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  Subjects -> ELECTRONICS (Total: 151 journals)
Advances in Biosensors and Bioelectronics     Open Access   (Followers: 2)
Advances in Magnetic and Optical Resonance     Full-text available via subscription   (Followers: 5)
Advances in Microelectronic Engineering     Open Access   (Followers: 1)
Advances in Power Electronics     Open Access   (Followers: 8)
Aerospace and Electronic Systems, IEEE Transactions on     Hybrid Journal   (Followers: 58)
American Journal of Electrical and Electronic Engineering     Open Access   (Followers: 10)
Annals of Telecommunications     Hybrid Journal   (Followers: 4)
APL : Organic Electronics and Photonics     Hybrid Journal   (Followers: 2)
APSIPA Transactions on Signal and Information Processing     Open Access   (Followers: 6)
Archives of Electrical Engineering     Open Access   (Followers: 9)
Autonomous Mental Development, IEEE Transactions on     Hybrid Journal   (Followers: 5)
Bell Labs Technical Journal     Hybrid Journal   (Followers: 8)
Biomedical Engineering, IEEE Reviews in     Full-text available via subscription   (Followers: 15)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 12)
Biomedical Instrumentation & Technology     Full-text available via subscription   (Followers: 4)
Broadcasting, IEEE Transactions on     Hybrid Journal   (Followers: 5)
BULLETIN of National Technical University of Ukraine. Series RADIOTECHNIQUE. RADIOAPPARATUS BUILDING     Open Access   (Followers: 1)
Bulletin of the Polish Academy of Sciences : Technical Sciences     Open Access  
Canadian Journal of Remote Sensing     Full-text available via subscription   (Followers: 13)
China Communications     Full-text available via subscription   (Followers: 1)
Circuits and Systems     Open Access   (Followers: 9)
Consumer Electronics Times     Open Access   (Followers: 4)
Control Systems     Hybrid Journal   (Followers: 20)
Electronic Markets     Hybrid Journal   (Followers: 6)
Electronic Materials Letters     Hybrid Journal   (Followers: 3)
Electronics     Open Access   (Followers: 7)
Electronics and Communications in Japan     Hybrid Journal   (Followers: 5)
Electronics Letters     Hybrid Journal   (Followers: 17)
Embedded Systems Letters, IEEE     Hybrid Journal   (Followers: 18)
Energy Harvesting and Systems : Materials, Mechanisms, Circuits and Storage     Hybrid Journal   (Followers: 1)
EPJ Quantum Technology     Open Access  
EURASIP Journal on Embedded Systems     Open Access   (Followers: 8)
Facta Universitatis, Series : Electronics and Energetics     Open Access  
Foundations and TrendsĀ® in Communications and Information Theory     Full-text available via subscription   (Followers: 6)
Foundations and TrendsĀ® in Signal Processing     Full-text available via subscription   (Followers: 4)
Frequenz     Full-text available via subscription   (Followers: 2)
Frontiers of Optoelectronics     Hybrid Journal   (Followers: 2)
Geoscience and Remote Sensing, IEEE Transactions on     Hybrid Journal   (Followers: 20)
Haptics, IEEE Transactions on     Hybrid Journal   (Followers: 4)
IEEE Antennas and Propagation Magazine     Hybrid Journal   (Followers: 14)
IEEE Antennas and Wireless Propagation Letters     Hybrid Journal   (Followers: 12)
IEEE Consumer Electronics Magazine     Full-text available via subscription   (Followers: 11)
IEEE Journal of Emerging and Selected Topics in Power Electronics     Hybrid Journal   (Followers: 8)
IEEE Journal of the Electron Devices Society     Open Access   (Followers: 2)
IEEE Power Electronics Magazine     Full-text available via subscription   (Followers: 5)
IEEE Transactions on Antennas and Propagation     Full-text available via subscription   (Followers: 9)
IEEE Transactions on Audio, Speech, and Language Processing     Hybrid Journal   (Followers: 11)
IEEE Transactions on Automatic Control     Hybrid Journal   (Followers: 21)
IEEE Transactions on Consumer Electronics     Hybrid Journal   (Followers: 13)
IEEE Transactions on Electron Devices     Hybrid Journal   (Followers: 6)
IEEE Transactions on Information Theory     Hybrid Journal   (Followers: 13)
IEEE Transactions on Power Electronics     Hybrid Journal   (Followers: 16)
IEICE - Transactions on Electronics     Full-text available via subscription   (Followers: 8)
IEICE - Transactions on Information and Systems     Full-text available via subscription   (Followers: 7)
IET Microwaves, Antennas & Propagation     Hybrid Journal   (Followers: 6)
IET Power Electronics     Hybrid Journal   (Followers: 8)
IET Wireless Sensor Systems     Hybrid Journal   (Followers: 6)
IETE Journal of Education     Open Access   (Followers: 2)
IETE Journal of Research     Open Access   (Followers: 5)
IETE Technical Review     Open Access   (Followers: 1)
Industrial Electronics, IEEE Transactions on     Hybrid Journal   (Followers: 11)
Industry Applications, IEEE Transactions on     Hybrid Journal   (Followers: 5)
Informatik-Spektrum     Hybrid Journal   (Followers: 1)
Instabilities in Silicon Devices     Full-text available via subscription  
Intelligent Transportation Systems Magazine, IEEE     Full-text available via subscription   (Followers: 1)
International Journal of Advanced Electronics and Communication Systems     Open Access   (Followers: 5)
International Journal of Advanced Research in Computer Science and Electronics Engineering     Open Access   (Followers: 20)
International Journal of Advances in Telecommunications, Electrotechnics, Signals and Systems     Open Access   (Followers: 3)
International Journal of Aerospace Innovations     Full-text available via subscription   (Followers: 11)
International Journal of Antennas and Propagation     Open Access   (Followers: 8)
International Journal of Applied Electronics in Physics & Robotics     Open Access   (Followers: 1)
International Journal of Biomedical Nanoscience and Nanotechnology     Hybrid Journal   (Followers: 5)
International Journal of Computational Vision and Robotics     Hybrid Journal   (Followers: 4)
International Journal of Computer & Electronics Research     Full-text available via subscription   (Followers: 2)
International Journal of Control     Hybrid Journal   (Followers: 12)
International Journal of Electronics     Hybrid Journal   (Followers: 2)
International Journal of Electronics & Data Communication     Open Access   (Followers: 4)
International Journal of Electronics and Telecommunications     Open Access   (Followers: 3)
International Journal of Granular Computing, Rough Sets and Intelligent Systems     Hybrid Journal   (Followers: 1)
International Journal of High Speed Electronics and Systems     Hybrid Journal  
International Journal of Microwave and Wireless Technologies     Hybrid Journal   (Followers: 1)
International Journal of Nano Devices, Sensors and Systems     Open Access   (Followers: 4)
International Journal of Nanoscience     Hybrid Journal   (Followers: 1)
International Journal of Numerical Modelling:Electronic Networks, Devices and Fields     Hybrid Journal   (Followers: 2)
International Journal of Power Electronics     Hybrid Journal   (Followers: 4)
International Journal of Review in Electronics & Communication Engineering     Open Access   (Followers: 2)
International Journal of Sensors, Wireless Communications and Control     Hybrid Journal   (Followers: 2)
International Journal of Systems, Control and Communications     Hybrid Journal   (Followers: 2)
International Journal on Communication     Full-text available via subscription   (Followers: 8)
International Journal on Electrical and Power Engineering     Full-text available via subscription   (Followers: 11)
International Transaction of Electrical and Computer Engineers System     Open Access  
ISRN Electronics     Open Access   (Followers: 1)
ISRN Signal Processing     Open Access  
Journal of Advanced Dielectrics     Open Access   (Followers: 1)
Journal of Artificial Intelligence     Open Access   (Followers: 5)
Journal of Circuits, Systems, and Computers     Hybrid Journal   (Followers: 1)
Journal of Electrical and Electronics Engineering Research     Open Access   (Followers: 2)
Journal of Electrical Bioimpedance     Full-text available via subscription   (Followers: 2)
Journal of Electrical Engineering & Electronic Technology     Full-text available via subscription   (Followers: 1)
Journal of Electromagnetic Analysis and Applications     Open Access   (Followers: 3)

