Subjects -> INSTRUMENTS (Total: 63 journals)
Showing 1 - 16 of 16 Journals sorted alphabetically
Applied Mechanics Reviews     Full-text available via subscription   (Followers: 27)
Computational Visual Media     Open Access   (Followers: 5)
Devices and Methods of Measurements     Open Access  
Documenta & Instrumenta - Documenta et Instrumenta     Open Access  
EPJ Techniques and Instrumentation     Open Access  
European Journal of Remote Sensing     Open Access   (Followers: 18)
Experimental Astronomy     Hybrid Journal   (Followers: 38)
Flow Measurement and Instrumentation     Hybrid Journal   (Followers: 15)
Geoscientific Instrumentation, Methods and Data Systems     Open Access   (Followers: 2)
Geoscientific Instrumentation, Methods and Data Systems Discussions     Open Access   (Followers: 1)
IEEE Journal on Miniaturization for Air and Space Systems     Hybrid Journal   (Followers: 2)
IEEE Sensors Journal     Hybrid Journal   (Followers: 107)
IEEE Sensors Letters     Hybrid Journal   (Followers: 4)
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems)     Open Access   (Followers: 3)
Imaging & Microscopy     Hybrid Journal   (Followers: 7)
InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan     Open Access  
Instrumentation Science & Technology     Hybrid Journal   (Followers: 7)
Instruments and Experimental Techniques     Hybrid Journal   (Followers: 1)
International Journal of Applied Mechanics     Hybrid Journal   (Followers: 8)
International Journal of Instrumentation Science     Open Access   (Followers: 41)
International Journal of Measurement Technologies and Instrumentation Engineering     Full-text available via subscription   (Followers: 1)
International Journal of Metrology and Quality Engineering     Full-text available via subscription   (Followers: 6)
International Journal of Remote Sensing     Hybrid Journal   (Followers: 144)
International Journal of Remote Sensing Applications     Open Access   (Followers: 49)
International Journal of Sensor Networks     Hybrid Journal   (Followers: 2)
International Journal of Testing     Hybrid Journal   (Followers: 1)
Invention Disclosure     Open Access   (Followers: 1)
Journal of Astronomical Instrumentation     Open Access   (Followers: 3)
Journal of Instrumentation     Hybrid Journal   (Followers: 31)
Journal of Instrumentation Technology & Innovations     Full-text available via subscription   (Followers: 2)
Journal of Medical Devices     Full-text available via subscription   (Followers: 4)
Journal of Medical Signals and Sensors     Open Access   (Followers: 1)
Journal of Optical Technology     Full-text available via subscription   (Followers: 4)
Journal of Research of NIST     Open Access   (Followers: 1)
Journal of Sensors and Sensor Systems     Open Access   (Followers: 12)
Journal of Vacuum Science & Technology B     Hybrid Journal   (Followers: 1)
Jurnal Informatika Upgris     Open Access  
Measurement : Sensors     Open Access   (Followers: 5)
Measurement and Control     Open Access   (Followers: 36)
Measurement Instruments for the Social Sciences     Open Access  
Measurement Techniques     Hybrid Journal   (Followers: 3)
Medical Devices & Sensors     Hybrid Journal   (Followers: 1)
Metrology and Instruments / Метрологія та прилади     Open Access  
Metrology and Measurement Systems     Open Access   (Followers: 8)
Microscopy     Hybrid Journal   (Followers: 7)
Modern Instrumentation     Open Access   (Followers: 57)
Optoelectronics, Instrumentation and Data Processing     Hybrid Journal   (Followers: 4)
PFG : Journal of Photogrammetry, Remote Sensing and Geoinformation Science     Hybrid Journal   (Followers: 4)
Photogrammetric Engineering & Remote Sensing     Full-text available via subscription   (Followers: 32)
Remote Sensing     Open Access   (Followers: 57)
Remote Sensing Applications : Society and Environment     Full-text available via subscription   (Followers: 9)
Remote Sensing of Environment     Hybrid Journal   (Followers: 94)
Remote Sensing Science     Open Access   (Followers: 30)
Review of Scientific Instruments     Hybrid Journal   (Followers: 20)
Science of Remote Sensing     Open Access   (Followers: 7)
Sensors International     Open Access   (Followers: 3)
Solid State Nuclear Magnetic Resonance     Hybrid Journal   (Followers: 3)
Standards     Open Access  
Transactions of the Institute of Measurement and Control     Hybrid Journal   (Followers: 12)
Videoscopy     Full-text available via subscription   (Followers: 5)
Труды СПИИРАН     Open Access  
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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 2314: A Cloud Classification Method Based
           on a Convolutional Neural Network for FY-4A Satellites

    • Authors: Yuhang Jiang, Wei Cheng, Feng Gao, Shaoqing Zhang, Shudong Wang, Chang Liu, Juanjuan Liu
      First page: 2314
      Abstract: The study of cloud types is critical for understanding atmospheric motions and climate predictions; for example, accurately classified cloud products help improve meteorological predicting accuracies. However, the current satellite cloud classification methods generally analyze the threshold change in a single pixel and do not consider the relationship between the surrounding pixels. The classification development relies heavily on human recourses and does not fully utilize the data-driven advantages of computer models. Here, a new intelligent cloud classification method based on the U-Net network (CLP-CNN) is developed to obtain more accurate, higher frequency, and larger coverage cloud classification products. The experimental results show that the CLP-CNN network can complete a cloud classification task of 800 × 800 pixels in 0.9 s. The classification area covers most of China, and the classification task only needs to use the original L1-level data, which can meet the requirements of a real-time operation. With the Himawari-8 CLTYPE product and the CloudSat 2B-CLDCLASS product as the test comparison target, the CLP-CNN network results match the Himawari-8 product highly, by 84.4%. The probability of detection (POD) is greater than 0.83 for clear skies, deep-convection, and Cirrus–Stratus type clouds. The probability of detection (POD) and accuracy are improved compared with other deep learning methods.
      Citation: Remote Sensing
      PubDate: 2022-05-11
      DOI: 10.3390/rs14102314
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2315: Unexpected Regional Zonal Structures
           in Low Latitude Ionosphere Call for a High Longitudinal Resolution of the
           Global Ionospheric Maps

    • Authors: Libo Liu, Yuyan Yang, Huijun Le, Yiding Chen, Ruilong Zhang, Hui Zhang, Wenjie Sun, Guozhu Li
      First page: 2315
      Abstract: This study reports unexpected strong longitudinal structures from Global Navigation Satellite System (GNSS) derived total electron content (TEC) observations in the low-latitude ionosphere over Asia. The observations during 2019–2020 show diverse patterns in the zonal difference of regional TEC, even under geomagnetically quiet conditions. The TEC in the northern hemisphere occasionally exhibits drastic zonal gradients. The intense regional gradients in TEC span a longitudinal extent of about 20°. The higher values may appear on the east or the west side. Strong zonal gradients may appear in all seasons, regardless of geomagnetically quiet or active conditions. The 15 December 2019 and 16 March 2020 cases depict an intense zonal differences cluster in the narrow latitudinal band of 16°N to 28°N, spanning a regional scale smaller than the normal longitudinal structures. In contrast, the Global Ionospheric Maps (GIMs) with a longitudinal resolution of 5° show a much flatter zonal picture. Such intense and regional-scale zonal structures in the low-latitude ionosphere call for a high zonal resolution of GIMs in terms of better geographically distributed observations. Notably, no counterpart regional structures are found at the conjugated points in the southern hemisphere during the two cases. Although the physical drivers are not certain, the appearance only in the northern hemisphere possibly excludes the dominant contribution to forming the regional structures from the equatorial electric field.
      Citation: Remote Sensing
      PubDate: 2022-05-11
      DOI: 10.3390/rs14102315
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2316: Climatological Aspects of Active
           Fires in Northeastern China and Their Relationship to Land Cover

    • Authors: Li Sun, Lei Yang, Xiangao Xia, Dongdong Wang, Tiening Zhang
      First page: 2316
      Abstract: Biomass burning (BB) is a driving force for heavy haze in northeastern China (NEC) and shows distinct seasonal features. However, little is known about its climatological aspects, which are important for regional BB management and understanding BB effects on climate and environment. Here, the climatological characteristics of active fires and their dependence on land cover in NEC were studied using Moderate Resolution Imaging Spectroradiometer (MODIS) products. Moreover, the influence of meteorological factors on fire activities was explored. The number of fires was found to have increased significantly from 2003 to 2018; and the annual total FRP (FRPtot) showed a generally consistent variation with fire counts. However, the mean fire radiative power for each spot (FRPmean) decreased. Fire activity showed distinctive seasonal variations. Most fires and intense burning events occurred in spring and autumn. Spatially, fires were mainly concentrated in cropland areas in plains, where the frequency of fires increased significantly, especially in spring and autumn. The annual percentage of agricultural fires increased from 34% in 2003 to over 60% after 2008 and the FRPtot of croplands increased from 12% to over 55%. Fires in forests, savannas, and grasslands tended to be associated with higher FRPmean than those in croplands. Analysis indicated that the increasing fire count in NEC is mainly caused by agricultural fires. Although the decreasing FRPmean represents an effective management of BB in recent years, high fire counts and FRPtot in croplands indicate that the crop residue burning cannot be simply banned and a need instead for effective applications. More efforts should be made on clean utilization of straw. The accumulation of dry biomass, high temperature, and low humidity, and weak precipitation are conducive to the fire activities. This study provides a comprehensive analysis of BB in NEC and provides a reference for regional BB management and control.
      Citation: Remote Sensing
      PubDate: 2022-05-11
      DOI: 10.3390/rs14102316
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2317: Multiscale Diagnosis of Mangrove
           Status in Data-Poor Context Using Very High Spatial Resolution Satellite
           Images: A Case Study in Pichavaram Mangrove Forest, Tamil Nadu, India

    • Authors: Shuvankar Ghosh, Christophe Proisy, Gowrappan Muthusankar, Christiane Hassenrück, Véronique Helfer, Raphaël Mathevet, Julien Andrieu, Natesan Balachandran, Rajendran Narendran
      First page: 2317
      Abstract: Highlighting spatiotemporal changes occurring within mangrove habitats at the finest possible scale could contribute fundamental knowledge and data for local sustainable management. This study presents the current situation of the Pichavaram mangrove area, a coastal region of Southeast India prone to both cyclones and reduced freshwater inflow. Based on the supervised classification and visual inspection of very high spatial resolution (VHSR) satellite images provided with a pixel size of <4 m, we generated time-series maps to analyze the changes that occurred in both the natural and planted mangroves between 2003 and 2019. We achieved a high mapping accuracy (>85%), which confirmed the potential of classification techniques applied to VHSR images in capturing changes in mangroves on a very fine scale. Our diagnosis reveals variable expansion rates in plantations made by the local authorities. We also report an ongoing mangrove dieback and confirm progressive shoreline erosion along the coastline. Despite a lack of field data, VHSR images allowed for the multiscale diagnosis of the ecosystem situation, thus constituting the first fine-scale assessment of the fragile Pichavaram mangrove area upon which the coastal community is dependent.
      Citation: Remote Sensing
      PubDate: 2022-05-11
      DOI: 10.3390/rs14102317
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2318: The Seasonality of Surface Urban Heat
           Islands across Climates

    • Authors: Panagiotis Sismanidis, Benjamin Bechtel, Mike Perry, Darren Ghent
      First page: 2318
      Abstract: In this work, we investigate how the seasonal hysteresis of the Surface Urban Heat Island Intensity (SUHII) differs across climates and provide a detailed typology of the daytime and nighttime SUHII hysteresis loops. Instead of the typical tropical/dry/temperate/continental grouping, we describe Earth’s climate using the Köppen–Geiger system that empirically maps Earth’s biome distribution into 30 climate classes. Our thesis is that aggregating multi-city data without considering the biome of each city results in temporal means that fail to reflect the actual SUHII characteristics. This is because the SUHII is a function of both urban and rural features and the phenology of the rural surroundings can differ considerably between cities, even in the same climate zone. Our investigation covers all the densely populated areas of Earth and uses 18 years (2000–2018) of land surface temperature and land cover data from the European Space Agency’s Climate Change Initiative. Our findings show that, in addition to concave-up and -down shapes, the seasonal hysteresis of the SUHII also exhibits twisted, flat, and triangle-like patterns. They also suggest that, in wet climates, the daytime SUHII hysteresis is almost universally concave-up, but they paint a more complex picture for cities in dry climates.
      Citation: Remote Sensing
      PubDate: 2022-05-11
      DOI: 10.3390/rs14102318
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2319: Harmonizing Definitions and Methods
           to Estimate Deforestation at the Lacandona Tropical Region in Southern
           Mexico

    • Authors: Ana Fernández-Montes de Oca, Adrián Ghilardi, Edith Kauffer, José Alberto Gallardo-Cruz, Juan Manuel Núñez, Víctor Sánchez-Cordero
      First page: 2319
      Abstract: Deforestation is a major factor reducing natural habitats, leading to tropical ecosystems and biodiversity loss worldwide. The Lacandona region in southern Mexico holds one of the largest fragments of tropical rainforest in North America. We evaluated the deforestation of the Lacandona region harmonizing concepts and methodologies. An international (FAO definition), governmental (national definition), and regional definition of deforestation with applications at different scales were analyzed and harmonized with two classification methods (likelihood and spectral angle mapper (SAM)). We used 2015 and 2018 Landsat 8 images, and likelihood and SAM classifications were applied for FAO and regional definitions of deforestation. Overall, the best evaluated classifier in quantity was likelihood for 2015 and 2018 (kappa: 0.87 and 0.70, overall accuracy: 91.8 and 80.4%, and quantity disagreement: 4.1 and 10 %, respectively). The allocation disagreement only showed exchange between classes. Nevertheless, they did not show differences between classifiers, although 2015 had less disagreement than 2018: exchange, 4.1% for likelihood and SAM; shift: 0% for likelihood and SAM. Maps based on the regional definition of deforestation showed that the likelihood classification detected 11,441 ha less deforestation than SAM (40,538 and 51,979 ha, respectively). The FAO definition of deforestation showed that likelihood classification detected 11,914 ha less deforestation than SAM classification (37,152 and 49,066 ha, respectively). Further, the likelihood classification showed 3387 ha more of deforestation according to the regional definition than the FAO definition of deforestation (40,538 and 37,152 ha, respectively). SAM classification showed that the regional definition showed 2913 ha more deforestation than the FAO definition (51,979 and 49,066, respectively). We concluded that implementation of governmental programs in the Lacandona region requires estimations based on a careful selection of deforestation definitions and methods.
      Citation: Remote Sensing
      PubDate: 2022-05-11
      DOI: 10.3390/rs14102319
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2320: Integration of VIIRS Observations
           with GEDI-Lidar Measurements to Monitor Forest Structure Dynamics from
           2013 to 2020 across the Conterminous United States

    • Authors: Khaldoun Rishmawi, Chengquan Huang, Karen Schleeweis, Xiwu Zhan
      First page: 2320
      Abstract: Consistent and spatially explicit periodic monitoring of forest structure is essential for estimating forest-related carbon emissions, analyzing forest degradation, and supporting sustainable forest management policies. To date, few products are available that allow for continental to global operational monitoring of changes in canopy structure. In this study, we explored the synergy between the NASA’s spaceborne Global Ecosystem Dynamics Investigation (GEDI) waveform LiDAR and the Visible Infrared Imaging Radiometer Suite (VIIRS) data to produce spatially explicit and consistent annual maps of canopy height (CH), percent canopy cover (PCC), plant area index (PAI), and foliage height diversity (FHD) across the conterminous United States (CONUS) at a 1-km resolution for 2013–2020. The accuracies of the annual maps were assessed using forest structure attribute derived from airborne laser scanning (ALS) data acquired between 2013 and 2020 for the 48 National Ecological Observatory Network (NEON) field sites distributed across the CONUS. The root mean square error (RMSE) values of the annual canopy height maps as compared with the ALS reference data varied from a minimum of 3.31-m for 2020 to a maximum of 4.19-m for 2017. Similarly, the RMSE values for PCC ranged between 8% (2020) and 11% (all other years). Qualitative evaluations of the annual maps using time series of very high-resolution images further suggested that the VIIRS-derived products could capture both large and “more” subtle changes in forest structure associated with partial harvesting, wind damage, wildfires, and other environmental stresses. The methods developed in this study are expected to enable multi-decadal analysis of forest structure and its dynamics using consistent satellite observations from moderate resolution sensors such as VIIRS onboard JPSS satellites.
      Citation: Remote Sensing
      PubDate: 2022-05-11
      DOI: 10.3390/rs14102320
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2321: Wind and Turbulence Statistics in the
           Urban Boundary Layer over a Mountain–Valley System in Granada, Spain
           

    • Authors: Pablo Ortiz-Amezcua, Alodía Martínez-Herrera, Antti J. Manninen, Pyry P. Pentikäinen, Ewan J. O’Connor, Juan Luis Guerrero-Rascado, Lucas Alados-Arboledas
      First page: 2321
      Abstract: Urban boundary layer characterization is currently a challenging and relevant issue, because of its role in weather and air quality modelling and forecast. In many cities, the effect of complex topography at local scale makes this modelling even more complicated. This is the case of mid-latitude urban areas located in typical basin topographies, which usually present low winds and high turbulence within the atmospheric boundary layer (ABL). This study focuses on the analysis of the first ever measurements of wind with high temporal and vertical resolution throughout the ABL over a medium-sized city surrounded by mountains in southern Spain. These measurements have been gathered with a scanning Doppler lidar system and analyzed using the Halo lidar toolbox processing chain developed at the Finnish Meteorological Institute. We have used the horizontal wind product and the ABL turbulence classification product to carry out a statistical study using a two-year database. The data availability in terms of maximum analyzed altitudes for statistically significant results was limited to around 1000–1500 m above ground level (a.g.l.) due to the decreasing signal intensity with height that also depends on aerosol load. We have analyzed the differences and similarities in the diurnal evolution of the horizontal wind profiles for different seasons and their modelling with Weibull and von Mises probability distributions, finding a general trend of mean daytime wind from the NW with mean speeds around 3–4 m/s at low altitudes and 6–10 m/s at higher altitudes, and weaker mean nocturnal wind from the SE with similar height dependence. The highest speeds were observed during spring, and the lowest during winter. Finally, we studied the turbulent sources at the ABL with temporal (for each hour of the day) and height resolution. The results show a clear convective activity during daytime at altitudes increasing with time, and a significant wind-shear-driven turbulence during night-time.
      Citation: Remote Sensing
      PubDate: 2022-05-11
      DOI: 10.3390/rs14102321
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2322: Using Satellite NDVI Time-Series to
           Monitor Grazing Effects on Vegetation Productivity and Phenology in
           Heterogeneous Mediterranean Forests

    • Authors: Duarte Balata, Ivo Gama, Tiago Domingos, Vânia Proença
      First page: 2322
      Abstract: The reintroduction of livestock grazing to regulate biomass load is being tested for large-scale restoration in Mediterranean landscapes affected by rural abandonment. Concurrently, there is a need to develop cost-effective methods to monitor such interventions. Here, we investigate if satellite data can be used to monitor the response of vegetation phenology and productivity to grazing disturbance in a heterogenous forest mosaic with herbaceous, shrub, and tree cover. We identify which vegetation seasonal metrics respond most to grazing disturbances and are relevant to monitoring efforts. The study follows a BACI (Before-After-Control-Impact) design applied to a grazing intervention in a Pyrenean oak forest (Quercus pyrenaica) in central Portugal. Using NDVI time-series from Sentinel-2 imagery for the period between June 2016 and June 2021, we observed that each type of vegetation exhibited a distinct phenology curve. Herbaceous vegetation was the most responsive to moderate grazing disturbances with respect to changes in phenology and productivity metrics, namely an anticipation of seasonal events. Results for shrubs and trees suggest a decline in peak productivity in grazed areas but no changes in phenology patterns. The techniques demonstrated in this study are relevant to a broad range of use cases in the large-scale monitoring of fine-grained heterogeneous landscapes.
      Citation: Remote Sensing
      PubDate: 2022-05-11
      DOI: 10.3390/rs14102322
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2323: Lunar Terrestrial Analog Experiment
           on the Spectral Interpretations of Rocks Observed by the Yutu-2 Rover

    • Authors: Rui Chang, Wei Yang, Honglei Lin, Rui Xu, Sheng Gou, Rong Wang, Yangting Lin
      First page: 2323
      Abstract: A visible and near-infrared imaging spectrometer (VNIS) loaded by the Chang’e-4 rover is the primary method for detecting the mineral composition of the lunar surface in the landing region. However, different data processing methods yield inconsistent mineral modes in measured lunar soil and rocks. To better constrain the mineral modes of the soil and rocks measured by Chang’e-4 VNIS, a noritic-gabbroic rock with a mineral composition similar to that of the lunar highland rocks is measured by scanning electron microscopy (SEM), the spare flight model of Chang’e-4 VNIS and TerraSpec-4 of ASD. Backscattered electron and energy dispersive spectrometry show that olivine, pyroxene, and plagioclase modal mineral abundances are 12.9, 35.0, and 52.2%, respectively. The estimated results of the spectrum by the Hapke radiative transfer model are 7.5, 39.3, and 53.2% for olivine, pyroxene, and plagioclase, respectively, which is consistent with to those of SEM mapping within error. In contrast, the estimated results of the modified Gaussian model are 29 and 71% for olivine and pyroxene, respectively, indicating the absence of plagioclase. Based on our implemented Hapke model, we decode the data of the two rocks detected by the rover on the 3rd and 26th lunar days of mission operations. The obtained results suggest that both rocks are norite or gabbro with noticeable differences. The first rock, with more olivine and pyroxene, may have been excavated from the Finsen crater. The second rock, with more plagioclase, may have been ejected from the southwestern edge of the Von Kármán crater, indicating the initial lunar crust.
      Citation: Remote Sensing
      PubDate: 2022-05-11
      DOI: 10.3390/rs14102323
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2324: A Method for Classifying Complex
           Features in Urban Areas Using Video Satellite Remote Sensing Data

