Speckle noise and soil heterogeneities as error sources in a bayesian soil moisture retrieval scheme for SAR data
Soil moisture retrieval from SAR images is always affected by speckle noise and uncertainties associated to soil parameters, which impact negatively on the accuracy of soil moisture estimates. In this paper a soil moisture Bayesian estimator from polarimetric SAR images is proposed to address these issues. This estimator is based on a set of statistical distributions derived for the polarimetric soil backscattering coefficients, which naturally includes models for the soil scattering, the speckle and the soil spatial heterogeneity. As a natural advantage of the Bayesian approach, prior information about soil condition can be easily included, enhancing the performance of the retrieval. The Oh's model is used as scattering model, although it presents a limiting range of validity for the retrieval of soil moisture. After fully stating the mathematical modeling, numerical simulations are presented. First, traditional minimization-based retrieval is investigated. Then, it is compared with the Bayesian retrieval scheme. The results indicate that the Bayesian model enlarges the validity region of the minimization-based procedure. Moreover, as speckle effects are reduced by multilooking, Bayesian retrieval approaches the minimization-based retrieval. On the other hand, when speckle effects are large, an improvement in the accuracy of the retrieval is achieved by using a precise prior. The proposed algorithm can be applied to investigate which are the optimum parameters regarding multilooking process and prior information required to perform a precise retrieval in a given soil condition.
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KOHA-OAI-AGRO:46853 |
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koha |
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UBA FA |
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Koha |
country |
Argentina |
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AR |
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Bibliográfico |
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En linea En linea |
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cat-ceiba |
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biblioteca |
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America del Sur |
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Biblioteca Central FAUBA |
language |
eng |
topic |
BAYESIAN METHODS INVERSE PROBLEMS RADAR APPLICATIONS SOIL MOISTURE SYNTHETIC APERTURE RADAR BACKSCATTERING COEFFICIENTS BAYESIAN APPROACHES BAYESIAN ESTIMATORS BAYESIAN METHODS BAYESIAN MODEL BAYESIAN RETRIEVAL ERROR SOURCES MATHEMATICAL MODELING MULTILOOKING OPTIMUM PARAMETERS POLARIMETRIC SAR PRIOR INFORMATION RADAR APPLICATIONS SAR DATA SAR IMAGES SCATTERING MODEL SOIL CONDITIONS SOIL HETEROGENEITY SOIL MOISTURE RETRIEVALS SOIL PARAMETERS SPATIAL HETEROGENEITY SPECKLE EFFECTS SPECKLE NOISE STATISTICAL DISTRIBUTION ALGORITHMS BAYESIAN NETWORKS COMPUTER SIMULATION ESTIMATION INVERSE PROBLEMS POLARIMETERS SOIL MOISTURE SPECKLE SYNTHETIC APERTURE RADAR UNCERTAINTY ANALYSIS GEOLOGIC MODELS ACCURACY ASSESSMENT ALGORITHM BACKSCATTER BAYESIAN ANALYSIS ERROR ANALYSIS HETEROGENEITY INVERSE PROBLEM NOISE NUMERICAL MODEL STATISTICAL DISTRIBUTION SYNTHETIC APERTURE RADAR BAYESIAN METHODS INVERSE PROBLEMS RADAR APPLICATIONS SOIL MOISTURE SYNTHETIC APERTURE RADAR BACKSCATTERING COEFFICIENTS BAYESIAN APPROACHES BAYESIAN ESTIMATORS BAYESIAN METHODS BAYESIAN MODEL BAYESIAN RETRIEVAL ERROR SOURCES MATHEMATICAL MODELING MULTILOOKING OPTIMUM PARAMETERS