and the staggered (high PRF) ambiguous modes, make use of a wide elevation beam, which can be obtained by phase tapering. The first mode is a conventional stripmap mode with a PRF much lower than the nominal Doppler bandwidth, allowing for the imaging of a large swath, because the ships’ azimuth ambiguities can be recognized as they appear at known positions. The second mode exploits a continuous variation of the pulse repetition interval, with a mean PRF greater than the nominal Doppler bandwidth as the range ambiguities of the ships are smeared and are unlikely to determine false alarms. Both modes are thought to operate in open sea surveillance, monitoring Exclusive Economic Zones or international waters. Examples of implementation of both modes for TerraSAR-X show that ground swaths of 120 km or 240 km can be mapped with 2 m2 resolution, ensuring outstanding detection performance even for small ships. The importance of resolution over noise and ambiguity level was highlighted by a comparison with ScanSAR modes that image comparable swaths with better noise and ambiguity levels but coarser resolutions.
      Citation: Remote Sensing
      PubDate: 2022-06-28
      DOI: 10.3390/rs14133102
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3103: A Study on TGF Detectability at 2165
           m Altitude: Estimates for the Mountain-Based Gamma-Flash Experiment

    • Authors: Alessandro Ursi, Gonzalo Rodriguez Fernandez, Alessandra Tiberia, Enrico Virgilli, Enrico Arnone, Enrico Preziosi, Riccardo Campana, Marco Tavani
      First page: 3103
      Abstract: Gamma-Flash is an Italian program devoted to the realization of both a ground-based and an airborne gamma-ray and neutron detection system, for in situ measurements of high-energy phenomena correlated to thunderstorm activity, such as Terrestrial Gamma-ray Flashes (TGFs), gamma-ray glows, and associated neutron emissions. The ground-based Gamma-Flash experiment is currently under installation at the Osservatorio Climatico “Ottavio Vittori” (CNR-ISAC) on Mt. Cimone, in Northern-Central Italy (2165 m a.s.l.), and it will be operational starting in Summer 2022. We studied the detectability of TGFs in the surroundings of the ground-based Gamma-Flash experiment, to identify an investigable spatial region around the detectors from which typical TGFs can survive and be revealed onground. We carried out numerical simulations of gamma-ray propagation in the mid-latitude atmosphere, and we developed a qualitative analytical model to integrate the results. This analysis allows one to identify a spatial region extending up to 4 km distance on ground and up to 10 km altitude a.s.l., considering typical TGFs emitting ∼1018 gamma-ray photons at the source. Lightning sferics data acquired by the LINET network demonstrate that such a region is interested by frequent cloud-to-ground and intra-cloud lightning, pointing out the suitability of the location for the purposes of the Gamma-Flash program.
      Citation: Remote Sensing
      PubDate: 2022-06-28
      DOI: 10.3390/rs14133103
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3104: Inter-Annual Climate Variability
           Impact on Oil Palm Mapping

    • Authors: Fernando Troya, Paulo N. Bernardino, Ben Somers
      First page: 3104
      Abstract: The contribution of oil palm plantations to the economic growth of tropical developing countries makes it essential to monitor their expansion into the tropical forest; consequently, most studies focus on improving mapping accuracy while using satellite imagery. However, accuracy can be hampered by atmospheric phenomena that can drastically change climatic conditions in tropical regions, affecting the spectral properties of the vegetation. In this sense, we studied the accuracy of palm plantation mapping by using features from different regions of the electromagnetic spectrum and a data fusion approach, and then compared the changes in accuracy over the years 2016, 2017, and 2018 (two of them with reported climatic anomalies). Optical-based maps obtained higher accuracy than thermal- and microwave-based maps, but they were the most affected by inter-annual climate variability (error margin between 5 and 10%), while thermal-based maps were the least affected (error margin between 8 and 9%). Data fusion combinations improved accuracy and reduced dissimilarities between years (e.g., phenology-based map accuracy changed by up to 20.8%, while phenology fused with microwave features changed by up to 6.8%). We conclude that inter-annual climate variability on land-cover mapping should be considered, especially if the outputs will be used as input in future studies.
      Citation: Remote Sensing
      PubDate: 2022-06-28
      DOI: 10.3390/rs14133104
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3105: Underwater Multispectral Laser Serial
           Imager for Spectral Differentiation of Macroalgal and Coral Substrates

    • Authors: Matthieu Huot, Fraser Dalgleish, Eric Rehm, Michel Piché, Philippe Archambault
      First page: 3105
      Abstract: The advancement of innovative underwater remote sensing detection and imaging methods, such as continuous wave laser line scan or pulsed laser (i.e., LiDAR—Light Detection and Ranging) imaging approaches can provide novel solutions for studying biological substrates and manmade objects/surfaces often encountered in underwater coastal environments. Such instruments can be used shipboard or coupled with proven and available deployment platforms as AUVs (Autonomous Underwater Vehicles). With the right planning, large areas can be surveyed, and more extreme and difficult-to-reach environments can be studied. A prime example, and representing a certain navigational challenge, is the under ice in the Arctic/Antarctic or winter/polar environments or deep underwater survey. Among many marine biological substrates, numerous species of macroalgae can be found worldwide in shallow down to 70+ m (clear water) coastal habitats and are essential ecosystem service providers through the habitat they provide for other species, the potential food resource value, and carbon sink they represent. Similarly, corals also provide important ecosystem services through their structure and diversity, are found to harbor increased local diversity, and are equally valid targets as “keystone” species. Hence, we expand current underwater remote sensing methods to combine macroalgal and coral surveys via the development of a multispectral laser serial imager designed for classification via spectral response. By using multiple continuous wave laser wavelength sources to scan and illuminate recreated benthic environments composed of macroalgae and coral, we show how elastic (i.e., reflectance) and inelastic (i.e., fluorescence) spectral responses can potentially be used to differentiate algal color groups and certain coral genus. Experimentally, three laser diodes (450 nm, 490 nm, 520 nm) are sequentially used in conjunction with up to 5 emission filters (450 nm, 490 nm, 520 nm, 580 nm, 685 nm) to acquire images generated by laser line scan pattern via high-speed galvanometric mirrors. Placed directly adjacent to a large saltwater imaging tank fitted with optical viewports, the optical system records target substrate spectral response using a photomultiplier preceded by a filter and is synchronously digitized to the scan rate by a high sample rate Analog-to-Digital Converter (ADC). Acquired images are normalized to correct for imager optical effects allowing for fluorescence intensity-based pixel segmentation via intensity thresholding. Overall, the multispectral laser serial imaging technique shows that the resulting high resolution data can be used for detection and classification of benthic substrates by their spectral response. These methods highlight a path towards eventual pixel-wise spectral response analysis for spectral differentiation.
      Citation: Remote Sensing
      PubDate: 2022-06-28
      DOI: 10.3390/rs14133105
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3106: Laboratory Investigation on Detecting
           Bridge Scour Using the Indirect Measurement from a Passing Vehicle

    • Authors: Bin Zhang, Hua Zhao, Chengjun Tan, Eugene J. OBrien, Paul C. Fitzgerald, Chul-Woo Kim
      First page: 3106
      Abstract: For bridges with surface foundations, scour is one of the main reasons for bridge failures. In regard to structural health monitoring, vibration-based scour detection techniques have received increasing attention over the past two decades. Scour occurs below the water surface in rivers or sea, leading to difficulty in equipment installation and maintenance. Recently, the concept of “drive-by” SHM using the indirect measurement of passing vehicle responses has been developed rapidly due to its convenience and low cost. This paper proposes a method to detect scour using the vehicle responses under an operational vehicle speed. The wavelet transform was applied to vehicle accelerations to obtain the wavelet energy. It was found that the wavelet energy increases with the increase in the scour damage level. However, the wavelet energy may also be affected by the on-site operating environments, such as sensor noise and other variabilities, which interferes with the identification of scour in practice. Hence, in this work, a statistical-wavelet-based approach was presented to effectively detect the presence of scour and even its location. The feasibility of the proposed approach is verified in both numerical simulation and lab experiments. The results show that the proposed method has a good potential to detect scour using indirect measurements.
      Citation: Remote Sensing
      PubDate: 2022-06-28
      DOI: 10.3390/rs14133106
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3107: Monitoring and Predicting Channel
           Morphology of the Tongtian River, Headwater of the Yangtze River Using
           Landsat Images and Lightweight Neural Network

    • Authors: Bin Deng, Kai Xiong, Zhiyong Huang, Changbo Jiang, Jiang Liu, Wei Luo, Yifei Xiang
      First page: 3107
      Abstract: The Tongtian River is the source of the Yangtze River and is a national key ecological reserve in China. Monitoring and predicting the changes and mechanisms of the Tongtian River channel morphology are beneficial to protecting the “Asian Water Tower”. This study aims to quantitatively monitor and predict the accretion and erosion area of the Tongtian River channel morphology during the past 30 years (1990–2020). Firstly, the water bodies of the Tongtian River were extracted and the accretion and erosion areas were quantified using 1108 Landsat images based on the combined method of three water-body indices and a threshold, and the surface-water dataset provided by the European Commission Joint Research Centre. Secondly, an intelligent lightweight neural-network model was constructed to predict and analyze the accretion and erosion area of the Tongtian River. Results indicate that the Tongtian River experienced apparent accretion and erosion with a total area of 98.3 and 94.9 km2, respectively, during 1990–2020. The braided (meandering) reaches at the upper (lower) Tongtian River exhibit an overall trend of accretion (erosion). The Tongtian River channel morphology was determined by the synergistic effect of sediment-transport velocity and streamflow. The lightweight neural network well-reproduced the complex nonlinear processes in the river-channel morphology with a final prediction error of 0.0048 km2 for the training session and 4.6 km2 for the test session. Results in this study provide more effective, reasonable, and scientific decision-making aids for monitoring, protecting, understanding, and mining the evolution characteristics of rivers, especially the complex change processes of braided river channels in alpine regions and developing countries.
      Citation: Remote Sensing
      PubDate: 2022-06-28
      DOI: 10.3390/rs14133107
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3108: RAMC: A Rotation Adaptive Tracker
           with Motion Constraint for Satellite Video Single-Object Tracking

    • Authors: Yuzeng Chen, Yuqi Tang, Te Han, Yuwei Zhang, Bin Zou, Huihui Feng
      First page: 3108
      Abstract: Single-object tracking (SOT) in satellite videos (SVs) is a promising and challenging task in the remote sensing community. In terms of the object itself and the tracking algorithm, the rotation of small-sized objects and tracking drift are common problems due to the nadir view coupled with a complex background. This article proposes a novel rotation adaptive tracker with motion constraint (RAMC) to explore how the hybridization of angle and motion information can be utilized to boost SV object tracking from two branches: rotation and translation. We decouple the rotation and translation motion patterns. The rotation phenomenon is decomposed into the translation solution to achieve adaptive rotation estimation in the rotation branch. In the translation branch, the appearance and motion information are synergized to enhance the object representations and address the tracking drift issue. Moreover, an internal shrinkage (IS) strategy is proposed to optimize the evaluation process of trackers. Extensive experiments on space-born SV datasets captured from the Jilin-1 satellite constellation and International Space Station (ISS) are conducted. The results demonstrate the superiority of the proposed method over other algorithms. With an area under the curve (AUC) of 0.785 and 0.946 in the success and precision plots, respectively, the proposed RAMC achieves optimal performance while running at real-time speed.
      Citation: Remote Sensing
      PubDate: 2022-06-28
      DOI: 10.3390/rs14133108
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3109: RAANet: A Residual ASPP with
           Attention Framework for Semantic Segmentation of High-Resolution Remote
           Sensing Images

    • Authors: Runrui Liu, Fei Tao, Xintao Liu, Jiaming Na, Hongjun Leng, Junjie Wu, Tong Zhou
      First page: 3109
      Abstract: Classification of land use and land cover from remote sensing images has been widely used in natural resources and urban information management. The variability and complex background of land use in high-resolution imagery poses greater challenges for remote sensing semantic segmentation. To obtain multi-scale semantic information and improve the classification accuracy of land-use types in remote sensing images, the deep learning models have been wildly focused on. Inspired by the idea of the atrous-spatial pyramid pooling (ASPP) framework, an improved deep learning model named RAANet (Residual ASPP with Attention Net) is constructed in this paper, which constructed a new residual ASPP by embedding the attention module and residual structure into the ASPP. There are 5 dilated attention convolution units and a residual unit in its encoder. The former is used to obtain important semantic information at more scales, and residual units are used to reduce the complexity of the network to prevent the disappearance of gradients. In practical applications, according to the characteristics of the data set, the attention unit can select different attention modules such as the convolutional block attention model (CBAM). The experimental results obtained from the land-cover domain adaptive semantic segmentation (LoveDA) and ISPRS Vaihingen datasets showed that this model can enhance the classification accuracy of semantic segmentation compared to the current deep learning models.
      Citation: Remote Sensing
      PubDate: 2022-06-28
      DOI: 10.3390/rs14133109
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3110: Multiscale Perspectives on an Extreme
           Warm-Sector Rainfall Event over Coastal South China

    • Authors: Yiliang Pu, Sheng Hu, Yali Luo, Xiantong Liu, Lihua Hu, Langming Ye, Huiqi Li, Feng Xia, Lingyu Gao
      First page: 3110
      Abstract: On 22 June 2017, an extreme warm-sector rainfall event hit the western coastal area of South China, with maximum hourly and 12-h rainfall accumulations of 189.4 and 464.8 mm, respectively, which broke local historical records. Multisource observations were used to reveal multiscale processes contributing to the extreme rainfall. The results showed that a marine boundary layer jet (BLJ) coupled with a synoptic low-level jet (LLJ) inland played an important role in the formation of an extremely humid environment with a very low lifting condensation level of near-surface air. Under the favorable pre-convective conditions, convection was initialized at a mesoscale convergence line, aided by topographic lifting in the evening. During the nocturnal hours, the rainstorm developed and was maintained by a quasi-stationary mesoscale outflow boundary, which continuously lifted warm, moist air transported by the enhanced BLJ. When producing the extreme rainfall rates, the storm possessed relatively weak convection, with the 40 dBZ echo top hardly reaching 6 km. The extreme rainfall was produced mainly by the warm rain microphysical processes, mainly because the humid environment and the deep warm cloud layer facilitated the clouds’ condensational growth and collision–coalescence, and also reduced rain evaporation. As the storm evolved, the raindrop concentration increased rapidly from its initial stage and remained high until its weakening stage, but the mean raindrop size changed little. The extreme rain was characterized by the highest concentration of raindrops during the storm’s lifetime with a mean size of raindrops slightly larger than the maritime regime.
      Citation: Remote Sensing
      PubDate: 2022-06-28
      DOI: 10.3390/rs14133110
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3111: Self-Supervision and
           Self-Distillation with Multilayer Feature Contrast for Supervision
           Collapse in Few-Shot Remote Sensing Scene Classification

    • Authors: Haonan Zhou, Xiaoping Du, Sen Li
      First page: 3111
      Abstract: Although the means of catching remote sensing images are becoming more effective and more abundant, the samples that can be collected in some specific environments can be quite scarce. When there are limited labeled samples, the methods for analyzing remote sensing images for scene classification perform drastically worse. Methods that classify few-shot remote sensing image scenes are often based on meta-learning algorithms for the handling of sparse data. However, this research shows they will be affected by supervision collapse where features in remote sensing images that help with out-of-distribution classes are discarded, which is harmful for the generation of unseen classes and new tasks. In this work, we wish to remind readers of the existence of supervision collapse in scene classification of few-shot remote sensing images and propose a method named SSMR based on multi-layer feature contrast to overcome supervision collapse. First of all, the method makes use of the label information contained in a finite number of samples for supervision and guides self-supervised learning to train the embedding network with supervision generated by multilayer feature contrast. This can prevent features from losing intra-class variation. Intra-class variation is always useful in classifying unseen data. What is more, the multi-layer feature contrast is merged with self-distillation, and the modified self-distillation is used to encourage the embedding network to extract sufficiently general features that transfer better to unseen classes and new domains. We demonstrate that most of the existing few-shot scene classification methods suffer from supervision collapse and that SSMR overcomes supervision collapse well in the experiments on the new dataset we specially designed for examining the problem, with a 2.4–17.2% increase compared to the available methods. Furthermore, we performed a series of ablation experiments to demonstrate how effective and necessary each structure of the proposed method is and to show how different choices in training impact final performance.
      Citation: Remote Sensing
      PubDate: 2022-06-28
      DOI: 10.3390/rs14133111
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3112: An Improved Submerged Mangrove
           Recognition Index-Based Method for Mapping Mangrove Forests by Removing
           the Disturbance of Tidal Dynamics and S. alterniflora