    • Authors: Fanghong Ye, Tinghua Ai, Jiaming Wang, Yuan Yao, Zheng Zhou
      First page: 2324
      Abstract: The classification of optical satellite-derived remote sensing images is an important satellite remote sensing application. Due to the wide variety of artificial features and complex ground situations in urban areas, the classification of complex urban features has always been a focus of and challenge in the field of remote sensing image classification. Given the limited information that can be obtained from traditional optical satellite-derived remote sensing data of a classification area, it is difficult to classify artificial features in detail at the pixel level. With the development of technologies, such as satellite platforms and sensors, the data types acquired by remote sensing satellites have evolved from static images to dynamic videos. Compared with traditional satellite-derived images, satellite-derived videos contain increased ground object reflection information, especially information obtained from different observation angles, and can thus provide more information for classifying complex urban features and improving the corresponding classification accuracies. In this paper, first, we analyze urban-area, ground feature characteristics and satellite-derived video remote sensing data. Second, according to these characteristics, we design a pixel-level classification method based on the application of machine learning techniques to video remote sensing data that represents complex, urban-area ground features. Last, we conduct experiments on real data. The test results show that applying the method designed in this paper to classify dynamic, satellite-derived video remote sensing data can improve the classification accuracy of complex features in urban areas compared with the classification results obtained using static, satellite-derived remote sensing image data at the same resolution.
      Citation: Remote Sensing
      PubDate: 2022-05-11
      DOI: 10.3390/rs14102324
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2325: Structural Stability Evaluation of
           Existing Buildings by Reverse Engineering with 3D Laser Scanner

    • Authors: Arum Jang, Young K. Ju, Min Jae Park
      First page: 2325
      Abstract: In the Fourth Industrial Revolution, research and development of application technologies that combine high-tech technologies have been actively conducted. Building information modeling (BIM) technology using advanced equipment is considered promising for future construction projects. In particular, using a 3D laser scanner, LIDAR is expected to be a solution for future building safety inspections. This work proposes a new method for evaluating building stability using a 3D laser scanner. In this study, an underground parking lot was analyzed using a 3D laser scanner. Further, structural analysis was performed using the finite element method (FEM) by applying the figure and geometry data acquired from the laser scan. This process includes surveying the modeled point cloud data of the scanned building, such as identifying the relative deflection of the floor slab, and the sectional shape and inclination of the column. Consequently, safety diagnosis was performed using the original evaluation criteria. This confirms that it is precise and efficient to use a 3D laser scanner for building stability assessment. This paper presents a digital point cloud-based approach using a 3D laser scanner to evaluate the stability of buildings.
      Citation: Remote Sensing
      PubDate: 2022-05-11
      DOI: 10.3390/rs14102325
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2326: Parameter Estimation for Sea Clutter
           Pareto Distribution Model Based on Variable Interval

    • Authors: Yifei Fan, Duo Chen, Mingliang Tao, Jia Su, Ling Wang
      First page: 2326
      Abstract: The generalized Pareto (GP) distribution model is often used to describe the amplitude statistical feature of sea clutter. Generally, the parameters of GP distribution are estimated by moments estimators. However, when the sea state is high, the appearance of sea spikes will increase the echo of the anomalous scattering units, which leads to a decrease in the parameter estimation accuracy and target detection performance. To improve the parameter estimation accuracy, this paper proposes a novel parameter estimation method based on variable intervals. Considering the local properties of sea clutter, we take a variable interval of the entire sea clutter series for parameter estimation, where the interval position is selected according to the sea state conditions. For contrast, the bipercentile parameter estimation and truncate moment estimation are also introduced. Finally, the experiment based on the real measured X-band sea clutter datasets indicates that the proposed method outperforms the state-of-the-art moments estimators.
      Citation: Remote Sensing
      PubDate: 2022-05-11
      DOI: 10.3390/rs14102326
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2327: The Impact of Central Heating on the
           Urban Thermal Environment Based on Multi-Temporal Remote Sensing Images

    • Authors: Xinran Chen, Xingfa Gu, Yulin Zhan, Dakang Wang, Yazhou Zhang, Faisal Mumtaz, Shuaiyi Shi, Qixin Liu
      First page: 2327
      Abstract: Research on the impact of anthropogenic heat discharge in a thermal environment is significant in climate change research. Central heating is more common in the winter in Northeast China as an anthropogenic heat. This study investigates the impact of central heating on the thermal environment in Shenyang, Changchun, and Harbin based on multi-temporal land surface temperature retrieval from remote sensing. An equivalent heat island index method was proposed to overcome the problem of the method based on a single-phase image, which cannot evaluate all the central heating season changes. The method improves the comprehensiveness of a thermal environment evaluation by considering the long-term heat accumulation. The results indicated a significant increase in equivalent heat island areas at night with 22.1%, 17.3%, and 19.5% over Shenyang, Changchun, and Harbin. The increase was significantly positively correlated with the central heating supply (with an R-value of 0.89 for Shenyang, 0.93 for Changchun, and 0.86 for Harbin; p < 0.05). The impact of central heating has a more significant effect than the air temperature. The results provide a reference for future studies of urban thermal environment changes.
      Citation: Remote Sensing
      PubDate: 2022-05-11
      DOI: 10.3390/rs14102327
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2328: Investigating the Magnetotelluric
           Responses in Electrical Anisotropic Media

    • Authors: Tianya Luo, Xiangyun Hu, Longwei Chen, Guilin Xu
      First page: 2328
      Abstract: When interpreting magnetotelluric (MT) data, because of the inherent anisotropy of the earth, considering electrical anisotropy is crucial. Accordingly, using the edge-based finite element method, we calculated the responses of MT data for electrical isotropic and anisotropic models, and subsequently used the anisotropy index and polar plot to depict MT responses. High values of the anisotropy index were mainly yielded at the boundary domains of anomalous bodies for isotropy cases because the conductive differences among isotropic anomalous bodies or among anomalous bodies and background earth can be regarded as macro-anisotropy. However, they only appeared across anomalous bodies in the anisotropy cases. The anisotropy index can directly differentiate isotropy from anisotropy but exhibits difficulty in reflecting the azimuth of the principal conductivities. For the isotropy cases, polar plots are approximately circular and become curves with a big ratio of the major axis to minor axis, such as an 8-shaped curve for the anisotropic earth. Furthermore, the polar plot can reveal the directions of principal conductivities. However, distorted by anomalous bodies, polar plots with a large ratio of the major axis to minor axis occur in isotropic domains around the anomalous bodies, which may lead to the misinterpretation of these domains as anisotropic earth. Therefore, combining the anisotropy index with a polar plot facilitates the identification of the electrical anisotropy.
      Citation: Remote Sensing
      PubDate: 2022-05-11
      DOI: 10.3390/rs14102328
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2329: A Comprehensive Clear-Sky Database
           for the Development of Land Surface Temperature Algorithms

    • Authors: Sofia L. Ermida, Isabel F. Trigo
      First page: 2329
      Abstract: Land surface temperature is linked to a wide range of surface processes. Given the increased development of earth observation systems, a large effort has been put into advancing land surface temperature retrieval algorithms from remote sensors. Due to the very limited number of reliable in situ observations matching the spatial scales of satellite observations, algorithm development relies on synthetic databases, which then constitute a crucial part of algorithm development. Here we provide a database of atmospheric profiles and respective surface conditions that can be used to train and verify algorithms for land surface temperature retrieval, including machine learning techniques. The database was built from ERA5 data resampled through a dissimilarity criterion applied to the temperature and specific humidity profiles. This criterion aims to obtain regular distributions of these variables, ensuring a good representation of all atmospheric conditions. The corresponding vertical profiles of ozone and relevant surface and vertically integrated variables are also included in the dataset. Information on the surface conditions (i.e., temperature and emissivity) was complemented with data from a wide array of satellite products, enabling a more realistic surface representation. The dataset is freely available online at Zenodo.
      Citation: Remote Sensing
      PubDate: 2022-05-11
      DOI: 10.3390/rs14102329
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2330: Dynamic Simulation of Land Use/Cover
           Change and Assessment of Forest Ecosystem Carbon Storage under Climate
           Change Scenarios in Guangdong Province, China

    • Authors: Lei Tian, Yu Tao, Wenxue Fu, Tao Li, Fang Ren, Mingyang Li
      First page: 2330
      Abstract: Exploring the spatial distribution of land use/cover change (LUCC) and ecosystem carbon storage under future climate change scenarios can provide the scientific basis for optimizing land resource redistribution and formulating policies for sustainable socioeconomic development. We proposed a framework that integrates the patch-generating land use simulation (PLUS) model and integrated valuation of ecosystem services and tradeoffs (InVEST) model to assess the spatiotemporal dynamic changes in LUCC and ecosystem carbon storage in Guangdong based on shared socioeconomic pathways and representative concentration pathways (SSP-RCP) scenarios provided by the Coupled Model Intercomparison Project 6 (CMIP6). The future simulation results showed that the distribution patterns of LUCC were similar under SSP126 and SSP245 scenarios, but the artificial surface expanded more rapidly, and the increase in forest land slowed down under the SPP245 scenario. Conversely, under the SSP585 scenario, the sharply expanded artificial surface resulted in a continuous decrease in forest land. Under the three scenarios, population, elevation, temperature, and distance to water were the highest contributing driving factors for the growth of cultivated land, forest land, grassland, and artificial surface, respectively. By 2060, the carbon storage in terrestrial ecosystems increased from 240.89 Tg in 2020 to 247.16 Tg and 243.54 Tg under SSP126 and SSP245 scenarios, respectively, of which forest ecosystem carbon storage increased by 17.65 Tg and 15.34 Tg, respectively; while it decreased to 226.54 Tg under the SSP585 scenario, and the decreased carbon storage due to forest destruction accounted for 81.05% of the total decreased carbon storage. Overall, an important recommendation from this study is that ecosystem carbon storage can be increased by controlling population and economic growth, and balancing urban expansion and ecological conservation, as well as increasing forest land area.
      Citation: Remote Sensing
      PubDate: 2022-05-11
      DOI: 10.3390/rs14102330
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2331: Application of the Doppler Spectrum
           of the Backscattering Microwave Signal for Monitoring of Ice Cover: A
           Theoretical View

    • Authors: Vladimir Karaev, Yury Titchenko, Maria Panfilova, Maria Ryabkova, Eugeny Meshkov, Kirill Ponur
      First page: 2331
      Abstract: In the radar remote sensing of sea ice, the main informative parameter is the backscattering radar cross section (RCS), which does not always make it possible to unambiguously determine the kind of scattering surface (ice/sea waves) and therefore leads to errors in estimating the area of the ice cover. This paper provides a discussion of the possibility of using the Doppler spectrum of the reflected microwave signal to solve this problem. For the first time, a semi-empirical model of the Doppler spectrum of a radar microwave signal reflected by an ice cover was developed for a radar with a wide antenna beam mounted on a moving carrier at small incidence angles of electromagnetic waves (0°–19°). To describe the Doppler spectrum of the reflected microwave signal, the following parameters were used: shift and width of the Doppler spectrum, as well as skewness and kurtosis coefficients. Research was conducted on the influence of the main parameters of the measurement scheme (movement velocity, width of antenna beam, sounding direction, incidence angle) and the sea ice concentration (SIC) on the parameters of the Doppler spectrum. It was shown that, in order to determine the kind of scattering surface, it is necessary to use a wide or knife-like (by the incidence angle) antenna. Calculations confirmed the assumption that, when measured from a moving carrier, the Doppler spectrum is a reliable indicator of the transition from one kind of scattering surface to another. The advantage of using the coefficients of skewness and kurtosis in the analysis is that it is not necessary to keep the radar velocity unchanged during the measurement process.
      Citation: Remote Sensing
      PubDate: 2022-05-11
      DOI: 10.3390/rs14102331
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2332: Monitoring and Predicting the
           Subsidence of Dalian Jinzhou Bay International Airport, China by
           Integrating InSAR Observation and Terzaghi Consolidation Theory

    • Authors: Xianlin Shi, Chen Chen, Keren Dai, Jin Deng, Ningling Wen, Yong Yin, Xiujun Dong
      First page: 2332
      Abstract: Dalian Jinzhou Bay International Airport (DJBIA) is an offshore artificial island airport, where the reclaimed land is prone to uneven land subsidence due to filling consolidation and construction. Monitoring and predicting the subsidence are essential to assist the subsequent subsidence control and ensure the operational safety of DJBIA. However, the accurate monitoring and prediction of reclaimed subsidence for such a wide area under construction are hard and challenging. This paper utilized the Small Baseline Subset Synthetic Aperture Radar (SBAS-InSAR) technology based on Sentinel-1 images from 2017 to 2021 to obtain the subsidence over the land reclamation area of the DJBIA, in which the results from ascending and descending orbit data were compared to verify the reliability of the results. The SBAS-InSAR results reveal that uneven subsidence is continuously occurring, especially on the runway, terminal, and building area of the airport, with the maximum subsidence rate exceeding 100 mm/year. It was found that there is a strong correlation between the subsidence rate and backfilling time. This study provides important information on the reclaimed subsidence for DJBIA and demonstrates a novel method for reclaimed subsidence monitoring and prediction by integrating the advanced InSAR technology and Terzaghi Consolidation Theory modeling. Moreover, based on the Terzaghi consolidation theory and the corresponding geological parameters of the airport, predicted subsidence curves in this area are derived. The comparison between predicted curves and the actual subsidence revealed by InSAR in 2017–2021 is highly consistent, with a similar trend and falling in a range of ±25 mm/year, which verifies that the subsidence in this area conforms to Terzaghi Consolidation Theory. Therefore, it can be predicted that in the future, the subsidence rate of the new reclamation area in this region will reach about 80 mm/year ± 25 mm/year, and the subsidence rate will gradually slow down with the accumulation of reclamation time. The subsidence rate will slow down to about 30 mm/year ± 25 mm/year after 10 years.
      Citation: Remote Sensing
      PubDate: 2022-05-11
      DOI: 10.3390/rs14102332
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2333: The Post-Failure Spatiotemporal
           Deformation of Certain Translational Landslides May Follow the Pre-Failure
           Pattern

    • Authors: Luyao Wang, Haijun Qiu, Wenqi Zhou, Yaru Zhu, Zijing Liu, Shuyue Ma, Dongdong Yang, Bingzhe Tang
      First page: 2333
      Abstract: Investigating landslide deformation patterns in different evolution stages is important for understanding landslide movement. Translational landslides generally slide along a relatively straight surface of rupture. Whether the post-failure spatiotemporal deformation for certain translational landslides follows the pre-failure pattern remains untested. Here, the pre- and post-failure spatiotemporal deformations of the Simencun landslide along the Yellow River in 2018 were analyzed through multi-temporal remote sensing image analysis, Interferometric Synthetic Aperture Radar (InSAR) deformation monitoring and intensive field investigations. The results show that the pre- and post-failure spatial deformations both follow a retrogressive failure pattern. The long time series of the displacement before and after failure is characterized by obvious seasonal and periodic stage acceleration movements. Effective rainfall played an important role in the increase of the displacement acceleration, and the change in temperature might have accelerated the displacement. Finally, there is a possibility that the post-failure spatiotemporal deformation pattern of translational landslides does follow the pre-failure pattern when certain conditions are satisfied. The results are of great significance to improving our understanding of the spatiotemporal deformation pattern of landslides and to post-failure risk prevention and control.
      Citation: Remote Sensing
      PubDate: 2022-05-12
      DOI: 10.3390/rs14102333
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2334: The Potential of Optical UAS Data for
           Predicting Surface Soil Moisture in a Peatland across Time and Sites

    • Authors: Raul Sampaio de Lima, Kai-Yun Li, Ants Vain, Mait Lang, Thaisa Fernandes Bergamo, Kaupo Kokamägi, Niall G. Burnside, Raymond D. Ward, Kalev Sepp
      First page: 2334
      Abstract: Advances in unmanned aerial systems (UASs) have increased the potential of remote sensing to overcome scale issues for soil moisture (SM) quantification. Regardless, optical imagery is acquired using various sensors and platforms, resulting in simpler operations for management purposes. In this respect, we predicted SM at 10 cm depth using partial least squares regression (PLSR) models based on optical UAS data and assessed the potential of this framework to provide accurate predictions across dates and sites. For this, we evaluated models’ performance using several datasets and the contribution of spectral and photogrammetric predictors on the explanation of SM. The results indicated that our models predicted SM at comparable accuracies as other methods relying on more expensive and complex sensors; the best R2 was 0.73, and the root-mean-squared error (RMSE) was 13.1%. Environmental conditions affected the predictive importance of different metrics; photogrammetric-based metrics were relevant over exposed surfaces, while spectral predictors were proxies of water stress status over homogeneous vegetation. However, the models demonstrated limited applicability across times and locations, particularly in highly heterogeneous conditions. Overall, our findings indicated that integrating UAS imagery and PLSR modelling is suitable for retrieving SM measures, offering an improved method for short-term monitoring tasks.
      Citation: Remote Sensing
      PubDate: 2022-05-12
      DOI: 10.3390/rs14102334
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2335: High Wind Geophysical Model Function
           Modeling for the HY-2A Scatterometer Using Neural Network

    • Authors: Xuetong Xie, Jing Wang, Mingsen Lin
      First page: 2335
      Abstract: Under low to medium wind speeds and no rainfall, the retrieved vector wind from a scatterometer is accurate and reliable. However, under high wind conditions, the currently used geophysical model function (GMF), such as NSCAT-2, for wind vector retrieval has the disadvantage of overestimating the backscattering coefficient, which leads to a decrease in the quality of the retrieved ocean surface winds. To enhance the wind retrieval precision of the HY-2A scatterometer under high wind conditions, a new GMF for high wind (HW-GMF) is established by using the neural network method based on the backscattering coefficient data of the HY-2A scatterometer combined with the wind speed data of the Special Sensor Microwave Imager (SSM/I) and the Final (FNL) operational global analysis wind direction data from the National Centers for Environmental Prediction (NCEP). The absolute value of the mean deviation between the predicted σ0 by the HW-GMF and the measured σ0 by the HY-2A scatterometer is less than 0.1 dB, indicating that the HW-GMF has high accuracy. To verify the HW-GMF performance, the wind field inversion accuracy of the HW-GMF is compared with that of the NSCAT-2 GMF, a GMF currently used in the data processing of the HY-2A scatterometer. The experimental results show that the deviation between the HW-GMF retrieved wind speed and the SSM/I wind speed is within 2 m/s in the high wind speed range of 15–35 m/s, indicating that the HW-GMF improves the precision of the wind speed inversion of the HY-2A scatterometer under high wind speed conditions.
      Citation: Remote Sensing
      PubDate: 2022-05-12
      DOI: 10.3390/rs14102335
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2336: Classification of Eurasian
           Watermilfoil (Myriophyllum spicatum) Using Drone-Enabled Multispectral
           Imagery Analysis

    • Authors: Colin Brooks, Amanda Grimm, Amy M. Marcarelli, Nicholas P. Marion, Robert Shuchman, Michael Sayers
      First page: 2336
      Abstract: Remote sensing approaches that could identify species of submerged aquatic vegetation (SAV) and measure their extent in lake littoral zones would greatly enhance SAV study and management, especially if these approaches can provide faster or more accurate results than traditional field methods. Remote sensing with multispectral sensors can provide this capability, but SAV identification with this technology must address the challenges of light extinction in aquatic environments where chlorophyll, dissolved organic carbon, and suspended minerals can affect water clarity and the strength of the sensed light signal. Here, we present an uncrewed aerial system (UAS)-enabled methodology to identify the extent of the invasive SAV species Myriophyllum spicatum (Eurasian watermilfoil, or EWM), primarily using a six-band Tetracam multispectral camera, flown over sites in the Les Cheneaux Islands area of northwestern Lake Huron, Michigan, USA. We analyzed water chemistry and light data and found our sites clustered into sites with higher and lower water clarity, although all sites had relatively high water clarity. The overall average accuracy achieved was 76.7%, with 78.7% producer’s and 77.6% user’s accuracy for the EWM. These accuracies were higher than previously reported from other studies that used remote sensing to map SAV. Our study found that two tested scale parameters did not lead to significantly different classification accuracies between sites with higher and lower water clarity. The EWM classification methodology described here should be applicable to other SAV species, especially if they have growth patterns that lead to high amounts of biomass relative to other species in the upper water column, which can be detected with the type of red-edge and infrared sensors deployed for this study.
      Citation: Remote Sensing
      PubDate: 2022-05-12
      DOI: 10.3390/rs14102336
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2337: Antarctic Basal Water Storage
           Variation Inferred from Multi-Source Satellite Observation and Relevant
           Models