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Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of
   [18 followers]  Follow    
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
     ISSN (Print) 1939-1404
     Published by Institute of Electrical and Electronics Engineers (IEEE) Homepage  [174 journals]   [SJR: 1.232]   [H-I: 14]
  • IEEE Transactions on Geoscience and Remote Sensing institutional listings
    • Pages: C4 - C4
      Abstract: Advertisement.
      PubDate: Sept. 2014
      Issue No: Vol. 7, No. 9 (2014)
       
  • IEEE Journal of Selected Topics in Applied Earth Observations and Remote
           Sensing Information for Authors
    • Pages: C3 - C3
      Abstract: Provides instructions and guidelines to prospective authors who wish to submit manuscripts.
      PubDate: Sept. 2014
      Issue No: Vol. 7, No. 9 (2014)
       
  • IEEE Journal of Selected Topics in Applied Earth Observations and Remote
           Sensing publication information
    • Pages: C2 - C2
      Abstract: Provides a listing of current staff, committee members and society officers.
      PubDate: Sept. 2014
      Issue No: Vol. 7, No. 9 (2014)
       
  • [Front cover]
    • Pages: C1 - C1
      Abstract: Presents the front cover for this issue of the publication.
      PubDate: Sept. 2014
      Issue No: Vol. 7, No. 9 (2014)
       
  • Table of contents
    • Pages: 3681 - 3682
      Abstract: Presents the table of contents for this issue of the periodical.
      PubDate: Sept. 2014
      Issue No: Vol. 7, No. 9 (2014)
       
  • Foreword to the Special Issue on the 2013 IEEE International Geoscience
           and Remote Sensing Symposium
    • Authors: Fraser; C.;Walker, J.;Williams, M.L.;
      Pages: 3683 - 3684
      PubDate: Sept. 2014
      Issue No: Vol. 7, No. 9 (2014)
       
  • Sea Ice Surface Temperature Estimation Using MODIS and AMSR-E Data Within
           a Guided Variational Model Along the Labrador Coast
    • Authors: Scott; K.A.;Li, E.;Wong, A.;
      Pages: 3685 - 3694
      Abstract: In this study, a new method, entitled as the multi-modality guided variational (MGV) method, is proposed, in which the data from a passive microwave sensor is used jointly with the data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to estimate the sea ice surface temperature (IST). The method augments existing sea IST values from the MODIS IST map, while filling in areas in the MODIS image that may be sparsely sampled due to the cloud cover, or due to increased spacing between the pixels at the swath edges. The former issue is particularly problematic in the marginal ice zone, where the atmospheric conditions often lead to persistent cloud cover. The sea IST is of interest because it can be used to estimate the sea ice thickness, an important parameter for shipping, climate change, and weather forecasting applications. The impact of the MGV method is checked through a comparison between the sea ice thickness calculated using the swath surface temperature and that calculated using the surface temperature from MGV. Using the operational ice charts as a guideline, it is found that the sea ice thickness values calculated using the MGV surface temperature are realistic, and there is a 16% increase in the number of sea ice thickness data points available when the MGV method is used as compared to when the swath data are used.
      PubDate: Sept. 2014
      Issue No: Vol. 7, No. 9 (2014)
       
  • Estimation of Surface Longwave Radiation over the Tibetan Plateau Region
           Using MODIS Data for Cloud-Free Skies
    • Authors: Wang; J.;Tang, B.;Zhang, X.;Wu, H.;Li, Z.;
      Pages: 3695 - 3703
      Abstract: This study addressed the estimation of downward surface longwave radiation ( ${R_{L,D}}$ ) for the Tibetan Plateau region using the MODerate resolution Imaging Spectroradiometer (MODIS) measurements observed at the top of the atmosphere (TOA) for clear-sky conditions. To extend, the method developed by Tang and Li with only regard to MODIS/Terra data and with surface elevation only lower than 2.5 km, two separate look-up table (LUT) coefficients corresponding to different elevations and viewing zenith angles (VZAs) were developed for MODIS/Terra and MODIS/Aqua, respectively. The upward surface longwave radiation ( ${R_{L,U}}$ ) was estimated using the Stefan–Boltzmann law with MODIS surface temperature/emissivity products (MOD11_L2/MYD11_L2). Combining the ${R_{L,D}}$ and ${R_{L,U}}$ , the net surface longwave radiation ${R_{L,N}}$ can then be obtained. The ${R_{L,D}}$ and ${R_{L,N}}$ were validated using some in situ measurements from two sites of Asian Automatic Weather Station Network Project (AAN) (Dunhuang and Amdo) and three sites of Environmental and Ecological Science Data Center for West China (Huazhaizi, Yingke, and A’rou) in the Heihe River Basin. The results showed that the root mean square errors (RMSEs) between the estimated ${R_{L,N}}$ and in situ measurements were about ${bf 30}~{bf W/m}^{bf 2}$ . In addition, the schemes developed, respectively, by Bisht et al. and Brutsaert were used to estimate the ${R_{L,D}}$ and ${R_{L,N}}$ for comparison with the proposed method. The results showed that the RMSE of the proposed method was the lowest one among the three methods, which indicated that the proposed method can estimate the ${R_{L,N}}$ accurately for the Tibetan Plateau region.
      PubDate: Sept. 2014
      Issue No: Vol. 7, No. 9 (2014)
       