    • Authors: Qing Xia, Ting-Ting He, Cheng-Zhi Qin, Xue-Min Xing, Wu Xiao
      First page: 3112
      Abstract: Currently, it is a great challenge for remote sensing technology to accurately map mangrove forests owing to periodic inundation. A submerged mangrove recognition index (SMRI) using two high- and low-tide images was recently proposed to remove the influence of tides and identify mangrove forests. However, when the tidal height of the selected low-tide image is not at the lowest tidal level, the corresponding SMRI does not function well, which results in mangrove forests below the low tidal height being undetected. Furthermore, Spartina alterniflora Loisel (S. alterniflora) was introduced to China in 1979 and rapidly spread to become the most serious invasive plant along the Chinese coastline. The current SMRI has failed to distinguish S. alterniflora from submerged mangrove forests because of their similar spectral signatures. In this study, an SMRI-based mangrove forest mapping method was developed using the time series of Sentinel-2 images to mitigate the two aforementioned issues. In the proposed method, quantile synthesis was applied to the time series of Sentinel-2 images to generate a lowest-tide synthetic image for creating SMRI to identify submerged mangrove forests. Unsubmerged mangrove forests were classified using a support vector machine, and a preliminary mangrove forest map was created by merging them. In addition, S. alterniflora was distinguished from the mangrove forests by analyzing their phenological differences. Finally, mangrove forest mapping was performed by masking S. alterniflora. The proposed method was applied to the entire coastline of the Guangxi Province, China. The results showed that it can reliably and accurately identify submerged mangrove forests derived from SMRI by synthesizing low- and high-tide images using quantile synthesis, and the differentiation of S. alterniflora using phenological differences results in more accurate mangrove mapping. This work helps to improve the accuracy of mangrove forest mapping using SMRI and its feasibility for coastal wetland monitoring. It also provides data for sustainable management, ecological protection, and restoration of vegetation in coastal zones.
      Citation: Remote Sensing
      PubDate: 2022-06-28
      DOI: 10.3390/rs14133112
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3113: Dynamic Characteristics Monitoring of
           Large Wind Turbine Blades Based on Target-Free DSST Vision Algorithm and

    • Authors: Wanrun Li, Wenhai Zhao, Jiaze Gu, Boyuan Fan, Yongfeng Du
      First page: 3113
      Abstract: The structural condition of blades is mainly evaluated using manual inspection methods. However, these methods are time-consuming, labor-intensive, and costly, and the detection results significantly depend on the experience of inspectors, often resulting in lower precision. Focusing on the dynamic characteristics (i.e., natural frequencies) of large wind turbine blades, this study proposes a monitoring method based on the target-free DSST (Discriminative Scale Space Tracker) vision algorithm and UAV. First, the displacement drift of UAV during hovering is studied. Accordingly, a displacement compensation method based on high-pass filtering is proposed herein, and the scale factor is adaptive. Then, the machine learning is employed to map the position and scale filters of the DSST algorithm to highlight the features of the target image. Subsequently, a target-free DSST vision algorithm is proposed, in which illumination changes and complex backgrounds are considered. Additionally, the algorithm is verified using traditional computer vision algorithms. Finally, the UAV and the target-free DSST vision algorithm are used to extract the dynamic characteristic of the wind turbine blades under shutdown. Results show that the proposed method can accurately identify the dynamic characteristics of the wind turbine blade. This study can serve as a reference for assessment of the condition of wind turbine blades.
      Citation: Remote Sensing
      PubDate: 2022-06-28
      DOI: 10.3390/rs14133113
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3114: Adaptive DDK Filter for GRACE
           Time-Variable Gravity Field with a Novel Anisotropic Filtering Strength

    • Authors: Nijia Qian, Guobin Chang, Jingxiang Gao, Wenbin Shen, Zhengwen Yan
      First page: 3114
      Abstract: Filtering for GRACE temporal gravity fields is a necessary step before calculating surface mass anomalies. In this study, we propose a new denoising and decorrelation kernel (DDK) filtering scheme called adaptive DDK filter. The involved error covariance matrix (ECM) adopts nothing but the monthly time-variable released by several data centers. The signal covariance matrix (SCM) involved is monthly time-variable also. Specifically, it is parameterized into two parameters, namely the regularization coefficient and the power index of signal covariances, which are adaptively determined from the data themselves according to the generalized cross validation (GCV) criterion. The regularization coefficient controls the global constraint on the signal variances of all degrees, while the power index adjusts the attenuation of the signal variances from low to high degrees, namely local constraint. By tuning these two parameters for the monthly SCM, the adaptability to the data and the optimality of filtering strength can be expected. In addition, we also devise a half-weight polygon area (HWPA) of the filter kernel to measure the filtering strength of the anisotropic filter more reasonably. The proposed adaptive DDK filter and filtering strength metric are tested based on CSR GRACE temporal gravity solutions with their ECMs from January 2004 to December 2010. Results show that the selected optimal power indices range from 3.5 to 6.9, with the corresponding regularization parameters range from 1 × 1014 to 5 × 1019. The adaptive DDK filter can retain comparable/more signal amplitude and suppress more high-degree noise than the conventional DDK filters. Compared with the equivalent smoothing radius (ESR) of filtering strength, the HWPA has stronger a distinguishing ability, especially when the filtering strength is similar.
      Citation: Remote Sensing
      PubDate: 2022-06-28
      DOI: 10.3390/rs14133114
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3115: Assessment of Moraine Cliff
           Spatio-Temporal Erosion on Wolin Island Using ALS Data Analysis

    • Authors: Marcin Winowski, Jacek Tylkowski, Marcin Hojan
      First page: 3115
      Abstract: The aim of the article is to present the temporal and spatial variability of the cliff coast erosion of the Wolin Island in 2012–2020 in three time periods (2012–2015, 2015–2018, 2018–2020). The research used data from airborne laser scanning (ALS), based on which DEM models were made. Based on the differences between the models, the amount of sediment that was eroded by the sea waves was determined. The conducted research showed that, in the analyzed period, the dynamics of the Wolin cliffs were characterized by high variability. The greatest erosion was observed on sandy cliffs, and the smallest on clay cliffs and on cliffs that are densely covered with vegetation. In the sediment budget studies, two seashore erosivity indicators were proposed: length-normalized sediment budget (LB) (m3/m) and area-normalized sediment budget (AB) (m3/m2). The average annual dynamics of the cliff edge erosion on the Wolin Island was found to be LB = 6.6 ± 0.3 m3/m/a, AB = 0.17 ± 0.01 m3/m2/a. The results obtained are comparable with other postglacial cliffs. The use of the differential analysis of DEM models allows for the determination of the dynamics of the cliff coast and may be used in spatial development and planning of seashore protection zones.
      Citation: Remote Sensing
      PubDate: 2022-06-28
      DOI: 10.3390/rs14133115
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3116: A Universal Multi-Frequency
           Micro-Resistivity Array Imaging Method for Subsurface Sensing

    • Authors: Haining Yang, Yuting Liu, Tingjun Li, Shijia Yi, Na Li
      First page: 3116
      Abstract: In this paper, a universal multi–frequency micro-resistivity array imaging (UMMAI) system for subsurface sensing is developed and verified. Different from conventional micro-resistivity imaging equipments, UMMAI is capable to provide high-resolution fullbore formation images in multiple logging environments including an oil-based mud scene, water-based mud scene and water-oil mixed mud scene, owning to the large dynamic range and good linearity of transceivers. With the advantage of diversity in excitation signal frequency, UMMAI presents abundant amplitude–frequency characteristics response images and phase–frequency characteristics response images of subsurface formations at the same time, which is beneficial to multi–frequency image fusion in the future. The fullbore imaging ability of UMMAI is evaluated in three different field tests, and the results show that UMMAI can give satisfactory credible formation images with high resolution, which is suitable for subsurface formation discrimination and useful for reservoir identification.
      Citation: Remote Sensing
      PubDate: 2022-06-28
      DOI: 10.3390/rs14133116
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3117: PolSAR Scene Classification via
           Low-Rank Constrained Multimodal Tensor Representation

    • Authors: Bo Ren, Mengqian Chen, Biao Hou, Danfeng Hong, Shibin Ma, Jocelyn Chanussot, Licheng Jiao
      First page: 3117
      Abstract: Polarimetric synthetic aperture radar (PolSAR) data can be acquired at all times and are not impacted by weather conditions. They can efficiently capture geometrical and geographical structures on the ground. However, due to the complexity of the data and the difficulty of data availability, PolSAR image scene classification remains a challenging task. To this end, in this paper, a low-rank constrained multimodal tensor representation method (LR-MTR) is proposed to integrate PolSAR data in multimodal representations. To preserve the multimodal polarimetric information simultaneously, the target decompositions in a scene from multiple spaces (e.g., Freeman, H/A/α, Pauli, etc.) are exploited to provide multiple pseudo-color images. Furthermore, a representation tensor is constructed via the representation matrices and constrained by the low-rank norm to keep the cross-information from multiple spaces. A projection matrix is also calculated by minimizing the differences between the whole cascaded data set and the features in the corresponding space. It also reduces the redundancy of those multiple spaces and solves the out-of-sample problem in the large-scale data set. To support the experiments, two new PolSAR image data sets are built via ALOS-2 full polarization data, covering the areas of Shanghai, China, and Tokyo, Japan. Compared with state-of-the-art (SOTA) dimension reduction algorithms, the proposed method achieves the best quantitative performance and demonstrates superiority in fusing multimodal PolSAR features for image scene classification.
      Citation: Remote Sensing
      PubDate: 2022-06-28
      DOI: 10.3390/rs14133117
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3118: RSSGG_CS: Remote Sensing Image Scene
           Graph Generation by Fusing Contextual Information and Statistical

    • Authors: Zhiyuan Lin, Feng Zhu, Qun Wang, Yanzi Kong, Jianyu Wang, Liang Huang, Yingming Hao
      First page: 3118
      Abstract: To semantically understand remote sensing images, it is not only necessary to detect the objects in them but also to recognize the semantic relationships between the instances. Scene graph generation aims to represent the image as a semantic structural graph, where objects and relationships between them are described as nodes and edges, respectively. Some existing methods rely only on visual features to sequentially predict the relationships between objects, ignoring contextual information and making it difficult to generate high-quality scene graphs, especially for remote sensing images. Therefore, we propose a novel model for remote sensing image scene graph generation by fusing contextual information and statistical knowledge, namely RSSGG_CS. To integrate contextual information and calculate attention among all objects, the RSSGG_CS model adopts a filter module (FiM) that is based on adjusted transformer architecture. Moreover, to reduce the blindness of the model when searching semantic space, statistical knowledge of relational predicates between objects from the training dataset and the cleaned Wikipedia text is used as supervision when training the model. Experiments show that fusing contextual information and statistical knowledge allows the model to generate more complete scene graphs of remote sensing images and facilitates the semantic understanding of remote sensing images.
      Citation: Remote Sensing
      PubDate: 2022-06-29
      DOI: 10.3390/rs14133118
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3119: Ionospheric Behavior during the 10
           June 2021 Annular Solar Eclipse and Its Impact on GNSS Precise Point

    • Authors: Juan Carlos Valdés-Abreu, Marcos A. Díaz, Manuel Bravo, Juan Carlos Báez, Yohadne Stable-Sánchez
      First page: 3119
      Abstract: The main effects of the 10 June 2021 annular solar eclipse on GNSS position estimation accuracy are presented. The analysis is based on TEC measurements made by 2337 GNSS stations around the world. TEC perturbations were obtained by comparing results 2 days prior to and after the day of the event. For the analysis, global TEC maps were created using ordinary Kriging interpolation. From TEC changes, the apparent position variation was obtained using the post-processing kinematic precise point positioning with ambiguity resolution (PPP-AR) mode. We validated the TEC measurements by contrasting them with data from the Swarm-A satellite and four digiosondes in Central/South America. The TEC maps show a noticeable TEC depletion (<−60%) under the moon’s shadow. Important variations of TEC were also observed in both crests of the Equatorial Ionization Anomaly (EIA) region over the Caribbean and South America. The effects on GNSS precision were perceived not only close to the area of the eclipse but also as far as the west coast of South America (Chile) and North America (California). The number of stations with positioning errors of over 10 cm almost doubled during the event in these regions. The effects were sustained longer (∼10 h) than usually assumed.
      Citation: Remote Sensing
      PubDate: 2022-06-29
      DOI: 10.3390/rs14133119
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3120: Correction: Almar et al. Sea State
           from Single Optical Images: A Methodology to Derive Wind-Generated Ocean
           Waves from Cameras, Drones and Satellites. Remote Sens. 2021, 13, 679

    • Authors: Rafael Almar, Erwin W. J. Bergsma, Patricio A. Catalan, Rodrigo Cienfuegos, Leandro Suarez, Felipe Lucero, Alexandre Nicolae Lerma, Franck Desmazes, Eleonora Perugini, Margaret L. Palmsten, Chris Chickadel
      First page: 3120
      Abstract: There was an error in the original article [...]
      Citation: Remote Sensing
      PubDate: 2022-06-29
      DOI: 10.3390/rs14133120
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3121: Wavefield Decomposition of
           Ocean-Bottom Multicomponent Seismic Data with Composite Calibration

    • Authors: Mingzhi Chu, Pengfei Yu
      First page: 3121
      Abstract: Downgoing/upgoing P/S-wave decomposition of ocean-bottom seismic (OBS) multicomponent data can help suppress the water-layer multiples and cross-talks between P- and S-waves, and therefore plays an important role in seismic migration and construction of P- and S-wave velocity models. We proposed novel composite calibration filters by introducing an additional dimension to the calibration of the particle velocity components, extending the wave-equation-based adaptive decomposition method. We also modified the existing workflow by jointly using primary reflections at near-to-medium offsets and ocean-bottom refractions at far offsets in the calibration optimization. The decomposition scheme with the novel calibration filters yielded satisfactory results in a deep-water OBS field data decomposition example. Expected decomposition effects, such as the enhancement of primary reflections and the attenuation of water-layer multiple events, can be observed in the decomposed upgoing wavefields. An experiment illustrated the effectiveness of composite calibration filters that compensated for unexpected velocity errors along the offset dimension.
      Citation: Remote Sensing
      PubDate: 2022-06-29
      DOI: 10.3390/rs14133121
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3122: Evaluating a New Relative
           Phenological Correction and the Effect of Sentinel-Based Earth Engine
           Compositing Approaches to Map Fire Severity and Burned Area

    • Authors: Adrián Israel Silva-Cardoza, Daniel José Vega-Nieva, Jaime Briseño-Reyes, Carlos Ivan Briones-Herrera, Pablito Marcelo López-Serrano, José Javier Corral-Rivas, Sean A. Parks, Lisa M. Holsinger
      First page: 3122
      Abstract: The remote sensing of fire severity and burned area is fundamental in the evaluation of fire impacts. The current study aimed to: (i) compare Sentinel-2 (S2) spectral indices to predict field-observed fire severity in Durango, Mexico; (ii) evaluate the effect of the compositing period (1 or 3 months), techniques (average or minimum), and phenological correction (constant offset, c, against a novel relative phenological correction, rc) on fire severity mapping, and (iii) determine fire perimeter accuracy. The Relative Burn Ratio (RBR), using S2 bands 8a and 12, provided the best correspondence with field-based fire severity (FBS). One-month rc minimum composites showed the highest correspondence with FBS (R2 = 0.83). The decrease in R2 using 3 months rather than 1 month was ≥0.05 (0.05–0.15) for c composites and <0.05 (0.02–0.03) for rc composites. Furthermore, using rc increased the R2 by 0.05–0.09 and 0.10–0.15 for the 3-month RBR and dNBR compared to the corresponding c composites. Rc composites also showed increases of up to 0.16–0.22 and 0.08–0.11 in kappa values and overall accuracy, respectively, in mapping fire perimeters against c composites. These results suggest a promising potential of the novel relative phenological correction to be systematically applied with automated algorithms to improve the accuracy and robustness of fire severity and perimeter evaluations.
      Citation: Remote Sensing
      PubDate: 2022-06-29
      DOI: 10.3390/rs14133122
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3123: Spatio-Temporal Variability of
           Suspended Particulate Matter in a High-Arctic Estuary (Adventfjorden,
           Svalbard) Using Sentinel-2 Time-Series