    • Authors: Jingyu Kang, Yang Lu, Yan Li, Zizhan Zhang, Hongling Shi
      First page: 2337
      Abstract: Antarctic basal water storage variation (BWSV) refers to mass changes of basal water beneath the Antarctic ice sheet (AIS). Identifying these variations is critical for understanding Antarctic basal hydrology variations and basal heat conduction, yet they are rarely accessible due to a lack of direct observation. This paper proposes a layered gravity density forward/inversion iteration method to investigate Antarctic BWSV based on multi-source satellite observations and relevant models. During 2003–2009, BWSV increased at an average rate of 43 ± 23 Gt/yr, which accounts for 29% of the previously documented total mass loss rate (−76 ± 20 Gt/yr) of AIS. Major uncertainty arises from satellite gravimetry, satellite altimetry, the glacial isostatic adjustment (GIA) model, and the modelled basal melting rate. We find that increases in basal water mainly occurred in regions with widespread active subglacial lakes, such as the Rockefeller Plateau, Siple Coast, Institute Ice Stream regions, and marginal regions of East Antarctic Ice Sheet (EAIS), which indicates the increased water storage in these active subglacial lakes, despite the frequent water drainage events. The Amundsen Sea coast experienced a significant loss during the same period, which is attributed to the basal meltwater discharging into the Amundsen Sea through basal channels.
      Citation: Remote Sensing
      PubDate: 2022-05-12
      DOI: 10.3390/rs14102337
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2338: Dynamic Range Compression
           Self-Adaption Method for SAR Image Based on Deep Learning

    • Authors: Hao Shi, Qingqing Sheng, Yupei Wang, Bingying Yue, Liang Chen
      First page: 2338
      Abstract: The visualization of synthetic aperture radar (SAR) images involves the mapping of high dynamic range (HDR) amplitude values to gray levels for lower dynamic range (LDR) display devices. This dynamic range compression process determines the visibility of details in the displayed result. It therefore plays a critical role in remote sensing applications. There are some problems with existing methods, such as poor adaptability, detail loss, imbalance between contrast improvement and noise suppression. To effectively obtain the images suitable for human observation and subsequent interpretation, we introduce a novel self-adaptive SAR image dynamic range compression method based on deep learning. Its designed objective is to present the maximal amount of information content in the displayed image and eliminate the contradiction between contrast and noise. Considering that, we propose a decomposition-fusion framework. The input SAR image is rescaled to a certain size and then put into a bilateral feature enhancement module to remap high and low frequency features to realize noise suppression and contrast enhancement. Based on the bilateral features, a feature fusion module is employed for feature integration and optimization to achieve a more precise reconstruction result. Visual and quantitative experiments on synthesized and real-world SAR images show that the proposed method notably realizes visualization which exceeds several statistical methods. It has good adaptability and can improve SAR images’ contrast for interpretation.
      Citation: Remote Sensing
      PubDate: 2022-05-12
      DOI: 10.3390/rs14102338
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2339: Spatiotemporal Evolution Pattern and
           Driving Mechanisms of Landslides in the Wenchuan Earthquake-Affected
           Region: A Case Study in the Bailong River Basin, China

    • Authors: Linxin Lin, Guan Chen, Wei Shi, Jiacheng Jin, Jie Wu, Fengchun Huang, Yan Chong, Yang Meng, Yajun Li, Yi Zhang
      First page: 2339
      Abstract: Understanding the spatiotemporal evolution and driving mechanisms of landslides following a mega-earthquake at the catchment scale can lead to improved landslide hazard assessment and reduced related risk. However, little effort has been made to undertake such research in the Wenchuan earthquake-affected region, outside Sichuan Province, China. In this study, we used the Goulinping valley in the Bailong River basin in southern Gansu Province, China, as an example. By examining the multitemporal inventory, we revealed various characteristics of the spatiotemporal evolution of landslides over the past 13 years (2007–2020). We evaluated the activity of landslides using multisource remote-sensing technology, analyzed the driving mechanisms of landslides, and further quantified the contribution of landslide evolution to debris flow in the catchment. Our results indicate that the number of landslides increased by nearly six times from 2007 to 2020, and the total volume of landslides approximately doubled. The evolution of landslides in the catchment can be divided into three stages: the earthquake driving stage (2008), the coupled driving stage of earthquake and rainfall (2008–2017), and the rainfall driving stage (2017–present). Landslides in the upstream limestone area were responsive to earthquakes, while the middle–lower loess–phyllite-dominated reaches were mainly controlled by rainfall. Thus, the current landslides in the upstream region remain stable, and those in the mid-downstream are vigorous. Small landslides and mid-downstream slope erosion can rapidly provide abundant debris flow and reduce its threshold, leading to an increase in the frequency and scale of debris flow. This study lays the foundation for studying landslide mechanisms in the Bailong River basin or similar regions. It also aids in engineering management and landslide risk mitigation under seismic activity and climate change conditions.
      Citation: Remote Sensing
      PubDate: 2022-05-12
      DOI: 10.3390/rs14102339
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2340: Improving Spatial Disaggregation of
           Crop Yield by Incorporating Machine Learning with Multisource Data: A Case
           Study of Chinese Maize Yield

    • Authors: Shuo Chen, Weihang Liu, Puyu Feng, Tao Ye, Yuchi Ma, Zhou Zhang
      First page: 2340
      Abstract: Spatially explicit crop yield datasets with continuous long-term series are essential for understanding the spatiotemporal variation of crop yield and the impact of climate change on it. There are several spatial disaggregation methods to generate gridded yield maps, but these either use an oversimplified approach with only a couple of ancillary data or an overly complex approach with limited flexibility and scalability. This study developed a spatial disaggregation method using improved spatial weights generated from machine learning. When applied to Chinese maize yield, extreme gradient boosting (XGB) derived the best prediction results, with a cross-validation coefficient of determination (R2) of 0.81 at the municipal level. The disaggregated yield at 1 km grids could explain 54% of the variance of the county-level statistical yield, which is superior to the existing gridded maize yield dataset in China. At the site level, the disaggregated yields also showed much better agreement with observations than the existing gridded maize yield dataset. This lightweight method is promising for generating spatially explicit crop yield datasets with finer resolution and higher accuracy, and for providing necessary information for maize production risk assessment in China under climate change.
      Citation: Remote Sensing
      PubDate: 2022-05-12
      DOI: 10.3390/rs14102340
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2341: Evaluating the Effectiveness of
           Machine Learning and Deep Learning Models Combined Time-Series Satellite
           Data for Multiple Crop Types Classification over a Large-Scale Region

    • Authors: Xue Wang, Jiahua Zhang, Lan Xun, Jingwen Wang, Zhenjiang Wu, Malak Henchiri, Shichao Zhang, Sha Zhang, Yun Bai, Shanshan Yang, Shuaishuai Li, Xiang Yu
      First page: 2341
      Abstract: Accurate extraction of crop cultivated area and spatial distribution is essential for food security. Crop classification methods based on machine learning and deep learning and remotely sensed time-series data are widely utilized to detect crop planting area. However, few studies assess the effectiveness of machine learning and deep learning algorithm integrated time-series satellite data for identifying multiple crop type classification over a large-scale region. Hence, this study aims to evaluate the effectiveness of machine learning and deep learning models in crop classification and provide a framework for large-scale multiple crop type classification based on time-series of satellite data. The time-series of the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and NaE (combined NDVI and EVI) were adopted as input features, and four widely used machine learning models, including Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN), and their integration (Stacking) were compared to examine the performance of multiple crop types (i.e., wheat, corn, wheat-corn, early rice, and early rice-late rice) classification in the North China Plain. The performance of two types of deep learning classifiers: the One-dimensional Convolutional Neural Network (Conv1D) and Long Short-Term Memory Networks (LSTM), were also tested. The results showed that the NaE feature performed best among three input features, and the Stacking model produced the highest accuracy (77.12%) compared to other algorithms.
      Citation: Remote Sensing
      PubDate: 2022-05-12
      DOI: 10.3390/rs14102341
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2342: Numerical Weather Predictions and
           Re-Analysis as Input for Lidar Inversions: Assessment of the Impact on
           Optical Products

    • Authors: Yuanzu Wang, Aldo Amodeo, Ewan J. O’Connor, Holger Baars, Daniele Bortoli, Qiaoyun Hu, Dongsong Sun, Giuseppe D’Amico
      First page: 2342
      Abstract: The atmospheric molecular number density can be obtained from atmospheric temperature and pressure profiles and is a significant input parameter for the inversion of lidar measurements. When measurements of vertical profiles of temperature and pressure are not available, atmospheric models are typically considered a valid alternative option. This paper investigates the influence of different atmospheric models (forecast and reanalysis) on the retrieval of aerosol optical properties (extinction and backscatter coefficients) by applying Raman and elastic-only methods to lidar measurements, to assess their use in lidar data processing. In general, reanalyzes are more accurate than forecasts, but, typically, they are not delivered in time for allowing near-real-time lidar data analysis. However, near-real-time observation is crucial for real-time monitoring of the environment and meteorological studies. The forecast models used in the paper are provided by the Integrated Forecasting System operated by the European Centre for Medium-Range Weather Forecasts (IFS_ECMWF) and the Global Data Assimilation System (GDAS), whereas the reanalysis model is obtained from the fifth-generation European Centre for Medium-Range Weather Forecasts ReAnalysis v5 (ERA5). The lidar dataset consists of measurements collected from four European Aerosol Research Lidar Network (EARLINET) stations during two intensive measurement campaigns and includes more than 200 cases at wavelengths of 355 nm, 532 nm, and 1064 nm. We present and discuss the results and influence of the forecast and reanalysis models in terms of deviations of the derived aerosol optical properties. The results show that the mean relative deviation in molecular number density is always below ±3%, while larger deviations are shown in the derived aerosol optical properties, and the size of the deviation depends on the retrieval method together with the different wavelengths. In general, the aerosol extinction coefficient retrieval is more dependent on the model used than the aerosol backscatter retrievals are. The larger influence on the extinction retrieval is mainly related to the deviation in the gradient of the temperature profile provided by forecast and reanalysis models rather than the absolute deviation of the molecular number density. We found that deviations in extinction were within ±5%, with a probability of 83% at 355 nm and 60% at 532 nm. Moreover, for aerosol backscatter coefficient retrievals, different models can have a larger impact when the backscatter coefficient is retrieved with the elastic method than when the backscatter coefficient is calculated using the Raman method at both 355 nm and 532 nm. In addition, the atmospheric aerosol load can also influence the deviations in the aerosol extinction and backscatter coefficients, showing a larger impact under low aerosol loading scenarios.
      Citation: Remote Sensing
      PubDate: 2022-05-12
      DOI: 10.3390/rs14102342
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2343: Pre-Seismic Temporal Integrated
           Anomalies from Multiparametric Remote Sensing Data

    • Authors: Zhonghu Jiao, Xinjian Shan
      First page: 2343
      Abstract: Pre-seismic anomalies have the potential to indicate imminent strong earthquakes in the short to medium terms. However, an improved understanding of the statistical significance between anomalies and earthquakes is required to develop operational forecasting systems. We developed a temporal integrated anomaly (TIA) method to obtain the temporal trends of multiparametric anomalies derived from the Atmospheric Infrared Sounder (AIRS) product before earthquakes. A total of 169 global earthquakes that occurred from 2006 to 2020 and had magnitudes of ≥7.0 and focal depths of ≤70 km were used to test this new method in a retrospective manner. In addition, 169 synthetic earthquakes were randomly generated to demonstrate the suppression capacity of the TIA method for false alarms. We identified four different TIA trends according to the temporal characteristics of positive and negative TIAs. Long-term correlation analyses show that the recognition ability was 12.4–28.4% higher for true earthquakes than for synthetic earthquakes (i.e., higher than that of a random guess). Incorporating 2–5 kinds of TIAs offered the best chance of recognizing imminent shocks, highlighting the importance of multiparameter anomalies. Although the TIA trend characteristics before the earthquakes were not unique, we identified certain unexplained pre-seismic phenomena within the remote sensing data. The results provide new insight into the relationships between pre-seismic anomalies and earthquakes; moreover, the recognition ability of the proposed approach exceeds that of random guessing.
      Citation: Remote Sensing
      PubDate: 2022-05-12
      DOI: 10.3390/rs14102343
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2344: Characterization of Wildfire Smoke
           over Complex Terrain Using Satellite Observations, Ground-Based
           Observations, and Meteorological Models

    • Authors: Makiko Nakata, Itaru Sano, Sonoyo Mukai, Alexander Kokhanovsky
      First page: 2344
      Abstract: The severity of wildfires is increasing globally. In this study, we used data from the Global Change Observation Mission-Climate/Second-generation Global Imager (GCOM-C/SGLI) to characterize the biomass burning aerosols that are generated by large-scale wildfires. We used data from the September 2020 wildfires in western North America. The target area had a complex topography, comprising a basin among high mountains along a coastal region. The SGLI was essential for dealing with the complex topographical changes in terrain that we encountered, as it contains 19 polarization channels ranging from near ultraviolet (380 nm and 412 nm) to thermal infrared (red at 674 nm and near-infrared at 869 nm) and has a fine spatial resolution (1 km). The SGLI also proved to be efficient in the radiative transfer simulations of severe wildfires through the mutual use of polarization and radiance. We used a regional numerical model SCALE (Scalable Computing for Advanced Library and Environment) to account for variations in meteorological conditions and/or topography. Ground-based aerosol measurements in the target area were sourced from the National Aeronautics and Space Administration-Aerosol Robotic Network; currently, official satellite products typically do not provide the aerosol properties for very optically thick cases of wildfires. This paper used satellite observations, ground-based observations, and a meteorological model to define an algorithm for retrieving the aerosol properties caused by severe wildfire events.
      Citation: Remote Sensing
      PubDate: 2022-05-12
      DOI: 10.3390/rs14102344
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2345: Monitoring Sand Spit Variability
           Using Sentinel-2 and Google Earth Engine in a Mediterranean Estuary

    • Authors: Mar Roca, Gabriel Navarro, Javier García-Sanabria, Isabel Caballero
      First page: 2345
      Abstract: Estuarine degradation is a major concern worldwide, and is rapidly increasing due to anthropogenic pressures. The Mediterranean Guadiaro estuary, located in San Roque (Cadiz, Spain), is an example of a highly modified estuary, showing severe negative effects of eutrophication episodes and beach erosion. The migration of its river mouth sand spit causes the closure of the estuary, resulting in serious water quality issues and flora and fauna mortality due to the lack of water renewal. With the aim of studying the Guadiaro estuary throughout a 4-year period (2017–2020), the Sentinel-2 A/B twin satellites of the Copernicus programme were used thanks to their 5-day and 10 m temporal and spatial resolution, respectively. Sea–land mapping was performed using the Normalized Difference Water Index (NDWI) in the Google Earth Engine (GEE) platform, selecting cloud-free Sentinel-2 Level 2A images and computing statistics. Results show a closure trend of the Guadiaro river mouth and no clear sand spit seasonal patterns. The study also reveals the potential of both Sentinel-2 and GEE for estuarine monitoring by means of an optimized processing workflow. This improvement will be useful for coastal management to ensure a continuous and detailed monitoring in the area, contributing to the development of early-warning tools, which can be helpful for supporting an ecosystem-based approach to coastal areas.
      Citation: Remote Sensing
      PubDate: 2022-05-12
      DOI: 10.3390/rs14102345
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2346: Editorial for Special Issue:
           “New Insights into Ecosystem Monitoring Using Geospatial
           Techniques”

    • Authors: Emiliano Agrillo, Nicola Alessi, Jose Manuel Álvarez-Martínez, Laura Casella, Federico Filipponi, Bing Lu, Simona Niculescu, Mária Šibíková, Kathryn E. L. Smith
      First page: 2346
      Abstract: Recent global-scale environmental issues from climate change to biodiversity loss are generating an intense social pressure on the scientific community [...]
      Citation: Remote Sensing
      PubDate: 2022-05-12
      DOI: 10.3390/rs14102346
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2347: Differential Strategy-Based
           Multi-Level Dense Network for Pansharpening

    • Authors: Junru Yin, Jiantao Qu, Qiqiang Chen, Ming Ju, Jun Yu
      First page: 2347
      Abstract: Due to the discrepancy in spatial structure between multispectral (MS) and panchromatic (PAN) images, the general fusion scheme will lead to image error in the fused result. To solve this issue, a differential strategy-based multi-level dense network is proposed, and it regards the image pairs at different scales as the input of the network at different levels and is able to map the spatial information in PAN images to each band of MS images well by learning the differential information of different levels, which effectively solves the scale effect of remote sensing images. An improved dense network with the same hierarchical structure is used to obtain richer spatial features to enhance the spatial information of the fused result. Meanwhile, a hybrid loss strategy is used to constrain the network at different levels for obtaining better results. Qualitative and quantitative analyses show that the result has a uniform spectral distribution, a complete spatial structure, and optimal evaluation criteria, which fully demonstrate the superior performance of the proposed method.
      Citation: Remote Sensing
      PubDate: 2022-05-12
      DOI: 10.3390/rs14102347
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2348: Hyperspectral Image Denoising via
           Group Sparsity Regularized Hybrid Spatio-Spectral Total Variation

    • Authors: Pengdan Zhang, Jifeng Ning
      First page: 2348
      Abstract: In this paper, we propose a new hyperspectral image (HSI) denoising model with the group sparsity regularized hybrid spatio-spectral total variation (GHSSTV) and low-rank tensor decomposition, which is based on the analysis of structural sparsity of HSIs. First, the global correlations among all modes are explored by the Tucker decomposition, which applies low-rank constraints to the clean HSIs. To avoid over-smoothing, we propose GHSSTV regularization to ensure the group sparsity not only in the first-order gradient domain but also in the second-order ones along the spatio-spectral dimensions. Then, the sparse noise in HSI can be detected by the ℓ1 norm. Furthermore, strong Gaussian noise is simulated by the Frobenius norm. The alternating direction multiplier method (ADMM) algorithm is employed to effectively solve the GHSSTV model. Finally, experimental results from a series of simulations and real-world data suggest a superior performance of the GHSSTV method in HSIs denoising.
      Citation: Remote Sensing
      PubDate: 2022-05-12
      DOI: 10.3390/rs14102348
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2349: Analysing Process and Probability of
           Built-Up Expansion Using Machine Learning and Fuzzy Logic in English
           Bazar, West Bengal

    • Authors: Tanmoy Das, Shahfahad, Mohd Waseem Naikoo, Swapan Talukdar, Ayesha Parvez, Atiqur Rahman, Swades Pal, Md Sarfaraz Asgher, Abu Reza Md. Towfiqul Islam, Amir Mosavi
      First page: 2349
      Abstract: The study sought to investigate the process of built-up expansion and the probability of built-up expansion in the English Bazar Block of West Bengal, India, using multitemporal Landsat satellite images and an integrated machine learning algorithm and fuzzy logic model. The land use and land cover (LULC) classification were prepared using a support vector machine (SVM) classifier for 2001, 2011, and 2021. The landscape fragmentation technique using the landscape fragmentation tool (extension for ArcGIS software) and frequency approach were proposed to model the process of built-up expansion. To create the built-up expansion probability model, the dominance, diversity, and connectivity index of the built-up areas for each year were created and then integrated with fuzzy logic. The results showed that, during 2001–2021, the built-up areas increased by 21.67%, while vegetation and water bodies decreased by 9.28 and 4.63%, respectively. The accuracy of the LULC maps for 2001, 2011, and 2021 was 90.05, 93.67, and 96.24%, respectively. According to the built-up expansion model, 9.62% of the new built-up areas was created in recent decades. The built-up expansion probability model predicted that 21.46% of regions would be converted into built-up areas. This study will assist decision-makers in proposing management strategies for systematic urban growth that do not damage the environment.
      Citation: Remote Sensing
      PubDate: 2022-05-12
      DOI: 10.3390/rs14102349
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2350: Performance and Uncertainty of
           Satellite-Derived Bathymetry Empirical Approaches in an Energetic Coastal
           Environment

    • Authors: Bertrand Lubac, Olivier Burvingt, Alexandre Nicolae Lerma, Nadia Sénéchal
      First page: 2350
      Abstract: Objectives of this study are to evaluate the performance of different satellite-derived bathymetry (SDB) empirical models developed for multispectral satellite mission applications and to propose an uncertainty model based on inferential statistics. The study site is the Arcachon Bay inlet (France). A dataset composed of 450,837 echosounder data points and 89 Sentinel-2 A/B and Landsat-8 images acquired from 2013 to 2020, is generated to test and validate SDB and uncertainty models for various contrasting optical conditions. Results show that water column optical properties are characterized by a high spatio-temporal variability controlled by hydrodynamics and seasonal conditions. The best performance and highest robustness are found for the cluster-based approach using a green band log-linear regression model. A total of 80 satellite images can be exploited to calibrate SDB models, providing average values of root mean square error and maximum bathymetry of 0.53 m and 7.3 m, respectively. The uncertainty model, developed to extrapolate information beyond the calibration dataset, is based on a multi-scene approach. The sensitivity of the model to the optical variability not explained by the calibration dataset is demonstrated but represents a risk of error of less than 5%. Finally, the uncertainty model applied to a diachronic analysis definitively demonstrates the interest in SDB maps for a better understanding of morphodynamic evolutions of large-scale and complex coastal systems.
      Citation: Remote Sensing
      PubDate: 2022-05-12
      DOI: 10.3390/rs14102350
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2351: The Changes of Spatiotemporal Pattern
           of Rocky Desertification and Its Dominant Driving Factors in Typical Karst
           Mountainous Areas under the Background of Global Change