POLARIMETRIC SAR PRIOR INFORMATION RADAR APPLICATIONS SAR DATA SAR IMAGES SCATTERING MODEL SOIL CONDITIONS SOIL HETEROGENEITY SOIL MOISTURE RETRIEVALS SOIL PARAMETERS SPATIAL HETEROGENEITY SPECKLE EFFECTS SPECKLE NOISE STATISTICAL DISTRIBUTION ALGORITHMS BAYESIAN NETWORKS COMPUTER SIMULATION ESTIMATION INVERSE PROBLEMS POLARIMETERS SOIL MOISTURE SPECKLE SYNTHETIC APERTURE RADAR UNCERTAINTY ANALYSIS GEOLOGIC MODELS ACCURACY ASSESSMENT ALGORITHM BACKSCATTER BAYESIAN ANALYSIS ERROR ANALYSIS HETEROGENEITY INVERSE PROBLEM NOISE NUMERICAL MODEL STATISTICAL DISTRIBUTION SYNTHETIC APERTURE RADAR |
spellingShingle |
BAYESIAN METHODS INVERSE PROBLEMS RADAR APPLICATIONS SOIL MOISTURE SYNTHETIC APERTURE RADAR BACKSCATTERING COEFFICIENTS BAYESIAN APPROACHES BAYESIAN ESTIMATORS BAYESIAN METHODS BAYESIAN MODEL BAYESIAN RETRIEVAL ERROR SOURCES MATHEMATICAL MODELING MULTILOOKING OPTIMUM PARAMETERS POLARIMETRIC SAR PRIOR INFORMATION RADAR APPLICATIONS SAR DATA SAR IMAGES SCATTERING MODEL SOIL CONDITIONS SOIL HETEROGENEITY SOIL MOISTURE RETRIEVALS SOIL PARAMETERS SPATIAL HETEROGENEITY SPECKLE EFFECTS SPECKLE NOISE STATISTICAL DISTRIBUTION ALGORITHMS BAYESIAN NETWORKS COMPUTER SIMULATION ESTIMATION INVERSE PROBLEMS POLARIMETERS SOIL MOISTURE SPECKLE SYNTHETIC APERTURE RADAR UNCERTAINTY ANALYSIS GEOLOGIC MODELS ACCURACY ASSESSMENT ALGORITHM BACKSCATTER BAYESIAN ANALYSIS ERROR ANALYSIS HETEROGENEITY INVERSE PROBLEM NOISE NUMERICAL MODEL STATISTICAL DISTRIBUTION SYNTHETIC APERTURE RADAR BAYESIAN METHODS INVERSE PROBLEMS RADAR APPLICATIONS SOIL MOISTURE SYNTHETIC APERTURE RADAR BACKSCATTERING COEFFICIENTS BAYESIAN APPROACHES BAYESIAN ESTIMATORS BAYESIAN METHODS BAYESIAN MODEL BAYESIAN RETRIEVAL ERROR SOURCES MATHEMATICAL MODELING MULTILOOKING OPTIMUM PARAMETERS POLARIMETRIC SAR PRIOR INFORMATION RADAR APPLICATIONS SAR DATA SAR IMAGES SCATTERING MODEL SOIL CONDITIONS SOIL HETEROGENEITY SOIL MOISTURE RETRIEVALS SOIL PARAMETERS SPATIAL HETEROGENEITY SPECKLE EFFECTS SPECKLE NOISE STATISTICAL DISTRIBUTION ALGORITHMS BAYESIAN NETWORKS COMPUTER SIMULATION ESTIMATION INVERSE PROBLEMS POLARIMETERS SOIL MOISTURE SPECKLE SYNTHETIC APERTURE RADAR UNCERTAINTY ANALYSIS GEOLOGIC MODELS ACCURACY ASSESSMENT ALGORITHM BACKSCATTER BAYESIAN ANALYSIS ERROR ANALYSIS HETEROGENEITY INVERSE PROBLEM NOISE NUMERICAL MODEL STATISTICAL DISTRIBUTION SYNTHETIC APERTURE RADAR Barber, Matías Grings, Francisco Perna, Pablo Piscitelli, Marcela Maas, Martin Bruscantini, Cintia Jacobo Berlles, Julio Karszenbaum, Haydeé Speckle noise and soil heterogeneities as error sources in a bayesian soil moisture retrieval scheme for SAR data |
description |
Soil moisture retrieval from SAR images is always affected by speckle noise and uncertainties associated to soil parameters, which impact negatively on the accuracy of soil moisture estimates. In this paper a soil moisture Bayesian estimator from polarimetric SAR images is proposed to address these issues. This estimator is based on a set of statistical distributions derived for the polarimetric soil backscattering coefficients, which naturally includes models for the soil scattering, the speckle and the soil spatial heterogeneity. As a natural advantage of the Bayesian approach, prior information about soil condition can be easily included, enhancing the performance of the retrieval. The Oh's model is used as scattering model, although it presents a limiting range of validity for the retrieval of soil moisture. After fully stating the mathematical modeling, numerical simulations are presented. First, traditional minimization-based retrieval is investigated. Then, it is compared with the Bayesian retrieval