  • Surface Energy Fluxes Estimation Over the South Asia Subcontinent Through
           Assimilating MODIS/TERRA Satellite Data With In Situ
           Observations and GLDAS Product by SEBS Model
    • Authors: Zhao; W.;Li, A.;Deng, W.;
      Pages: 3704 - 3712
      Abstract: Evapotranspiration (ET) estimation is important to water resource management in the South Asia subcontinent, and remote sensing method is a good choice to get surface energy fluxes for ET estimation. However, the accuracy of regional atmospheric parameters plays a vital role especially for South Asia with few meteorological stations available. In this study, to seek a practical way to derive surface energy fluxes for ET estimation, we compare the performances of three methods based on SEBS model using different meteorological data sources (meteorological stations, GLDAS product and the combination of in situ observation, and satellite retrieval). The spatial distribution and value reasonability analysis of the estimates indicate that the three methods cannot provide reliable estimates because of the constraint of station number and GLDAS product accuracy. Correlation analysis finds that there is a good agreement between GLDAS air temperature product and in situ observations with the coefficient of determination (R2) above 0.4 for 5 days in 2008. According to the relationship, an integration method is proposed by recalibrating GLDAS air temperature with in situ observations with other atmospheric parameters supplied by GLDAS product, and the average values of the estimated LE calculated at 100 NDVI intervals are well correlated with NDVI with ${{bf R}^{bf 2}}$ more than 0.695. The LE values are also within a reasonable value range with the maximum about ${bf 700nbsphbox{W}/hbox{m}^2}$ occurred at the full vegetation cover condition. The results indicate that the integration method is a practical way of obtaining surface energy fluxes and ET estimation for South Asia.
      PubDate: Sept. 2014
      Issue No: Vol. 7, No. 9 (2014)
       
  • Near-Real Time Detection of Beetle Infestation in Pine Forests Using MODIS
           Data
    • Authors: Anees; A.;Aryal, J.;
      Pages: 3713 - 3723
      Abstract: This paper considers near-real time detection of beetle infestation in North American pine forests using MODIS 8-days 500 m data. Two methods are considered, both using a single time series for detection of beetle infestation by analyzing the statistics of the trend component of the signal. The first method estimates the trend component of the vegetation index time series by fitting an underlying triply modulated cosine model over a sliding window, using nonlinear least squares (NLS), and the second method uses a ${mbi{T}}$ -point moving average finite impulse response (FIR) filter. Both the methods perform well and show similar performance on simulated datasets. The methods are also tested on many difference and ratio-indices of a real-world dataset with change and no-change examples taken from the Rocky Mountain region of the United States and of British Columbia in Canada. The results suggest that both the methods detect beetle infestation reliably in almost all the vegetation index datasets. However, the model-based method (NLS-based) performs better in terms of the detection delay. Red Green Index (RGI), when used with the model-based method, provides the best tradeoff between the detection delay and accuracy. Furthermore, 90%, 50%, and 25% cross-validations are also performed for the threshold selection on RGI dataset, and it is shown that the selected threshold works well on the test data. In the end, it is also shown that the model-based method outperforms a recently published method for near-real time disturbance detection in MODIS data, in both accuracy and detection delay.
      PubDate: Sept. 2014
      Issue No: Vol. 7, No. 9 (2014)
       
  • Managing Wheat From Space: Linking MODIS NDVI and Crop Models for
           Predicting Australian Dryland Wheat Biomass
    • Authors: Perry; E.M.;Morse-McNabb, E.M.;Nuttall, J.G.;O Leary, G.J.;Clark, R.;
      Pages: 3724 - 3731
      Abstract: This study explored the relationships between moderate resolution imaging spectroradiometer (MODIS) NDVI observations with both measured and simulated fractional green cover (FGrC), leaf area index (LAI), and above ground biomass (AGB) for dryland wheat in Australia. A total of 37 paddocks in north-western Victoria, Australia, were sampled during 2003–2006 for AGB at anthesis, and for FGrC, NDVI (from an active optical sensor), and AGB during 2012. The 2012 FGrC and NDVI measurements were fitted to MODIS NDVI, resulting in positive, linear relationships when the MODIS NDVI values were $ leq {bf 0.80}$ . Measured AGB was also positively, linearly related to MODIS summed NDVI, resulting in an overall ${{bf R}^{bf 2}}$ of 0.81 and root mean square error (RMSE) of 1397 kg/ha. Crop simulations were run for the fourteen paddocks from 2003 to 2006, and six paddocks from 2012. Four crop phenological points were selected to extract corresponding NDVI and simulated crop parameters: emergence, peak LAI, the mid-point between emergence and peak LAI, and anthesis. Linear models were fit between the MODIS NDVI and simulated values of FGrC, LAI, and AGB. Overall, the highest ${{bf R}^{bf 2}}$ values corresponded to using all of the dates for FGrC ( ${{bf R}^{bf 2}} = {bf 0.82}$ ) and AGB ( ${{bf R}^{bf 2}} = {bf 0.92}$ ), and anthesis dates for LAI ( ${{bf R}^{bf 2}} = {bf 0.74}$ ). For FGrC and AGB, the RMSE with simulated parameters were comparable or better than the equivalent results from the in situ measurements (note th- t there were no LAI in situ measurements to compare with). The results support the notion for extending the value of the MODIS NDVI using crop simulation models. The combination of remotely sensed and simulation data might offer regional maps of spatial AGB and ultimately grain yield, which would have high value for research, resource management, policy, and potentially, crop management.
      PubDate: Sept. 2014
      Issue No: Vol. 7, No. 9 (2014)
       
  • Automated Retrieval of Cloud Masks from the HJ-1 WVC Imagery
    • Authors: Li; D.;Ge, Y.;Wang, B.;
      Pages: 3732 - 3741
      Abstract: Robust and automated cloud discrimination is regarded as an important step toward the extraction and analysis of cloud-free surface features, especially for high-resolution images only covering visible and near-infrared spectrum, like data from the wide view CCD cameras (WVC) of HJ-1 minisatellites. A cloud screening algorithm designed for the HJ-1 (China) multispectral WVC data is presented using a combination of Tasseled Cap (TC) transformation and an unsupervised classification known as the iterative self-organizing data analysis (ISODATA). Two filters, one of which is cluster-based and the other uses a pixel-based analysis that employs greenness index and wetness index both extracted from the TC transformation, are successively driven to filter out cloud-pixels. The performance of the proposed algorithm is investigated for six HJ-1 WVC scenes with typical cloud coverage. Two comparisons, respectively, with one method based on the combination of the TC transformation and linear constrains (TCLC) and the other using the maximum-likelihood classification (MLC), are given in this study. The initial results from both comparisons show an impressive agreement, especially for regions covered by thick clouds or large thin clouds. Although this agreement decreases significantly for other complex situations, such as the presence of numerous small thin clouds, overall agreements still stay on an acceptable level. A considerable contribution, therefore, can be expected by this method for those high-resolution data with less frequent observations and a narrow spectrum cover.
      PubDate: Sept. 2014
      Issue No: Vol. 7, No. 9 (2014)
       