    • Authors: Bing Guo, Fei Yang, Junfu Fan, Yuefeng Lu
      First page: 2351
      Abstract: There are significant differences in the dominant driving factors of rocky desertification evolution in different historical periods in southwest karst mountainous areas. However, previous studies were mostly conducted in specific periods. In this study, taking Bijie City as an example, the spatial and temporal evolution pattern of rocky desertification in Bijie City in the recent 35 years was analyzed by introducing the feature space model and the gravity center model, and then the dominant driving factors of rocky desertification in the study area in different historical periods were clarified based on GeoDetector. The results were as follows: (1) The point-to-point B (bare land index)-DI (dryness index) feature space model has high applicability for rocky desertification monitoring, and its inversion accuracy was 91.3%. (2) During the past 35 years, the rocky desertification in Bijie belonged to the moderate rocky desertification on the whole, and zones of intensive and severe rocky desertification were mainly distributed in the Weining Yi, Hui, and Miao Autonomous Region. (3) During 1985–2020, the rocky desertification in Bijie City showed an overall weakening trend (‘weakening–aggravating–weakening’). (4) From 1985 to 2020, the gravity center of rocky desertification in Bijie City moved westward, indicating that the aggravating degree of rocky desertification in the western region of the study area was higher than that in the eastern region. (5) The dominant factors affecting the evolution of rocky desertification in the past 35 years shifted from natural factor (vegetation coverage) to human activity factor (population density). The research results could provide decision supports for the prevention and control of rocky desertification in Bijie City and even the southwest karst mountainous area.
      Citation: Remote Sensing
      PubDate: 2022-05-12
      DOI: 10.3390/rs14102351
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2352: Influence of Open-Pit Coal Mining on
           Ground Surface Deformation of Permafrost in the Muli Region in the
           Qinghai-Tibet Plateau, China

    • Authors: Hongwei Wang, Yuan Qi, Juan Zhang, Jinlong Zhang, Rui Yang, Junyu Guo, Dongliang Luo, Jichun Wu, Shengming Zhou
      First page: 2352
      Abstract: The Qinghai-Tibet Plateau (QTP) is the largest mid-to low latitude and high-altitude permafrost. Open-pit coal mining and other activities have caused serious damage to the alpine ecological environment and have accelerated the degradation of permafrost on the QTP. In this study, the influence of open-pit coal mining on the time series ground surface deformation of the permafrost in the Muli region of the QTP was analyzed from 19 January 2018 to 22 December 2020 based on Landsat, Gaofen, and Sentinel remote sensing data. The primary methods include human-computer interactive visual interpretation and the small baseline subsets interferometric synthetic aperture radar (SBAS-InSAR) method. The results showed that the spatial distribution of displacement velocity exhibits a considerably different pattern in the Muli region. Alpine meadow is the main land use/land cover (LULC) in the Muli region, and the surface displacement was mainly subsidence. The surface subsidence trend in alpine marsh meadows was obvious, with a subsidence displacement velocity of 10–30 mm/a. Under the influence of changes in temperature, the permafrost surface displacement was characteristics of regular thaw subsidence and freeze uplift. Surface deformation of the mining area is relatively severe, with maximum uplift displacement velocity of 74.31 mm/a and maximum subsidence displacement velocity of 167.51 mm/a. Open-pit coal mining had resulted in the destruction of 48.73 km2 of natural landscape in the Muli region. Mining development in the Muli region had increased the soil moisture of the alpine marsh meadow around the mining area, resulting in considerable cumulative displacement near the mining area and the acceleration of permafrost degradation.
      Citation: Remote Sensing
      PubDate: 2022-05-12
      DOI: 10.3390/rs14102352
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2353: Challenges in Diurnal Humidity
           Analysis from Cellular Microwave Links (CML) over Germany

    • Authors: Yoav Rubin, Dorita Rostkier-Edelstein, Christian Chwala, Pinhas Alpert
      First page: 2353
      Abstract: Near-surface humidity is a crucial variable in many atmospheric processes, mostly related to the development of clouds and rain. The humidity at the height of a few tens of meters above ground level is highly influenced by surface characteristics. Measuring the near-surface humidity at high resolution, where most of the humidity’s sinks and sources are found, is a challenging task using classical tools. A novel approach for measuring the humidity is based on commercial microwave links (CML), which provide a large part of the cellular networks backhaul. This study focuses on employing humidity measurements with high spatio–temporal resolution in Germany. One major goal is to assess the errors and the environmental influence by comparing the CML-derived humidity to in-situ humidity measurements at weather stations and reanalysis (COSMO-Rea6) products. The method of retrieving humidity from the CML has been improved as compared to previous studies due to the use of new data at high temporal resolution. The results show a similar correlation on average and generally good agreement between both the CML retrievals and the reanalysis, and 32 weather stations near Siegen, West Germany (CML—0.84, Rea6—0.85). Higher correlations are observed for CML-derived humidity during the daytime (0.85), especially between 9–17 LT (0.87) and a maximum at 12 LT (0.90). During the night, the correlations are lower on average (0.81), with a minimum at 3 LT (0.74). These results are discussed with attention to the diurnal boundary layer (BL) height variation which has a strong effect on the BL humidity temporal profile. Further metrics including root mean square errors, mean values and standard deviations, were also calculated.
      Citation: Remote Sensing
      PubDate: 2022-05-12
      DOI: 10.3390/rs14102353
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2354: Comparison between the Mesospheric
           Winds Observed by Two Collocated Meteor Radars at Low Latitudes

    • Authors: Jie Zeng, Wen Yi, Xianghui Xue, Iain Reid, Xiaojing Hao, Na Li, Jinsong Chen, Tingdi Chen, Xiankang Dou
      First page: 2354
      Abstract: This study compares the hourly mesospheric horizontal winds observed by two collocated and independent low-latitude meteor radars operating at 37.5 MHz and 53.1 MHz in Kunming, China (25.6°N, 103.8°E). Upon analyzing simultaneously detected meteor echoes, we find a fixed angular deviation between the baselines of the two meteor radar antenna arrays within the east–north–up coordinate system. Then, we correct the deviation in the antenna azimuth direction using a novel method and recalculate the horizontal zonal and meridional winds. A comparison of the results before and after the correction shows strong consistency between the winds observed by both meteor radars within the entire detection altitude range. Furthermore, we summarize the performance of different techniques for measuring mesospheric winds. Ultimately, our statistical analysis approach allows the uncertainties associated with meteor radar wind observations to be more precisely estimated.
      Citation: Remote Sensing
      PubDate: 2022-05-13
      DOI: 10.3390/rs14102354
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2355: Integrating Hybrid Pyramid Feature
           Fusion and Coordinate Attention for Effective Small Sample Hyperspectral
           Image Classification

    • Authors: Chen Ding, Youfa Chen, Runze Li, Dushi Wen, Xiaoyan Xie, Lei Zhang, Wei Wei, Yanning Zhang
      First page: 2355
      Abstract: In recent years, hyperspectral image (HSI) classification (HSIC) methods that use deep learning have proved to be effective. In particular, the utilization of convolutional neural networks (CNNs) has proved to be highly effective. However, some key issues need to be addressed when classifying hyperspectral images (HSIs), such as small samples, which can influence the generalization ability of the CNNs and the HSIC results. To address this problem, we present a new network that integrates hybrid pyramid feature fusion and coordinate attention for enhancing small sample HSI classification results. The innovative nature of this paper lies in three main areas. Firstly, a baseline network is designed. This is a simple hybrid 3D-2D CNN. Using this baseline network, more robust spectral-spatial feature information can be obtained from the HSI. Secondly, a hybrid pyramid feature fusion mechanism is used, meaning that the feature maps of different levels and scales can be effectively fused to enhance the feature extracted by the model. Finally, coordinate attention mechanisms are utilized in the network, which can not only adaptively capture the information of the spectral dimension, but also include the direction-aware and position sensitive information. By doing this, the proposed CNN structure can extract more useful HSI features and effectively be generalized to test samples. The proposed method was shown to obtain better results than several existing methods by experimenting on three public HSI datasets.
      Citation: Remote Sensing
      PubDate: 2022-05-13
      DOI: 10.3390/rs14102355
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2356: Geometric Construction of Video
           Stereo Grid Space

    • Authors: Huangchuang Zhang, Ruoping Shi, Ge Li
      First page: 2356
      Abstract: The construction of digital twin cities is a current research hotspot. Video data are one of the important aspects of digital twin cities, and their digital modeling is one of the important foundations of its construction. For this reason, the construction and digital analysis of video data space has become an urgent problem to be solved. After in-depth research, this study found that the existing video space construction methods have three shortcomings: first, the problem of high requirements for objective conditions or low accuracy; second, the lack of easy and efficient mapping algorithms from 2D video pixel coordinates to 3D; and third, the lack of efficient correlation mechanisms between video space and external geographic information, making it difficult to integrate video space with external information, and thus prevent a more effective analysis. In view of the above problems, this paper proposes a video stereo grid geometric space construction method based on GeoSOT-3D stereo grid coding and a camera imaging model to form a video stereo grid space model. Finally, targeted experiments of video stereo grid space geometry construction were conducted to analyze the experimental results before and after optimization and compare the variance size to verify the feasibility and effectiveness of the model.
      Citation: Remote Sensing
      PubDate: 2022-05-13
      DOI: 10.3390/rs14102356
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2357: Robot Path Planning Method Based on
           Indoor Spacetime Grid Model

    • Authors: Huangchuang Zhang, Qingjun Zhuang, Ge Li
      First page: 2357
      Abstract: In the context of digital twins, smart city construction and artificial intelligence technology are developing rapidly, and more and more mobile robots are performing tasks in complex and time-varying indoor environments, making, at present, the unification of modeling, dynamic expression, visualization of operation, and wide application between robots and indoor environments a pressing problem to be solved. This paper presents an in-depth study on this issue and summarizes three major types of methods: geometric modeling, topological modeling, and raster modeling, and points out the advantages and disadvantages of these three types of methods. Therefore, in view of the current pain points of robots and complex time-varying indoor environments, this paper proposes an indoor spacetime grid model based on the three-dimensional division framework of the Earth space and innovatively integrates time division on the basis of space division. On the basis of the model, a dynamic path planning algorithm for the robot in the complex time-varying indoor environment is designed, that is, the Spacetime-A* algorithm (STA* for short). Finally, the indoor spacetime grid modeling experiment is carried out with real data, which verifies the feasibility and correctness of the spacetime relationship calculation algorithm encoded by the indoor spacetime grid model. Then, experiments are carried out on the multi-group path planning algorithms of the robot under the spacetime grid, and the feasibility of the STA* algorithm under the indoor spacetime grid and the superiority of the spacetime grid are verified.
      Citation: Remote Sensing
      PubDate: 2022-05-13
      DOI: 10.3390/rs14102357
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2358: Using Remote Sensing to Estimate
           Understorey Biomass in Semi-Arid Woodlands of South-Eastern Australia

    • Authors: Linda Riquelme, David H. Duncan, Libby Rumpff, Peter Anton Vesk
      First page: 2358
      Abstract: Monitoring ground layer biomass, and therefore forage availability, is important for managing large, vertebrate herbivore populations for conservation. Remote sensing allows for frequent observations over broad spatial scales, capturing changes in biomass over the landscape and through time. In this study, we explored different satellite-derived vegetation indices (VIs) for their utility in estimating understorey biomass in semi-arid woodlands of south-eastern Australia. Relationships between VIs and understorey biomass data have not been established in these particular semi-arid communities. Managers want to use forage availability to inform cull targets for western grey kangaroos (Macropus fuliginosus), to minimise the risk that browsing poses to regeneration in threatened woodland communities when grass biomass is low. We attempted to develop relationships between VIs and understorey biomass data collected over seven seasons across open and wooded vegetation types. Generalised Linear Mixed Models (GLMMs) were used to describe relationships between understorey biomass and VIs. Total understorey biomass (live and dead, all growth forms) was best described using the Tasselled Cap (TC) greenness index. The combined TC brightness and Modified Soil Adjusted Vegetation Index (MSAVI) ranked best for live understorey biomass (all growth forms), and grass (live and dead) biomass was best described by a combination of TC brightness and greenness indices. Models performed best for grass biomass, explaining 70% of variation in external validation when predicting to the same sites in a new season. However, we found empirical relationships were not transferrable to data collected from new sites. Including other variables (soil moisture, tree cover, and dominant understorey growth form) improved model performance when predicting to new sites. Anticipating a drop in forage availability is critical for the management of grazing pressure for woodland regeneration, however, predicting understorey biomass through space and time is a challenge. Whilst remotely sensed VIs are promising as an easily-available source of vegetation information, additional landscape-scale data are required before they can be considered a cost-efficient method of understorey biomass estimation in this semi-arid landscape.
      Citation: Remote Sensing
      PubDate: 2022-05-13
      DOI: 10.3390/rs14102358
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2359: Non-Uniform Synthetic Aperture
           Radiometer Image Reconstruction Based on Deep Convolutional Neural Network
           

    • Authors: Chengwang Xiao, Xi Wang, Haofeng Dou, Hao Li, Rongchuan Lv, Yuanchao Wu, Guangnan Song, Wenjin Wang, Ren Zhai
      First page: 2359
      Abstract: When observing the Earth from space, the synthetic aperture radiometer antenna array is sometimes set as a non-uniform array. In non-uniform synthetic aperture radiometer image reconstruction, the existing brightness temperature image reconstruction methods include the grid method and array factor forming (AFF) method. However, when using traditional methods for imaging, errors are usually introduced or some prior information is required. In this article, we propose a new IASR imaging method with deep convolution neural network (CNN). The frequency domain information is extracted through multiple convolutional layers, global pooling layers, and fully connected layers to achieve non-uniform synthetic aperture radiometer imaging. Through extensive numerical experiments, we demonstrate the performance of the proposed imaging method. Compared to traditional imaging methods such as the grid method and AFF method, the proposed method has advantages in image quality, computational efficiency, and noise suppression.
      Citation: Remote Sensing
      PubDate: 2022-05-13
      DOI: 10.3390/rs14102359
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2360: A New Coupling Method for PM2.5
           Concentration Estimation by the Satellite-Based Semiempirical Model and
           Numerical Model

    • Authors: Shuyun Yuan, Ying Li, Jinhui Gao, Fangwen Bao
      First page: 2360
      Abstract: Aerosol optical and chemical properties play a major role in the retrieval of PM2.5 concentrations based on aerosol optical depth (AOD) data from satellites in the conventional semiempirical model (SEM). However, limited observation information hinders the high-resolution estimation of PM2.5. Therefore, a new method for evaluating near-surface PM2.5 at high spatial resolution is developed by coupling the SEM and the chemical transport model (CTM)-based numerical (CSEN) model. The numerical model can provide large-scale information for aerosol properties with high spatial resolution at a large scale based on emissions and meteorology, though it can still be biased in simulating absolute PM2.5 concentrations. Therefore, the two crucial aerosol characteristic parameters, including the coefficient integrated humidity effect (γ′) and the comprehensive reference value of aerosol properties (K) in SEM, have been redefined using the WRF-Chem numerical model. Improved model performance was observed for these results compared with the original SEM results. The monthly averaged correlation coefficients (R) by CSEN were 0.92, 0.82, 0.84, and 0.83 in January, April, July, and October, respectively, whereas those of the SEM were 0.80, 0.77, 0.72, and 0.72, respectively. All the statistical metrics of the model validation showed significant improvements in all seasons. The reduced biases of estimated PM2.5 by CSEN indicated the effect of hygroscopic growth and aerosol properties affected by the meteorology on the relationship between AOD and estimated PM2.5 concentrations, especially in winter and summer. The better performance of the CSEN model provides insight for air quality monitoring at different scales, which supplies important information for air pollution control policies and health impact analysis.
      Citation: Remote Sensing
      PubDate: 2022-05-13
      DOI: 10.3390/rs14102360
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2361: Correcting GEDI Water Level Estimates
           for Inland Waterbodies Using Machine Learning

    • Authors: Ibrahim Fayad, Nicolas Baghdadi, Jean-Stéphane Bailly, Frédéric Frappart, Núria Pantaleoni Reluy
      First page: 2361
      Abstract: The Global Ecosystem Dynamics Investigation (GEDI) LiDAR on the International Space Station has acquired more than 35 billion shots globally in the period between April 2019 and August 2021. The acquired shots could offer a significant database for the measure and monitoring of inland water levels over the Earth’s surface. Nonetheless, previous and current studies have shown that the provided GEDI elevation estimates are significantly less accurate than any available radar or LiDAR altimeter. Indeed, our analysis of GEDI’s altimetric capabilities to retrieve water levels over the five North American Great Lakes presented estimates with a bias that ranged between 0.26 and 0.35 m and a root mean squared error (RMSE) ranging between 0.54 and 0.68 m. Therefore, our objective in this study is to post-process the original GEDI water level estimates from an error model taking instrumental, atmospheric, and lakes surface state factors as proxies, which affect the physical shape of the waveforms, hence introducing uncertainties on the elevation estimates. The first tested model, namely a random forest regressor (RFICW) with the instrumental, atmospheric, and water surface state factors as inputs, was validated temporally (trained on a given year and validated on another) and spatially (trained on a given lake and validated on the remaining four). The results showed a significant decrease in elevation estimation errors both temporally and spatially. The temporally validated models showed an RMSE on the corrected elevation estimates of 0.18 m. Concerning the spatially validated model, the results varied based on the lake data used for training. Indeed, the most accurate spatially validated model showed an RMSE of 0.17 m, while the least accurate model showed an RMSE of 0.26 m. Finally, given that an elevation correction model using all the factors (instrumental, atmospheric, and water surface state factors) presents a best-case scenario, as water surface state factors are only available over a selected number of lakes globally, three additional models based on random forest were tested. The first, RFI, uses only instrumental factors as correction factors, RFIC uses both instrumental and atmospheric factors, while the third, RFIW, uses instrumental and water surface state factors. The temporal validation of these models showed that the model using instrumental factors, while less accurate than the remaining two models, was capable of correcting the original GEDI elevation estimates by a factor of two across the five lakes. On the other hand, the RFIC model was the most accurate between the three, with a slight degradation in comparison to the full model. Indeed, the RFIC model showed an RMSE on the estimation of water levels of 0.21 m.
      Citation: Remote Sensing
      PubDate: 2022-05-13
      DOI: 10.3390/rs14102361
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2362: Performance Analysis of Ku/Ka
           Dual-Band SAR Altimeter from an Airborne Experiment over South China Sea

    • Authors: Xiaonan Liu, Weiya Kong, Hanwei Sun, Yaobing Lu
      First page: 2362
      Abstract: Satellite radar altimeters have been successfully used for sea surface height (SSH) measurement for decades, gaining great insight in oceanography, meteorology, marine geology, etc. To further improve the observation precision and spatial resolution, radar altimeters have evolved from real aperture to synthetic aperture, from the Ku-band to Ka-band. Future synthetic aperture radar (SAR) altimeter of the Ka-band is expected to achieve better performance than its predecessors. To verify the SAR altimeter data processing method and explore the system advantage of the Ka-band, a Ku/Ka dual-band SAR altimeter airborne experiment was carried out over South China Sea on 6 November 2021. Through dedicated hardware design, this campaign has acquired the Ku and Ka dual-band echo data simultaneously. The airborne data are processed to estimate the SSH retrieval precision after a series of procedures (including height compensation, range migration correction, multi-look processing, waveform re-tracking). To accustom to the airborne experiment design, a SAR echo model that fully considers both the attitude variation of the aircraft and the elliptical footprint of radar beam is established. The retrieved SSH data are compared with the public SSH data along the flight path at the experiment day, showing good consistence for both bands. By calculating the theoretical precision of waveform re-tracking and re-processing the dual-band airborne data into different bandwidths, it is demonstrated that the Ku/Ka precision ratio is possible to achieve 1.4 within the 27 km offshore area, which indicates that Ka-band has better performance.
      Citation: Remote Sensing
      PubDate: 2022-05-13
      DOI: 10.3390/rs14102362
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2363: Evaluation of FY-4A Temperature
           Profile Products and Application to Winter Precipitation Type Diagnosis in
           Southern China