    • Authors: Daniela M. R. Walch, Rakesh K. Singh, Janne E. Søreide, Hugues Lantuit, Amanda Poste
      First page: 3123
      Abstract: Arctic coasts, which feature land-ocean transport of freshwater, sediments, and other terrestrial material, are impacted by climate change, including increased temperatures, melting glaciers, changes in precipitation and runoff. These trends are assumed to affect productivity in fjordic estuaries. However, the spatial extent and temporal variation of the freshwater-driven darkening of fjords remain unresolved. The present study illustrates the spatio-temporal variability of suspended particulate matter (SPM) in the Adventfjorden estuary, Svalbard, using in-situ field campaigns and ocean colour remote sensing (OCRS) via high-resolution Sentinel-2 imagery. To compute SPM concentration (CSPMsat), a semi-analytical algorithm was regionally calibrated using local in-situ data, which improved the accuracy of satellite-derived SPM concentration by ~20% (MRD). Analysis of SPM concentration for two consecutive years (2019, 2020) revealed strong seasonality of SPM in Adventfjorden. Highest estimated SPM concentrations and river plume extent (% of fjord with CSPMsat > 30 mg L−1) occurred during June, July, and August. Concurrently, we observed a strong relationship between river plume extent and average air temperature over the 24 h prior to the observation (R2 = 0.69). Considering predicted changes to environmental conditions in the Arctic region, this study highlights the importance of the rapidly changing environmental parameters and the significance of remote sensing in analysing fluxes in light attenuating particles, especially in the coastal Arctic Ocean.
      Citation: Remote Sensing
      PubDate: 2022-06-29
      DOI: 10.3390/rs14133123
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3124: Seaweed Habitats on the Shore:
           Characterization through Hyperspectral UAV Imagery and Field Sampling

    • Authors: Wendy Diruit, Anthony Le Bris, Touria Bajjouk, Sophie Richier, Mathieu Helias, Thomas Burel, Marc Lennon, Alexandre Guyot, Erwan Ar Gall
      First page: 3124
      Abstract: Intertidal macroalgal habitats are major components of temperate coastal ecosystems. Their distribution was studied using field sampling and hyperspectral remote mapping on a rocky shore of Porspoder (western Brittany, France). Covers of both dominating macroalgae and the sessile fauna were characterized in situ at low tide in 24 sampling spots, according to four bathymetric levels. A zone of ca. 17,000 m2 was characterized using a drone equipped with a hyperspectral camera. Macroalgae were identified by image processing using two classification methods to assess the representativeness of spectral classes. Finally, a comparison of the remote imaging data to the field sampling data was conducted. Seven seaweed classes were distinguished by hyperspectral pictures, including five different species of Fucales. The maximum likelihood (MLC) and spectral angle mapper (SAM) were both trained using image-derived spectra. MLC was more accurate to classify the main dominating species (Overall Accuracy (OA) 95.1%) than SAM (OA 87.9%) at a site scale. However, at sampling points scale, the results depend on the bathymetric level. This study evidenced the efficiency and accuracy of hyperspectral remote sensing to evaluate the distribution of dominating intertidal seaweed species and the potential for a combined field/remote approach to assess the ecological state of macroalgal communities.
      Citation: Remote Sensing
      PubDate: 2022-06-29
      DOI: 10.3390/rs14133124
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3125: New Reprocessing towards Life-Time
           Quality-Consistent Suomi NPP OMPS Nadir Sensor Data Records (SDR):
           Calibration Improvements and Impact Assessments on Long-Term Quality
           Stability of OMPS SDR Data Sets

    • Authors: Banghua Yan, Chunhui Pan, Trevor Beck, Xin Jin, Likun Wang, Ding Liang, Lawrence Flynn, Junye Chen, Jingfeng Huang, Steven Buckner, Cheng-Zhi Zou, Ninghai Sun, Lin Lin, Alisa Young, Lihang Zhou, Wei Hao
      First page: 3125
      Abstract: The Nadir Mapper (NM) and Nadir Profiler (NP) within the Ozone Mapping and Profiler Suites (OMPS) are ultraviolet spectrometers to measure Earth radiance and Solar irradiance spectra from 300–380 nm and 250–310 nm, respectively. The OMPS NM and NP instruments flying on the Suomi-NPP (SNPP) satellite have provided over ten years of operational Sensor Data Records (SDRs) data sets to support a variety of OMPS Environmental Data Record (EDR) applications. However, the discrepancies of quality remain in the operational OMPS SDR data prior to 28 June 2021 due to changes in calibration algorithms associated with the calibration coefficient look-up tables (LUTs) during this period. In this study, we present results for the newly (v2) reprocessed SNPP OMPS NM and NP SDR data prior to 30 June 2021, which uses consistent calibration tables with improved accuracy. Compared with a previous (v1) reprocessing, this new reprocessing includes the improvements associated with the following updated tables or error correction: an updated stray light correction table for the NM, an off-nadir geolocation error correction for the NM, an artificial offset error correction in the NM dark processing code, and biweekly solar wavelength LUTs for the NP. This study further analyzes the impact of each improvement on the quality of the OMPS SDR data by taking advantage of the existing OMPS SDR calibration/validation studies. Finally, this study compares the v2 reprocessed OMPS data sets with the operational and the v1 reprocessed data sets. The results demonstrate that the new reprocessing significantly improves the accuracy and consistency of the life-time SNPP OMPS NM and NP SDR data sets. It also advances the uniformity of the data over the dichroic range from 300 to 310 nm between the NM and NP. The normalized radiance differences at the same wavelength between the NM and NP observations are reduced from 0.001 order (v1 reprocessing) or 0.01 order (operational processing) to 0.001 order or smaller. The v2 reprocessed data are archived in the NOAA CLASS data center with the same format as the operational data.
      Citation: Remote Sensing
      PubDate: 2022-06-29
      DOI: 10.3390/rs14133125
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3126: Exploring VIIRS Night Light Long-Term

    • Authors: Changyong Cao, Bin Zhang, Frank Xia, Yan Bai
      First page: 3126
      Abstract: There is a great need to study the decadal long-term time series of urban night-light changes since the launch of Suomi NPP, NOAA-20, to future JPSS-2, 3, and 4 in the next decades. The recently recalibrated and reprocessed Suomi NPP VIIRS/DNB dataset overcomes a number of limitations in the operational data stream for time series studies. However, new methodologies are desirable to explore the large volume of historical data to reveal long-term socio-economic and environmental changes. In this study, we introduce a novel algorithm using convolutional neural network similarity index (CNN/SI) to rapidly and automatically identify cloud-free observations for selected cities. The derived decadal clear sky mean radiance time series allows us to study the urban night light changes over a long period of time. Our results show that the radiometric changes for some metropolitan areas changed on the order of 29% in the past decade, while others had no appreciable change. The strong seasonal variation in the mean radiance appears to be highly correlated with seasonal aerosol optical thickness. This study will facilitate the use of recalibrated/reprocessed data, and improve our understanding of urban night light changes due to geophysical, climatological, and socio-economic factors.
      Citation: Remote Sensing
      PubDate: 2022-06-29
      DOI: 10.3390/rs14133126
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3127: Hydrological Evaluation of
           Satellite-Based Precipitation Products in Hunan Province

    • Authors: Yan Yan, Guihua Wang, Nergui Nanding, Weitian Chen
      First page: 3127
      Abstract: The quality of satellite-based precipitation products including TMPA 3B42, IMERG-early, IMERG-final, and CMORPH-CRT, is evaluated by comparing with gauge observations in Hunan province of China between 2017 and 2019. By using the outputs of the Dominant River Routing Integrated with VIC Environment (DRIVE) model, the hydrological applications of gauge- and satellite-based precipitation products are analyzed by comparing them with streamflow observations. Furthermore, we conduct a case study considering Typhoon Bailu. It is found that IMERG-final can produce better results compared to the other three satellite-based products against gauge-based precipitation. In terms of discharge simulations, the gauge-based precipitation provides the most accurate results, followed by IMERG-final. During Typhoon Bailu, the peak of the mean gauge-based precipitation in the rainfall center (24.5°N–26°N, 111°E–114°E) occurred on 25 August 2019, whereas the daily streamflow reached its peak one day later, suggesting the lagged impact of precipitation on streamflow. From the Taylor diagram, the gauge-based precipitation is the most accurate for estimating the streamflow during Typhoon Bailu, followed by IMERG-final, IMERG-early, TMPA 3B42, and CMORPH-CRT, respectively. Overall, gauge-based precipitation has the best performance in terms of hydrological application, whereas IMERG-final performs the best among four satellite-based precipitation products.
      Citation: Remote Sensing
      PubDate: 2022-06-29
      DOI: 10.3390/rs14133127
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3128: Densely Residual Network with Dual
           Attention for Hyperspectral Reconstruction from RGB Images

    • Authors: Lixia Wang, Aditya Sole, Jon Yngve Hardeberg
      First page: 3128
      Abstract: In the last several years, deep learning has been introduced to recover a hyperspectral image (HSI) from a single RGB image and demonstrated good performance. In particular, attention mechanisms have further strengthened discriminative features, but most of them are learned by convolutions with limited receptive fields or require much computational cost, which hinders the function of attention modules. Furthermore, the performance of these deep learning methods is hampered by tackling multi-level features equally. To this end, in this paper, based on multiple lightweight densely residual modules, we propose a densely residual network with dual attention (DRN-DA), which utilizes advanced attention and adaptive fusion strategy for more efficient feature correlation learning and more powerful feature extraction. Specifically, an SE layer is applied to learn channel-wise dependencies, and dual downsampling spatial attention (DDSA) is developed to capture long-range spatial contextual information. All the intermediate-layer feature maps are adaptively fused. Experimental results on four data sets from the NTIRE 2018 and NTIRE 2020 Spectral Reconstruction Challenges demonstrate the superiority of the proposed DRN-DA over state-of-the-art methods (at least −6.19% and −1.43% on NTIRE 2018 “Clean” and “Real World” track, −6.85% and −5.30% on NTIRE 2020 “Clean” and “Real World” track) in terms of mean relative absolute error.
      Citation: Remote Sensing
      PubDate: 2022-06-29
      DOI: 10.3390/rs14133128
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3129: GNSS-R Soil Moisture Retrieval for
           Flat Vegetated Surfaces Using a Physics-Based Bistatic Scattering Model
           and Hybrid Global/Local Optimization

    • Authors: Amir Azemati, Amer Melebari, James D. Campbell, Jeffrey P. Walker, Mahta Moghaddam
      First page: 3129
      Abstract: This paper presents a soil moisture retrieval scheme from Cyclone Global Navigation Satellite System (CYGNSS) DDM over land. The proposed inversion method consists of a hybrid global and local optimization method and a physics-based bistatic scattering forward model. The forward model was developed for bare-to-densely vegetated terrains, and it predicts the circularly polarized BRCS DDM of the land surface. This method was tested on both simulated DDM and CYGNSS DDM over the SMAP Yanco core validation site in Australia. About 250 CYGNSS DDMs from 2019 and 2020 over the Yanco site were used for validation. The simulated DDM were for grassland and forest vegetation types. The vegetation type of the Yanco validation site was grassland. The VWC was 0.19kg/m2 and 4.89kg/m2 for the grassland and forest terrains, respectively. For the case when the surface roughness is known to the algorithm, the unbiased root mean square error (ubRMSE) of soil moisture estimates was less than 0.03m3/m3 while it was approximately 0.06m3/m3 and 0.09m3/m3 for the validation results from 2019 and 2020, respectively. The retrieval algorithm generally had enhanced performance for smaller values of soil moisture. For the case when both the soil moisture and surface roughness are unknown to the algorithm and only a single DDM is used for retrieval, the validation results showed an expected reduced performance, with an ubRMSE of less than 0.12m3/m3.
      Citation: Remote Sensing
      PubDate: 2022-06-29
      DOI: 10.3390/rs14133129
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3130: Automatic Deployment of Convolutional
           Neural Networks on FPGA for Spaceborne Remote Sensing Application

    • Authors: Tianwei Yan, Ning Zhang, Jie Li, Wenchao Liu, He Chen
      First page: 3130
      Abstract: In recent years, convolutional neural network (CNN)-based algorithms have been widely used in remote sensing image processing and show tremendous performance in a variety of application fields. However, large amounts of data and intensive computations make the deployment of CNN-based algorithms a challenging problem, especially for the spaceborne scenario where resources and power consumption are limited. To tackle this problem, this paper proposes an automatic CNN deployment solution on resource-limited field-programmable gate arrays (FPGAs) for spaceborne remote sensing applications. Firstly, a series of hardware-oriented optimization methods are proposed to reduce the complexity of the CNNs. Secondly, a hardware accelerator is designed. In this accelerator, a reconfigurable processing engine array with efficient convolutional computation architecture is used to accelerate CNN-based algorithms. Thirdly, to bridge the optimized CNNs and hardware accelerator, a compilation toolchain is introduced into the deployment solution. Through the automatic conversion from CNN models to hardware instructions, various networks can be deployed on hardware in real-time. Finally, we deployed an improved VGG16 network and an improved YOLOv2 network on Xilinx AC701 to evaluate the effectiveness of the proposed deployment solution. The experiments show that with only 3.407 W power consumption and 94 DSP consumption, our solution achieves 23.06 giga operations per second (GOPS) throughput in the improved VGG16 and 22.17 GOPS throughput in the improved YOLOv2. Compared to the related works, the DSP efficiency of our solution is improved by 1.3–2.7×.
      Citation: Remote Sensing
      PubDate: 2022-06-29
      DOI: 10.3390/rs14133130
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3131: Generalized Labeled Multi-Bernoulli
           Multi-Target Tracking with Doppler-Only Measurements

    • Authors: Yun Zhu, Mahendra Mallick, Shuang Liang, Junkun Yan
      First page: 3131
      Abstract: The paper addresses the problem of tracking multiple targets with Doppler-only measurements in multi-sensor systems. It is well known that the observability of the target state measured using Doppler-only measurements is very poor, which makes it difficult to initialize the tracking target and produce the target trajectory in any tracking algorithm. Within the framework of random finite sets, we propose a novel constrained admissible region (CAR) based birth model that instantiates the birth distribution using Doppler-only measurements. By combining physics-based constraints in the unobservable subspace of the state space, the CAR based birth model can effectively reduce the ambiguity of the initial state. The CAR based birth model combines physics-based constraints in the unobservable subspace of the state space to reduce the ambiguity of the initial state. We implement the CAR based birth model with the generalized labeled multi-Bernoulli tracking filter to demonstrate the effectiveness of our proposed algorithm in Doppler-only tracking. The performance of the proposed approach is tested in two simulation scenarios in terms of the optimal subpattern assignment (OSPA) error, OSPA(2) (2)error, and computing efficiency. The simulation results demonstrate the superiority of the proposed approach. Compared to the approach taken by the state-of-the-art methods, the proposed approach can at most reduce the OSPA error by 58.77%, reduce the OSPA(2) error by 43.51%, and increase the computing efficiency by 9.56 times in the first scenario. In the second scenario, the OSPA error is reduced by 62.80%, the OSPA(2) (2)error is reduced by 43.65%, and the computing efficiency is increased by 2.61 times at most.
      Citation: Remote Sensing
      PubDate: 2022-06-29
      DOI: 10.3390/rs14133131
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3132: RADAR-Vegetation Structural
           Perpendicular Index (R-VSPI) for the Quantification of Wildfire Impact and
           Post-Fire Vegetation Recovery

    • Authors: Aakash Chhabra, Christoph Rüdiger, Marta Yebra, Thomas Jagdhuber, James Hilton
      First page: 3132
      Abstract: The precise information on fuel characteristics is essential for wildfire modelling and management. Satellite remote sensing can provide accurate and timely measurements of fuel characteristics. However, current estimates of fuel load changes from optical remote sensing are obstructed by seasonal cloud cover that limits their continuous assessments. This study utilises remotely sensed Synthetic-Aperture Radar (SAR) (Sentinel-1 backscatter) data as an alternative to optical-based imaging (Sentinel-2 scaled surface reflectance). SAR can penetrate clouds and offers high-spatial and medium-temporal resolution datasets and can hence complement the optical dataset. Inspired by the optical-based Vegetation Structural Perpendicular Index (VSPI), an SAR-based index termed RADAR-VSPI (R-VSPI) is introduced in this study. R-VSPI characterises the spatio-temporal changes in fuel load due to wildfire and the subsequent vegetation recovery thereof. The R-VSPI utilises SAR backscatter (σ°) from the co-polarized (VV) and cross-polarized (VH) channels at a centre frequency of 5.4 GHz. The newly developed index is applied over major wildfire events that occurred during the “Black Summer” wildfire season (2019–2020) in southern Australia. The condition of the fuel load was mapped every 5 (any orbit) to 12 (same orbit) days at an aggregated spatial resolution of 110 m. The results show that R-VSPI was able to quantify fuel depletion by wildfire (relative to healthy vegetation) and monitor its subsequent post-fire recovery. The information on fuel condition and heterogeneity improved at high-resolution by adapting the VSPI on a dual-polarization SAR dataset (R-VSPI) compared to the historic forest fuel characterisation methods (that used visible and infrared bands only for fuel estimations). The R-VSPI thus provides a complementary source of information on fuel load changes in a forest landscape compared to the optical-based VSPI, in particular when optical observations are not available due to cloud cover.
      Citation: Remote Sensing
      PubDate: 2022-06-29
      DOI: 10.3390/rs14133132
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3133: Evaluation of Field Applicability of
           High-Speed 3D Digital Image Correlation for Shock Vibration Measurement in
           Underground Mining

