info:eu-repo/semantics/article
Speckle noise and soil heterogeneities as error sources in a Bayesian soil moisture retrieval scheme for SAR data
Fecha
2012-06Registro en:
Barber, Matias Ernesto; Grings, Francisco Matias; Perna, Pablo Alejandro; Piscitelli, Marcela; Maas, Martín Daniel; et al.; Speckle noise and soil heterogeneities as error sources in a Bayesian soil moisture retrieval scheme for SAR data; Institute of Electrical and Electronics Engineers; Ieee Journal Of Selected Topics In Applied Earth Observations And Remote Sensing; 5; 3; 6-2012; 942-951
1939-1404
CONICET Digital
CONICET
Autor
Barber, Matias Ernesto
Grings, Francisco Matias
Perna, Pablo Alejandro
Piscitelli, Marcela
Maas, Martín Daniel
Bruscantini, Cintia Alicia
Jacobo Berlles, Julio César Alberto
Karszenbaum, Haydee
Resumen
—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 minimizationbased 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.