    • Authors: Yang Gao, Dongyan Mao, Xin Wang, Danyu Qin
      First page: 2363
      Abstract: FY-4A GIIRS temperature profile products have provided unprecedented information for studying the atmospheric characteristics of thermal structures since 2020. The main objective of this paper is to evaluate GIIRS temperature profile products by using radiosonde observations and then apply them to the diagnosis of winter precipitation types in southern China. GIIRS temperature profile products for four types (clear sky perfect quality, cloudy sky perfect quality, cloudy sky good quality and cloudy sky bad quality) show different performances. Relatively, the cloud can affect the quality and quantity of GIIRS products. At different pressure levels, the perfect flagged data under the clear or cloudy sky show the best agreement with radiosonde observations, yielding the highest Pearson correlation coefficient and lowest mean bias as well as root mean square error. The good flagged data have a slight deviation from the perfect data. The impact on the quantity of the GIIRS temperature data is greater than that on the quality with an increase in cloud top height. A case investigation was carried out to analyze the performance of GIIRS temperature profiles for the diagnosis of precipitation types in a winter storm of 2022. The GIIRS temperature profiles represent the reasonable atmospheric thermal structures in the rain and snow in Hubei and Hunan provinces. The GIIRS temperature below 700 hPa is an important indictor to precipitation type diagnosis. Furthermore, two critical thresholds for GIIRS temperatures, which are below −2 °C at 850 hPa and below 0 °C at 925 hPa, respectively, are proposed for the occurrence of snowfall in this winter storm. In addition, the distribution of GIIRS temperature at different pressure levels is consistent with radiosonde observations in a freezing rain event in Guiyang, all of which show the warm rain mechanism by combining the cloud top information.
      Citation: Remote Sensing
      PubDate: 2022-05-13
      DOI: 10.3390/rs14102363
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2364: Air–Sea Interface Parameters
           and Heat Flux from Neural Network and Advanced Microwave Scanning
           Radiometer Observations

    • Authors: Biao Zhang, Xiaotong Yu, William Perrie, Fenghua Zhou
      First page: 2364
      Abstract: We present a new approach, based on a multi-parameter back-propagation neural network (BPNN) model, to simultaneously retrieve sea surface wind speed, sea surface temperature, near-surface air temperature, and dewpoint temperature over the global oceans from the Advanced Microwave Scanning Radiometer 2 (AMSR2) onboard the Global Change Observation Mission 1st-Water (GCOM-W1). The model is trained and validated with the collocations of AMSR2 multi-channel (6.9–36.5 GHz) brightness temperatures, under both clear and cloudy conditions, and National Data Buoy Center (NDBC) and Tropical Atmosphere Ocean Project (TAO) buoy measurements along with ECMWF ERA5 reanalysis data. The root-mean-square (rms) errors of BPNN-retrieved sea surface wind speed, sea surface temperature, near-surface air temperature, and dewpoint temperature are 1.13 m/s, 1.02 °C, 1.20 °C, and 1.57 °C, respectively. The first three retrieved geophysical parameters and the estimated relative humidity from near-surface air temperature and dewpoint temperature are used to compute the sensible heat flux (SHF) and latent heat flux (LHF), using an improved bulk flux parametrization. The rms errors of the estimated SHF and LHF from BPNN-derived air–sea interface variables, and those from buoy and reanalysis data, are 18.13 W/m2 and 39.56 W/m2. We also compare SHF and LHF estimates with the Yongxing air–sea flux tower measurements in the northern South China Sea. The estimated SHF and LHF in summer and autumn periods are closer to observations than in winter and spring. The proposed method has potential to derive instantaneous air–sea interface atmospheric and oceanic parameters as well as surface sensible and latent heat fluxes from AMSR2 along-track wide swath observations.
      Citation: Remote Sensing
      PubDate: 2022-05-13
      DOI: 10.3390/rs14102364
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2365: A Machine Learning Approach to
           Extract Rock Mass Discontinuity Orientation and Spacing, from Laser
           Scanner Point Clouds

    • Authors: Elisa Mammoliti, Francesco Di Stefano, Davide Fronzi, Adriano Mancini, Eva Savina Malinverni, Alberto Tazioli
      First page: 2365
      Abstract: This study wants to give a contribution to the semi-automatic evaluation of rock mass discontinuities, orientation and spacing, as important parameters used in Engineering. In complex and inaccessible study areas, a traditional geological survey is hard to conduct, therefore, remote sensing techniques have proven to be a very useful tool for discontinuity analysis. However, critical expert judgment is necessary to make reliable analyses. For this reason, the open-source Python tool named DCS (Discontinuities Classification and Spacing) was developed to manage point cloud data. The tool is written in Python and is based on semi-supervised clustering. By this approach the users can: (a) estimate the number of discontinuity sets (here referred to as “clusters”) using the Error Sum of Squares (SSE) method and the K-means algorithm; (b) evaluate step by step the quality of the classification visualizing the stereonet and the scatterplot of dip vs. dip direction from the clustering; (c) supervise the clustering procedure through a manual initialization of centroids; (d) calculate the normal spacing. In contrast to other algorithms available in the literature, the DCS method does not require complex parameters as inputs for the classification and permits the users to supervise the procedure at each step. The DCS approach was tested on the steep coastal cliff of Ancona town (Italy), called the Cardeto–Passetto cliff, which is characterized by a complex fracturing and is largely affected by rockfall phenomena. The results of discontinuity orientation were validated with the field survey and compared with the ones of the FACETS plug-in of CloudCompare. In addition, the algorithm was tested and validated on regular surfaces of an anthropic wall located at the bottom of the cliff. Eventually, a kinematic analysis of rock slope stability was performed, discussing the advantages and limitations of the methods considered and making fundamental considerations on their use.
      Citation: Remote Sensing
      PubDate: 2022-05-13
      DOI: 10.3390/rs14102365
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2366: Remote Sensing of Global Sea Surface
           pH Based on Massive Underway Data and Machine Learning

    • Authors: Zhiting Jiang, Zigeng Song, Yan Bai, Xianqiang He, Shujie Yu, Siqi Zhang, Fang Gong
      First page: 2366
      Abstract: Seawater pH is a direct proxy of ocean acidification, and monitoring the global pH distribution and long-term series changes is critical to understanding the changes and responses of the marine ecology and environment under climate change. Owing to the lack of sufficient global-scale pH data and the complex relationship between seawater pH and related environmental variables, generating time-series products of satellite-derived global sea surface pH poses a great challenge. In this study, we solved the problem of the lack of sufficient data for pH algorithm development by using the massive underway sea surface carbon dioxide partial pressure (pCO2) dataset to structure a large data volume of near in situ pH based on carbonate calculation between underway pCO2 and calculated total alkalinity from sea surface salinity and relevant parameters. The remote sensing inversion model of pH was then constructed through this massive pH training dataset and machine learning methods. After several tests of machine learning methods and groups of input parameters, we chose the random forest model with longitude, latitude, sea surface temperature (SST), chlorophyll a (Chla), and Mixed layer depth (MLD) as model inputs with the best performance of correlation coefficient (R2 = 0.96) and root mean squared error (RMSE = 0.008) in the training set and R2 = 0.83 (RMSE = 0.017) in the testing set. The sensitivity analysis of the error variation induced by the uncertainty of SST and Chla (SST ≤ ±0.5 °C and Chla ≤ ±20%; RMSESST ≤ 0.011 and RMSEChla ≤ 0.009) indicated that our sea surface pH model had good robustness. Monthly average global sea surface pH products from 2004 to 2019 with a spatial resolution of 0.25° × 0.25° were produced based on the satellite-derived SST and Chla products and modeled MLD dataset. The pH model and products were validated using another independent station-measured pH dataset from the Global Ocean Data Analysis Project (GLODAP), showing good performance. With the time-series pH products, refined interannual variability and seasonal variability were presented, and trends of pH decline were found globally. Our study provides a new method of directly using remote sensing to invert pH instead of indirect calculation based on the construction of massive underway calculated pH data, which would be made useful by comparing it with satellite-derived pCO2 products to understand the carbonate system change and the ocean ecological environments responding to the global change.
      Citation: Remote Sensing
      PubDate: 2022-05-14
      DOI: 10.3390/rs14102366
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2367: Combined Assimilation of Doppler Wind
           Lidar and Tail Doppler Radar Data over a Hurricane Inner Core for Improved
           Hurricane Prediction with the NCEP Regional HWRF System

    • Authors: Xin Li, Zhaoxia Pu, Jun A. Zhang, George David Emmitt
      First page: 2367
      Abstract: Accurate specification of hurricane inner-core structure is critical to predicting the evolution of a hurricane. However, observations over hurricane inner cores are generally lacking. Previous studies have emphasized Tail Doppler radar (TDR) data assimilation to improve hurricane inner-core representation. Recently, Doppler wind lidar (DWL) has been used as an observing system to sample hurricane inner-core and environmental conditions. The NOAA P3 Hurricane Hunter aircraft has DWL installed and can obtain wind data over a hurricane’s inner core when the aircraft passes through the hurricane. In this study, we examine the impact of assimilating DWL winds and TDR radial winds on the prediction of Hurricane Earl (2016) with the NCEP operational Hurricane Weather Research and Forecasting (HWRF) system. A series of data assimilation experiments are conducted with the Gridpoint Statistical Interpolation (GSI)-based ensemble-3DVAR hybrid system to identify the best way to assimilate TDR and DWL data into the HWRF forecast system. The results show a positive impact of DWL data on hurricane analysis and prediction. Compared with the assimilation of u and v components, assimilation of DWL wind speed provides better hurricane track and intensity forecasts. Proper choices of data thinning distances (e.g., 5 km horizontal thinning and 70 hPa vertical thinning for DWL) can help achieve better analysis in terms of hurricane vortex representation and forecasts. In the analysis and forecast cycles, the combined TDR and DWL assimilation (DWL wind speed and TDR radial wind, along with other conventional data, e.g., NCEP Automated Data Processing (ADP) data) offsets the downgrade analysis from the absence of DWL observations in an analysis cycle and outperforms assimilation of a single type of data (either TDR or DWL) and leads to improved forecasts of hurricane track, intensity, and structure. Overall, assimilation of DWL observations has been beneficial for analysis and forecasts in most cases. The outcomes from this study demonstrate the great potential of including DWL wind profiles in the operational HWRF system for hurricane forecast improvement.
      Citation: Remote Sensing
      PubDate: 2022-05-13
      DOI: 10.3390/rs14102367
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2368: Shipborne GNSS-Determined Sea Surface
           Heights Using Geoid Model and Realistic Dynamic Topography

    • Authors: Sander Varbla, Aive Liibusk, Artu Ellmann
      First page: 2368
      Abstract: With an increasing demand for accurate and reliable estimates of sea surface heights (SSH) from coastal and marine applications, approaches based on GNSS positioning have become favored, to bridge the gap between tide gauge (TG) and altimetry measurements in the coastal zone, and to complement offshore altimetry data. This study developed a complete methodology for jointly deriving and validating shipborne GNSS-determined SSH, using a geoid model and realistic dynamic topography estimates. An approach that combines the properties of hydrodynamic models and TG data was developed to obtain the latter. Tide gauge data allow estimating the spatiotemporal bias of a hydrodynamic model and, thus, linking it to the used vertical datums (e.g., a novel geoid-based Baltic Sea Chart Datum 2000). However, TG data may be erroneous and represent different conditions than offshore locations. The qualities of spatiotemporal bias are, hence, used to constrain TG data errors. Furthermore, a rigid system of four GNSS antennas was used to ensure SSH accuracy. Besides eliminating the vessel’s attitude effect on measurement data, the rigid system also provides a means for internal validation, suggesting a 4.1 cm height determination accuracy in terms of standard deviation. The methodology also involves eliminating the effect of sea state conditions via a low-pass filter and empirical estimation of vessel sailing-related corrections, such as the squat effect. The different data validation (e.g., examination of residual values and intersection analyses) results, ranging from 1.8 cm to 5.5 cm in terms of standard deviation, indicate an SSH determination accuracy of around 5 cm.
      Citation: Remote Sensing
      PubDate: 2022-05-13
      DOI: 10.3390/rs14102368
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2369: Leveraging Machine Learning and
           Geo-Tagged Citizen Science Data to Disentangle the Factors of Avian
           Mortality Events at the Species Level

    • Authors: Anni Yang, Matthew Rodriguez, Di Yang, Jue Yang, Wenwen Cheng, Changjie Cai, Han Qiu
      First page: 2369
      Abstract: Abrupt environmental changes can affect the population structures of living species and cause habitat loss and fragmentations in the ecosystem. During August–October 2020, remarkably high mortality events of avian species were reported across the western and central United States, likely resulting from winter storms and wildfires. However, the differences of mortality events among various species responding to the abrupt environmental changes remain poorly understood. In this study, we focused on three species, Wilson’s Warbler, Barn Owl, and Common Murre, with the highest mortality events that had been recorded by citizen scientists. We leveraged the citizen science data and multiple remotely sensed earth observations and employed the ensemble random forest models to disentangle the species responses to winter storm and wildfire. We found that the mortality events of Wilson’s Warbler were primarily impacted by early winter storms, with more deaths identified in areas with a higher average daily snow cover. The Barn Owl’s mortalities were more identified in places with severe wildfire-induced air pollution. Both winter storms and wildfire had relatively mild effects on the mortality of Common Murre, which might be more related to anomalously warm water. Our findings highlight the species-specific responses to environmental changes, which can provide significant insights into the resilience of ecosystems to environmental change and avian conservations. Additionally, the study emphasized the efficiency and effectiveness of monitoring large-scale abrupt environmental changes and conservation using remotely sensed and citizen science data.
      Citation: Remote Sensing
      PubDate: 2022-05-14
      DOI: 10.3390/rs14102369
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2370: Automatic Extraction of Mountain
           River Surface and Width Based on Multisource High-Resolution Satellite
           Images

    • Authors: Yuan Xue, Chao Qin, Baosheng Wu, Dan Li, Xudong Fu
      First page: 2370
      Abstract: The extraction of high-resolution geomorphic information from remote sensing images is a key technology for supporting mountain river research. Extracting small rivers (width < 90 m) from complex backgrounds based on satellite images remains a challenging issue. In this research, we propose an improved random forest (RF) algorithm, RF-ANN (artificial neural network), by using neural networks and thermal infrared data for the extraction of river surfaces. We also develop an automated river width extraction (ARWE) method based on the central axis transformation algorithm and centerline automatic correction algorithm for the automatic extraction of the river widths across the whole basin. We chose the Huangfuchuan River Basin on the Loess Plateau, China, as a case study area. Chinese GF-1 and ZY-3 satellite images were implemented as the primary data source. We extracted the bankfull river surface and river widths of the Huangfuchuan River by using these two improved methods. The results show that the RF-ANN method has a total river surface extraction accuracy of 94.7%, and the extracted river surfaces cover more than 85% of the order 3 DEM river network. By implementing high-resolution DEM and thermal infrared data, RF-ANN effectively eliminates the disturbance of shadows of mountains and other features, which ensures the high accuracy of the extracted widths. It was verified that the maximum and minimum river widths that can be extracted in the Huangfuchuan River Basin are 297.4 m and 6.1 m, respectively. The overall error of river width extraction is 0.97 m, which is less than half of the pixel length of remote sensing images. The R2 and root mean square error (RMSE) of the estimated river width values are 0.99 and 1.49, respectively. For tiny rivers with widths narrower than 10 m, the error of river width extraction is 10.9%. The error of thin rivers whose widths range from 10 to 30 m is 4.9%. For small rivers ranging from 30 to 90 and rivers wider than 90 m, the error is 1.1% and 0.6%, respectively. The new approach provides an effective method for extracting the surface and width of mountain rivers in topographically complex regions by using high-resolution satellite images, which may provide a database for estimating river carbon emissions and related research in fluvial morphology and water resource management.
      Citation: Remote Sensing
      PubDate: 2022-05-14
      DOI: 10.3390/rs14102370
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2371: MIMO: A Unified Spatio-Temporal Model
           for Multi-Scale Sea Surface Temperature Prediction

    • Authors: Siyun Hou, Wengen Li, Tianying Liu, Shuigeng Zhou, Jihong Guan, Rufu Qin, Zhenfeng Wang
      First page: 2371
      Abstract: Sea surface temperature (SST) is a crucial factor that affects global climate and marine activities. Predicting SST at different temporal scales benefits various applications, from short-term SST prediction for weather forecasting to long-term SST prediction for analyzing El Niño–Southern Oscillation (ENSO). However, existing approaches for SST prediction train separate models for different temporal scales, which is inefficient and cannot take advantage of the correlations among the temperatures of different scales to improve the prediction performance. In this work, we propose a unified spatio-temporal model termed the Multi-In and Multi-Out (MIMO) model to predict SST at different scales. MIMO is an encoder–decoder model, where the encoder learns spatio-temporal features from the SST data of multiple scales, and fuses the learned features with a Cross Scale Fusion (CSF) operation. The decoder utilizes the learned features from the encoder to adaptively predict the SST of different scales. To our best knowledge, this is the first work to predict SST at different temporal scales simultaneously with a single model. According to the experimental evaluation on the Optimum Interpolation SST (OISST) dataset, MIMO achieves the state-of-the-art prediction performance.
      Citation: Remote Sensing
      PubDate: 2022-05-14
      DOI: 10.3390/rs14102371
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2372: Luojia-1 Nightlight Image
           Registration Based on Sparse Lights

    • Authors: Zhichao Guan, Guo Zhang, Yonghua Jiang, Xin Shen, Zhen Li
      First page: 2372
      Abstract: When mosaicking adjacent nightlight images of a large area that lacks human activities, traditional registration methods have difficulty realizing the tie point registrations due to the lack of structural information. In order to address this issue, this study devises an easy-to-implement engineering solution that allows for the registration of sparse light areas with high efficiency while guaranteeing accuracy in non-sparse light areas. The proposed method first extracts the sparsely distributed light point positions through use of roundness detection and the centroid method. Then, geometric positioning forward and backward algorithms and the random consistency sampling detection algorithm (RANSAC) are used in order to achieve a rough registration of the nightlight images and the remaining tie points are expanded through the affine model. Through experimentation it was found that, compared with traditional registration methods, the proposed method is more reliable and has a wider distribution in sparse light areas. Finally, through the registration test of 275 scenes of nightlight images of China from Luojia-1, the coverage ratio of the tie points was increased from 59.3% from the traditional method to 95.3% in this study and the accuracy of the block adjustment was 0.63 pixels, which verifies the effectiveness of the method. The proposed method provides a basis for the registration, block adjustment, and mosaicking of nightlight images.
      Citation: Remote Sensing
      PubDate: 2022-05-14
      DOI: 10.3390/rs14102372
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2373: Mapping and Spatial Variation of
           Seagrasses in Xincun, Hainan Province, China, Based on Satellite Images

    • Authors: Yiqiong Li, Junwu Bai, Li Zhang, Zhaohui Yang
      First page: 2373
      Abstract: Seagrass is an important structural and functional component of the global marine ecosystem and is of high value for its ecological services. This paper took Xincun Bay (including Xincun Harbor and Li’an Harbor) of Hainan Province as the study area, combined ground truth data, and adopted two methods to map seagrass in 2020 using Chinese GF2 satellite images: maximum-likelihood and object-oriented classification. Sentinel-2 images from 2016 to 2020 were used to extract information on seagrass distribution changes. The following conclusions were obtained. (1) Based on GF2 imagery, both the classical maximum likelihood classification (MLC) method and the object-based image analysis (OBIA) method can effectively extract seagrass information, and OBIA can also portray the overall condition of seagrass patches. (2) The total seagrass area in the study area in 2020 was about 395 hectares, most of which was distributed in Xincun Harbor. The southern coast of Xincun Harbor is an important area where seagrass is concentrated over about 228 hectares in a strip-like continuous distribution along the coastline. (3) The distribution of seagrasses in the study area showed a significant decaying trend from 2016 to 2020. The total area of seagrass decreased by 79.224 ha during the five years from 2016 to 2020, with a decay rate of 16.458%. This study is the first on the comprehensive monitoring of seagrass in Xincun Bay using satellite remote sensing images, and comprises the first use of GF2 data in seagrass research, aiming to provide a reference for remote sensing monitoring of seagrass in the South China Sea.
      Citation: Remote Sensing
      PubDate: 2022-05-14
      DOI: 10.3390/rs14102373
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2374: Multi-Hand Gesture Recognition Using
           Automotive FMCW Radar Sensor

    • Authors: Yong Wang, Di Wang, Yunhai Fu, Dengke Yao, Liangbo Xie, Mu Zhou
      First page: 2374
      Abstract: With the development of human–computer interaction(s) (HCI), hand gestures are playing increasingly important roles in our daily lives. With hand gesture recognition (HGR), users can play virtual games together, control the smart equipment, etc. As a result, this paper presents a multi-hand gesture recognition system using automotive frequency modulated continuous wave (FMCW) radar. Specifically, we first constructed the range-Doppler map (RDM) and range-angle map (RAM), and then suppressed the spectral leakage, and dynamic and static interferences. Since the received echo signals with multi-hand gestures are mixed together, we propose a spatiotemporal path selection algorithm to separate the mixed multi-hand gestures. A dual 3D convolutional neural network-based feature fusion network is proposed for feature extraction and classification. We developed the FMCW radar-based platform to evaluate the performance of the proposed multi-hand gesture recognition method; the experimental results show that the proposed method can achieve an average recognition accuracy of 93.12% when eight gestures with two hands are performed simultaneously.
      Citation: Remote Sensing
      PubDate: 2022-05-14
      DOI: 10.3390/rs14102374
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2375: Can Forel–Ule Index Act as a
           Proxy of Water Quality in Temperate Waters' Application of Plume
           Mapping in Liverpool Bay, UK