  • Denoising of Hyperspectral Images Employing Two-Phase Matrix Decomposition
    • Authors: Li; Q.;Li, H.;LU, Z.;Lu, Q.;Li, W.;
      Pages: 3742 - 3754
      Abstract: Noise reduction is a significant preprocessing step for hyperspectral image (HSI) analysis. There are various noise sources, leading to the difficulty in developing a somewhat universal technique for noise reduction. A majority of the existing denoising strategies are designed to tackle a certain kind of noise, with somewhat idealized hypotheses imposed on them. Therefore, it is desirable to develop a noise reduction technique with high universality for various noise patterns. Matrix decomposition can decompose a given matrix into two components if they have low-rank and sparse properties. This fits the case of HSI denoising when an HSI is reorganized as a matrix, because the noise-free signal of HSI has low rank due to the high correlations within its content, while the noise of HSI has structured sparsity with respect to the big volume of data. Moreover, matrix decomposition avoids denoising process falling into the dependence on distribution characteristics of the noise or making some idealized assumptions on HSI signal and noise. In this paper, a two-phase matrix decomposition scheme is presented. First, by employing the low-rank property of HSI signal and the structured sparsity of HSI noise, the hyperspectral data matrix is decomposed into a basic signal component and a rough noise component. Then, the latter is further decomposed into a spatial compensation part and a final noise part, via using the band-by-band total variation (TV) regularization. A number of simulated and real data experiments demonstrate that the proposed approach produces superior denoising results for different HSI noise patterns within a wide range of noise levels.
      PubDate: Sept. 2014
      Issue No: Vol. 7, No. 9 (2014)
       
  • Influence of Sand-Grain Morphology and Iron-Oxide Distribution Patterns on
           the Visible and Near-Infrared Reflectance of Sand-Textured Soils
    • Authors: Baranoski; G.V.G.;Kimmel, B.W.;Chen, T.F.;Miranda, E.;
      Pages: 3755 - 3763
      Abstract: The overall shape of a sand grain can be defined by two morphological properties, namely sphericity and roundness, and it is largely determined by soil-formation and weathering processes. In this paper, we investigate the effects of these properties on the visible and near-infrared reflectance of sand-textured soils characterized by the presence of iron oxides. Our investigation is supported by computer simulations performed using the SPLITS (Spectral Light Transport Model for Sand) model and considering actual sand characterization data. Our findings indicate that the influence of grain morphology may vary considerably depending on the distribution patterns of iron oxides present in sand-textured soils. These minerals may occur as pure particles, as contaminants mixed with the grain parent material, or as coatings. Since these distribution patterns are also significantly affected by soil-formation and weathering processes, we believe that the combined influence of sand-grain shape and iron-oxide distribution patterns on the reflectance of sandy landscapes should be carefully taken into account in the retrieval of information about their mineralogy and environmental history.
      PubDate: Sept. 2014
      Issue No: Vol. 7, No. 9 (2014)
       
  • Using Boruta-Selected Spectroscopic Wavebands for the Asymptomatic
           Detection of Fusarium Circinatum Stress
    • Authors: Poona; N.K.;Ismail, R.;
      Pages: 3764 - 3772
      Abstract: High spectral resolution multitemporal data were used to model asymptomatic stress caused by Fusarium circinatum in 3-month old Pinus radiata seedlings. The objectives of the study were: 1) to identify an optimal subset of wavebands that could model asymptomatic stress in P. radiata seedlings and 2) to develop a robust classification model for discriminating healthy and stressed seedlings. To achieve these objectives, spectral data were collected for healthy, infected, and damaged seedlings using a hand-held field spectroradiometer. The data were analyzed, first for combined classes and then for class pairs using the Boruta algorithm. Results indicated that the best discrimination was possible at week three for all classes, with a KHAT value of 0.79 and an out of bag error of 14.00% ( ${bf CV},{bf error} = {bf 16.00% }$ ), using a subset of 107 wavebands. A closer examination of the class pairs, namely healthy-infected (H-I) and infected-damaged (I-D), showed improved discrimination with KHAT values of 0.82 and 0.84, respectively. The H-I class pair was classified using a subset of just 38 wavebands, whereas the I-D class pair was classified using a subset of just 40 wavebands. Overall, this study demonstrated that it is more difficult to discriminate asymptomatic stress when additional stress related classes are present. Nonetheless, the methodology developed in this study has the potential to be operationalized within a nursery environment for the early detection of F. circinatum-induced stress in P. radiata seedlings.
      PubDate: Sept. 2014
      Issue No: Vol. 7, No. 9 (2014)
       
  • Characteristics Analysis and Classification of Crop Harvest Patterns by
           Exploiting High-Frequency MultiPolarization SAR Data
    • Authors: Zhao; L.;Yang, J.;Li, P.;Zhang, L.;
      Pages: 3773 - 3783
      Abstract: At harvest season, crops are often harvested using various methods at different times. Mapping and monitoring of the patterns of croplands during the harvest period provide information for farmers to help guide the harvest practices that are time critical and to support early warning of threats to food security. This study discusses the feasibility of high-frequency (C/X) polarimetric synthetic aperture radar (PolSAR) for the discrimination of crop patterns during harvest. The polarimetric signals gathered from a farmland area during harvest in Inner Mongolia, China, have been evaluated to investigate the properties of different harvest patterns by using the fully polarimetric Radarsat-2 and dual-pol TerraSAR-X images. A set of polarimetric parameters were derived from the datasets to interpret the radar signatures. The statistics show the sensitivity of the polarimetric parameters to the properties of the harvest patterns. The crop type, biomass, water content held by plants, crop swath direction, and crop state make a large contribution to the fluctuation of the polarimetric scattering characteristics. By exploring the polarimetric characteristics across different harvest patterns, a new method of mapping the harvest state is proposed by utilizing the decision tree algorithm. In the proposed method, GIS data are exploited to avoid the confusion of similar harvest patterns for different species. The harvest pattern mapping results by using the multipolarimetric data acquired over the study area in different years, demonstrate the feasibility and potential of polarimetric data of short wavelength for harvest pattern monitoring during harvest.
      PubDate: Sept. 2014
      Issue No: Vol. 7, No. 9 (2014)
       
  • An Improved Model for L-Band Brightness Temperature Estimation Over
           Foam-Covered Seas Under Low and Moderate Winds
    • Authors: Ma; W.;Yang, X.;Liu, G.;Ma, S.;Yu, Y.;Li, Z.;Liu, Y.;
      Pages: 3784 - 3793
      Abstract: In this study, an improved ${{mbi{T}}_{mbi{B}}}$ model is proposed on the basis of the SSA model and Monahan foam coverage model. A 22-month matched dataset from the Aquarius observation and Argo buoys was collected to build the improved model. However, it was determined that the wind-induced emissivity calculated from the SSA model at low wind speed (WS) displays obvious deviation compared with the satellite observations, which can be corrected using the systematical error correct coefficient (SECC). To evaluate the effects of sea wave spectra on ${{mbi{T}}_{mbi{B}}}$ , different sea wave spectra are used and compared in the SSA model and the foam coverage model. A new set of parameters for the foam coverage model for different spectra is obtained by fitting the wind-induced emissivity calculated from a 16-month matching dataset of the Aquarius and Argo buoys. The SSA model with a Kudryavtsev spectrum, as well as the improved foam coverage model, is selected for simulating the satellite ${{mbi{T}}_{mbi{B}}}$ under low and moderate winds. Finally, a 6-month match dataset obtained in 2013 is used for model validation. The root mean square error (RMSE) of the estimated brightness temperature is approximately 0.5 K in V polarization and less than 0.7 K in H polarization. The correlation coefficient is approximately 0.9, except for the case with ${bf 45.6}^{{bf{circ}}}$ in H polarization. The bias between simulation and observation in V polarization is lower than that in H polarization, and the biases in both polarizations are acceptable when the WS is less than 20 m/s.
      PubDate: Sept. 2014
      Issue No: Vol. 7, No. 9 (2014)
       
