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Speckle noise and soil heterogeneities as error sources in a bayesian soil moisture retrieval scheme for SAR data

Por: Barber, M.
Colaborador(es): Grings, F | Perna, P | Piscitelli, M | Maas, M | Bruscantini, C | Jacobo Berlles, J | Karszenbaum, H.
ISSN: 1939-1404.Tipo de material: Artículos y capítulos. Recurso electrónico.Tema(s): 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 | Recursos en línea: Haga clic para acceso en línea | LINK AL EDITOR. En: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 5, no. 3 (2012) 942-951Resumen: 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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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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