    • Authors: Lenka Fronkova, Naomi Greenwood, Roi Martinez, Jennifer A. Graham, Richard Harrod, Carolyn A. Graves, Michelle J. Devlin, Caroline Petus
      First page: 2375
      Abstract: The use of ocean colour classification algorithms, linked to water quality gradients, can be a useful tool for mapping river plumes in both tropical and temperate systems. This approach has been applied in operational water quality programs in the Great Barrier Reef to map river plumes and assess trends in marine water composition and ecosystem health during flood periods. In this study, we used the Forel–Ule colour classification algorithm for Sentinel-3 OLCI imagery in an automated process to map monthly, annual and long-term plume movement in the temperate coastal system of Liverpool Bay (UK). We compared monthly river plume extent to the river flow and in situ water quality data between 2017–2020. The results showed a strong positive correlation (Spearman’s rho = 0.68) between the river plume extent and the river flow and a strong link between the FUI defined waterbodies and nutrients, SPM, turbidity and salinity, hence the potential of the Forel–Ule index to act as a proxy for water quality in the temperate Liverpool Bay water. The paper discusses how the Forel–Ule index could be used in operational water quality programs to better understand river plumes and the land-based inputs to the coastal zones in UK waters, drawing parallels with methods that have been developed in the GBR and Citclops project. Overall, this paper provides the first insight into the systematic long-term river plume mapping in UK coastal waters using a fast, cost-effective, and reproducible workflow. The study created a novel water assessment typology based on the common physical, chemical and biological ocean colour properties captured in the Forel–Ule index, which could replace the more traditional eutrophication assessment regions centred around strict geographic and political boundaries. Additionally, the Forel–Ule assessment typology is particularly important since it identifies areas of the greatest impact from the land-based loads into the marine environment, and thus potential risks to vulnerable ecosystems.
      Citation: Remote Sensing
      PubDate: 2022-05-14
      DOI: 10.3390/rs14102375
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2376: Optimization Framework for
           Spatiotemporal Analysis Units Based on Floating Car Data

    • Authors: Haifu Cui, Liang Wu, Zhenming He
      First page: 2376
      Abstract: Spatiotemporal scale is a basic component of geographical problems because the size of spatiotemporal units may have a significant impact on the aggregation of spatial data and the corresponding analysis results. However, there is no clear standard for measuring the representativeness of conclusions when geographical data with different temporal and spatial units are used in geographical calculations. Therefore, a spatiotemporal analysis unit optimization framework is proposed to evaluate candidate analysis units using the distribution patterns of spatiotemporal data. The framework relies on Pareto optimality to select the spatiotemporal analysis unit, thereby overcoming the subjectivity and randomness of traditional unit setting methods and mitigating the influence of the modifiable areal unit problem (MAUP) to a certain extent. The framework is used to analyze floating car trajectory data, and the spatiotemporal analysis unit is optimized by using a combination of global spatial autocorrelation coefficients and the coefficients of variation of local spatial autocorrelation. Moreover, based on urban hotspot calculations, the effectiveness of the framework is further verified. The proposed optimization framework for spatiotemporal analysis units based on multiple criteria can provide suitable spatiotemporal analysis scales for studies of geographical phenomena.
      Citation: Remote Sensing
      PubDate: 2022-05-14
      DOI: 10.3390/rs14102376
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2377: Automated Glacier Snow Line Altitude
           Calculation Method Using Landsat Series Images in the Google Earth Engine
           Platform

    • Authors: Xiang Li, Ninglian Wang, Yuwei Wu
      First page: 2377
      Abstract: Glacier snow line altitude (SLA) at the end of the ablation season is an indicator of the equilibrium line altitude (ELA), which is a key parameter for calculating and assessing glacier mass balance. Here, we present an automated algorithm to classify bare ice and snow cover on glaciers using Landsat series images and calculate the minimum annual glacier snow cover ratio (SCR) and maximum SLA for reference glaciers during the 1985–2020 period in Google Earth Engine. The calculated SCR and SLA values are verified using the observed glacier accumulation area ratio (AAR) and ELA. We select 14 reference glaciers from High Mountain Asia (HMA), the Caucasus, the Alps, and Western Canada, which represent four mountainous regions with extensive glacial development in the northern hemisphere. The SLA accuracy is ~73%, with a mean uncertainty of ±24 m, for 13 of the reference glaciers. Eight of these glaciers yield R2 > 0.5, and the other five glaciers yield R2 > 0.3 for their respective SCR–AAR relationships. Furthermore, 10 of these glaciers yield R2 > 0.5 and the other three glaciers yield R2 > 0.3 for their respective SLA–ELA relationships, which indicate that the calculated SLA from this algorithm provides a good fit to the ELA observations. However, Careser Glacier yields a poor fit between the SLA calculations and ELA observations owing to tremendous surface area changes during the analyzed time series; this indicates that glacier surface shape changes due to intense ablation will lead to a misclassification of the glacier surface, resulting in deviations between the SLA and ELA. Furthermore, cloud cover, shadows, and the Otsu method limitation will further affect the SLA calculation. The post-2000 SLA values are better than those obtained before 2000 because merging the Landsat series images reduces the temporal resolution, which allows the date of the calculated SLA to be closer to the date of the observed ELA. From a regional perspective, the glaciers in the Caucasus, HMA and the Alps yield better results than those in Western Canada. This algorithm can be applied to large regions, such as HMA, to obtain snow line estimates where manual approaches are exhaustive and/or unfeasible. Furthermore, new optical data, such as that from Sentinel-2, can be incorporated to further improve the algorithm results.
      Citation: Remote Sensing
      PubDate: 2022-05-14
      DOI: 10.3390/rs14102377
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2378: Efficient Shallow Network for River
           Ice Segmentation

    • Authors: Daniel Sola, K. Andrea Scott
      First page: 2378
      Abstract: River ice segmentation, used for surface ice concentration estimation, is important for validating river processes and ice-formation models, predicting ice jam and flooding risks, and managing water supply and hydroelectric power generation. Furthermore, discriminating between anchor ice and frazil ice is an important factor in understanding sediment transport and release events. Modern deep learning techniques have proved to deliver promising results; however, they can show poor generalization ability and can be inefficient when hardware and computing power is limited. As river ice images are often collected in remote locations by unmanned aerial vehicles with limited computation power, we explore the performance-latency trade-offs for river ice segmentation. We propose a novel convolution block inspired by both depthwise separable convolutions and local binary convolutions giving additional efficiency and parameter savings. Our novel convolution block is used in a shallow architecture which has 99.9% fewer trainable parameters, 99% fewer multiply–add operations, and 69.8% less memory usage than a UNet, while achieving virtually the same segmentation performance. We find that the this network trains fast and is able to achieve high segmentation performance early in training due to an emphasis on both pixel intensity and texture. When compared to very efficient segmentation networks such as LR-ASPP with a MobileNetV3 backbone, we achieve good performance (mIoU of 64) 91% faster during training on a CPU and an overall mIoU that is 7.7% higher. We also find that our network is able to generalize better to new domains such as snowy environments.
      Citation: Remote Sensing
      PubDate: 2022-05-15
      DOI: 10.3390/rs14102378
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2379: Machine Learning Algorithms for
           Modeling and Mapping of Groundwater Pollution Risk: A Study to Reach Water
           Security and Sustainable Development (Sdg) Goals in a Mediterranean
           Aquifer System

    • Authors: Safae Ijlil, Ali Essahlaoui, Meriame Mohajane, Narjisse Essahlaoui, El Mostafa Mili, Anton Van Rompaey
      First page: 2379
      Abstract: Groundwater pollution poses a severe threat and issue to the environment and humanity overall. That is why mitigative strategies are urgently needed. Today, studies mapping groundwater risk pollution assessment are being developed. In this study, five new hybrid/ensemble machine learning (ML) models are developed, named DRASTIC-Random Forest (RF), DRASTIC-Support Vector Machine (SVM), DRASTIC-Multilayer Perceptron (MLP), DRASTIC-RF-SVM, and DRASTIC-RF-MLP, for groundwater pollution assessment in the Saiss basin, in Morocco. The performances of these models are evaluated using the Receiver Operating Characteristic curve (ROC curve), precision, and accuracy. Based on the results of the ROC curve method, it is indicated that the use of hybrid/ensemble machine learning (ML) models improves the performance of the individual machine learning (ML) algorithms. In effect, the AUC value of the original DRASTIC is 0.51. Furthermore, both hybrid/ensemble models, DRASTIC-RF-MLP (AUC = 0.953) and DRASTIC-RF-SVM, (AUC = 0.901) achieve the best accuracy among the other models, followed by DRASTIC-RF (AUC = 0.852), DRASTIC-SVM (AUC = 0.802), and DRASTIC-MLP (AUC = 0.763). The results delineate areas vulnerable to pollution, which require urgent actions and strategies to improve the environmental and social qualities for the local population.
      Citation: Remote Sensing
      PubDate: 2022-05-15
      DOI: 10.3390/rs14102379
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2380: Soil Moisture Influence on the FTIR
           Spectrum of Salt-Affected Soils

    • Authors: Le Thi Thu Hien, Anne Gobin, Duong Thi Lim, Dang Tran Quan, Nguyen Thi Hue, Nguyen Ngoc Thang, Nguyen Thanh Binh, Vu Thi Kim Dung, Pham Ha Linh
      First page: 2380
      Abstract: Soil salinity has a major impact on agricultural production. In a changing climate with rising sea-levels, low-lying coastal areas are increasingly inundated whereby saltwater gradually contaminates the soil. Drought prone areas may suffer from salinity due to high evapotranspiration rates in combination with the use of saline irrigation water. Salinity is difficult to monitor because soil moisture affects the soil’s spectral signature. We conducted Fourier-transform infrared spectroscopy on alluvial and sandy soil samples in the coastal estuary of the Red River Delta. The soils are contaminated with NaCl, Na2CO3 and Na2SO4 salts. In an experiment of salt contamination, we established that three ranges of the spectrum were strongly influenced by both salt and moisture content in the soil, at wavenumbers 3200–3400 cm−1 (2.9–3.1 µm); 1600–1700 cm−1 (5.9–6.3 µm); 900–1100 cm−1 (9.1–11.1 µm). The Na2CO3 contaminated soil and the spectral value had a linear relationship between wavelengths 6.9 and 7.4 µm. At wavelength 6.99 µm, there was no relationship between absorbance and soil moisture, but the absorbance was proportional to the salt content (R2 = 0.85; RMSE = 0.68 g) and electrical conductivity (R2 = 0.50; RMSE = 3.8 dS/m). The relationship between soil moisture and spectral absorbance value was high at wavelengths below 6.7 µm, resulting in a quadratic relation between soil moisture and absorbance at wavelength 6.13 µm (R2 = 0.80; RMSE = 5.2%). The spectral signatures and equations might be useful for mapping salt-affected soils, particularly in difficult to access locations. Technological advances in thermal satellite sensors may offer possibilities for monitoring soil salinity.
      Citation: Remote Sensing
      PubDate: 2022-05-15
      DOI: 10.3390/rs14102380
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2381: Spatial and Temporal Drought
           Characteristics in the Huanghuaihai Plain and Its Influence on Cropland
           Water Use Efficiency

    • Authors: Weiyin Wang, Junli Li, Hongjiao Qu, Wenwen Xing, Cheng Zhou, Youjun Tu, Zongyi He
      First page: 2381
      Abstract: Understanding the relationship between drought and the water use efficiency (WUE) in terrestrial ecosystems can help reduce drought risk. It remains unclear what the correlation between the cropland water use efficiency (CWUE) and drought during drought events. We aim to identify the spatiotemporal relationship between drought and the CWUE and to ensure the service capacity of cultivated land ecosystems. In this study, the cubist algorithm was used to establish a monthly integrated surface drought index (mISDI) dataset for the Huang–Huai–Hai Plain (HHHP), and the run theory was used to identify drought events. We assessed the spatio-temporal variations of drought in the HHHP during 2000–2020 and its influence on the CWUE. The research results were as follows: from the overall perspective of the HHHP, the mISDI showed a downward trend. Drought had an enhanced effect on the CWUE of the HHHP, and the enhancement of the CWUE in the eastern hilly area was more significant. The CWUE response to drought had a three-month lag period and a significant positive correlation, and it was shown that the cultivated land ecosystems in this area had strong drought resistance ability. This study provides a new framework for understanding the response of the CWUE to drought and formulating reasonable vegetation management strategies for the HHHP.
      Citation: Remote Sensing
      PubDate: 2022-05-15
      DOI: 10.3390/rs14102381
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2382: Characterizing the Patterns and
           Trends of Urban Growth in Saudi Arabia’s 13 Capital Cities Using a
           Landsat Time Series

    • Authors: Amal H. Aljaddani, Xiao-Peng Song, Zhe Zhu
      First page: 2382
      Abstract: Development and a growing population in Saudi Arabia have led to a substantial increase in the size of its urban areas. This sustained development has increased policymakers’ need for reliable data and analysis regarding the patterns and trends of urban expansion throughout the country. Although previous studies on urban growth in Saudi cities exist, there has been no comprehensive research that focused on all 13 regional capitals within the country. Our study addressed this gap by producing a new annual long-term dataset of 30 m spatial resolution that covered 35 years (1985–2019) and maintained a high overall accuracy of annual classifications across the study period, ranging between 93 and 98%. Utilizing the continuous change detection and classification (CCDC) algorithm and all available Landsat data, we classified Landsat pixels into urban and non-urban classes with an annual frequency and quantified urban land cover change over these 35 years. We implemented a stratified random sampling design to assess the accuracy of the annual classifications and the multi-temporal urban change. The results revealed that Saudi capitals experienced massive urban growth, from 1305.28 ± 348.71 km2 in 1985 to 2704.94 ± 554.04 km2 in 2019 (±values represent the 95% confidence intervals). In addition to the high accuracy of the annual classifications, the overall accuracy of the multi-temporal urban change map was also high and reached 91%. The urban expansion patterns varied from city to city and from year to year. Most capital cities showed clear growth patterns of edge development, that is, a continuous expansion of built-up lands radiating from existing urban areas. This study provides distinct insights into the urban expansion characteristics of each city in Saudi Arabia and a synoptic view of the country as a whole over the past four decades. Our results provided a dataset that can be used as the foundation for future socioeconomic and environmental studies.
      Citation: Remote Sensing
      PubDate: 2022-05-15
      DOI: 10.3390/rs14102382
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2383: Sea Surface Temperature Variability
           and Marine Heatwaves in the Black Sea

    • Authors: Bayoumy Mohamed, Omneya Ibrahim, Hazem Nagy
      First page: 2383
      Abstract: Marine heatwaves (MHWs) have recently been at the forefront of climate research due to their devastating impacts on the marine environment. In this study, we have evaluated the spatiotemporal variability and trends of sea surface temperature (SST) and MHWs in the Black Sea. Furthermore, we investigated the relationship between the El Niño–Southern Oscillation (ENSO) and MHW frequency. This is the first attempt to investigate MHWs and their characteristics in the Black Sea using high-resolution remote-sensing daily satellite SST data (0.05° × 0.05°) from 1982 to 2020. The results showed that the spatial average of the SST warming rate over the entire basin was about 0.65 ± 0.07 °C/decade. Empirical orthogonal function (EOF) analysis revealed that SST in the Black Sea exhibited inter-annual spatiotemporal coherent variability. The maximum spatial SST variability was discovered in the central Black Sea, whereas the lowest variability was in the Batumi and Caucasus anti-cyclonic eddies in the eastern Black Sea. The highest SST temporal variability was found in 1994. More than two-thirds of all MHW events were recorded in the last decade (2010–2020). The highest annual MHW durations were reported in 1994 and 2020. The highest MHW frequency was detected in 2018 (7 waves). Over the whole study period (1982–2020), a statistically significant increase in annual MHW frequency and duration was detected, with trends of 1.4 ± 0.3 waves/decade and 2.8 ± 1.3 days/decade, respectively. A high number of MHW events coincided with El Niño (e.g., 1996, 1999, 2007, 2010, 2018, and 2020). A strong correlation (R = 0.90) was observed between the annual mean SST and the annual MHW frequency, indicating that more MHWs can be expected in the Black Sea, with serious consequences for the marine ecosystem.
      Citation: Remote Sensing
      PubDate: 2022-05-16
      DOI: 10.3390/rs14102383
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2384: On the Exploitation of Remote Sensing
           Technologies for the Monitoring of Coastal and River Delta Regions

    • Authors: Qing Zhao, Jiayi Pan, Adam Thomas Devlin, Maochuan Tang, Chengfang Yao, Virginia Zamparelli, Francesco Falabella, Antonio Pepe
      First page: 2384
      Abstract: Remote sensing technologies are extensively applied to prevent, monitor, and forecast hazardous risk conditions in the present-day global climate change era. This paper presents an overview of the current stage of remote sensing approaches employed to study coastal and delta river regions. The advantages and limitations of Earth Observation technology in characterizing the effects of climate variations on coastal environments are also presented. The role of the constellations of satellite sensors for Earth Observation, collecting helpful information on the Earth’s system and its temporal changes, is emphasized. For some key technologies, the principal characteristics of the processing chains adopted to obtain from the collected raw data added-value products are summarized. Emphasis is put on studying various disaster risks that affect coastal and megacity areas, where heterogeneous and interlinked hazard conditions can severely affect the population.
      Citation: Remote Sensing
      PubDate: 2022-05-16
      DOI: 10.3390/rs14102384
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2385: Deep Learning-Based Object Detection
           Techniques for Remote Sensing Images: A Survey

    • Authors: Zheng Li, Yongcheng Wang, Ning Zhang, Yuxi Zhang, Zhikang Zhao, Dongdong Xu, Guangli Ben, Yunxiao Gao
      First page: 2385
      Abstract: Object detection in remote sensing images (RSIs) requires the locating and classifying of objects of interest, which is a hot topic in RSI analysis research. With the development of deep learning (DL) technology, which has accelerated in recent years, numerous intelligent and efficient detection algorithms have been proposed. Meanwhile, the performance of remote sensing imaging hardware has also evolved significantly. The detection technology used with high-resolution RSIs has been pushed to unprecedented heights, making important contributions in practical applications such as urban detection, building planning, and disaster prediction. However, although some scholars have authored reviews on DL-based object detection systems, the leading DL-based object detection improvement strategies have never been summarized in detail. In this paper, we first briefly review the recent history of remote sensing object detection (RSOD) techniques, including traditional methods as well as DL-based methods. Then, we systematically summarize the procedures used in DL-based detection algorithms. Most importantly, starting from the problems of complex object features, complex background information, tedious sample annotation that will be faced by high-resolution RSI object detection, we introduce a taxonomy based on various detection methods, which focuses on summarizing and classifying the existing attention mechanisms, multi-scale feature fusion, super-resolution and other major improvement strategies. We also introduce recognized open-source remote sensing detection benchmarks and evaluation metrics. Finally, based on the current state of the technology, we conclude by discussing the challenges and potential trends in the field of RSOD in order to provide a reference for researchers who have just entered the field.
      Citation: Remote Sensing
      PubDate: 2022-05-16
      DOI: 10.3390/rs14102385
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2386: Ecological Engineering Projects
           Shifted the Dominance of Human Activity and Climate Variability on
           Vegetation Dynamics

    • Authors: Jie Gao, Yangjian Zhang, Zhoutao Zheng, Nan Cong, Guang Zhao, Yixuan Zhu, Yao Chen, Yihan Sun, Jianshuang Zhang, Yu Zhang
      First page: 2386
      Abstract: Global greening and its eco-environmental outcomes are getting mounting international focus. The important contribution of China to the global greening is highly appreciated. However, the basic driving forces are still elusive. The Loess Plateau (LP) and Three-River Source Region (TRSR) were chased as study areas in Northern China. The prior one represents the region experiencing intensive human interventions from ecological engineering projects, while the latter is a typical region that is experiencing faster climate change. Hypothesized to be driven by a disproportionate rate of human activities and climates, also being regions of typical large-scale ecological engineering projects, the study goal is to identify the actual driving forces on vegetation dynamics in these two regions. Trend analysis, correlation analysis, and residual trend-based method (RESTREND) were utilized to understand the relationships between climate variability, human activities, and vegetation dynamics. The spatiotemporal variations of vegetation from 1982 to 2019 were evaluated and the respective impacts of climatic and anthropogenic factors on vegetation dynamics were disentangled. Indicating apparent vegetation restoration in LP and TRSR, the results depict that annual LAI has remarkably increased during the 38 years. Temperature and precipitation promoted vegetation growth, whereas the solar radiation and vapor pressure deficit hampered it. After implementing the ecological engineering projects, the primary climatic factor changed from temperature to precipitation. Meanwhile, human activities act as the major driving factor in vegetation greening in the entire study area, with a contribution rate exceeding 70%. This information highlights that ecological engineering can significantly reduce the risks of ecosystem degradation and effectively restore vegetation, especially in ecologically sensitive and vulnerable areas.
      Citation: Remote Sensing
      PubDate: 2022-05-16
      DOI: 10.3390/rs14102386
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2387: A Multi-Objective Quantum Genetic
           Algorithm for MIMO Radar Waveform Design