  • Snow Height Determination by Polarimetric Phase Differences in X-Band SAR
           Data
    • Authors: Leinss; S.;Parrella, G.;Hajnsek, I.;
      Pages: 3794 - 3810
      Abstract: The copolar phase difference (CPD) between VV and HH polarization of X-band SAR acquisitions shows a significant dependence on the depth of fresh snow. Phase differences of 5–15 deg/10 cm fresh snow were determined at a frequency of 9.65 GHz by comparing spatial and temporal variations of snow depth (SD) with the CPD. Spatial correlations were derived from snow transect measurements during January 2012 and TanDEM-X acquisitions. Temporal correlations were derived from weather station data and TerraSAR-X acquisitions between December 2012 and April 2013. All measurements were done at a test field near the city Sodankylae, Finland. To explain the observed CPD, a model derives birefringent properties from the microstructure of snow, which is described as aligned elliptical particles. The microscopic description is based on computer tomography observations. Different incidence angles were analyzed in consistency with the model. The temporal evolution of the CPD was linked to the temperature-gradient-driven recrystallization process. Sudden increases in the CPD indicate fresh snow. Slow decreases indicate the subsequent recrystallization process. The background signal of wet soil was considered and causes a small negative offset to the CPD. A quantitative determination of the depth of fresh snow is possible, because the specific CPD per meter of snow can be estimated. Spatial resolutions below ${bf 100 times 100}~hbox{bf m}$ are achievable with sensors such as TerraSAR-X or TanDEM-X. This paper presents a theoretical relationship between the microstructure of snow and the CPD and relates the CPD theoretically and empirically to the depth of fresh snow.
      PubDate: Sept. 2014
      Issue No: Vol. 7, No. 9 (2014)
       
  • Dense Media Radiative Transfer Applied to SnowScat and SnowSAR
    • Authors: Chang; W.;Tan, S.;Lemmetyinen, J.;Tsang, L.;Xu, X.;Yueh, S.H.;
      Pages: 3811 - 3825
      Abstract: The dense media radiative transfer (DMRT) theory is applied to data analysis of recent measurements of multifrequency microwave backscatter from the snow cover on earth. Measurement includes ground-based campaign (SnowScat) and airborne mission (SnowSAR). Both the quasi-crystalline approximation (QCA) model and the bicontinuous model are used for a multilayer snow medium. Two size parameters are used for both models. Grain size and stickiness parameter are used for QCA model. The bicontinuous model has two parameters: the mean wave number $langle bm{zeta }rangle$ and the parameter ${bm{b}}$ . The mean wave number $langle bm{zeta}rangle $ corresponds to the inverse of the grain size, while the ${bm{b}}$ parameter controls the width of the wave number distribution and is related to the clustering property. The bicontinuous model is used to generate the microstructures of snow by computer, and Maxwell equations are solved numerically for each sample of computer-generated structure to calculate the extinction coefficient and the phase matrix. Other geometric descriptors of the bicontinuous medium include correlation functions and specific surface areas, both of which can be calculated from the parameters $langle bm{zeta }rangle $ and ${bm{b}}$ . In making comparisons, we use ground measurements of specific surface area, grain size, densities, and layering of snow cover as input for the theoretical models. The geometric properties and the scattering properties of the bicontinuous model are also compared with past models. In making the mult- frequency comparisons, we use the same physical parameters of all three frequencies: 1) X band; 2) Ku bands of 13.3 GHz; and 3) 16.7 GHz. It is emphasized that the DMRT models provide frequency, size, and angular dependence that depart from the classical model of Rayleigh scattering and are in better agreement with experimental observations.
      PubDate: Sept. 2014
      Issue No: Vol. 7, No. 9 (2014)
       
  • Resilience Aspects in the Sensor Web Infrastructure for Natural Disaster
           Monitoring and Risk Assessment Based on Earth Observation Data
    • Authors: Kussul; N.;Skakun, S.;Shelestov, A.Y.;Kussul, O.;Yailymov, B.;
      Pages: 3826 - 3832
      Abstract: This paper focuses on enabling resilience of the Sensor Web system for disaster monitoring and risk assessment that is based on the use of Earth Observation (EO) data. Resilience is the ability of the system to maintain trustworthy service delivery in spite of changes. Resilience of the EO infrastructure becomes an extremely important issue, especially in the disaster management domain. We propose to enable resilience through: 1) the use of grid resources and services to meet high performance computing requirements; and 2) assessment of resource reputation to select reliable resources. Grid services enable redundancy through integration of heterogeneous resources administrated by geographically distributed organizations. The same services could be provided by different organizations within the infrastructure, and could be of different levels of quality. Therefore, we propose to incorporate a utility-based reputation model to assess reliability of the resources. The proposed approach is implemented within the Namibia Sensor Web Pilot Project that was created as a testbed for evaluating and prototyping key technologies for rapid acquisition and distribution of data products for decision support systems to monitor floods.
      PubDate: Sept. 2014
      Issue No: Vol. 7, No. 9 (2014)
       
  • SMOS and Aquarius Radiometers: Inter-Comparison Over Selected Targets
    • Authors: Pablos; M.;Piles, M.;Gonzalez-Gambau, V.;Vall-llossera, M.;Camps, A.;Martinez, J.;
      Pages: 3833 - 3844
      Abstract: Passive microwave remote sensing at L-band is considered to be the most suitable technique to measure soil moisture and ocean salinity. These two variables are needed as inputs of predictive models, to improve climate and weather forecast, and to increase our knowledge of the water cycle. Nowadays, there are two space missions providing frequent and global observations of moisture and salinity of the Earth’s surface with L-band radiometers on-board. The first one is the ESA’s SMOS satellite, launched on November 2, 2009, which carries a two-dimensional, multi-angular, and full-polarimetric synthetic aperture radiometer. The second one is the NASA/CONAE’s Aquarius/SAC-D mission, launched on June 10, 2011, which includes three beam push-broom real aperture radiometers. The objective of this work is to compare SMOS and Aquarius brightness temperatures and verify the continuity and consistency of the data over the entire dynamic range of observations. This is paramount if data from both radiometers are used for any long term enviromental, meteorological, hydrological, or climatological studies. The inter-comparison approach proposed is based on the study of 1 year of measurements over key target regions selected as representative of land, ice, and sea surfaces. The level of linearity, the correlation, and the differences between the observations of the two radiometers are analyzed. Results show a higher linear correlation between SMOS and Aquarius brightness temperatures over land than over sea. A seasonal effect and spatial inhomogeneities are observed over ice, at the Dome-C region. In all targets, better agreement is found in horizontal than in vertical polarization. Also, the correlation is higher at higher incidence angles. These differences indicate that there is a non-linear effect between the two instruments, not only a bias.
      PubDate: Sept. 2014
      Issue No: Vol. 7, No. 9 (2014)
       
