Browse Conference Publications > Geoscience and Remote Sensing ... Prev | Back to Results | Next » A Bayesian methodology for soil parameters retrieval from SAR images

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

Vancouver

Simposio; Geoscience and Remote Sensing Symposium (IGARSS), 2011 IEEE International; 2011

IEEE

Soil moisture retrieval from SAR data presents two main sources of uncertainty: terrain heterogeneity and speckle noise. In this paper, these issues will be addressed by using a Bayesian approach. Such a Bayesian approach (1) needs only a forward model (no retrieval model required), (2) gives the optimal unbiased estimator for the soil moisture and its error and (3) can include as many error sources as required. Through numerical simulations, a standard Oh retrieval procedure and the Bayesian approach were tested for different number of looks (n = 3 and n = 64). The results indicate that for a large number of looks the region of validity of both approaches are similar. Furthermore, contrary to the Oh model retrieval procedure which is only valid in a bounded region of the (hh, vv, hv)-space, the Bayesian approach gives an estimation of soil moisture and its error for any combination of hh, vv and hv, so enlarging the region where the retrieval is possible.