    • Authors: Tianqu Liu, Jinping Sun, Guohua Wang, Yilong Lu
      First page: 2387
      Abstract: Aiming at maximizing waveform diversity gain when designing a phase-coded multiple-input multiple-output (MIMO) radar waveform set, it is desirable that all waveforms are orthogonal to each other. Hence, the lowest possible peak cross-correlation ratio (PCCR) is expected. Meanwhile, low peak auto-correlation side-lobe ratio (PASR) is needed for good detection performance. However, it is difficult to obtain a closed form solution to the waveform set from the expected values of the PASR and PCCR. In this paper, the waveform set design problem is modeled as a multi-objective, NP-hard constrained optimization problem. Unlike conventional approaches that design the waveform set through optimizing a weighted sum objective function, the proposed optimization model evaluates the performance of multi-objective functions based on Pareto level and obtains a set of Pareto non-dominated solutions. That means that the MIMO radar system can trade off each objective function for different requirements. To solve this problem, this paper presents a multi-objective quantum genetic algorithm (MoQGA) based on the framework of quantum genetic algorithm. A new population update strategy for the MoQGA is designed based on the proposed model. Compared to the state-of-the-art methods, like BiST and Multi-CAN, the PASR and PCCR metrics of the waveform set are 0.95–3.91 dB lower with the parameters of the numerical simulation. The MoQGA is able to minimize PASR and PCCR of the MIMO radar waveform set simultaneously.
      Citation: Remote Sensing
      PubDate: 2022-05-16
      DOI: 10.3390/rs14102387
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2388: Diagnostic Multidisciplinary
           Investigations for Cultural Heritage at Etna Volcano: A Case Study from
           the 1669 Eruption in the Mother Church at the Old Settlement of
           Misterbianco

    • Authors: Carla Bottari, Patrizia Capizzi, Raffaele Martorana, Raffaele Azzaro, Stefano Branca, Riccardo Civico, Mario Fucile, Emilio Pecora
      First page: 2388
      Abstract: Misterbianco is located on the southern flank of Mt. Etna (Unesco site), in eastern Sicily (Italy). This site, also known as Monasterium Album, has a long and tormented history linked with volcanic activity of Mt. Etna and regional seismicity. This site received much attention in the 2000s when excavation works brought to light a 14th century church remains below the thick layer of the 1669 lava. This study documents the first diagnostic multidisciplinary survey performed at this site 350 years after the eruption: the investigations were performed by using techniques such as ground-penetrating radar, infrared thermography, a terrestrial laser scanner and a drone survey to analyze the site’s topography, to adequately map the hidden structures inside the building and to identify fractures and deformations in the church. Starting from the site history, we present the results of the multidisciplinary approach aimed at reconstructing the historical events that led to the damage in the church.
      Citation: Remote Sensing
      PubDate: 2022-05-16
      DOI: 10.3390/rs14102388
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2389: Multi-Level Alignment Network for
           Cross-Domain Ship Detection

    • Authors: Chujie Xu, Xiangtao Zheng, Xiaoqiang Lu
      First page: 2389
      Abstract: Ship detection is an important research topic in the field of remote sensing. Compared with optical detection methods, Synthetic Aperture Radar (SAR) ship detection can penetrate clouds to detect hidden ships in all-day and all-weather. Currently, the state-of-the-art methods exploit convolutional neural networks to train ship detectors, which require a considerable labeled dataset. However, it is difficult to label the SAR images because of expensive labor and well-trained experts. To address the above limitations, this paper explores a cross-domain ship detection task, which adapts the detector from labeled optical images to unlabeled SAR images. There is a significant visual difference between SAR images and optical images. To achieve cross-domain detection, the multi-level alignment network, which includes image-level, convolution-level, and instance-level, is proposed to reduce the large domain shift. First, image-level alignment exploits generative adversarial networks to generate SAR images from the optical images. Then, the generated SAR images and the real SAR images are used to train the detector. To further minimize domain distribution shift, the detector integrates convolution-level alignment and instance-level alignment. Convolution-level alignment trains the domain classifier on each activation of the convolutional features, which minimizes the domain distance to learn domain-invariant features. Instance-level alignment reduces domain distribution shift on the features extracted from the region proposals. The entire multi-level alignment network is trained end-to-end and its effectiveness is proved on multiple cross-domain ship detection datasets.
      Citation: Remote Sensing
      PubDate: 2022-05-16
      DOI: 10.3390/rs14102389
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2390: Localized Downscaling of Urban Land
           Surface Temperature—A Case Study in Beijing, China

    • Authors: Nana Li, Hua Wu, Xiaoying Ouyang
      First page: 2390
      Abstract: High-resolution land surface temperature (LST) data are essential for fine-scale urban thermal environment studies. Urban LST downscaling studies mostly remain focused on only two-dimensional (2-D) data, and neglect the impact of three-dimensional (3-D) surface structure on LST. In addition, the choice of window size is also important for LST downscaling over heterogeneous surfaces. In this study, we downscaled Landsat-LST using localized and stepwise approaches in a random forest model (RF). In addition, both 2- and 3-D building morphologies were included. Our results show that: (1) The performances of a local moving window and stepwise downscaling are dependent on the extent of surface heterogeneity. For mixed surfaces, a localized window performed better than the global window, and a stepwise approach performed better than a single-step approach. However, for monotonous surfaces (e.g., urban impervious surfaces), the global window performed better than a localized window; (2) That multi-scale geographically weighted regression (MGWR) could provide a possibility for selection of the optimal moving window. 7 × 7 windows derived from MGWR by the minimum bandwidth of predictors, performed better than other windows (3 × 3, 5 × 5, and 11 × 11) in the Beijing area; (3) That the morphology of buildings has a non-negligible impact and scaling effect on urban LST. When building morphologies were included in downscaling, the performance of the RF model improved. Furthermore, the importance of the sky view factor, building height, and building density was greater at a higher resolution than at a lower resolution.
      Citation: Remote Sensing
      PubDate: 2022-05-16
      DOI: 10.3390/rs14102390
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2391: Potential of Ultra-High-Resolution
           UAV Images with Centimeter GNSS Positioning for Plant Scale Crop
           Monitoring

    • Authors: Jean-Marc Gilliot, Dalila Hadjar, Joël Michelin
      First page: 2391
      Abstract: To implement agricultural practices that are more respectful of the environment, precision agriculture methods for monitoring crop heterogeneity are becoming more and more spatially detailed. The objective of this study was to evaluate the potential of Ultra-High-Resolution UAV images with centimeter GNSS positioning for plant-scale monitoring. A Dji Phantom 4 RTK UAV with a 20 MPixel RGB camera was used, flying at an altitude of 25 m (0.7 cm resolution). This study was conducted on an experimental plot sown with maize. A centimeter-precision Trimble Geo7x GNSS receiver was used for the field measurements. After evaluating the precision of the UAV’s RTK antenna in static mode on the ground, the positions of 17 artificial targets and 70 maize plants were measured during a series of flights in different RTK modes. Agisoft Metashape software was used. The error in position of the UAV RTK antenna in static mode on the ground was less than one centimeter, in terms of both planimetry and elevation. The horizontal position error measured in flight on the 17 targets was less than 1.5 cm, while it was 2.9 cm in terms of elevation. Finally, according to the RTK modes, at least 81% of the corn plants were localized to within 5 cm of their position, and 95% to within 10 cm.
      Citation: Remote Sensing
      PubDate: 2022-05-16
      DOI: 10.3390/rs14102391
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2392: Comparison between Automated and
           Manual Detection of Lava Fountains from Fixed Monitoring Thermal Cameras
           at Etna Volcano, Italy

    • Authors: Sonia Calvari, Giuseppe Nunnari
      First page: 2392
      Abstract: The Etna volcano is renowned worldwide for its extraordinary lava fountains that rise several kilometers above the vent and feed eruptive columns, then drift hundreds of kilometers away from the source. The Italian Istituto Nazionale di Geofisica e Vulcanologia-Osservatorio Etneo (INGV-OE) is responsible for the monitoring of Mt. Etna, and for this reason, has deployed a network of visible and thermal cameras around the volcano. From these cameras, INGV-OE keeps a keen eye, and is able to observe the eruptive activity, promptly advising the civil protection and aviation authorities of any changes, as well as quantifying the spread of lava flows and the extent of pyroclastic and ash plumes by using a careful analysis of the videos recorded by the monitoring cameras. However, most of the work involves analysis carried out by hand, which is necessarily approximate and time-consuming, thus limiting the usefulness of these results for a prompt hazard assessment. In addition, the start of lava fountains is often a gradual process, increasing in strength from Strombolian activity, to intermediate explosive activity, and eventually leading to sustained lava fountains. The thresholds between these different fields (Strombolian, Intermediate, and lava fountains) are not clear cut, and are often very difficult to distinguish by a manual analysis of the images. In this paper, we presented an automated routine that, when applied to thermal images and with good weather conditions, allowed us to detect (1) the starting and ending time of each lava fountain, (2) the area occupied by hot pyroclasts, (3) the elevation reached by the lava fountains over time, and (4) eventually, to calculate in real-time the erupted volume of pyroclasts, giving results close to the manual analysis but more focused on the sustained portion of the lava fountain, which is also the most dangerous. This routine can also be applied to other active volcanoes, allowing a prompt and uniform definition of the timing of the lava fountain eruptive activity, as well as the magnitude and intensity of the event.
      Citation: Remote Sensing
      PubDate: 2022-05-16
      DOI: 10.3390/rs14102392
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2393: The Spatiotemporal Change of Glacier
           Runoff Is Comparably Attributed to Climatic Factors and Physical
           Properties in Northwestern China

    • Authors: Xuejing Leng, Xiaoming Feng, Bojie Fu, Yu Zhang
      First page: 2393
      Abstract: The spatiotemporal regimes of glacier runoff (GR) under a warming climate are of great concern, especially in dryland areas in northwestern China (DAC). Due to the difficulty of observing GR, little attention has been given to the spatiotemporal change in GR at regional scales. This study uses the regional individual glacier mass balance (GMB) dataset developed by digital elevation models (DEMs) to simulate the spatiotemporal regime of GR using atmospheric parameters considering both ablation and accumulation processes on glaciers. In this study, GR, including glacier meltwater runoff (MR) and delayed water runoff (DR) of the DAC, was quantitatively assessed at a catchment scale from 1961 to 2015. The total annual GR in the DAC was (100.81 ± 68.71) × 108 m3 in 1961–2015, where MR accounted for 68%. Most basins had continuously increasing tendencies of different magnitudes from 1961 to 2015. The least absolute shrinkage and selection operator (LASSO) and random forest techniques were used to explore the contributions of climate factors and glacier physical properties to GR, and the results indicated that climate factors could explain 56.64% of the variation. In comparison, the remaining 43.36% could be explained by the physical properties of glaciers themselves (i.e., degree-day factor on ice, degree-day factor on snow, glacier median height, aspect, and slope). This study not only improves our understanding of the spatiotemporal change in GR in the drylands of northwestern China at spatial and temporal resolutions but also highlights the role of physical properties in explaining the heterogeneous dynamics among GRs unlike previous studies that only emphasize rising temperatures.
      Citation: Remote Sensing
      PubDate: 2022-05-16
      DOI: 10.3390/rs14102393
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2394: Combining Passive Acoustics and
           Environmental Data for Scaling Up Ecosystem Monitoring: A Test on Coral
           Reef Fishes

    • Authors: Simon Elise, François Guilhaumon, Gérard Mou-Tham, Isabel Urbina-Barreto, Laurent Vigliola, Michel Kulbicki, J. Henrich Bruggemann
      First page: 2394
      Abstract: Ecological surveys of coral reefs mostly rely on visual data collected by human observers. Although new monitoring tools are emerging, their specific advantages should be identified to optimise their simultaneous use. Based on the goodness-of-fit of linear models, we compared the potential of passive acoustics and environmental data for predicting the structure of coral reef fish assemblages in different environmental and biogeographic settings. Both data types complemented each other. Globally, the acoustic data showed relatively low added value in predicting fish assemblage structures. The predictions were best for the distribution of fish abundance among functional entities (i.e., proxies for fish functional groups, grouping species that share similar eco-morphological traits), for the simplest functional entities (i.e., combining two eco-morphological traits), and when considering diet and the level in the water column of the species. Our study demonstrates that Passive Acoustic Monitoring (PAM) improves fish assemblage assessment when used in tandem with environmental data compared to using environmental data alone. Such combinations can help with responding to the current conservation challenge by improving our surveying capacities at increased spatial and temporal scales, facilitating the identification and monitoring of priority management areas.
      Citation: Remote Sensing
      PubDate: 2022-05-16
      DOI: 10.3390/rs14102394
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2395: HTC+ for SAR Ship Instance
           Segmentation

    • Authors: Tianwen Zhang, Xiaoling Zhang
      First page: 2395
      Abstract: Existing instance segmentation models mostly pay less attention to the targeted characteristics of ships in synthetic aperture radar (SAR) images, which hinders further accuracy improvements, leading to poor segmentation performance in more complex SAR image scenes. To solve this problem, we propose a hybrid task cascade plus (HTC+) for better SAR ship instance segmentation. Aiming at the specific SAR ship task, seven techniques are proposed to ensure the excellent performance of HTC+ in more complex SAR image scenes, i.e., a multi-resolution feature extraction network (MRFEN), an enhanced feature pyramid net-work (EFPN), a semantic-guided anchor adaptive learning network (SGAALN), a context ROI extractor (CROIE), an enhanced mask interaction network (EMIN), a post-processing technique (PPT), and a hard sample mining training strategy (HSMTS). Results show that each of them offers an observable accuracy gain, and the instance segmentation performance in more complex SAR image scenes becomes better. On two public datasets SSDD and HRSID, HTC+ surpasses the other nine competitive models. It achieves 6.7% higher box AP and 5.0% higher mask AP than HTC on SSDD. These are 4.9% and 3.9% on HRSID.
      Citation: Remote Sensing
      PubDate: 2022-05-17
      DOI: 10.3390/rs14102395
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2396: Optimization of UAV-Based Imaging and
           Image Processing Orthomosaic and Point Cloud Approaches for Estimating
           Biomass in a Forage Crop

    • Authors: Worasit Sangjan, Rebecca J. McGee, Sindhuja Sankaran
      First page: 2396
      Abstract: Forage and field peas provide essential nutrients for livestock diets, and high-quality field peas can influence livestock health and reduce greenhouse gas emissions. Above-ground biomass (AGBM) is one of the vital traits and the primary component of yield in forage pea breeding programs. However, a standard method of AGBM measurement is a destructive and labor-intensive process. This study utilized an unmanned aerial vehicle (UAV) equipped with a true-color RGB and a five-band multispectral camera to estimate the AGBM of winter pea in three breeding trials (two seed yields and one cover crop). Three processing techniques—vegetation index (VI), digital surface model (DSM), and 3D reconstruction model from point clouds—were used to extract the digital traits (height and volume) associated with AGBM. The digital traits were compared with the ground reference data (measured plant height and harvested AGBM). The results showed that the canopy volume estimated from the 3D model (alpha shape, α = 1.5) developed from UAV-based RGB imagery’s point clouds provided consistent and high correlation with fresh AGBM (r = 0.78–0.81, p < 0.001) and dry AGBM (r = 0.70–0.81, p < 0.001), compared with other techniques across the three trials. The DSM-based approach (height at 95th percentile) had consistent and high correlation (r = 0.71–0.95, p < 0.001) with canopy height estimation. Using the UAV imagery, the proposed approaches demonstrated the potential for estimating the crop AGBM across winter pea breeding trials.
      Citation: Remote Sensing
      PubDate: 2022-05-17
      DOI: 10.3390/rs14102396
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2397: Z-Transform-Based FDTD
           Implementations of Biaxial Anisotropy for Radar Target Scattering Problems
           

    • Authors: Yuxian Zhang, Naixing Feng, Jinfeng Zhu, Guoda Xie, Lixia Yang, Zhixiang Huang
      First page: 2397
      Abstract: In this article, an efficient Z-transform-based finite-difference time-domain (Z-FDTD) is developed to implement and analyze electromagnetic scatterings in the 3D biaxial anisotropy. In terms of the Z-transform technique, we first discuss the conversion relationship between time- or frequency-domain derivative operators and the corresponding Z-domain operator, then build up the Z-transform-based iteration from the electric flux D converted to the electric field E based on dielectric tensor ε (and from the magnetic flux B converted to the magnetic field H in line with permeability tensor μ) by combining the constitutive formulations about the biaxial anisotropy. As a result, the iterative process about the Z-FDTD implementation can be smoothly carried out by means of combining with the Maxwell’s equations. To our knowledge, it is inevitably necessary for the absorbing boundary condition (ABC) to be considered in the electromagnetic scattering; hence, we utilize the unsplit-field complex-frequency-shifted perfectly matched layer (CFS-PML) to truncate the Z-FDTD’s physical region, and then capture time- and frequency-domain radiation with the electric dipole. In the 3D simulations, we select two different biaxial anisotropic models to validate the proposed formulations by using the popular commercial software COMSOL. Moreover, it is certain that those results are effective and available for electromagnetic scattering problems under the oblique incidence executed by the Z-FDTD method.
      Citation: Remote Sensing
      PubDate: 2022-05-17
      DOI: 10.3390/rs14102397
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2398: Exploring the Potential of Optical
           Polarization Remote Sensing for Oil Spill Detection: A Case Study of
           Deepwater Horizon

    • Authors: Zihan Zhang, Lei Yan, Xingwei Jiang, Jing Ding, Feizhou Zhang, Kaiwen Jiang, Ke Shang
      First page: 2398
      Abstract: Oil spills lead to catastrophic problems. In most oil spill cases, the spatial and temporal intractability of the detriment cannot be neglected, and problems related to economic, social and environmental factors constantly appear for a long time. Remote sensing has been widely used as a powerful means to conduct oil spill detection. Optical polarization remote sensing, thriving in recent years, shows a novel potential for oil spill detection. This paper provides a demonstration of the use of open-source POLDER/PARASOL polarization time-series data to detect oil spill. The Deepwater Horizon oil spill, one of the largest oil spill disasters, is utilized to explore the potential of optical polarization remote sensing for oil spill detection. A total of 24 feature combinations are organized to quantitatively study the positive effect of adding polarization information and the appropriate way to describe polarization characteristics. Random forest classifier models are trained with different combinations, and the results are assessed by 10-fold cross-validation. The improvement from adding polarization characteristics is remarkable ((average) accuracy: +0.51%; recall: +2.83%; precision: +3.49%; F1 score: +3.01%, (maximum) accuracy: +0.80%; recall: +5.09%; precision: +6.92%; F1 score: +4.72%), and coupling between the degree of polarization and the phase angle of polarization provides the best description of polarization information. This study confirms the potential of optical polarization remote sensing for oil spill detection, and some detailed problems related to model establishment and polarization feature characterization are discussed for the further application of polarization information.
      Citation: Remote Sensing
      PubDate: 2022-05-17
      DOI: 10.3390/rs14102398
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2399: Assessment of Maize Drought Risk in
           Midwestern Jilin Province: A Comparative Analysis of TOPSIS and VIKOR
           Models

    • Authors: Yining Ma, Suri Guga, Jie Xu, Xingpeng Liu, Zhijun Tong, Jiquan Zhang
      First page: 2399
      Abstract: Drought is a major natural disaster that causes a reduction in rain-fed maize yield. Agricultural drought risk assessment is conducive to improving regional disaster management ability, thereby reducing food security risks and economic losses. Considering the complexity of risk assessment research, an increasing number of researchers are focusing on the multiple-criteria decision-making (MCDM) method. However, the applicability of the MCDM method to agro-meteorological disaster risk assessments is not clear. Therefore, this study comprehensively evaluated hazard, exposure, vulnerability, and emergency response and recovery capability using the TOPSIS and VIKOR models to generate a maize drought risk map in mid-western Jilin Province and ranked the drought risk of each county. The results showed that: (1) maize drought risk in the middle and west of Jilin province showed an increasing trend. Spatially, the risk diminished from west to east. The drought risks faced by Tongyu, Changchun, and Dehui were more severe; (2) the evaluation results of the two models were verified using the yield reduction rate. The VIKOR model was found to be more suitable for agrometeorological drought risk assessments; (3) according to the damage degree of drought disaster to maize, the cluster analysis method was used to divide the study area into three sub-regions: safe, moderate drought, and severe drought. Combined with the characteristics of different regions, suggestions on disaster prevention and mitigation are proposed. The results of this study can provide a basis for formulating strategies to alleviate drought, reduce losses, and ensure the sustainable development of agriculture.
      Citation: Remote Sensing
      PubDate: 2022-05-17
      DOI: 10.3390/rs14102399
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2400: Quantifying the Trends and Variations
           in the Frost-Free Period and the Number of Frost Days across China under
           Climate Change Using ERA5-Land Reanalysis Dataset

    • Authors: Hongyuan Li, Guohua Liu, Chuntan Han, Yong Yang, Rensheng Chen
      First page: 2400
      Abstract: Understanding the spatio-temporal variations in the frost-free period (FFP) and the number of frost days (FD) is beneficial to reduce the harmful effects of climate change on agricultural production and enhancing agricultural adaptation. However, the spatio-temporal variations in FFP and FD and their response to climate change remain unclear across China. To investigate the impact of climate change on FFP and FD, the trends and variations in FFP and FD across China from 1950 to 2020 were quantified using ERA5-Land, a reanalysis dataset with high spatial and temporal resolution. The results showed that ERA5-Land has good applicability in quantifying the trends and variations in FFP and FD across China under climate change. The spatial distribution of multi-year average FFP and FD across China showed significant latitudinal zonality and altitude dependence, i.e., FFP decreased with increasing latitude and altitude, while FD increased with increasing latitude and altitude. As a result of climate warming across China, the FFP showed an increasing trend with an increase rate of 1.25 d/10a and the maximum increasing rate of FFP in the individual region was 6.2 d/10a, while the FD showed a decreasing trend with a decrease rate of 1.41 d/10a and the maximum decreasing rate of FD in the individual region was −6.7 d/10a. Among the five major climate zones in China, the subtropical monsoon climate zone (SUMZ) with the greatest increasing rate of 1.73 d/10a in FFP, while the temperate monsoon climate zone (TEMZ) with the greatest decreasing rate of −1.72 d/10a in FD. In addition, the coefficient of variation (Cv) of FFP showed greater variability at higher altitudes, while the Cv of FD showed greater variability at lower latitudes in southern China. Without considering the adaptation to temperature of crops, a general increase in FFP and a general decrease in FD were both beneficial to agricultural production in terms of FFP and FD promoting a longer growing period and reducing frost damage on crops. This study provides a comprehensive understanding of the trends and variations in FFP and FD under climate change, which is of great scientific significance for the adjustment of the agricultural production layout to adapt to climate change in China.
      Citation: Remote Sensing
      PubDate: 2022-05-17
      DOI: 10.3390/rs14102400
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2401: Temperature Mediates the Dynamic of
           MODIS NPP in Alpine Grassland on the Tibetan Plateau, 2001–2019