    • Authors: Seunghwan Seo, Younghun Ko, Moonkyung Chung
      First page: 3133
      Abstract: When combined with high-speed photography technology, the digital image correlation (DIC) method provides an excellent photographic image processing capability that can be used to convert the evolving full-field surface properties of structures to sets of two-dimensional (2D) or three-dimensional (3D) coordinate values. In this study, the applicability of the DIC method and high-speed cameras in underground mining was investigated by measuring the displacement and vibration of rock pillars caused by blasting. This technique is used extensively in engineering and is increasingly being applied to new fields. As a result of comparing the DIC results for blast vibration with the measured values of the contact sensor through field tests, the maximum displacement and vibration speed were found to be 86% and 93% accurate, respectively. In addition, the obtained values appeared similar to those predicted through numerical analysis. Field test results indicate that both methods yielded similar results. Therefore, it is concluded that it is feasible to use the DIC and high-speed camera to measure ground displacements and vibrations from blasting in underground mining. In addition, the system conditions required for blast vibration measurement were considered by comparing the accuracy with the existing measurement methods.
      Citation: Remote Sensing
      PubDate: 2022-06-29
      DOI: 10.3390/rs14133133
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3134: Evaluating Effects of
           Medium-Resolution Optical Data Availability on Phenology-Based Rice
           Mapping in China

    • Authors: Ruoqi Liu, Geli Zhang, Jinwei Dong, Yan Zhou, Nanshan You, Yingli He, Xiangming Xiao
      First page: 3134
      Abstract: The phenology-based approach has proven effective for paddy rice mapping due to the unique flooding and transplanting features of rice during the early growing season. However, the method may be greatly affected if no valid observations are available during the flooding and rice transplanting phase. Here, we compare the effects of data availability of different sensors in the critical phenology phase, thereby supporting paddy rice mapping based on phenology-based approaches. Importantly, our study further analyzed the effects of the spatial pattern of the valid observations related to certain factors (i.e., sideslips, clouds, and temporal window lengths of flooding and rice transplanting), which supply the applicable area of the phenology-based approach indications. We first determined the flooding and rice transplanting phase using in situ observational data from agrometeorological stations and remote sensing data, then evaluated the effects of data availability in this phase of 2020 in China using all Landsat-7 and 8 and Sentinel-2 data. The results show that on the country level, the number of average valid observations during the flooding and rice transplanting phase was more than ten for the integration of Landsat and Sentinel images. On the sub-country level, the number of average valid observations was high in the cold temperate zone (17.4 observations), while it was relatively lower in southern China (6.4 observations), especially in Yunnan–Guizhou Plateau, which only had three valid observations on average. Based on the multicollinearity test, the three factors are significantly correlated with the absence of valid observations: (R2 = 0.481) and Std.Coef. (Std. Err.) are 0.306 (0.094), −0.453 (0.003) and −0.547 (0.019), respectively. Overall, these results highlight the substantial spatial heterogeneity of valid observations in China, confirming the reliability of the integration of Landsat-7 and 8 and Sentinel-2 imagery for paddy rice mapping based on phenology-based approaches. This can pave the way for a national-scale effort of rice mapping in China while further indicating potential omission errors in certain cloud-prone regions without sufficient optical observation data, i.e., the Sichuan Basin.
      Citation: Remote Sensing
      PubDate: 2022-06-29
      DOI: 10.3390/rs14133134
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3135: Mapping a European Spruce Bark Beetle
           Outbreak Using Sentinel-2 Remote Sensing Data

    • Authors: Michele Dalponte, Yady Tatiana Solano-Correa, Lorenzo Frizzera, Damiano Gianelle
      First page: 3135
      Abstract: Insect outbreaks affect forests, causing the deaths of trees and high economic loss. In this study, we explored the detection of European spruce bark beetle (Ips typographus, L.) outbreaks at the individual tree crown level using multispectral satellite images. Moreover, we explored the possibility of tracking the progression of the outbreak over time using multitemporal data. Sentinel-2 data acquired during the summer of 2020 over a bark beetle–infested area in the Italian Alps were used for the mapping and tracking over time, while airborne lidar data were used to automatically detect the individual tree crowns and to classify tree species. Mapping and tracking of the outbreak were carried out using a support vector machine classifier with input vegetation indices extracted from the multispectral data. The results showed that it was possible to detect two stages of the outbreak (i.e., early, and late) with an overall accuracy of 83.4%. Moreover, we showed how it is technically possible to track the evolution of the outbreak in an almost bi-weekly period at the level of the individual tree crowns. The outcomes of this paper are useful from both a management and ecological perspective: it allows forest managers to map a bark beetle outbreak at different stages with a high spatial accuracy, and the maps describing the evolution of the outbreak could be used in further studies related to the behavior of bark beetles.
      Citation: Remote Sensing
      PubDate: 2022-06-29
      DOI: 10.3390/rs14133135
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3136: Investigating Phases of Thermal
           Unrest at Ambrym (Vanuatu) Volcano through the Normalized Hot Spot Indices
           Tool and the Integration with the MIROVA System

    • Authors: Francesco Marchese, Diego Coppola, Alfredo Falconieri, Nicola Genzano, Nicola Pergola
      First page: 3136
      Abstract: Ambrym is an active volcanic island, located in the Vanuatu archipelago, consisting of a 12 km-wide summit caldera. This open vent volcano is characterized by an almost persistent degassing activity which occurs in the Benbow and Marum craters, which were also the site of recent lava lakes. On 15 December 2018, about three years after an intense lava effusion, the first recorded since 1989, a small-scale intra-caldera fissure eruption occurred. On 16 December, the eruption stopped, and the lava lakes at the Benbow and Marum craters were drained. In this work, we investigated the thermal activity of the Ambrym volcano, before, during, and after the 15 December 2018 eruption, using daytime Sentinel-2 (S2) Multispectral Instruments (MSI) and Landsat-8 (L8) Operational Land Imager (OLI) data, at a mid-high spatial resolution. The results were integrated with Moderate Resolution Imaging Spectroradiometer (MODIS) observations. Outputs of the Normalized Hotspot Indices (NHI) tool, retrieved from S2-MSI and L8-OLI data, show that the thermal activity at the Ambrym craters increased about three weeks before the 15 December 2018 lava effusion. This information is consistent with the estimates of volcanic radiative power (VRP), which were performed by the Middle Infrared Observation of Volcanic Activity (MIROVA) system, by analyzing the nighttime MODIS data. The latter revealed a significant increase of VRP, with values above 700 MW at the end of the October–November 2018 period. Moreover, the drastic reduction of thermal emissions at the craters, marked by the NHI tool since the day of the fissure eruption, is consistent with the drop in the lava lake level that was independently suggested in a previous study. These results demonstrate that the S2-MSI and L8-OLI time series, combined with infrared MODIS observations, may contribute to detecting increasing trends in lava lake activity, which may precede effusive eruptions at the open vent volcanoes. This study addresses some challenging scenarios regarding the definition of possible threshold levels (e.g., in terms of VRP and total Short Wave Infrared radiance) from the NHI and MIROVA datasets, which could require special attention from local authorities in terms of the occurrence of possible future eruptions.
      Citation: Remote Sensing
      PubDate: 2022-06-29
      DOI: 10.3390/rs14133136
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3137: Downscaling Satellite Soil Moisture
           Using a Modular Spatial Inference Framework

    • Authors: Ricardo M. Llamas, Leobardo Valera, Paula Olaya, Michela Taufer, Rodrigo Vargas
      First page: 3137
      Abstract: Soil moisture is an important parameter that regulates multiple ecosystem processes and provides important information for environmental management and policy decision-making. Spaceborne sensors provide soil moisture information over large areas, but information is commonly available at coarse resolution with spatial and temporal gaps. Here, we present a modular spatial inference framework to downscale satellite-derived soil moisture using terrain parameters and test the performance of two modeling methods (Kernel-Weighted K-Nearest Neighbor <KKNN> and Random Forest <RF>). We generate monthly and weekly gap-free spatial predictions on soil moisture at 1 km using data from the European Space Agency Climate Change Initiative (ESA-CCI; version 6.1) over two regions in the conterminous United States. RF was the method that performed better in cross-validation when comparing with the reference ESA-CCI data, but KKNN showed a slightly higher agreement with ground-truth information as part of independent validation. We postulate that more heterogeneous landscapes (i.e., high topographic variation) may be more challenging for downscaling and predicting soil moisture; therefore, moisture networks should increase monitoring efforts across these complex landscapes. Future opportunities for development of modular cyberinfrastructure tools for downscaling satellite-derived soil moisture are discussed.
      Citation: Remote Sensing
      PubDate: 2022-06-29
      DOI: 10.3390/rs14133137
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3138: Initial Tropospheric Wind
           Observations by Sanya Incoherent Scatter Radar

    • Authors: Ning Zhang, Xinan Yue, Feng Ding, Baiqi Ning, Junyi Wang, Junhao Luo, Yonghui Wang, Mingyuan Li, Yihui Cai
      First page: 3138
      Abstract: Sanya incoherent scatter radar (SYISR) is a newly developed phased array incoherent scatter radar in the low latitudes of China located at Sanya (18.3°N, 109.6°E), Hainan Province. The main objective of SYISR is to observe the ionosphere. Given its frequency and power, it should have the capability to observe the troposphere. In this study, we show several tropospheric wind experiments that may indicate radar function expansion and capability verification, although observing the troposphere will not be an operation mode in the future. Reliable radar echoes were detected by SYISR up to 20 km with a turbulence scale of 0.35 m and a frequency of 430 MHz. Generally, both the geometric (GEO) method and the velocity azimuth display (VAD) method give similar wind profiles. Above 10 km, the discrepancy between the two methods becomes nonnegligible. For the same method, the discrepancy above 15–20 km among winds derived from different zenith angle measurements is nonnegligible. The VAD methods give more reasonable results at higher altitudes. The standard deviation of the difference (SYISR radar minus the reanalysis data ERA5) for zonal wind and meridional wind was 1.1 m/s and 0.78 m/s, respectively. During rainfall, we can distinguish the spectrum of rainfall and atmospheric turbulence from the power spectrum according to the spectral widths and Doppler frequency shifts.
      Citation: Remote Sensing
      PubDate: 2022-06-29
      DOI: 10.3390/rs14133138
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3139: Can Satellite-Based Thermal Anomalies
           Be Indicative of Heatwaves' An Investigation for MODIS Land Surface
           Temperatures in the Mediterranean Region

    • Authors: Ilias Agathangelidis, Constantinos Cartalis, Anastasios Polydoros, Thaleia Mavrakou, Kostas Philippopoulos
      First page: 3139
      Abstract: In recent years, an exceptional number of record-shattering temperature extremes have been observed, resulting in significant societal and environmental impacts. The Mediterranean region is particularly thermally vulnerable, frequently suffering from intense and severe heatwaves. Using daily temperature observations from 58 weather stations (NOAA Global Historical Climatology Network daily database) in the Mediterranean area, past heatwave episodes were initially detected. A daily LST time series was developed using Land Surface Temperature (LST) products from Moderate Resolution Imaging Spectroradiometer (MODIS) (Terra & Aqua satellites) for a 19-year period (2002–2020) at the station locations. LST anomalies were identified using percentile-based indices. It was found that remotely sensed-based LST presents the potential for understanding and monitoring heatwave events, as surface thermal anomalies were generally indicative of heatwaves. Approximately 42% (39%) of heatwave days during daytime (nighttime) coincided with LST anomalies; conversely, 51% of daytime LST anomalies overlapped with the exact days of a heatwave (38% at night). Importantly, the degree of association was significantly higher for extremely hot days (up to an 80% match) and long-lasting heatwaves (up to an 85% match). Rising trends in frequency and duration were observed for both heatwaves and LST anomalies. The results advance the understanding of surface-atmosphere coupling during extreme temperature days and reflect the suitability of thermal remote sensing in heatwave preparedness strategies.
      Citation: Remote Sensing
      PubDate: 2022-06-29
      DOI: 10.3390/rs14133139
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3140: Quantifying the Impacts of the 2020
           Flood on Crop Production and Food Security in the Middle Reaches of the
           Yangtze River, China

    • Authors: Liang-Chen Wang, Duc Vinh Hoang, Yuei-An Liou
      First page: 3140
      Abstract: This study uses satellite imagery and geospatial data to examine the impact of floods over the main planting areas for double-cropping rice and grain crops in the middle reaches of the Yangtze River. During summer 2020, a long-lasting 62-day heavy rainfall caused record-breaking floods over large areas of China, especially the Yangtze basin. Through close examination of Sentinel-1/2 satellite imagery and Copernicus Global Land Cover, between July and August 2020, the inundation area reached 21,941 and 23,063 km2, and the crop-affected area reached 11,649 and 11,346 km2, respectively. We estimated that approximately 4.66 million metric tons of grain crops were seriously affected in these two months. While the PRC government denied that food security existed, the number of Grains and Feeds imported from the U.S. between January to July 2021 increased by 316%. This study shows that with modern remote sensing techniques, stakeholders can obtain critical estimates of large-scale disaster events much earlier than other indicators, such as disaster field surveys or crop price statistics. Potential use could include but is not limited to monitoring floods and land use coverage changes.
      Citation: Remote Sensing
      PubDate: 2022-06-30
      DOI: 10.3390/rs14133140
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3141: Near Real-Time Fire Detection and
           Monitoring in the MATOPIBA Region, Brazil

    • Authors: Mikhaela A. J. S. Pletsch, Thales S. Körting, Felipe C. Morita, Celso H. L. Silva-Junior, Liana O. Anderson, Luiz E. O. C. Aragão
      First page: 3141
      Abstract: MATOPIBA is an agricultural frontier, where fires are essential for its biodiversity maintenance. However, the increase in its recurrence and intensity, as well as accidental fires can lead to socioeconomic and environmental losses. Due to this dual relationship with fire, near real-time (NRT) fire management is required throughout the region. In this context, we developed, to the best of our knowledge, the first Machine Learning (ML) algorithm based on the GOES-16 ABI sensor able to detect and monitor Active Fires (AF) in NRT in MATOPIBA. To do so, we analyzed the best combination of three ML algorithms and how long it takes to consider a historical time series able to support accurate AF predictions. We used the most accurate combination for the final model (FM) development. The results show that the FM ensures an overall accuracy rate of approximately 80%. The FM potential is remarkable not only for single detections but also for a consecutive sequence of positive predictions. Roughly, the FM achieves an accuracy rate peak after around 20 h of consecutive AF detections, but there is an important trade-off between the accuracy and the time required to assemble more fire indications, which can be decisive for firefighters in real life.
      Citation: Remote Sensing
      PubDate: 2022-06-30
      DOI: 10.3390/rs14133141
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3142: Analysis of the Spatial and Temporal
           Evolution of Land Subsidence in Wuhan, China from 2017 to 2021