  • A Downscaling Approach for SMOS Land Observations: Evaluation of
           High-Resolution Soil Moisture Maps Over the Iberian Peninsula
    • Authors: Piles; M.;Sanchez, N.;Vall-llossera, M.;Camps, A.;Martinez-Fernandez, J.;Martinez, J.;Gonzalez-Gambau, V.;
      Pages: 3845 - 3857
      Abstract: The ESA’s Soil Moisture and Ocean Salinity (SMOS) mission is the first satellite devoted to measure the Earth’s surface soil moisture. It has a spatial resolution of ${bf sim! 40}$  km and a 3-day revisit. In this paper, a downscaling algorithm is presented as a new ability to obtain multiresolution soil moisture estimates from SMOS using visible-to-infrared remotely sensed observations. This algorithm is applied to combine 2 years of SMOS and MODIS Terra/Aqua data over the Iberian Peninsula into fine-scale (1 km) soil moisture estimates. Disaggregated soil moisture maps are compared to 0–5 cm ground-based measurements from the REMEDHUS network. Three matching strategies are employed: 1) a comparison at 40 km spatial resolution is undertaken to ensure SMOS sensitivity is preserved in the downscaled maps; 2) the spatio-temporal correlation of downscaled maps is analyzed through comparison with point-scale observations; and 3) high-resolution maps and ground-based observations are aggregated per land-use to identify spatial patterns related with vegetation activity and soil type. Results show that the downscaling method improves the spatial representation of SMOS coarse soil moisture estimates while maintaining temporal correlation and root mean squared differences with ground-based measurements. The dynamic range of in situ soil moisture measurements is reproduced in the high-resolution maps, including stations with different mean soil wetness conditions. Downscaled maps capture the soil moisture dynamics of general land uses, with the exception of irrigated crops. This evaluation study supports the use of this downscaling approach to enhance the spatial resolution of SMOS observations over semi-arid regions such as the Iberian Peninsula.
      PubDate: Sept. 2014
      Issue No: Vol. 7, No. 9 (2014)
       
  • SMOS Retrieval Results Over Forests: Comparisons With Independent
           Measurements
    • Authors: Rahmoune; R.;Ferrazzoli, P.;Singh, Y.K.;Kerr, Y.H.;Richaume, P.;Al Bitar, A.;
      Pages: 3858 - 3866
      Abstract: This paper shows results obtained by using SMOS Level 2 retrieval algorithm, run at prototype stage, over forests. For each SMOS pixel, the algorithm estimates the soil moisture (SM) and the vegetation optical depth ( ${taub}$ ). Average ${taub}$ values retrieved in 4 days of July 2011 over forest pixels are shown and compared against forest height estimated by GLAS Lidar on board ICEsat satellite. Results of the comparison show a significantly increasing trend of ${taub}$ with respect to forest height. For each 1-m interval of forest height estimated by Lidar, the standard deviation of optical depth is slightly higher than 0.1. The analysis is made again considering forest ${taub}$ retrieved in 4 days of February, May, and November 2011, and it is observed that seasonal effects over optical depth are low. As an insight, it is shown that the increasing trend is still observed after subdividing world forests into Coniferous, Deciduous Broadleaf, and Evergreen Broadleaf. Comparisons with independent information about biomass are also shown at regional level for the U.S. The increasing trend is still observed, but with a reduced range of values. For SM, 14 nodes of the SCAN/SNOTEL network in the U.S. are considered. Over 2 years of data, retrieved values of SM are compared against ground measurements. Obtained values of correlation coefficient, rms error, and bias are reported.
      PubDate: Sept. 2014
      Issue No: Vol. 7, No. 9 (2014)
       
  • Assimilation of SMOS Soil Moisture for Quantifying Drought Impacts on Crop
           Yield in Agricultural Regions
    • Authors: Chakrabarti; S.;Bongiovanni, T.;Judge, J.;Zotarelli, L.;Bayer, C.;
      Pages: 3867 - 3879
      Abstract: This study investigates the effects of agricultural drought on crop yields, through integration of crop growth models and remote sensing observations. The soil moisture (SM) product from SM and Ocean Salinity (SMOS) mission obtained at 25 km was downscaled to a spatial resolution of 1 km, compatible with the crop models. The downscaling algorithm is based upon information theoretic learning and uses data-driven probabilistic relationships between high-resolution remotely sensed products that are sensitive to SM and in situ SM. The downscaled SM values are assimilated in the crop model using an Ensemble Kalman filter-based augmented state-vector technique that estimates states and parameters simultaneously. The downscaling and assimilation framework are implemented for predominantly agricultural region of the lower La-Plata Basin (LPB) in Brazil during two growing seasons. This rain-fed region was affected by agricultural drought in the second season, indicated by markedly lower precipitation compared to the first growing season. The downscaled SM was compared with the in situ SM at a validation site and the root mean square difference (RMSD) was ${bf {0.045~{hbox {m}}^3/{hbox {m}}^3}}$ . The crop yields estimated by the downscaling–assimilation framework were compared with those provided by the Companhia Nacional de Asastecimento (CONAB) and Instituto Brasileiro de Geografia e Estatistica (IBGE). The assimilated yields are improved during both seasons with increased improvement during the second season that was affected by agricultural drought. The differences between the assimilated and observed crop yields were 16.8% during the first growing season and 4.37% during the second season.
      PubDate: Sept. 2014
      Issue No: Vol. 7, No. 9 (2014)
       
  • How do Spatial Scale, Noise, and Reference Data affect Empirical Estimates
           of Error in ASAR-Derived 1 km Resolution Soil Moisture?
    • Authors: Doubkova; M.;Dostalova, A.;van Dijk, A.I.J.M.;Bloschl, G.;Wagner, W.;Fernandez-Prieto, D.;
      Pages: 3880 - 3891
      Abstract: The performance of the advanced synthetic aperture radar (ASAR) global mode (GM) surface soil moisture (SSM) data was studied over Australia by means of two widely used bivariate measures, the root-mean-square error (RMSE) and the Pearson correlation coefficient ( $mbi R$ ). By computing RMSE and $mbi R$ at multiple spatial scales and for different data combinations, we assessed how, and at which scales, the spatial sampling error, noise, and the choice of the reference data impact on RMSE and $mbi R$ . The results reveal large changes in RMSE and $mbi R$ with continental average values of 8% and 18% for the RMSE of relative soil moisture saturation and between 0.4 and 0.7 for $mbi R$ depending on the spatial scale of aggregation and the choice of reference data. The combined effect of noise and spatial sampling error accounted for a 79% RMSE increase at 1 km and predominated over the error due to the choise of the reference data also at 5 km scale. The effect of noise on RMSE strongly diminished at spatial scales ${bf {geq! 2 nbsp {hbox {km}}}}$ . By contrast, the impact of uncertainties in the reference data was larger on $mbi R$ than on RMSE. This highlights the better potential of $mbi R$ to estimate the benefit of observations prior to data assimilation. Based on our results, it is further suggested that a potential way for an improved ASAR GM SSM error assessment is to: 1) aggregate the- data to ${bf {geq! 2nbsp {hbox {km}}}}$ resolution to minimize the noise; 2) subtract the spatial sampling error within the coarse resolution footprint; and 3) remove the reference uncertainty using advanced techniques such as triple collocation.
      PubDate: Sept. 2014
      Issue No: Vol. 7, No. 9 (2014)
       