scheme. The results indicate that the Bayesian model enlarges the validity region of the minimization-based procedure. Moreover, as speckle effects are reduced by multilooking, Bayesian retrieval approaches the minimization-based retrieval. On the other hand, when speckle effects are large, an improvement in the accuracy of the retrieval is achieved by using a precise prior. The proposed algorithm can be applied to investigate which are the optimum parameters regarding multilooking process and prior information required to perform a precise retrieval in a given soil condition. |
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Texto |
topic_facet |
BAYESIAN METHODS INVERSE PROBLEMS RADAR APPLICATIONS SOIL MOISTURE SYNTHETIC APERTURE RADAR BACKSCATTERING COEFFICIENTS BAYESIAN APPROACHES BAYESIAN ESTIMATORS BAYESIAN METHODS BAYESIAN MODEL BAYESIAN RETRIEVAL ERROR SOURCES MATHEMATICAL MODELING MULTILOOKING OPTIMUM PARAMETERS POLARIMETRIC SAR PRIOR INFORMATION RADAR APPLICATIONS SAR DATA SAR IMAGES SCATTERING MODEL SOIL CONDITIONS SOIL HETEROGENEITY SOIL MOISTURE RETRIEVALS SOIL PARAMETERS SPATIAL HETEROGENEITY SPECKLE EFFECTS SPECKLE NOISE STATISTICAL DISTRIBUTION ALGORITHMS BAYESIAN NETWORKS COMPUTER SIMULATION ESTIMATION INVERSE PROBLEMS POLARIMETERS SOIL MOISTURE SPECKLE SYNTHETIC APERTURE RADAR UNCERTAINTY ANALYSIS GEOLOGIC MODELS ACCURACY ASSESSMENT ALGORITHM BACKSCATTER BAYESIAN ANALYSIS ERROR ANALYSIS HETEROGENEITY INVERSE PROBLEM NOISE NUMERICAL MODEL STATISTICAL DISTRIBUTION SYNTHETIC APERTURE RADAR |
author |
Barber, Matías Grings, Francisco Perna, Pablo Piscitelli, Marcela Maas, Martin Bruscantini, Cintia Jacobo Berlles, Julio Karszenbaum, Haydeé |
author_facet |
Barber, Matías Grings, Francisco Perna, Pablo Piscitelli, Marcela Maas, Martin Bruscantini, Cintia Jacobo Berlles, Julio Karszenbaum, Haydeé |
author_sort |
Barber, Matías |
title |
Speckle noise and soil heterogeneities as error sources in a bayesian soil moisture retrieval scheme for SAR data |
title_short |
Speckle noise and soil heterogeneities as error sources in a bayesian soil moisture retrieval scheme for SAR data |
title_full |
Speckle noise and soil heterogeneities as error sources in a bayesian soil moisture retrieval scheme for SAR data |
title_fullStr |
Speckle noise and soil heterogeneities as error sources in a bayesian soil moisture retrieval scheme for SAR data |
title_full_unstemmed |
Speckle noise and soil heterogeneities as error sources in a bayesian soil moisture retrieval scheme for SAR data |
title_sort |
speckle noise and soil heterogeneities as error sources in a bayesian soil moisture retrieval scheme for sar data |
url |
http://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber=46853 |
work_keys_str_mv |
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_version_ |
1756046698737565696 |
spelling |
KOHA-OAI-AGRO:468532021-07-06T16:56:34Zhttp://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber=46853AAGSpeckle noise and soil heterogeneities as error sources in a bayesian soil moisture retrieval scheme for SAR dataBarber, MatíasGrings, FranciscoPerna, PabloPiscitelli, MarcelaMaas, MartinBruscantini, CintiaJacobo Berlles, JulioKarszenbaum, Haydeétextengapplication/pdfSoil moisture retrieval from SAR images is always affected by speckle noise and uncertainties associated to soil parameters, which impact negatively on the accuracy of soil moisture estimates. In this paper a soil moisture Bayesian estimator from polarimetric SAR images is proposed to address these issues. This estimator is based on a set of statistical distributions derived for the polarimetric soil backscattering