    • Authors: Yizhan Zhao, Lv Zhou, Cheng Wang, Jiahao Li, Jie Qin, Haiquan Sheng, Liangke Huang, Xin Li
      First page: 3142
      Abstract: Land subsidence is a common geological hazard. Rapid urban expansion has led to different degrees of ground subsidence within Wuhan in the past few years. The novel coronavirus outbreak in 2020 has seriously impacted urban construction and people’s lives in Wuhan. Land subsidence in Wuhan has changed greatly with the resumption of work and production. We used 80 Sentinel-1A Synthetic Aperture Radar (SAR) images covering Wuhan to obtain the land subsidence change information of Wuhan from July 2017 to September 2021 by using the small baseline subset interferometric SAR technique. Results show that the subsidence in Wuhan is uneven and concentrated in a few areas, and the maximum subsidence rate reached 57 mm/yr during the study period. Compared with land deformation before 2017, the land subsidence in Wuhan is more obvious after 2020. The most severe area of subsidence is located near Qingling in Hongshan District, with a maximum accumulated subsidence of 90 mm, and obvious subsidence funnels are observed in Qiaokou, Jiangan, Wuchang and Qingshan Districts. The location of subsidence centers in Wuhan is associated with building intensity, and most of the subsidence funnels are formed in connection with urban subway construction and building construction. Carbonate belt and soft ground cover areas are more likely to lead to karst collapse and land subsidence phenomena. Seasonal changes are observed in the land subsidence in Wuhan. A large amount of rainfall can replenish groundwater resources and reduce the rate of land subsidence. The change in water level in the Yangtze River has a certain impact on the land subsidence along the rivers in Wuhan, but the overall impact is small. An obvious uplift is observed in Caidian District in the south of Wuhan, and the reason may be related to the physical and chemical expansion effects of the expansive clay.
      Citation: Remote Sensing
      PubDate: 2022-06-30
      DOI: 10.3390/rs14133142
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3143: Comparison of Deep Learning Methods
           for Detecting and Counting Sorghum Heads in UAV Imagery

    • Authors: He Li, Peng Wang, Chong Huang
      First page: 3143
      Abstract: With the rapid development of remote sensing with small, lightweight unmanned aerial vehicles (UAV), efficient and accurate crop spike counting, and yield estimation methods based on deep learning (DL) methods have begun to emerge, greatly reducing labor costs and enabling fast and accurate counting of sorghum spikes. However, there has not been a systematic, comprehensive evaluation of their applicability in cereal crop spike identification in UAV images, especially in sorghum head counting. To this end, this paper conducts a comparative study of the performance of three common DL algorithms, EfficientDet, Single Shot MultiBox Detector (SSD), and You Only Look Once (YOLOv4), for sorghum head detection based on lightweight UAV remote sensing data. The paper explores the effects of overlap ratio, confidence, and intersection over union (IoU) parameters, using the evaluation metrics of precision P, recall R, average precision AP, F1 score, computational efficiency, and the number of detected positive/negative samples (Objects detected consistent/inconsistent with real samples). The experiment results show the following. (1) The detection results of the three methods under dense coverage conditions were better than those under medium and sparse conditions. YOLOv4 had the most accurate detection under different coverage conditions; on the contrary, EfficientDet was the worst. While SSD obtained better detection results under dense conditions, the number of over-detections was larger. (2) It was concluded that although EfficientDet had a good positive sample detection rate, it detected the fewest samples, had the smallest R and F1, and its actual precision was poor, while its training time, although medium, had the lowest detection efficiency, and the detection time per image was 2.82-times that of SSD. SSD had medium values for P, AP, and the number of detected samples, but had the highest training and detection efficiency. YOLOv4 detected the largest number of positive samples, and its values for R, AP, and F1 were the highest among the three methods. Although the training time was the slowest, the detection efficiency was better than EfficientDet. (3) With an increase in the overlap ratios, both positive and negative samples tended to increase, and when the threshold value was 0.3, all three methods had better detection results. With an increase in the confidence value, the number of positive and negative samples significantly decreased, and when the threshold value was 0.3, it balanced the numbers for sample detection and detection accuracy. An increase in IoU was accompanied by a gradual decrease in the number of positive samples and a gradual increase in the number of negative samples. When the threshold value was 0.3, better detection was achieved. The research findings can provide a methodological basis for accurately detecting and counting sorghum heads using UAV.
      Citation: Remote Sensing
      PubDate: 2022-06-30
      DOI: 10.3390/rs14133143
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3144: Accurate Recognition of Building
           Rooftops and Assessment of Long-Term Carbon Emission Reduction from
           Rooftop Solar Photovoltaic Systems Fusing GF-2 and Multi-Source Data

    • Authors: Shaofu Lin, Chang Zhang, Lei Ding, Jing Zhang, Xiliang Liu, Guihong Chen, Shaohua Wang, Jinchuan Chai
      First page: 3144
      Abstract: Rooftop solar photovoltaic (PV) retrofitting can greatly reduce the emissions of greenhouse gases, thus contributing to carbon neutrality. Effective assessment of carbon emission reduction has become an urgent challenge for the government and for business enterprises. In this study, we propose a method to assess accurately the potential reduction of long-term carbon emission by installing solar PV on rooftops. This is achieved using the joint action of GF-2 satellite images, Point of Interest (POI) data, and meteorological data. Firstly, we introduce a building extraction method that extends the DeepLabv3+ by fusing the contextual information of building rooftops in GF-2 images through multi-sensory fields. Secondly, a ridgeline detection algorithm for rooftop classification is proposed, based on the Hough transform and Canny edge detection. POI semantic information is used to calculate the usable area under different subsidy policies. Finally, a multilayer perceptron (MLP) is constructed for long-term PV electricity generation series with regional meteorological data, and carbon emission reduction is estimated for three scenarios: the best, the general, and the worst. Experiments were conducted with GF-2 satellite images collected in Daxing District, Beijing, China in 2021. Final results showed that: (1) The building rooftop recognition method achieved overall accuracy of 95.56%; (2) The best, the general and the worst amount of annual carbon emission reductions in the study area were 7,705,100 tons, 6,031,400 tons, and 632,300 tons, respectively; (3) Multi-source data, such as POIs and climate factors play an indispensable role for long-term estimation of carbon emission reduction. The method and conclusions provide a feasible approach for quantitative assessment of carbon reduction and policy evaluation.
      Citation: Remote Sensing
      PubDate: 2022-06-30
      DOI: 10.3390/rs14133144
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3145: An Ultra-Resolution Features
           Extraction Suite for Community-Level Vegetation Differentiation and
           Mapping at a Sub-Meter Resolution

    • Authors: Ram C. Sharma
      First page: 3145
      Abstract: This paper presents two categories of features extraction and mapping suite, a very high-resolution suite and an ultra-resolution suite at 2 m and 0.5 m resolutions, respectively, for the differentiation and mapping of land cover and community-level vegetation types. The features extraction flow of the ultra-resolution suite involves pan-sharpening of the multispectral image, color-transformation of the pan-sharpened image, and the generation of panchromatic textural features. The performance of the ultra-resolution features extraction suite was compared with the very high-resolution features extraction suite that involves the calculation of radiometric indices and color-transformation of the multi-spectral image. This research was implemented in three mountainous ecosystems located in a cool temperate region. Three machine learning classifiers, Random Forests, XGBoost, and SoftVoting, were employed with a 10-fold cross-validation method for quantitatively evaluating the performance of the two suites. The ultra-resolution suite provided 5.3% more accuracy than the very high-resolution suite using single-date autumn images. Addition of summer images gained 12.8% accuracy for the ultra-resolution suite and 13.2% accuracy for the very high-resolution suite across all sites, while the ultra-resolution suite showed 4.9% more accuracy than the very high-resolution suite. The features extraction and mapping suites presented in this research are expected to meet the growing need for differentiating land cover and community-level vegetation types at a large scale.
      Citation: Remote Sensing
      PubDate: 2022-06-30
      DOI: 10.3390/rs14133145
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3146: 2D&3DHNet for 3D Object
           Classification in LiDAR Point Cloud

    • Authors: Wei Song, Dechao Li, Su Sun, Lingfeng Zhang, Yu Xin, Yunsick Sung, Ryong Choi
      First page: 3146
      Abstract: Accurate semantic analysis of LiDAR point clouds enables the interaction between intelligent vehicles and the real environment. This paper proposes a hybrid 2D and 3D Hough Net by combining 3D global Hough features and 2D local Hough features with a classification deep learning network. Firstly, the 3D object point clouds are mapped into the 3D Hough space to extract the global Hough features. The generated global Hough features are input into the 3D convolutional neural network for training global features. Furthermore, a multi-scale critical point sampling method is designed to extract critical points in the 2D views projected from the point clouds to reduce the computation of redundant points. To extract local features, a grid-based dynamic nearest neighbors algorithm is designed by searching the neighbors of the critical points. Finally, the two networks are connected to the full connection layer, which is input into fully connected layers for object classification.
      Citation: Remote Sensing
      PubDate: 2022-06-30
      DOI: 10.3390/rs14133146
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3147: Suggestive Data Annotation for
           CNN-Based Building Footprint Mapping Based on Deep Active Learning and
           Landscape Metrics

    • Authors: Zhichao Li, Shuai Zhang, Jinwei Dong
      First page: 3147
      Abstract: Convolutional neural network (CNN)-based very high-resolution (VHR) image segmentation has become a common way of extracting building footprints. Despite publicly available building datasets and pre-trained CNN models, it is still necessary to prepare sufficient labeled image tiles to train CNN models from scratch or update the parameters of pre-trained CNN models to extract buildings accurately in real-world applications, especially the large-scale building extraction, due to differences in landscapes and data sources. Deep active learning is an effective technique for resolving this issue. This study proposes a framework integrating two state-of-the-art (SOTA) models, U-Net and DeeplabV3+, three commonly used active learning strategies, (i.e., margin sampling, entropy, and vote entropy), and landscape characterization to illustrate the performance of active learning in reducing the effort of data annotation, and then understand what kind of image tiles are more advantageous for CNN-based building extraction. The framework enables iteratively selecting the most informative image tiles from the unlabeled dataset for data annotation, training the CNN models, and analyzing the changes in model performance. It also helps us to understand the landscape features of iteratively selected image tiles via active learning by considering building as the focal class and computing the percent, the number of patches, edge density, and landscape shape index of buildings based on labeled tiles in each selection. The proposed method was evaluated on two benchmark building datasets, WHU satellite dataset II and WHU aerial dataset. Models in each iteration were trained from scratch on all labeled tiles. Experimental results based on the two datasets indicate that, for both U-Net and DeeplabV3+, the three active learning strategies can reduce the number of image tiles to be annotated and achieve good model performance with fewer labeled image tiles. Moreover, image tiles with more building patches, larger areas of buildings, longer edges of buildings, and more dispersed building distribution patterns were more effective for model training. The study not only provides a framework to reduce the data annotation efforts in CNN-based building extraction but also summarizes the preliminary suggestions for data annotation, which could facilitate and guide data annotators in real-world applications.
      Citation: Remote Sensing
      PubDate: 2022-06-30
      DOI: 10.3390/rs14133147
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3148: Using Remote Sensing to Identify
           Urban Fringe Areas and Their Spatial Pattern of Educational Resources: A
           Case Study of the Chengdu-Chongqing Economic Circle

    • Authors: Wei Lu, Yuechen Li, Rongkun Zhao, Yue Wang
      First page: 3148
      Abstract: Rapid urbanization has already caused many impacts, such as environmental degradation and imbalanced resource allocation. As the frontiers of urbanization, urban fringe areas (UFAs) present both urban and rural characteristics and undergo complex socio-economic structural changes. Accurately identifying the spatial extent of UFAs is highly significant because it contributes to understanding the pattern of urban spatial expansion and guides future urban planning. However, existing methods are strongly affected by subjective factors. To solve this problem, this study presents a new approach to identifying UFAs, with the Chengdu-Chongqing economic circle as the study area. The new method achieved an identification accuracy of 74.2%, effectively eliminated some noise points, and reduced the influence of subjective factors. From an applied perspective, this study employed the Geo-information Tupu and density-field-based hotspot detector to analyze the spatial pattern of educational resources. Overall, the results showed that hotspots of educational resources are concentrated in places with good transportation or near urban areas; and the generalized symmetric structure Tupu of hotspots is diverse. In addition, the results can reveal the hotspot formation mechanism and provide a reference for resource allocation.
      Citation: Remote Sensing
      PubDate: 2022-06-30
      DOI: 10.3390/rs14133148
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3149: Seasonal Variation in Microphysical
           Characteristics of Precipitation at the Entrance of Water Vapor Channel in
           Yarlung Zangbo Grand Canyon

    • Authors: Ran Li, Gaili Wang, Renran Zhou, Jingyi Zhang, Liping Liu
      First page: 3149
      Abstract: Mêdog is located at the entrance of the water vapor channel in the Yarlung Zangbo Grand Canyon (YGC). This area has the largest annual accumulated rainfall totals and precipitation frequency on the Tibetan Plateau (TP). This paper investigates the seasonal variation in raindrop size distribution (DSD) characteristics in Mêdog based on disdrometer observations from 1 July 2019 to 30 June 2020. The DSD characteristics are examined under six rain rate classes and two rainfall types (stratiform and convective) in the winter, premonsoon, monsoon and postmonsoon periods. The highest (lowest) concentration of small raindrops is observed in monsoon (winter) precipitation, whereas large raindrops predominate in premonsoon precipitation. For stratiform rainfall, the mean mass-weighted mean diameter (Dm) exhibits overlooked differences in the four periods, while the mean normalized intercept parameter (Nw) is significantly higher in the monsoon period than in the other three periods. The convective rainfall in the monsoon and postmonsoon periods is characterized by a high concentration of limited-size drops and can be classified as maritime-like. This is probably attributed to abundant warm and humid airflow transported by the Indian Ocean monsoon into Mêdog. The westerly winds prevail over the TP during the premonsoon period, and thereby the premonsoon convective rainfall in Mêdog has a larger mean Dm and a lower mean Nw. In addition, the relationships of radar reflectivity Z and rain rate R for different precipitation types in different periods are also derived. A better understanding of the seasonal variation in the microphysical characteristics of precipitation in Mêdog is important for improving the microphysical parameterization scheme and the precipitation forecast of models on the TP.
      Citation: Remote Sensing
      PubDate: 2022-06-30
      DOI: 10.3390/rs14133149
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3150: Distribution, Magnitude, and
           Variability of Natural Oil Seeps in the Gulf of Mexico

    • Authors: Carrie O’Reilly, Mauricio Silva, Samira Daneshgar Asl, William P. Meurer, Ian R. MacDonald
      First page: 3150
      Abstract: The Gulf of Mexico is a hydrocarbon-rich region characterized by the presence of floating oil slicks from persistent natural hydrocarbon seeps, which are reliably captured by synthetic aperture radar (SAR) satellite imaging. Improving the state of knowledge of hydrocarbon seepage in the Gulf of Mexico improves the understanding and quantification of natural seepage rates in North America. We used data derived from SAR scenes collected over the Gulf of Mexico from 1978 to 2018 to locate oil slick origins (OSOs), cluster the OSOs into discrete seep zones, estimate the flux of individual seepage events, and calculate seep recurrence rates. In total, 1618 discrete seep zones were identified, primarily concentrated in the northern Gulf of Mexico within the Louann salt formation, with a secondary concentration in the Campeche region. The centerline method was used to estimate flux based on the drift length of the slick (centerline), the slick area, and average current and wind speeds. Flux estimates from the surface area of oil slicks varied geographically and temporally; on average, seep zones exhibited an 11% recurrence rate, suggesting possible intermittent discharge from natural seeps. The estimated average instantaneous flux for natural seeps is 9.8 mL s−1 (1.9 × 103 bbl yr−1), with an annual discharge of 1.73–6.69 × 105 bbl yr−1 (2.75–10.63 × 104 m3 yr−1) for the entire Gulf of Mexico. The temporal variability of average flux suggests a potential decrease following 1995; however, analysis of flux in four lease blocks indicates that flux has not changed substantially over time. It is unlikely that production activities in the Gulf of Mexico impact natural seepage on a human timescale. Of the 1618 identified seep zones, 1401 are located within U.S. waters, with 70 identified as having flux and recurrence rates significantly higher than the average. Seep zones exhibiting high recurrence rates are more likely to be associated with positive seismic anomalies. Many of the methods developed for this study can be applied to SAR-detected oil slicks in other marine settings to better assess the magnitude of global hydrocarbon seepage.
      Citation: Remote Sensing
      PubDate: 2022-06-30
      DOI: 10.3390/rs14133150
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3151: Trend Changes of the Vegetation
           Activity in Northeastern East Asia and the Connections with Extreme
           Climate Indices

    • Authors: Zijing Guo, Wei Lou, Cheng Sun, Bin He
      First page: 3151
      Abstract: In the context of global warming, vegetation activity in northeastern East Asia (40–45°N, 105–130°E) (NEA) shows a significant growth trend on a multidecadal scale, but how vegetation changes on a decadal scale is unclear. In this study, we find a significant trend of vegetation greening in northeastern East Asia during 1982–1998 and a slowdown in the greening trend during 1998–2014. Trend analysis of the extreme climate indices reveals that the trends of precipitation-related extreme climate indices are similar to those of vegetation change, and further correlation analysis reveals that precipitation-related extreme climate indices have a strong positive correlation with the NDVI. The results indicate that the vegetation in northeastern East Asia is more sensitive to precipitation changes, especially extreme precipitation, compared with the temperature and related extreme indices. Furthermore, the analysis of large-scale atmospheric circulation changes suggests a role of Northwest Pacific subtropical high (NPSH) in the trend changes of precipitation-related extreme indices. The strengthening of NPSH before 1998 enhances the moisture transport to the NEA, providing abundant water vapor favorable for extreme precipitation events, while after 1998, the NPSH trend is much weakened, corresponding to a decrease in the moisture transport trend.
      Citation: Remote Sensing
      PubDate: 2022-06-30
      DOI: 10.3390/rs14133151
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3152: Dual Receiver EGNOS+SDCM Positioning
           with C1C and C1W Pseudo-Range Measurements

    • Authors: Mieczysław Bakuła, Kamil Krasuski, Karol Dawidowicz
      First page: 3152
      Abstract: The paper presents an approach to the simultaneous use of SDCM and EGNOS corrections for two GNSS receivers placed at a constant distance. The SDCM and EGNOS corrections were applied for two GPS code measurements on L1 frequency: C1C and C1W. The approach is based mainly on the constrained least squares adjustment, but for the horizontal and vertical coordinates, the Kalman Filter was applied in order to reduce pseudo-range noises. It allows for obtaining a higher autonomous accuracy of GPS/(SDCM+EGNOS) positioning than when using only the GPS/EGNOS or GPS/SDCM system. The final dual-redundant solution, in which two SBAS systems were used (EGNOS+SDCM) and two GPS pseudo-ranges (C1C+C1W) were present, yielded RMS errors of 0.11 m for the horizontal coordinates and 0.25 m for the vertical coordinates. Moreover, the accuracy analysis in the developed mathematical model for the determined 3D coordinates with simultaneous use of EGNOS and SDCM systems proved to be much more reliable than using only a single EGNOS or SDCM system. The presented approach can be used not only for precise navigation, but also for some geoscience applications and remote sensing where the reliable accuracy of autonomous GPS positioning is required.
      Citation: Remote Sensing
      PubDate: 2022-06-30
      DOI: 10.3390/rs14133152
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3153: Multi-Crop Classification Using
           Feature Selection-Coupled Machine Learning Classifiers Based on Spectral,
           Textural and Environmental Features

    • Authors: Shan He, Peng Peng, Yiyun Chen, Xiaomi Wang
      First page: 3153
      Abstract: Machine learning (ML) classifiers have been widely used in the field of crop classification. However, having inputs that include a large number of complex features increases not only the difficulty of data collection but also reduces the accuracy of the classifiers. Feature selection (FS), which can availably reduce the number of features by selecting and reserving the most essential features for crop classification, is a good tool to solve this problem effectively. Different FS methods, however, have dissimilar effects on various classifiers, so how to achieve the optimal combination of FS methods and classifiers to meet the needs of high-precision recognition of multiple crops remains an open question. This paper intends to address this problem by coupling the analysis of three FS methods and six classifiers. Spectral, textual, and environmental features are firstly extracted as potential classification indexes from time-series remote sensing images from France. Then, three FS methods are used to obtain feature subsets and combined with six classifiers for coupling analysis. On this basis, 18 multi-crop classification models (FS–ML models) are constructed. Additionally, six classifiers without FS are constructed for comparison. The training set and the validation set for these models are constructed by using the Kennard-Stone algorithm with 70% and 30% of the samples, respectively. The performance of the classification model is evaluated by Kappa, F1-score, accuracy, and other indicators. The results show that different FS methods have dissimilar effects on various models. The best FS–ML model is RFAA+-RF, and its Kappa coefficient can reach 0.7968, which is 0.33–46.67% higher than that of other classification models. The classification results are highly dependent on the original classification index sets. Hence, the reasonability of combining spectral, textural, and environmental indexes is verified by comparing them with the single feature index set. The results also show that the classification strategy combining spectral, textual, and environmental indexes can effectively improve the ability of crop recognition, and the Kappa coefficient is 9.06–65.52% higher than that of the single unscreened feature set.
      Citation: Remote Sensing
      PubDate: 2022-06-30
      DOI: 10.3390/rs14133153
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3154: Combined Radar Quality Index for
           Quantitative Precipitation Estimation of Heavy Rainfall Events

    • Authors: Yang Zhang, Liping Liu, Hao Wen, Benchao Yu, Huiying Wang, Yong Zhang
      First page: 3154
      Abstract: For quantitative precipitation estimation (QPE) based on polarimetric radar (PR) and rain gauges (RGs), the quality of the radar data is crucial for estimation accuracy. This paper proposes a combined radar quality index (CRQI) to represent the quality of the radar data used for QPE and an algorithm that uses CRQI to improve the QPE performance. Nine heavy rainfall events that occurred in Guangdong Province, China, were used to evaluate the QPE performance in five contrast tests. The QPE performance was evaluated in terms of the overall statistics, spatial distribution, near real-time statistics, and microphysics. CRQI was used to identify good-quality data pairs (i.e., PR-based QPE and RG observation) for correcting estimators (i.e., relationships between the rainfall rate and the PR parameters) in real-time. The PR-based QPE performance was improved because estimators were corrected according to variations in the drop size distribution, especially for data corresponding to 1.1 mm < average Dm < 1.4 mm, and 4 < average log10 Nw < 4.5. Some underestimations caused by the beam broadening effect, excessive beam height, and partial beam blockages, which could not be mitigated by traditional algorithms, were significantly mitigated by the proposed algorithm using CRQI. The proposed algorithm reduced the root mean square error by 17.5% for all heavy rainfall events, which included three precipitation types: convective precipitation (very heavy rainfall), squall line (huge raindrops), and stratocumulus precipitation (small but dense raindrops). Although the best QPE performance was observed for stratocumulus precipitation, the biggest improvement in performance with the proposed algorithm was observed for the squall line.
      Citation: Remote Sensing
      PubDate: 2022-06-30
      DOI: 10.3390/rs14133154
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3155: Evaluating Landsat-8 and Sentinel-2
           Data Consistency for High Spatiotemporal Inland and Coastal Water Quality

    • Authors: Sidrah Hafeez, Man Sing Wong, Sawaid Abbas, Muhammad Asim
      First page: 3155
      Abstract: The synergy of fine-to-moderate-resolutin (i.e., 10–60 m) satellite data of the Landsat-8 Operational Land Imager (OLI) and the Sentinel-2 Multispectral Instrument (MSI) provides a possibility to monitor the dynamics of sensitive aquatic systems. However, it is imperative to assess the spectral consistency of both sensors before developing new algorithms for their combined use. This study evaluates spectral consistency between OLI and MSI-A/B, mainly in terms of the top-of-atmosphere reflectance (ρt), Rayleigh-corrected reflectance (ρrc), and remote-sensing reflectance (Rrs). To check the spectral consistency under various atmospheric and aquatic conditions, near-simultaneous same-day overpass images of OLI and MSI-A/B were selected over diverse coastal and inland areas across Mainland China and Hong Kong. The results showed that spectral data obtained from OLI and MSI-A/B were consistent. The difference in the mean absolute percentage error (MAPE) of the OLI and MSI-A products was ~8% in ρt and ~10% in both ρrc and Rrs for all the matching bands, whereas the MAPE for OLI and MSI-B was ~3.7% in ρt, ~5.7% in ρrc, and ~7.5% in Rrs for all visible bands except the ultra-blue band. Overall, the green band was the most consistent, with the lowest MAPE of ≤ 4.6% in all the products. The linear regression model suggested that product difference decreased significantly after band adjustment with the highest reduction rate in Rrs (NIR band) and Rrs (red band) for the OLI–MSI-A and OLI–MSI-B comparison, respectively. Further, this study discussed the combined use of OLI and MSI-A/B data for (i) time series of the total suspended solid concentrations (TSS) over coastal and inland waters; (ii) floating algae area comparison; and (iii) tracking changes in coastal floating algae (FA). Time series analysis of the TSS showed that seasonal variation was well-captured by the combined use of sensors. The analysis of the floating algae bloom area revealed that the algae area was consistent, however, the difference increases as the time difference between the same-day overpasses increases. Furthermore, tracking changes in coastal FA over two months showed that thin algal slicks (width < 500 m) can be detected with an adequate spatial resolution of the OLI and the MSI.
      Citation: Remote Sensing
      PubDate: 2022-06-30
      DOI: 10.3390/rs14133155
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3156: A Variable-Scale Coherent Integration
           Method for Moving Target Detection in Wideband Radar

    • Authors: Tingkun Lu, Feng He, Lei Yu, Manqing Wu
      First page: 3156
      Abstract: Accurate integration of the extended target’s energy is one of the important challenges of moving target detection in wideband radar. In this paper, a coherent integration method for wideband radar, i.e., variable-scale moving target detection (VSMTD), is proposed to resist range migration and Doppler broadening. On the one hand, subband decomposition can effectively integrate the energy of the extended target in range using variable-scale transformation, accomplished by modulating the filter bank. On the other hand, it increases the coherent integration time by mitigating the range migration in a sufficiently narrow subband. The discrete Fourier transform (DFT) modulated filter bank and the fast Fourier transform (FFT) algorithm are also used to achieve fast VSMTD implementation. Finally, the simulation results demonstrate the superior performance of the proposed VSMTD method.
      Citation: Remote Sensing
      PubDate: 2022-07-01
      DOI: 10.3390/rs14133156
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3157: A Deep Learning Time Series Approach
           for Leaf and Wood Classification from Terrestrial LiDAR Point Clouds

    • Authors: Tao Han, Gerardo Arturo Sánchez-Azofeifa
      First page: 3157
      Abstract: The accurate separation between leaf and woody components from terrestrial laser scanning (TLS) data is vital for the estimation of leaf area index (LAI) and wood area index (WAI). Here, we present the application of deep learning time series separation of leaves and wood from TLS point clouds collected from broad-leaved trees. First, we use a multiple radius nearest neighbor approach to obtain a time series of the geometric features. Second, we compare the performance of Fully Convolutional Neural Network (FCN), Long Short-Term Memory Fully Convolutional Neural Network (LSTM-FCN), and Residual Network (ResNet) on leaf and wood classification. We also compare the effect of univariable (UTS) and multivariable (MTS) time series on classification accuracy. Finally, we explore the utilization of a class activation map (CAM) to reduce the black-box effect of deep learning. The average overall accuracy of the MTS method across the training data is 0.96, which is higher than the UTS methods (0.67 to 0.88). Meanwhile, ResNet spent much more time than FCN and LSTM-FCN in model development. When testing our method on an independent dataset, the MTS models based on FCN, LSTM-FCN, and ResNet all demonstrate similar performance. Our method indicates that the CAM can explain the black-box effect of deep learning and suggests that deep learning algorithms coupled with geometric feature time series can accurately separate leaf and woody components from point clouds. This provides a good starting point for future research into estimation of forest structure parameters.
      Citation: Remote Sensing
      PubDate: 2022-07-01
      DOI: 10.3390/rs14133157
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3158: A Compensation Method of Saturated
           Waveform for Space-Borne Laser Altimeter

    • Authors: Shaoning Li, Xiufang Fan, Hongbo Pan, Qifan Yu
      First page: 3158
      Abstract: Due to the difference in surface reflectivity, the laser measurement waveform data recorded in full waveform have a saturation phenomenon. When the signal is saturated, the echo waveform produces peak clipping and pulse spreading, which seriously restrict the accuracy of laser measurement results and the usability of data. Therefore, we conducted a ranging investigation on the “peak clipping” phenomenon of the saturated waveform and found a nonlinear time delay in the range, which is between the two extreme cases of saturated “dead time” and Gaussian fitting peak time as pulse signal reception time. Subsequently, based on the consistent relationship between the geometric characteristics of the high- and low-gain channels of the space-borne laser altimeter, we constructed a laser waveform saturation compensation model, namely, the laser pulse flight time delay compensation and the laser waveform peak intensity compensation, and carried out the data saturation compensation and validation with the dual-channel measurement data from the GaoFen-7 (GF-7) satellite. The experimental results showed that the saturation compensation model (SCM) proposed in this paper could restore the features of the saturated waveform signal and effectively improve the accuracy of the laser ranging. The accuracy of the laser waveform fitting result after saturation compensation improved from 0.7 ns (0.11 m) to 0.14 ns (0.02 m), which greatly improved the usability of the saturated laser measurement waveform data.
      Citation: Remote Sensing
      PubDate: 2022-07-01
      DOI: 10.3390/rs14133158
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3159: Forest Fire Segmentation from Aerial
           Imagery Data Using an Improved Instance Segmentation Model

    • Authors: Zhihao Guan, Xinyu Miao, Yunjie Mu, Quan Sun, Qiaolin Ye, Demin Gao
      First page: 3159
      Abstract: In recent years, forest-fire monitoring methods represented by deep learning have been developed rapidly. The use of drone technology and optimization of existing models to improve forest-fire recognition accuracy and segmentation quality are of great significance for understanding the spatial distribution of forest fires and protecting forest resources. Due to the spreading and irregular nature of fire, it is extremely tough to detect fire accurately in a complex environment. Based on the aerial imagery dataset FLAME, this paper focuses on the analysis of methods to two deep-learning problems: (1) the video frames are classified as two classes (fire, no-fire) according to the presence or absence of fire. A novel image classification method based on channel domain attention mechanism was developed, which achieved a classification accuracy of 93.65%. (2) We propose a novel instance segmentation method (MaskSU R-CNN) for incipient forest-fire detection and segmentation based on MS R-CNN model. For the optimized model, the MaskIoU branch is reconstructed by a U-shaped network in order to reduce the segmentation error. Experimental results show that the precision of our MaskSU R-CNN reached 91.85%, recall 88.81%, F1-score 90.30%, and mean intersection over union (mIoU) 82.31%. Compared with many state-of-the-art segmentation models, our method achieves satisfactory results on forest-fire dataset.
      Citation: Remote Sensing
      PubDate: 2022-07-01
      DOI: 10.3390/rs14133159
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3160: Evaluation of Vegetation Indexes and
           Green-Up Date Extraction Methods on the Tibetan Plateau

    • Authors: Jingyi Xu, Yao Tang, Jiahui Xu, Jin Chen, Kaixu Bai, Song Shu, Bailang Yu, Jianping Wu, Yan Huang
      First page: 3160
      Abstract: The vegetation green-up date (GUD) of the Tibetan Plateau (TP) is highly sensitive to climate change. Accurate estimation of GUD is essential for understanding the dynamics and stability of terrestrial ecosystems and their interactions with climate. The GUD is usually determined from a time-series of vegetation indices (VIs). The adoption of different VIs and GUD extraction methods can lead to different GUDs. However, our knowledge of the uncertainty in these GUDs on TP is still limited. In this study, we evaluated the performance of different VIs and GUD extraction methods on TP from 2003 to 2020. The GUDs were determined from six Moderate Resolution Imaging Spectroradiometer (MODIS) derived VIs: normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), normalized difference infrared index (NDII), phenology index (PI), normalized difference phenology index (NDPI), and normalized difference greenness index (NDGI). Four extraction methods (βmax, CCRmax, G20, and RCmax) were applied individually to each VI to determine GUD. The GUDs obtained from all VIs showed similar patterns of early green-up in the eastern and late green-up in the western plateau, and similar trend of GUD advancement in the eastern and postponement in the western plateau. The accuracy of the derived GUDs was evaluated by comparison with ground-observed GUDs from 19 agrometeorological stations. Our results show that two snow-free VIs, NDGI and NDPI, had better performance in GUD extraction than the snow-calibrated conventional VIs, NDVI and EVI. Among all the VIs, NDGI gave the highest GUD accuracy when combined with the four extraction methods. Based on NDGI, the GUD extracted by the CCRmax method was found to have the highest consistency (r = 0.62, p < 0.01, RMSE = 11 days, bias = −3.84 days) with ground observations. The NDGI also showed the highest accuracy for preseason snow-covered site-years (r = 0.71, p < 0.01, RMSE = 10.69 days, bias = −4.05 days), indicating its optimal resistance to snow cover influence. In comparison, NDII and PI hardly captured GUD. NDII was seriously affected by preseason snow cover, as indicated by the negative correlation coefficient (r = −0.34, p < 0.1), high RMSE and bias (RMSE = 50.23 days, bias = −24.25 days).
      Citation: Remote Sensing
      PubDate: 2022-07-01
      DOI: 10.3390/rs14133160
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3161: Improving the SSH Retrieval Precision
           of Spaceborne GNSS-R Based on a New Grid Search Multihidden Layer Neural
           Network Feature Optimization Method

    • Authors: Qiang Wang, Wei Zheng, Fan Wu, Huizhong Zhu, Aigong Xu, Yifan Shen, Yelong Zhao
      First page: 3161
      Abstract: The altimetry precision of conventional spaceborne Global Navigation Satellite Systems Reflectometry (GNSS-R) is limited, and the error models are complicated. To compensate for the shortcomings of conventional methods, we present a new grid search multihidden layer neural network feature optimization method (GSMHLFO) for sea surface height (SSH) retrieval. Firstly, the GSMHLFO is constructed by combining the multihidden layer neural network, feature engineering, and a grid search algorithm. Moreover, the retrieval performance of the GSMHLFO and its sensitivity to various features are analyzed. By analyzing 14 feature sets with different information details, we concluded that the elevation, signal-to-noise ratio (SNR), atmospheric delay, and ocean wind speed can provide essential contributions to the SSH retrieval based on GSMHLFO. Secondly, the Technical University of Denmark 18 mean sea surface (DTU18 MSS), which is corrected by the TPXO8 global tide model, was used to verify the GSMHLFO. The number of hidden layers and neurons was optimized using the grid search algorithm. The experimental results show that the proposed GSMHLFO with four hidden layers and 200 neurons per layer has a better retrieval performance. Compared with DTU18, the mean absolute difference (MAD), the root mean square error (RMSE), and the Pearson correlation coefficient (PCC) equal 4.23 m, 5.94 m, and 0.98, respectively. The retrieval precision obtained is significantly improved compared to that reported in the literature for the TDS-1 SSH retrieval. Finally, the retrieval performance of the GSMHLFO and the traditional HALF single-point retracking method were compared. The precision of GSMHLFO is higher than that of traditional retracking method according to MAD, RMSE, and PCC, which are increased by 32.86, 25.00, and 8.99%. The GSMHLFO will provide innovative theoretical and methodological support for the high-precision SSH retrieval of GNSS-R altimetry satellites in the future.
      Citation: Remote Sensing
      PubDate: 2022-07-01
      DOI: 10.3390/rs14133161
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3162: Live Fuel Moisture Content Mapping in
           the Mediterranean Basin Using Random Forests and Combining MODIS Spectral
           and Thermal Data

    • Authors: Àngel Cunill Camprubí, Pablo González-Moreno, Víctor Resco de Dios
      First page: 3162
      Abstract: Remotely sensed vegetation indices have been widely used to estimate live fuel moisture content (LFMC). However, marked differences in vegetation structure affect the relationship between field-measured LFMC and reflectance, which limits spatial extrapolation of these indices. To overcome this limitation, we explored the potential of random forests (RF) to estimate LFMC at the subcontinental scale in the Mediterranean basin wildland. We built RF models (LFMCRF) using a combination of MODIS spectral bands, vegetation indices, surface temperature, and the day of year as predictors. We used the Globe-LFMC and the Catalan LFMC monitoring program databases as ground-truth samples (10,374 samples). LFMCRF was calibrated with samples collected between 2000 and 2014 and validated with samples from 2015 to 2019, with overall root mean square errors (RMSE) of 19.9% and 16.4%, respectively, which were lower than current approaches based on radiative transfer models (RMSE ~74–78%). We used our approach to generate a public database with weekly LFMC maps across the Mediterranean basin.
      Citation: Remote Sensing
      PubDate: 2022-07-01
      DOI: 10.3390/rs14133162
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3163: SRT: A Spectral Reconstruction
           Network for GF-1 PMS Data Based on Transformer and ResNet

    • Authors: Kai Mu, Ziyuan Zhang, Yurong Qian, Suhong Liu, Mengting Sun, Ranran Qi
      First page: 3163
      Abstract: The time of acquiring remote sensing data was halved after the joint operation of Gao Fen-6 (GF-6) and Gao Fen-1 (GF-1) satellites. Meanwhile, GF-6 added four bands, including the “red-edge” band that can effectively reflect the unique spectral characteristics of crops. However, GF-1 data do not contain these bands, which greatly limits their application to crop-related joint monitoring. In this paper, we propose a spectral reconstruction network (SRT) based on Transformer and ResNet to reconstruct the missing bands of GF-1. SRT is composed of three modules: (1) The transformer feature extraction module (TFEM) fully extracts the correlation features between spectra. (2) The residual dense module (RDM) reconstructs local features and avoids the vanishing gradient problem. (3) The residual global construction module (RGM) reconstructs global features and preserves texture details. Compared with competing methods, such as AWAN, HRNet, HSCNN-D, and M2HNet, the proposed method proved to have higher accuracy by a margin of the mean relative absolute error (MRAE) and root mean squared error (RMSE) of 0.022 and 0.009, respectively. It also achieved the best accuracy in supervised classification based on support vector machine (SVM) and spectral angle mapper (SAM).
      Citation: Remote Sensing
      PubDate: 2022-07-01
      DOI: 10.3390/rs14133163
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3164: Impacts of Land-Use Change on the
           Spatio-Temporal Patterns of Terrestrial Ecosystem Carbon Storage in the
           Gansu Province, Northwest China

    • Authors: Lingge Wang, Rui Zhu, Zhenliang Yin, Zexia Chen, Chunshuang Fang, Rui Lu, Jiqiang Zhou, Yonglin Feng
      First page: 3164
      Abstract: Land-use change is supposed to exert significant effects on the spatio-temporal patterns of ecosystem carbon storage in arid regions, while the relative size of land-use change effect under future environmental change conditions is still less quantified. In this study, we combined a land-use change dataset with a satellite-based high-resolution biomass and soil organic carbon dataset to determine the role of land-use change in affecting ecosystem carbon storage from 1980 to 2050 in the Gansu province of China, using the MCE-CA-Markov and InVEST models. In addition, to quantify the relative size of the land-use change effect in comparison with other environmental drivers, we also considered the effects of climate change, CO2 enrichment, and cropland and forest managements in the models. The results show that the ecosystem carbon storage in the Gansu province increased by 208.9 ± 99.85 Tg C from 1980 to 2020, 12.87% of which was caused by land-use change, and the rest was caused by climate change, CO2 enrichment, and ecosystem managements. The land-use change-induced carbon sequestration was mainly associated with the land-use category conversion from farmland to grassland as well as from saline land and desert to farmland, driven by the grain-for-green projects in the Loess Plateau and oasis cultivation in the Hexi Corridor. Furthermore, it was projected that ecosystem carbon storage in the Gansu province from 2020 to 2050 will change from −14.69 ± 12.28 Tg C to 57.83 ± 53.42 Tg C (from 105.62 ± 51.83 Tg C to 177.03 ± 94.1 Tg C) for the natural development (ecological protection) scenario. By contrast, the land-use change was supposed to individually increase the carbon storage by 56.46 ± 9.82 (165.84 ± 40.06 Tg C) under the natural development (ecological protection) scenario, respectively. Our results highlight the importance of ecological protection and restoration in enhancing ecosystem carbon storage for arid regions, especially under future climate change conditions.
      Citation: Remote Sensing
      PubDate: 2022-07-01
      DOI: 10.3390/rs14133164
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3165: AVHRR GAC Sea Surface Temperature
           Reanalysis Version 2

    • Authors: Boris Petrenko, Victor Pryamitsyn, Alexander Ignatov, Olafur Jonasson, Yury Kihai
      First page: 3165
      Abstract: The 40+ years-long sea surface temperature (SST) dataset from 4 km Global Area Coverage (GAC) data of the Advanced Very High-Resolution Radiometers (AVHRR/2s and/3s) flown onboard ten NOAA satellites (N07/09/11/12/14/15/16/17/18/19) has been created under the NOAA AVHRR GAC SST Reanalysis 2 (RAN2) Project. The data were reprocessed with the NOAA Advanced Clear Sky Processor for Ocean (ACSPO) enterprise SST system. Two SST products are reported in the full ~3000 km AVHRR swath: ‘subskin’ (highly sensitive to true skin SST, but debiased with respect to in situ SST) and ‘depth’ (a closer proxy for in situ data, but with reduced sensitivity). The reprocessing methodology aims at close consistency of satellite SSTs with in situ SSTs, in an optimal retrieval domain. Long-term orbital and calibration trends were compensated by daily recalculation of regression coefficients using matchups with drifters and tropical moored buoys (supplemented by ships for N07/09), collected within limited time windows centered at the processed day. The nighttime Sun impingements on the sensor black body were mitigated by correcting the L1b calibration coefficients. The Earth view pixels contaminated with a stray light were excluded. Massive cold SST outliers caused by volcanic aerosols following three major eruptions were filtered out by a modified, more conservative ACSPO clear-sky mask. The RAN2 SSTs are available in three formats: swath L2P (144 10-min granules per 24 h interval) and two 0.02° gridded (uncollated L3U, also 144 granules/24 h; and collated L3C, two global maps per 24 h, one for day and one for the night). This paper evaluates the RAN2 SST dataset, with a focus on the L3C product and compares it with two other available AVHRR GAC L3C SST datasets, NOAA Pathfinder v5.3 and ESA Climate Change Initiative v2.1. Among the three datasets, the RAN2 covers the global ocean more completely and shows reduced regional and temporal biases, improved stability and consistency between different satellites, and in situ SSTs.
      Citation: Remote Sensing
      PubDate: 2022-07-01
      DOI: 10.3390/rs14133165
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3166: Inferring 2D Local
           Surface-Deformation Velocities Based on PSI Analysis of Sentinel-1 Data: A
           Case Study of Öræfajökull, Iceland

    • Authors: Jirathana Dittrich, Daniel Hölbling, Dirk Tiede, Þorsteinn Sæmundsson
      First page: 3166
      Abstract: Two-dimensional deformation estimates derived from Persistent Scatterer Interferometric (PSI) analysis of Synthetic Aperture Radar (SAR) data can improve the characterisation of spatially and temporally varying deformation processes of Earth’s surface. In this study, we examine the applicability of Persistent Scatterer (PS) Line-Of-Sight (LOS) estimates in providing two-dimensional deformation information, focusing on the retrieval of the local surface-movement processes. Two Sentinel-1 image stacks, ascending and descending, acquired from 2015 to 2018, were analysed based on a single master interferometric approach. First, Interferometric SAR (InSAR) deformation signals were corrected for divergent plate spreading and the Glacial Isostatic Adjustment (GIA) signals. To constrain errors due to rasterisation and interpolation of the pointwise deformation estimates, we applied a vector-based decomposition approach to solve the system of linear equations, resulting in 2D vertical and horizontal surface-deformation velocities at the PSs. We propose, herein, a two-step decomposition procedure that incorporates the Projected Local Incidence Angle (PLIA) to solve for the potential slope-deformation velocity. Our derived 2D velocities reveal spatially detailed movement patterns of the active Svínafellsjökull slope, which agree well with the independent GPS time-series measurements available for this area.
      Citation: Remote Sensing
      PubDate: 2022-07-01
      DOI: 10.3390/rs14133166
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3167: A Tracking Imaging Control Method for
           Dual-FSM 3D GISC LiDAR

    • Authors: Yu Cao, Xiuqin Su, Xueming Qian, Haitao Wang, Wei Hao, Meilin Xie, Xubin Feng, Junfeng Han, Mingliang Chen, Chenglong Wang
      First page: 3167
      Abstract: In this paper, a tracking and pointing control system with dual-FSM (fast steering mirror) composite axis is proposed. It is applied to the target-tracking accuracy control in a 3D GISC LiDAR (three-dimensional ghost imaging LiDAR via sparsity constraint) system. The tracking and pointing imaging control system of the dual-FSM 3D GISC LiDAR proposed in this paper is a staring imaging method with multiple measurements, which mainly solves the problem of high-resolution remote-sensing imaging of high-speed moving targets when the technology is transformed into practical applications. In the research of this control system, firstly, we propose a method that combines motion decoupling and sensor decoupling to solve the mechanical coupling problem caused by the noncoaxial sensor installation of the FSM. Secondly, we suppress the inherent mechanical resonance of the FSM in the control system. Thirdly, we propose the optical path design of a dual-FSM 3D GISC LiDAR tracking imaging system to solve the problem of receiving aperture constraint. Finally, after sufficient experimental verification, our method is shown to successfully reduce the coupling from 7% to 0.6%, and the precision tracking bandwidth reaches 300 Hz. Moreover, when the distance between the GISC system and the target is 2.74 km and the target flight speed is 7 m/s, the tracking accuracy of the system is improved from 15.7 μrad (σ) to 2.2 μrad (σ), and at the same time, the system recognizes the target contour clearly. Our research is valuable to put the GISC technology into practical applications.
      Citation: Remote Sensing
      PubDate: 2022-07-01
      DOI: 10.3390/rs14133167
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3168: Characteristics of Freeze–Thaw
           Cycles in an Endorheic Basin on the Qinghai-Tibet Plateau Based on
           SBAS-InSAR Technology

    • Authors: Huayun Zhou, Lin Zhao, Lingxiao Wang, Zanpin Xing, Defu Zou, Guojie Hu, Changwei Xie, Qiangqiang Pang, Guangyue Liu, Erji Du, Shibo Liu, Yongping Qiao, Jianting Zhao, Zhibin Li, Yadong Liu
      First page: 3168
      Abstract: The freeze–thaw (F-T) cycle of the active layer (AL) causes the “frost heave and thaw settlement” deformation of the terrain surface. Accurately identifying its amplitude and time characteristics is important for climate, hydrology, and ecology research in permafrost regions. We used Sentinel-1 SAR data and small baseline subset-interferometric synthetic aperture radar (SBAS-InSAR) technology to obtain the characteristics of F-T cycles in the Zonag Lake-Yanhu Lake permafrost-affected endorheic basin on the Qinghai-Tibet Plateau from 2017 to 2019. The results show that the seasonal deformation amplitude (SDA) in the study area mainly ranges from 0 to 60 mm, with an average value of 19 mm. The date of maximum frost heave (MFH) occurred between November 27th and March 21st of the following year, averaged in date of the year (DOY) 37. The maximum thaw settlement (MTS) occurred between July 25th and September 21st, averaged in DOY 225. The thawing duration is the thawing process lasting about 193 days. The spatial distribution differences in SDA, the date of MFH, and the date of MTS are relatively significant, but there is no apparent spatial difference in thawing duration. Although the SDA in the study area is mainly affected by the thermal state of permafrost, it still has the most apparent relationship with vegetation cover, the soil water content in AL, and active layer thickness. SDA has an apparent negative and positive correlation with the date of MFH and the date of MTS. In addition, due to the influence of soil texture and seasonal rivers, the seasonal deformation characteristics of the alluvial-diluvial area are different from those of the surrounding areas. This study provides a method for analyzing the F-T cycle of the AL using multi-temporal InSAR technology.
      Citation: Remote Sensing
      PubDate: 2022-07-01
      DOI: 10.3390/rs14133168
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3169: Unmanned Aircraft System (UAS)
           Structure-From-Motion (SfM) for Monitoring the Changed Flow Paths and
           Wetness in Minerotrophic Peatland Restoration