  • Using a Remote Sensing Driven Model to Analyze Effect of Land Use on Soil
           Moisture in the Weihe River Basin, China
    • Authors: Wang; Y.;Wang, S.;Yang, S.;Zhang, L.;Zeng, H.;Zheng, D.;
      Pages: 3892 - 3902
      Abstract: In-depth study of the soil moisture mechanisms and understanding of the soil moisture transport law has an important practical significance for regional water resources management and the challenge of the water resources scarcity. Using traditional methods of soil moisture monitoring, deep soil layers can be monitored, but continuous monitoring of soil moisture at the regional level cannot be achieved. Although remote sensing simulation models can meet regional scale needs, these models are confined to the surface soil layer, and research on deep soil moisture inversion is still lacking. This paper focuses on these two issues, and investigates a remote sensing-driven soil moisture monitoring model for the Weihe River Basin. Considering water resource management needs in the Weihe River Basin, we improved the structure of the soil moisture balance model and optimized model parameters to build the remote sensing driven soil moisture balance model (RS-SWBM). Based on soil moisture modeling, the effect of vegetation on soil moisture in the Weihe River Basin was analyzed. The RS-SWBM developed for the Weihe River Basin was validated with observational data and Global Land Data Assimilation System (GLDAS) soil moisture data products. Based on the correlation analysis, correlation coefficients were all above 0.80, reflecting the effectiveness of the model. The effects of different vegetation types on soil moisture dynamics and consumption efficiency were analyzed. The results indicated that different vegetation types experienced different seasonal variations, vertical patterns, and consumption efficiencies, with strong correlations existing between these parameters and land use as well as precipitation.
      PubDate: Sept. 2014
      Issue No: Vol. 7, No. 9 (2014)
       
  • River Water Level Prediction Using Passive Microwave Signatures—A
           Case Study: The Bermejo Basin
    • Authors: Vittucci; C.;Guerriero, L.;Ferrazzoli, P.;Rahmoune, R.;Barraza, V.;Grings, F.;
      Pages: 3903 - 3914
      Abstract: The aim of this work is to investigate the exploitation of radiometric acquisitions from satellite sensors at different microwave frequencies in view of the prediction of river water level. A case study has been identified in the Bermejo basin, in northern Argentina. This river is seasonally affected by severe flooding events in the lower part, mostly due to rains occurring in the upper basin, that produce sediment loadings flushing down along the lower basin thus changing the watercourse. While the effectiveness of microwave radiometry at Ka band for flood monitoring is consolidated in the literature, this study also considers X and C bands (provided by the Advanced Microwave Scanning Radiometer (AMSR) series together with the higher frequency) and highlights the better sensitivity to soil conditions of L band data (made recently available, thanks to SMOS) over moderately and densely vegetated areas. This study confirms, first, the well-known capability of passive microwave remote sensing instruments to record brightness temperature variations due to rainfall and floods occurred near river edges under different seasonal conditions. For this purpose, a multifrequency comparative analysis is conducted. Second, it investigates whether these properties can be exploited for flood forecasting: a model which directly links the daily satellite measurements to the river water level has been tested, considering 1- to 7-day forecast horizons. The results show that forecasting models can take advantage of the sensitivity of low frequencies to soil moisture conditions in order to predict flood peaks, despite the instrument’s low resolutions.
      PubDate: Sept. 2014
      Issue No: Vol. 7, No. 9 (2014)
       
  • Evaluation of TRMM Multisatellite Precipitation Analysis (TMPA) Products
           and Their Potential Hydrological Application at an Arid and Semiarid Basin
           in China
    • Authors: Peng; B.;Shi, J.;Ni-Meister, W.;Zhao, T.;Ji, D.;
      Pages: 3915 - 3930
      Abstract: Satellite-based precipitation products are promising data sources for the hydrometeorological community. However, many of these products lack quantitative estimates of their uncertainty. In this paper, we present a thorough evaluation study of the state-of-the-art satellite-based precipitation product: Tropical Rainfall Measurement Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) research product (TRMM 3B42) in the middle and upper reaches of Heihe River Basin. The study area locates in arid and semiarid regions in the northwest of China. Both version 6 (V6) and the newly released version 7 (V7) TRMM 3B42 research products are evaluated through statistical analysis and hydrological modeling in this study. We find that the performance of daily TMPA products is climatology-dependent. For the study basin, TMPA products generally perform better in wetter than in drier climatology conditions. High False Alarming Ratio (FAR) and positive bias occur in drier areas, whereas TMPA products display negative bias in wetter regions. TMPA products perform worse in winter when snowfall occurs than in nonsnow seasons. The new V7 TMPA products perform better than V6 on both daily and monthly scales. Hydrological modeling results show that TMPA products are promising in streamflow simulation at ungauged basins in arid and semiarid regions, especially the new V7 whose integrated performance is comparable with that of gauge-based precipitation estimate. When model parameters’ sensitivity in forcing precipitation is considered by model recalibration, performances of both two versions of TMPA products are improved, especially for the peak flow simulation in summer. This study can serve as a reference of uncertainty estimates of the TMPA research time precipitation products in hydrometeorological applications at a watershed scale.
      PubDate: Sept. 2014
      Issue No: Vol. 7, No. 9 (2014)
       
  • Evaluation of Precipitation Estimates by at-Launch Codes of GPM/DPR
           Algorithms Using Synthetic Data from TRMM/PR Observations
    • Authors: Kubota; T.;Yoshida, N.;Urita, S.;Iguchi, T.;Seto, S.;Meneghini, R.;Awaka, J.;Hanado, H.;Kida, S.;Oki, R.;
      Pages: 3931 - 3944
      Abstract: The Global Precipitation Measurement (GPM) Core Observatory will carry a Dual-frequency Precipitation Radar (DPR) consisting of a Ku-band precipitation radar (KuPR) and a Ka-band precipitation radar (KaPR). In this study, “at-launch” codes of DPR precipitation algorithms, which will be used in GPM ground systems at launch, were evaluated using synthetic data based upon the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) data. Results from the codes (Version 4.20131010) of the KuPR-only, KaPR-only, and DPR algorithms were compared with “true values” calculated based upon drop size distributions assumed in the synthetic data and standard results from the TRMM algorithms at an altitude of 2 km over the ocean. The results indicate that the total precipitation amounts during April 2011 from the KuPR and DPR algorithms are similar to the true values, whereas the estimates from the KaPR data are underestimated. Moreover, the DPR estimates yielded smaller precipitation rates for rates less than about 10 mm/h and greater precipitation rates above 10 mm/h. Underestimation of the KaPR estimates was analyzed in terms of measured radar reflectivity ( ${bf Z}_{bf m}$ ) of the KaPR at an altitude of 2 km. The underestimation of the KaPR data was most pronounced during strong precipitation events of ${bf Z}_{bf m} lt {bf 18}~{bf dBZ}$ (high attenuation cases) over heavy precipitation areas in the Tropics, whereas the underestimation was less pronounced when the ${bf Z}_{bf m}gt 26~{bf dBZ}$ (moderate attenuation cases). The results suggest that the underestimation is caused by a problem in the attenuation correction method, which was verified by the improved codes.
      PubDate: Sept. 2014
      Issue No: Vol. 7, No. 9 (2014)
       