coefficients, which naturally includes models for the soil scattering, the speckle and the soil spatial heterogeneity. As a natural advantage of the Bayesian approach, prior information about soil condition can be easily included, enhancing the performance of the retrieval. The Oh's model is used as scattering model, although it presents a limiting range of validity for the retrieval of soil moisture. After fully stating the mathematical modeling, numerical simulations are presented. First, traditional minimization-based retrieval is investigated. Then, it is compared with the Bayesian retrieval scheme. The results indicate that the Bayesian model enlarges the validity region of the minimization-based procedure. Moreover, as speckle effects are reduced by multilooking, Bayesian retrieval approaches the minimization-based retrieval. On the other hand, when speckle effects are large, an improvement in the accuracy of the retrieval is achieved by using a precise prior. The proposed algorithm can be applied to investigate which are the optimum parameters regarding multilooking process and prior information required to perform a precise retrieval in a given soil condition.Soil moisture retrieval from SAR images is always affected by speckle noise and uncertainties associated to soil parameters, which impact negatively on the accuracy of soil moisture estimates. In this paper a soil moisture Bayesian estimator from polarimetric SAR images is proposed to address these issues. This estimator is based on a set of statistical distributions derived for the polarimetric soil backscattering coefficients, which naturally includes models for the soil scattering, the speckle and the soil spatial heterogeneity. As a natural advantage of the Bayesian approach, prior information about soil condition can be easily included, enhancing the performance of the retrieval. The Oh's model is used as scattering model, although it presents a limiting range of validity for the retrieval of soil moisture. After fully stating the mathematical modeling, numerical simulations are presented. First, traditional minimization-based retrieval is investigated. Then, it is compared with the Bayesian retrieval scheme. The results indicate that the Bayesian model enlarges the validity region of the minimization-based procedure. Moreover, as speckle effects are reduced by multilooking, Bayesian retrieval approaches the minimization-based retrieval. On the other hand, when speckle effects are large, an improvement in the accuracy of the retrieval is achieved by using a precise prior. The proposed algorithm can be applied to investigate which are the optimum parameters regarding multilooking process and prior information required to perform a precise retrieval in a given soil condition.BAYESIAN METHODSINVERSE PROBLEMSRADAR APPLICATIONSSOIL MOISTURESYNTHETIC APERTURE RADARBACKSCATTERING COEFFICIENTSBAYESIAN APPROACHESBAYESIAN ESTIMATORSBAYESIAN METHODSBAYESIAN MODELBAYESIAN RETRIEVALERROR SOURCESMATHEMATICAL MODELINGMULTILOOKINGOPTIMUM PARAMETERSPOLARIMETRIC SARPRIOR INFORMATIONRADAR APPLICATIONSSAR DATASAR IMAGESSCATTERING MODELSOIL CONDITIONSSOIL HETEROGENEITYSOIL MOISTURE RETRIEVALSSOIL PARAMETERSSPATIAL HETEROGENEITYSPECKLE EFFECTSSPECKLE NOISESTATISTICAL DISTRIBUTIONALGORITHMSBAYESIAN NETWORKSCOMPUTER SIMULATIONESTIMATIONINVERSE PROBLEMSPOLARIMETERSSOIL MOISTURESPECKLESYNTHETIC APERTURE RADARUNCERTAINTY ANALYSISGEOLOGIC MODELSACCURACY ASSESSMENTALGORITHMBACKSCATTERBAYESIAN ANALYSISERROR ANALYSISHETEROGENEITYINVERSE PROBLEMNOISENUMERICAL MODELSTATISTICAL DISTRIBUTIONSYNTHETIC APERTURE RADARIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |