Speckle Noise and Soil Heterogeneities as Error Sources in a Bayesian Soil Moisture Retrieval Scheme for SAR Data

MATIAS BARBER; FRANCISCO MATIAS GRINGS; PABLO PERNA; MARCELA PISCITELLI; MARTIN MAAS; CINTIA BRUSCANTINI; JULIO CESAR JACOBO-BERLLES; HAYDEE KARSZENBAUM

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

Lugar: New York; Año: 2012

1939-1404

Soil moisture retrieval from SAR images is always affected by speckle noise and uncertainities associated to soil parameters, which impact negatively on the accuracy of soil moisture estimates. In this paper a Bayesian model is proposed to address these issues. A soil moisture Bayesian estimator from polarimetric SAR images is presented. This estimator is based on a set of stochastic equations for the polarimetric soil backscattering coefficients, which naturally includes models for the soil scattering, the speckle and the soil spatial heterogeneity. Since it is a Bayesian estimator, it may extensively use prior information about soil condition, 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. The Bayesian retrieval scheme is then compared with the minimization-based one. The results indicate that the Bayesian model enlarges the validity region of the minimizationbased procedure. Moreover, as speckle effects are reduced by multilooking, Bayesian retrieval approaches to the minimizationbased retrieval. On the other hand, an improvement in the accuracy of the retrieval is achieved by using a precise prior when speckle effects are large. The proposed algorithm can be applied to investigate which are the optimum parameters regarding multi-loking process and prior information required to perform a precise retrieval in a given soil condition.