    • Authors: Jinxia Cui, Yanding Wang, Tiancai Zhou, Lili Jiang, Qingwen Qi
      First page: 2401
      Abstract: Although alpine grassland net primary productivity (NPP) plays an important role in balancing the carbon cycle and is extremely vulnerable to climate factors, on the Tibetan Plateau, the generalized effect of climate factors on the NPP in areas with humid and arid conditions is still unknown. Hence, we determined the effects of precipitation and temperature on the MODIS NPP in alpine grassland areas from 2001 to 2019 according to information from humid and arid climatic regions. On a spatial scale, we found that temperature generated a larger effect on the NPP than precipitation did in humid regions, but as a primary factor, precipitation had an impact on the NPP in arid regions. These results suggest that temperature and precipitation are the primary limiting factors for plant growth in humid and arid regions. We also found that temperature produced a greater effect on the NPP in humid regions than in arid regions, but no significant differences were observed in the effects of precipitation on the NPP in humid and arid regions. In a time series (2001–2019), the effects of precipitation and temperature on the NPP presented fluctuating decrease (R2 = 0.28, p < 0.05) and increase (R2 = 0.24, p < 0.05) trends in arid regions. However, the effect of the climate on the NPP remained stable in humid regions. In both humid and arid regions, the dynamics of the NPP from 2001 to 2019 were mediated by an increase in temperature. Specifically, 35.9% and 2.57% of the dynamic NPP in humid regions and 45.1 and 7.53% of the dynamic NPP in arid regions were explained by variations in the temperature and precipitation, respectively. Our findings highlighted that grassland areas in humid regions can adapt to dynamic climates, but plants in arid regions are sensitive to changes in the climate. These findings can increase our understanding of climate and ecological responses and provide a framework for adapting management practices.
      Citation: Remote Sensing
      PubDate: 2022-05-17
      DOI: 10.3390/rs14102401
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2402: Heatwaves Significantly Slow the
           Vegetation Growth Rate on the Tibetan Plateau

    • Authors: Caixia Dong, Xufeng Wang, Youhua Ran, Zain Nawaz
      First page: 2402
      Abstract: In recent years, heatwaves have been reported frequently by literature and the media on the Tibetan Plateau. However, it is unclear how alpine vegetation responds to the heatwaves on the Tibetan Plateau. This study aimed to identify the heatwaves using long-term meteorological data and examine the impact of heatwaves on vegetation growth rate with remote sensing data. The results indicated that heatwaves frequently occur in June, July, and August on the Tibetan Plateau. The average frequency of heatwaves had no statistically significant trends from 2000 to 2020 for the entire Tibetan Plateau. On a monthly scale, the average frequency of heatwaves increased significantly (p < 0.1) in August, while no significant trends were in June and July. The intensity of heatwaves indicated a negative correlation with the vegetation growth rate anomaly (ΔVGR) calculated from the normalized difference vegetation index (NDVI) (r = −0.74, p < 0.05) and the enhanced vegetation index (EVI) (r = −0.61, p < 0.1) on the Tibetan Plateau, respectively. Both NDVI and EVI consistently demonstrate that the heatwaves slow the vegetation growth rate. This study outlines the importance of heatwaves to vegetation growth to enrich our understanding of alpine vegetation response to increasing extreme weather events under the background of climate change.
      Citation: Remote Sensing
      PubDate: 2022-05-17
      DOI: 10.3390/rs14102402
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2403: Revealing Active Mars with HiRISE
           Digital Terrain Models

    • Authors: Sarah S. Sutton, Matthew Chojnacki, Alfred S. McEwen, Randolph L. Kirk, Colin M. Dundas, Ethan I. Schaefer, Susan J. Conway, Serina Diniega, Ganna Portyankina, Margaret E. Landis, Nicole F. Baugh, Rodney Heyd, Shane Byrne, Livio L. Tornabene, Lujendra Ojha, Christopher W. Hamilton
      First page: 2403
      Abstract: Many discoveries of active surface processes on Mars have been made due to the availability of repeat high-resolution images from the High Resolution Imaging Science Experiment (HiRISE) onboard the Mars Reconnaissance Orbiter. HiRISE stereo images are used to make digital terrain models (DTMs) and orthorectified images (orthoimages). HiRISE DTMs and orthoimage time series have been crucial for advancing the study of active processes such as recurring slope lineae, dune migration, gully activity, and polar processes. We describe the process of making HiRISE DTMs, orthoimage time series, DTM mosaics, and the difference of DTMs, specifically using the ISIS/SOCET Set workflow. HiRISE DTMs are produced at a 1 and 2 m ground sample distance, with a corresponding estimated vertical precision of tens of cm and ∼1 m, respectively. To date, more than 6000 stereo pairs have been acquired by HiRISE and, of these, more than 800 DTMs and 2700 orthoimages have been produced and made available to the public via the Planetary Data System. The intended audiences of this paper are producers, as well as users, of HiRISE DTMs and orthoimages. We discuss the factors that determine the effective resolution, as well as the quality, precision, and accuracy of HiRISE DTMs, and provide examples of their use in time series analyses of active surface processes on Mars.
      Citation: Remote Sensing
      PubDate: 2022-05-17
      DOI: 10.3390/rs14102403
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2404: Cropping Patterns of Annual Crops: A
           Remote Sensing Review

    • Authors: Mbali Mahlayeye, Roshanak Darvishzadeh, Andrew Nelson
      First page: 2404
      Abstract: Cropping patterns are defined as the sequence and spatial arrangement of annual crops on a piece of land. Knowledge of cropping patterns is crucial for crop production and land-use intensity. While cropping patterns are related to crop production and land use intensity, they are rarely reported in agricultural statistics, especially those relating to small farms in developing countries. Remote sensing has enabled mapping cropping patterns by monitoring crops’ spatial and temporal dynamics. In this paper, we reviewed remote sensing studies of single, sequential and intercropping patterns of annual crops practiced at local and regional scales. A total of 89 studies were selected from 753 publications based on their cropping pattern types and relevance to the scope of this review. The review found that despite the increase in single cropping pattern studies due to the Sentinel missions, studies on intercropping patterns are rare, suggesting that mapping intercropping is still challenging. More so, microwave remote sensing for mapping intercropping has not been fully explored. Given the complexities in mapping intercropping, our review highlights how less frequently used vegetation indices (VIs) that benefit from red-edge and SWIR spectral bands may improve intercropping mapping.
      Citation: Remote Sensing
      PubDate: 2022-05-17
      DOI: 10.3390/rs14102404
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2405: Effect of Assimilating SMAP Soil
           Moisture on CO2 and CH4 Fluxes through Direct Insertion in a Land Surface
           Model

    • Authors: Zhen Zhang, Abhishek Chatterjee, Lesley Ott, Rolf Reichle, Andrew F. Feldman, Benjamin Poulter
      First page: 2405
      Abstract: Soil moisture impacts the biosphere–atmosphere exchange of CO2 and CH4 and plays an important role in the terrestrial carbon cycle. A better representation of soil moisture would improve coupled carbon–water dynamics in terrestrial ecosystem models and could potentially improve model estimates of large-scale carbon fluxes and climate feedbacks. Here, we investigate using soil moisture observations from the Soil Moisture Active Passive (SMAP) satellite mission to inform simulated carbon fluxes in the global terrestrial ecosystem model LPJ-wsl. Results suggest that the direct insertion of SMAP reduces the bias in simulated soil moisture at in situ measurement sites by 40%, with a greater improvement at temperate sites. A wavelet analysis between the model and measurements from 26 FLUXNET sites suggests that the assimilated run modestly reduces the bias of simulated carbon fluxes for boreal and subtropical sites at 1–2-month time scales. At regional scales, SMAP soil moisture can improve the estimated responses of CO2 and CH4 fluxes to extreme events such as the 2018 European drought and the 2019 rainfall event in the Sudd (Southern Sudan) wetlands. The simulated improvements to land–surface carbon fluxes using the direct insertion of SMAP are shown across a variety of timescales, which suggests the potential of SMAP soil moisture in improving the model representation of carbon–water coupling.
      Citation: Remote Sensing
      PubDate: 2022-05-17
      DOI: 10.3390/rs14102405
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2406: Backtracking Reconstruction Network
           for Three-Dimensional Compressed Hyperspectral Imaging

    • Authors: Xi Wang, Tingfa Xu, Yuhan Zhang, Axin Fan, Chang Xu, Jianan Li
      First page: 2406
      Abstract: Compressed sensing (CS) has been widely used in hyperspectral (HS) imaging to obtain hyperspectral data at a sub-Nyquist sampling rate, lifting the efficiency of data acquisition. Yet, reconstructing the acquired HS data via iterative algorithms is time consuming, which hinders the real-time application of compressed HS imaging. To alleviate this problem, this paper makes the first attempt to adopt convolutional neural networks (CNNs) to reconstruct three-dimensional compressed HS data by backtracking the entire imaging process, leading to a simple yet effective network, dubbed the backtracking reconstruction network (BTR-Net). Concretely, we leverage the divide-and-conquer method to divide the imaging process based on coded aperture tunable filter (CATF) spectral imager into steps, and build a subnetwork for each step to specialize in its reverse process. Consequently, BTR-Net introduces multiple built-in networks which performs spatial initialization, spatial enhancement, spectral initialization and spatial–spectral enhancement in an independent and sequential manner. Extensive experiments show that BTR-Net can reconstruct compressed HS data quickly and accurately, which outperforms leading iterative algorithms both quantitatively and visually, while having superior resistance to noise.
      Citation: Remote Sensing
      PubDate: 2022-05-17
      DOI: 10.3390/rs14102406
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2407: Prediction of GPS Satellite Clock
           Offset Based on an Improved Particle Swarm Algorithm Optimized BP Neural
           Network

    • Authors: Dong Lv, Genyou Liu, Jikun Ou, Shengliang Wang, Ming Gao
      First page: 2407
      Abstract: Satellite clock offset is an important factor affecting the accuracy of real-time precise point positioning (RT-PPP). Due to missing real-time service (RTS) products provided by the International GNSS Service (IGS) or network faults, users may not obtain effective real-time corrections, resulting in the unavailability of RT-PPP. Considering this issue, an improved back propagation (BP) neural network optimized by heterogeneous comprehensive learning and dynamic multi-swarm particle swarm optimizer (HPSO-BP) is proposed for clock offset prediction. The new model uses the particle swarm optimizer to optimize the initial parameters of the BP neural network, which can avoid the instability and over-fitting problems of the traditional BP neural network. IGS RTS product data is selected for the experimental analysis; the results demonstrate that the average prediction precision of the HPSO-BP model for 20-min and 60-min is better than 0.15 ns, improving by approximately 85% compared to traditional models including the linear polynomial (LP) model, the quadratic polynomial (QP) model, the gray system model (GM (1,1)), and the ARMA time series model. It indicates that the HPSO-BP model has reasonable practicability and stability in the short-term satellite clock offset prediction, and its prediction performance is superior to traditional models. Therefore, in practical applications, the clock offset products predicted by the HPSO-BP model can meet the centimeter-level positioning accuracy requirements of RT-PPP.
      Citation: Remote Sensing
      PubDate: 2022-05-17
      DOI: 10.3390/rs14102407
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2408: Characteristics of Greening along
           Altitudinal Gradients on the Qinghai–Tibet Plateau Based on
           Time-Series Landsat Images

    • Authors: Yuhao Pan, Yan Wang, Shijun Zheng, Alfredo R. Huete, Miaogen Shen, Xiaoyang Zhang, Jingfeng Huang, Guojin He, Le Yu, Xiyan Xu, Qiaoyun Xie, Dailiang Peng
      First page: 2408
      Abstract: The Qinghai–Tibet Plateau (QTP) is ecologically fragile and is especially sensitive to climate change. Previous studies have shown that the vegetation on the QTP is undergoing overall greening with variations along altitudinal gradients. However, the mechanisms that cause the differences in the spatiotemporal patterns of vegetation greening among different types of terrain and vegetation have not received sufficient attention. Therefore, in this study, we used a Landsat NDVI time-series for the period 1992–2020 and climate data to observe the effects of terrain and vegetation types on the spatiotemporal patterns in vegetation greening on the QTP and to analyze the factors driving this greening using the geographical detector and the velocity of the vertical movement of vegetation greenness isolines. The results showed the following: (1) The vertical movement of the vegetation greenness isolines was affected by the temperature and precipitation at all elevations. The precipitation had a more substantial effect than the temperature below 3000 m. In contrast, above 3000 m, the temperature had a greater effect than the precipitation. (2) The velocity of the vertical movement of the vegetation greenness isolines of woody plants was higher than that of herbaceous plants. (3) The influence of slope on the vertical movement of vegetation greenness isolines was more significant than that of the aspect. The results of this study provided details of the spatiotemporal differences in vegetation greening between different types of terrain and vegetation at a 30-m scale as well as of the underlying factors driving this greening. These results will help to support ecological protection policies on the QTP.
      Citation: Remote Sensing
      PubDate: 2022-05-17
      DOI: 10.3390/rs14102408
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2409: Marine Litter Detection by
           Sentinel-2: A Case Study in North Adriatic (Summer 2020)

    • Authors: Achille Carlo Ciappa
      First page: 2409
      Abstract: Aggregates of floating materials detected in North Adriatic in six Sentinel-2 scenes of August 2020 have been investigated. Most of the floating materials were identified by the chlorophyll red edge and consisted of vegetal materials, probably conveyed by rivers and exchanged with the lagoons. Traces of marine litter were looked for in the spectral anomalies of the Red Edge bands, assuming changes of the red edge in pixels where marine litter was mixed with vegetal materials. About half of the detected patches were unclassified due to the weakness of the useful signal (pixel filling percentage < 25%). The classification produced 59% of vegetal materials, 16% of marine litter mixed with vegetal materials and 22% of intermediate cases. A small percentage (2%) was attributed to submerged vegetal materials, found in isolated patches. The previous percentages were obtained with a separation criterion based on arbitrary thresholds. The patches were more concentrated at the mouths of the northern rivers, less off the Venice lagoon, and very few outside the Po River, with the minimal river outflow during the period. Sentinel-2 is a valid tool for the discrimination of marine litter in aggregates of floating matter. The proposed method requires validation, and the North Adriatic is an excellent site for field work, as in summer many patches of floating matter form in proximity to the coast.
      Citation: Remote Sensing
      PubDate: 2022-05-17
      DOI: 10.3390/rs14102409
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2410: Surface Water Dynamics from Space: A
           Round Robin Intercomparison of Using Optical and SAR High-Resolution
           Satellite Observations for Regional Surface Water Detection

    • Authors: Christian Tottrup, Daniel Druce, Rasmus Probst Meyer, Mads Christensen, Michael Riffler, Bjoern Dulleck, Philipp Rastner, Katerina Jupova, Tomas Sokoup, Arjen Haag, Mauricio C. R. Cordeiro, Jean-Michel Martinez, Jonas Franke, Maximilian Schwarz, Victoria Vanthof, Suxia Liu, Haowei Zhou, David Marzi, Rudiyanto Rudiyanto, Mark Thompson, Jens Hiestermann, Hamed Alemohammad, Antoine Masse, Christophe Sannier, Sonam Wangchuk, Guy Schumann, Laura Giustarini, Jason Hallowes, Kel Markert, Marc Paganini
      First page: 2410
      Abstract: Climate change, increasing population and changes in land use are all rapidly driving the need to be able to better understand surface water dynamics. The targets set by the United Nations under Sustainable Development Goal 6 in relation to freshwater ecosystems also make accurate surface water monitoring increasingly vital. However, the last decades have seen a steady decline in in situ hydrological monitoring and the availability of the growing volume of environmental data from free and open satellite systems is increasingly being recognized as an essential tool for largescale monitoring of water resources. The scientific literature holds many promising studies on satellite-based surface-water mapping, but a systematic evaluation has been lacking. Therefore, a round robin exercise was organized to conduct an intercomparison of 14 different satellite-based approaches for monitoring inland surface dynamics with Sentinel-1, Sentinel-2, and Landsat 8 imagery. The objective was to achieve a better understanding of the pros and cons of different sensors and models for surface water detection and monitoring. Results indicate that, while using a single sensor approach (applying either optical or radar satellite data) can provide comprehensive results for very specific localities, a dual sensor approach (combining data from both optical and radar satellites) is the most effective way to undertake largescale national and regional surface water mapping across bioclimatic gradients.
      Citation: Remote Sensing
      PubDate: 2022-05-17
      DOI: 10.3390/rs14102410
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2411: A Method of Soil Moisture Content
           Estimation at Various Soil Organic Matter Conditions Based on Soil
           Reflectance

    • Authors: Tianchen Li, Tianhao Mu, Guiwei Liu, Xiguang Yang, Gechun Zhu, Chuqing Shang
      First page: 2411
      Abstract: Soil moisture is one of the most important components of all the soil properties affecting the global hydrologic cycle. Optical remote sensing technology is one of the main parts of soil moisture estimation. In this study, we promote a soil moisture-estimating method with applications regarding various soil organic matters. The results indicate that the soil organic matter had a significant spectral feature at wavelengths larger than 900 nm. The existence of soil organic matter would lead to darker soil, and this feature was similar to the soil moisture. Meanwhile, the effect of the soil organic matter on its reflectance overlaps with the effect of soil moisture on its reflected spectrum. This can lead to the underestimation of the soil moisture content, with an MRE of 21.87%. To reduce this effect, the absorption of the soil organic matter was considered based on the Lambert–Beer law. Then, we established an SMCg-estimating model based on the radiative transform theory while considering the effect of the soil organic matter. The results showed that the effect of the soil organic matter can be effectively reduced and the accuracy of the soil moisture estimation was increased, while MRE decreased from 21.87% to 6.53%.
      Citation: Remote Sensing
      PubDate: 2022-05-17
      DOI: 10.3390/rs14102411
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2412: Tea Cultivation Suitability
           Evaluation and Driving Force Analysis Based on AHP and Geodetector
           Results: A Case Study of Yingde in Guangdong, China

    • Authors: Chen, Li, Chen, Li, Zhang, Zhao
      First page: 2412
      Abstract: Tea is an economically important crop. Evaluating the suitability of tea can better optimize the regional layout of the tea industry and provide a scientific basis for tea planting plans, which is also conducive to the sustainable development of the tea industry in the long run. Driving force analysis can be carried out to better understand the main influencing factors of tea growth. The main purpose of this study was to evaluate the suitability of tea planting in the study area, determine the prioritization of tea industry development in this area, and provide support for the government’s planning and decision making. This study used Sentinel image data to obtain the current land use data of the study area. The results show that the accuracy of tea plantation classification based on Sentinel images reached 86%, and the total accuracy reached 92%. Then, we selected 14 factors, including climate, soil, terrain, and human-related factors, using the analytic hierarchy process and spatial analysis technology to evaluate the suitability of tea cultivation in the study area and obtain a comprehensive potential distribution map of tea cultivation. The results show that the moderately suitable area (36.81%) accounted for the largest proportion of the tea plantation suitability evaluation, followed by the generally suitable area (31.40%), the highly suitable area (16.91%), and the unsuitable area (16.23%). Among these areas, the highly suitable area is in line with the distribution of tea cultivation at the Yingde municipal level. Finally, to better analyze the contribution of each factor to the suitability of tea, the factors were quantitatively evaluated by the Geodetector model. The most important factors affecting the tea cultivation suitability evaluation were temperature (0.492), precipitation (0.367), slope (0.302), and elevation (0.255). Natural factors influence the evaluation of the suitability of tea cultivation, and the influence of human factors is relatively minor. This study provides an important scientific basis for tea yield policy formulation, tea plantation site selection, and adaptation measures.
      Citation: Remote Sensing
      PubDate: 2022-05-17
      DOI: 10.3390/rs14102412
      Issue No: Vol. 14, No. 10 (2022)
       
  • Remote Sensing, Vol. 14, Pages 2413: Correction: Li et al. Automatic Point
           Cloud Registration for Large Outdoor Scenes Using a Priori Semantic
           Information. Remote Sens. 2021, 13, 3474

    • Authors: Remote Sensing Editorial Office Remote Sensing Editorial Office
      First page: 2413
      Abstract: In the original article [...]
      Citation: Remote Sensing
      PubDate: 2022-05-18
      DOI: 10.3390/rs14102413
      Issue No: Vol. 14, No. 10 (2022)
       
 
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