    • Authors: Lauri Ikkala, Anna-Kaisa Ronkanen, Jari Ilmonen, Maarit Similä, Sakari Rehell, Timo Kumpula, Lassi Päkkilä, Björn Klöve, Hannu Marttila
      First page: 3169
      Abstract: Peatland restoration aims to achieve pristine water pathway conditions to recover dispersed wetness, water quality, biodiversity and carbon sequestration. Restoration monitoring needs new methods for understanding the spatial effects of restoration in peatlands. We introduce an approach using high-resolution data produced with an unmanned aircraft system (UAS) and supported by the available light detection and ranging (LiDAR) data to reveal the hydrological impacts of elevation changes in peatlands due to restoration. The impacts were assessed by analyzing flow accumulation and the SAGA Wetness Index (SWI). UAS campaigns were implemented at two boreal minerotrophic peatland sites in degraded and restored states. Simultaneously, the control campaigns mapped pristine sites to reveal the method sensitivity of external factors. The results revealed that the data accuracy is sufficient for describing the primary elevation changes caused by excavation. The cell-wise root mean square error in elevation was on average 48 mm when two pristine UAS campaigns were compared with each other, and 98 mm when each UAS campaign was compared with the LiDAR data. Furthermore, spatial patterns of more subtle peat swelling and subsidence were found. The restorations were assessed as successful, as dispersing the flows increased the mean wetness by 2.9–6.9%, while the absolute changes at the pristine sites were 0.4–2.4%. The wetness also became more evenly distributed as the standard deviation decreased by 13–15% (a 3.1–3.6% change for pristine). The total length of the main flow routes increased by 25–37% (a 3.1–8.1% change for pristine), representing the increased dispersion and convolution of flow. The validity of the method was supported by the field-determined soil water content (SWC), which showed a statistically significant correlation (R2 = 0.26–0.42) for the restoration sites but not for the control sites, possibly due to their upslope catchment areas being too small. Despite the uncertainties related to the heterogenic soil properties and complex groundwater interactions, we conclude the method to have potential for estimating changed flow paths and wetness following peatland restoration.
      Citation: Remote Sensing
      PubDate: 2022-07-01
      DOI: 10.3390/rs14133169
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3170: UAV Video-Based Approach to Identify
           Damaged Trees in Windthrow Areas

    • Authors: Flavio Furukawa, Junko Morimoto, Nobuhiko Yoshimura, Takashi Koi, Hideaki Shibata, Masami Kaneko
      First page: 3170
      Abstract: Disturbances in forest ecosystems are expected to increase by the end of the twenty-first century. An understanding of these disturbed areas is critical to defining management measures to improve forest resilience. While some studies emphasize the importance of quick salvage logging, others emphasize the importance of the deadwood for biodiversity. Unmanned aerial vehicle (UAV) remote sensing is playing an important role to acquire information in these areas through the structure-from-motion (SfM) photogrammetry process. However, the technique faces challenges due to the fundamental principle of SfM photogrammetry as a passive optical method. In this study, we investigated a UAV video-based technology called full motion video (FMV) to identify fallen and snapped trees in a windthrow area. We compared the performance of FMV and an orthomosaic, created by the SfM photogrammetry process, to manually identify fallen and snapped trees, using a ground survey as a reference. The results showed that FMV was able to identify both types of damaged trees due to the ability of video to deliver better context awareness compared to the orthomosaic, although providing lower position accuracy. In addition to its processing being simpler, FMV technology showed great potential to support the interpretation of conventional UAV remote sensing analysis and ground surveys, providing forest managers with fast and reliable information about damaged trees in windthrow areas.
      Citation: Remote Sensing
      PubDate: 2022-07-01
      DOI: 10.3390/rs14133170
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3171: Mitigation of Systematic Noise in F16
           SSMIS LAS Channels Observations for Tropical Cyclone Applications

    • Authors: Huijie Dong, Xiaolei Zou
      First page: 3171
      Abstract: The Special Sensor Microwave Imager Sounder (SSMIS) onboard the Defense Meteorological Satellite Program (DMSP) F16, launched on 18 October 2003, was the first conical-scanning radiometer to combine the Special Sensor Microwave/Imagers (SSM/I), Special Sensor Microwave/Temperature Sounder (SSM/T), and the Special Sensor Microwave/Water Vapor Sounder (SSM/T2). Nearly 20 years of F16 SSMIS data are available to the general public, providing many opportunities to study the atmosphere at both the synoptic and decadal scales. However, data noise from complicated structures has occurred in the brightness temperature (TB) observations of lower atmospheric sounding (LAS) channels since 25 April 2013. We used a two-dimensional Fast Fourier Transform to analyze the characteristic features of data noise in cross-track and along-track directions. We found that the data noise is around 1–2 K and occurs at certain cross-track wavelengths (∆λ)noise. A latitudinal variation was found for (∆λ)noise. Due to noise interference, TB observations reflecting rain, clouds, tropical cyclone warm core, temperature, and water vapor distributions are not readily distinguishable, especially in channels above the middle troposphere (channels 4–7 and 24), whose dynamic TB range is smaller than low tropospheric channels 1–3. Examples are provided to show the impact of the proposed noise mitigation for conical-scanning TB observations to capture 3D structures of hurricanes directly. Once the noise in F16 SSMIS LAS channels from 25 April 2013to the present is eliminated, we may investigate the decadal change of many features of tropical cyclones derivable from these TB observations.
      Citation: Remote Sensing
      PubDate: 2022-07-01
      DOI: 10.3390/rs14133171
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3172: Analyzing Canopy Height Patterns and
           Environmental Landscape Drivers in Tropical Forests Using NASA’s
           GEDI Spaceborne LiDAR

    • Authors: Esmaeel Adrah, Wan Shafrina Wan Mohd Jaafar, Hamdan Omar, Shaurya Bajaj, Rodrigo Vieira Leite, Siti Munirah Mazlan, Carlos Alberto Silva, Maggie Chel Gee Ooi, Mohd Nizam Mohd Said, Khairul Nizam Abdul Maulud, Adrián Cardil, Midhun Mohan
      First page: 3172
      Abstract: Canopy height is a fundamental parameter for determining forest ecosystem functions such as biodiversity and above-ground biomass. Previous studies examining the underlying patterns of the complex relationship between canopy height and its environmental and climatic determinants suffered from the scarcity of accurate canopy height measurements at large scales. NASA’s mission, the Global Ecosystem Dynamic Investigation (GEDI), has provided sampled observations of the forest vertical structure at near global scale since late 2018. The availability of such unprecedented measurements allows for examining the vertical structure of vegetation spatially and temporally. Herein, we explore the most influential climatic and environmental drivers of the canopy height in tropical forests. We examined different resampling resolutions of GEDI-based canopy height to approximate maximum canopy height over tropical forests across all of Malaysia. Moreover, we attempted to interpret the dynamics underlining the bivariate and multivariate relationships between canopy height and its climatic and topographic predictors including world climate data and topographic data. The approaches to analyzing these interactions included machine learning algorithms, namely, generalized linear regression, random forest and extreme gradient boosting with tree and Dart implementations. Water availability, represented as the difference between precipitation and potential evapotranspiration, annual mean temperature and elevation gradients were found to be the most influential determinants of canopy height in Malaysia’s tropical forest landscape. The patterns observed are in line with the reported global patterns and support the hydraulic limitation hypothesis and the previously reported negative trend for excessive water supply. Nevertheless, different breaking points for excessive water supply and elevation were identified in this study, and the canopy height relationship with water availability observed to be less significant for the mountainous forest on altitudes higher than 1000 m. This study provides insights into the influential factors of tree height and helps with better comprehending the variation in canopy height in tropical forests based on GEDI measurements, thereby supporting the development and interpretation of ecosystem modeling, forest management practices and monitoring forest response to climatic changes in montane forests.
      Citation: Remote Sensing
      PubDate: 2022-07-01
      DOI: 10.3390/rs14133172
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3173: Spatiotemporal Distribution Patterns
           and Exposure Risks of PM2.5 Pollution in China

    • Authors: Jun Song, Chunlin Li, Miao Liu, Yuanman Hu, Wen Wu
      First page: 3173
      Abstract: The serious pollution of PM2.5 caused by rapid urbanization in recent years has become an urgent problem to be solved in China. Annual and daily satellite-derived PM2.5 datasets from 2001 to 2020 were used to analyze the temporal and spatial patterns of PM2.5 in China. The regional and population exposure risks of the nation and of urban agglomerations were evaluated by exceedance frequency and population weight. The results indicated that the PM2.5 concentrations of urban agglomerations decreased sharply from 2014 to 2020. The region with PM2.5 concentrations less than 35 μg·m−3 accounted for 80.27% in China, and the average PM2.5 concentrations in 8 urban agglomerations were less than 35 μg·m−3 in 2020. The spatial distribution pattern of PM2.5 concentrations in China revealed higher concentrations to the east of the Hu Line and lower concentrations to the west. The annual regional exposure risk (RER) in China was at a high level, with a national average of 0.75, while the average of 14 urban agglomerations was as high as 0.86. Among the 14 urban agglomerations, the average annual RER was the highest in the Shandong Peninsula (0.99) and lowest in the Northern Tianshan Mountains (0.76). The RER in China has obvious seasonality; the most serious was in winter, and the least serious was in summer. The population exposure risk (PER) east of the Hu Line was significantly higher than that west of the Hu Line. The average PER was the highest in Beijing-Tianjin-Hebei (4.09) and lowest in the Northern Tianshan Mountains (0.71). The analysis of air pollution patterns and exposure risks in China and urban agglomerations in this study could provide scientific guidance for cities seeking to alleviate air pollution and prevent residents’ exposure risks.
      Citation: Remote Sensing
      PubDate: 2022-07-01
      DOI: 10.3390/rs14133173
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3174: A Study of Simulation of the Urban
           Space 3D Temperature Field at a Community Scale Based on High-Resolution
           Remote Sensing and CFD

    • Authors: Hongyuan Huo, Fei Chen
      First page: 3174
      Abstract: This study used high-resolution remote-sensing technology and CFD models to carry out a simulation study of a three-dimensional (3D) USTE for daytime and nighttime at a block scale. Firstly, the influence of vegetation with different spatial layouts on the 3D USTE was analyzed. Moreover, the heat transfer process and heat conduction process between urban surface components at the block scale were simulated, and in the meanwhile, the distribution and changes of the 3D USTE and the regional wind pressure environment were monitored. The simulation results showed that (1) vegetation has a relatively significant mitigation effect on the thermal environment near the surface, (2) vegetation with different morphologies and layouts results in significant differences in the mitigation efficiency of wind speed and canyon USTE, and (3) the seasonal spatial 3D temperature can be mitigated as well. In addition, this study analyzed the mitigation effect of vegetation on the urban wind–heat environment during both daytime and nighttime. The results indicated that (1) the mitigation effect of vegetation is more significant during the daytime, while showing a small value at night with an even temperature distribution, and (2) convection heat transfer is the primary cause, or one of the major causes, of differences in the USTE.
      Citation: Remote Sensing
      PubDate: 2022-07-01
      DOI: 10.3390/rs14133174
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3175: CAISOV: Collinear Affine Invariance
           and Scale-Orientation Voting for Reliable Feature Matching

    • Authors: Haihan Luo, Kai Liu, San Jiang, Qingquan Li, Lizhe Wang, Wanshou Jiang
      First page: 3175
      Abstract: Reliable feature matching plays an important role in the fields of computer vision and photogrammetry. Due to the complex transformation model caused by photometric and geometric deformations, and the limited discriminative power of local feature descriptors, initial matches with high outlier ratios cannot be addressed very well. This study proposes a reliable outlier-removal algorithm by combining two affine-invariant geometric constraints. First, a very simple geometric constraint, namely, CAI (collinear affine invariance) has been implemented, which is based on the observation that the collinear property of any two points is invariant under affine transformation. Second, after the first-step outlier removal based on the CAI constraint, the SOV (scale-orientation voting) scheme was then adopted to remove remaining outliers and recover the lost inliers, in which the peaks of both scale and orientation voting define the parameters of the geometric transformation model. Finally, match expansion was executed using the Delaunay triangulation of refined matches. By using close-range (rigid and non-rigid images) and UAV (unmanned aerial vehicle) datasets, comprehensive comparison and analysis are conducted in this study. The results demonstrate that the proposed outlier-removal algorithm achieves the best overall performance when compared with RANSAC-like and local geometric constraint-based methods, and it can also be applied to achieve reliable outlier removal in the workflow of SfM-based UAV image orientation.
      Citation: Remote Sensing
      PubDate: 2022-07-01
      DOI: 10.3390/rs14133175
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3176: A Novel Topography Retrieval
           Algorithm Based on Single-Pass Polarimetric SAR Data and Terrain Dependent
           Error Analysis

    • Authors: Congrui Yang, Fengjun Zhao, Chunle Wang, Mengmeng Wang, Xiuqing Liu, Robert Wang
      First page: 3176
      Abstract: Polarimetric synthetic aperture radar (PolSAR) data provide an alternative way for topography retrieval, especially when limited PolSAR data are available. This article proposes a novel topography retrieval algorithm based on the Lambertian backscatter model that further improves the vertical precision of digital elevation model (DEM) generation and requires only one flight. The key idea of the proposed algorithm is to avoid data fluctuations caused by the ratio of the azimuth slope angle to the polarimetric orientation angle (POA). The previous research has confirmed the feasibility of generating a DEM based on single-pass PolSAR data, but its effect on the quality of reference DEM has not been well-explained. To analyze this effect, a large number of experiments on DEM with different resolutions are conducted. In addition, an in-depth analysis of non-linear and terrain-dependent errors is performed. The L-band PolSAR data of NASA/JPL TOPSAR and ALOS-2 PALSAR-2 and interferometric SAR (InSAR) DEM data are used to verify the proposed algorithm. The experimental results show that PolSAR data can be used as an additional reliable information source for DEM fusion under certain conditions to improve the quality of public DEM.
      Citation: Remote Sensing
      PubDate: 2022-07-01
      DOI: 10.3390/rs14133176
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3177: A Novel Polarimetric Channel
           Imbalance Phase Estimation Method Based on the Rotated Double-Bounce
           Backscatters in Urban Areas

    • Authors: Songtao Shangguan, Xiaolan Qiu, Bin Han, Wenju Liu, Kun Fu
      First page: 3177
      Abstract: Polarization calibration without artificial calibrators has been one of the focuses of research and discussion for PolSAR communities. However, there is limited research on the treatment of dual-polarization systems and the calibration methods for getting rid of distributed targets. In this paper, we contribute to proposing a new and convenient method for estimating the polarimetric channel imbalance phase at the transmitter and receiver, which can be used for both quad-pol and dual-pol SAR systems. We found a brand-new reference object in the urban area scene, namely the effective dihedrals. A statistical calculation method was proposed correspondingly, which obtained an effective estimation of the channel imbalance phases. The theoretical explanation of the proposed method was consistent with the statistical phenomena presented in the experiments. The technique was illustrated and verified through C-band SAR images, including GaoFen-3 (GF-3) data and Sentinel-1 data. The technique was also validated and successfully applied in airborne SAR data of P, L, S, C, and X bands. The estimation error could be within 7° when crosstalk items were less than −30 dB. The method realizes a fast and low-cost dual-polarization phase imbalance estimation and provides a new technical approach to supplement the traditional tropical-rainforest-based quad-pol system calibration. The method can be conveniently applied to the monitoring of polarization distortion parameters, ensuring good polarization SAR data quality.
      Citation: Remote Sensing
      PubDate: 2022-07-01
      DOI: 10.3390/rs14133177
      Issue No: Vol. 14, No. 13 (2022)
  • Remote Sensing, Vol. 14, Pages 3178: A Novel Frequency-Domain Focusing
           Method for Geosynchronous Low-Earth-Orbit Bistatic SAR in
           Sliding-Spotlight Mode

    • Authors: Zhichao Sun, Tianfu Chen, Huarui Sun, Junjie Wu, Zheng Lu, Zhongyu Li, Hongyang An, Jianyu Yang
      First page: 3178
      Abstract: The low-earth-orbit synthetic aperture radar (SAR) can achieve enhanced remote-sensing capabilities by exploiting the large-scale and long-duration beam coverage of a geosynchronous (GEO) SAR illuminator. Different bistatic imaging modes can be implemented by the steering of an antenna beam onboard the LEO receiver, such as high-resolution sliding-spotlight mode. In this paper, the accurate focusing of GEO-LEO bistatic SAR (GEO-LEO BiSAR) in sliding-spotlight mode is investigated. First, the two major problems of the accurate bistatic range model, i.e., curved trajectory within long integration time and ‘stop-and-go’ assumption error, for sliding-spotlight GEO-LEO BiSAR are analyzed. Then, a novel bistatic range model based on equivalent circular orbit trajectory is proposed to accurately represent the range history of GEO-LEO BiSAR in sliding-spotlight mode. Based on the proposed range model, a frequency-domain imaging method is put forward. First, a modified two-step preprocessing method is implemented to remove the Doppler aliasing caused by azimuth variance of Doppler centroid and beam steering. Then, an azimuth trajectory scaling is formulated to remove the azimuth variance of motion parameters due to curved trajectory. A modified frequency-domain imaging method is derived to eliminate the 2-D spatial variance and achieve accurate focusing of the echo data. Finally, imaging results and analysis on both simulated data and real data from an equivalent BiSAR experiment validate the effectiveness of the proposed method.
      Citation: Remote Sensing
      PubDate: 2022-07-01
      DOI: 10.3390/rs14133178
      Issue No: Vol. 14, No. 13 (2022)
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