  • A New FAPAR Analytical Model Based on the Law of Energy Conservation: A
           Case Study in China
    • Authors: Fan; W.;Liu, Y.;Xu, X.;Chen, G.;Zhang, B.;
      Pages: 3945 - 3955
      Abstract: The fraction of absorbed photosynthetically active radiation (FAPAR) characterizes the energy-absorption ability of the vegetation canopy. It is a critical input to many land-surface models such as crop growth models, net primary productivity models, and climate models. There is a great need for FAPAR products derived from remote-sensing data. The objective of this research is to develop a new instantaneous quantitative FAPAR model based on the law of energy conservation and the concept of recollision probability ( ${mbi p}$ ). Using the ray-tracing method, the FAPAR-P model separates direct energy absorption by the canopy from energy absorption caused by multiple scattering between the soil and the canopy. Direct sunlight and diffuse skylight are also considered. This model has a clear physical meaning and can be applied to continuous and discrete vegetation. The model was validated by Monte Carlo (MC) simulation and field measurements in the Heihe River basin, China, which proved its reliability for FAPAR calculations.
      PubDate: Sept. 2014
      Issue No: Vol. 7, No. 9 (2014)
       
  • Accelerating Time-Domain SAR Raw Data Simulation for Large Areas Using
           Multi-GPUs
    • Authors: Zhang; F.;Hu, C.;Li, W.;Hu, W.;Li, H.;
      Pages: 3956 - 3966
      Abstract: Large areas synthetic aperture radar (SAR) raw data simulation, which contains various actual system errors, is one of the main tasks in SAR system design and development. The growth of swath and resolution results in a significant increase in data volume as well as the simulation time. This poses a challenge for SAR raw data simulation considering system errors. For recent years, the graphics processing unit (GPU)-based scalable parallel method has been applied to raw data simulation. In this paper, we investigate time-domain SAR raw data simulation for large areas on multi-GPUs architecture, which can not only simulate raw data of large areas by task partitioning and scheduling, but also improve the efficiency of current GPU-based algorithm by access conflict optimization and fine-grained parallel pipeline. Experimental results show that the proposed multi-GPUs-based raw data simulation method achieves a ${bf 5 times }$ speedup compared to the current GPU-based method on single GPU, and a significant over ${bf 500 times}$ speedup on 4 GPUs compared to traditional CPU-based simulation. These results verify that multi-GPUs-based time-domain method is very suitable for large data volume raw data simulation, especially for the case of wide swath and high resolution.
      PubDate: Sept. 2014
      Issue No: Vol. 7, No. 9 (2014)
       
  • Filtering of Azimuth Ambiguity in Stripmap Synthetic Aperture Radar Images
    • Authors: Di Martino; G.;Iodice, A.;Riccio, D.;Ruello, G.;
      Pages: 3967 - 3978
      Abstract: Due to the specific characteristics of the SAR system, peculiar artifacts can appear on SAR images. In particular, finite pulse repetition frequency (PRF) and nonideal antenna pattern give rise to azimuth ambiguity, with the possible presence of “ghosts” on the image. They are due to the replica of strong targets located outside of the antenna main beam, superposed onto low intensity areas of the imaged scene. In this paper, we propose a method for the filtering of azimuth ambiguities on stripmap SAR images, that we name “asymmetric mapping and selective filtering” (AM&SF) method. Our framework is based on the theory of selective filtering and on a two-step procedure. In the first step, two asymmetric filters are used to suppress ambiguities due to each sidelobe of the antenna pattern, and the ratios between the original and filtered images are used to produce two maps of the ambiguity-affected areas (one for each sidelobe). In the second step, these maps are used to produce a final image in which only the areas affected by the ambiguities are replaced by their filtered (via the proper of the two filters) versions. The proposed method can be employed in situations in which similar approaches fail, and it has a smaller computational burden. The framework is positively tested on TerraSAR-X and COSMO/SkyMed SAR images of different marine scenes.
      PubDate: Sept. 2014
      Issue No: Vol. 7, No. 9 (2014)
       
  • Susceptibility Assessment of Landslides Triggered by the Lushan
           Earthquake, April 20, 2013, China
    • Authors: Niu; R.;Wu, X.;Yao, D.;Peng, L.;Ai, L.;Peng, J.;
      Pages: 3979 - 3992
      Abstract: The Lushan earthquake ( ${bf Ms = 7.0}$ ; epicenter located at $bf{30^circ 17^prime N}$ , $bf{102^circ 57^{prime} E}$ ) occurred on April 20, 2013 and had a focal depth of 12.3 km. The earthquake was triggered by the reactivation of the Longmenshan Fault in Lushan County, Sichuan Province, China. This earthquake caused massive landslides that resulted in tragic loss of life and economic devastation. Strong earthquakes are among the prime triggering factors of landslides. The zone of highest seismic intensity for this earthquake was selected as a case study to assess the susceptibility to earthquake-induced landslides. Visual interpretation of color aerial photographs with 0.4- and 0.6-m spatial resolution and extensive field surveys provided a detailed landslide inventory map that included 226 landslides. Nine primary landslide-related factors were selected as predictor variables, including elevation, slope, aspect, curvature classification, distance from drainages, slope structure, lithology, distance from faults, and peak ground acceleration. The support vector machine (SVM) is a popular learning procedure that is based on statistical learning theory and utilizes a kernel function to map data from the original feature space to a high-dimensional space. Using an SVM, a nonlinear landslide system can be converted into a linear landslide system. Two parameters C and $mbi {sigma }$ must be carefully predetermined to establish an efficient SVM. Therefore, a genetic algorithm (GA) was adopted to optimize the parameters of the SVM. The proposed GA-SVM model with the highest predictive accuracy and generalization ability was trained and then used to predict landslide susceptibility. The analytic- l results were validated by comparing them with known landslides using a success rate curve and classification accuracy. The GA-SVM model has an area ratio of 0.9586 and a kappa coefficient of 0.9575 and outperforms the SVM. Approximately, 94.97% of the landslides lie in the very-high-susceptibility region, 2.17% of the landslides lie in the high-susceptibility region, 1.13% of the landslides lie in the moderate-susceptibility region, and 1.73% of the landslides lie in the low- and very-low-susceptibility regions. The experimental results demonstrate that the GA-SVM model provides the best predictive accuracy. The model can effectively assess landslide susceptibility and provides a novel method for landslide prediction.
      PubDate: Sept. 2014
      Issue No: Vol. 7, No. 9 (2014)
       
 
 
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