In this paper, a study on the influence of a priori information on the retrieval of soil moisture from SAR data is carried out. A priori information on the surface state has been exploited in two different soil moisture retrieval algorithms and subsequently their performances are compared. The first algorithm is based on a Neural Network trained by the Integral Equation Method (IEM) model. The second algorithm is an ITerative Method, based on the direct IEM model, which estimates soil moisture by an iterative search. The paper investigates the difference between the algorithms performances as a function of the accuracy of the a priori information. In addition, the algorithms robustness versus measurement errors is evaluated. Finally, the two approaches are applied to experimental data acquired during the 1st SIR-C/X-SAR mission and results are discussed.
The influence of a priori information on soil moisture retrieval from SAR data
Satalino G;Pasquariello G;Mattia F;
2002
Abstract
In this paper, a study on the influence of a priori information on the retrieval of soil moisture from SAR data is carried out. A priori information on the surface state has been exploited in two different soil moisture retrieval algorithms and subsequently their performances are compared. The first algorithm is based on a Neural Network trained by the Integral Equation Method (IEM) model. The second algorithm is an ITerative Method, based on the direct IEM model, which estimates soil moisture by an iterative search. The paper investigates the difference between the algorithms performances as a function of the accuracy of the a priori information. In addition, the algorithms robustness versus measurement errors is evaluated. Finally, the two approaches are applied to experimental data acquired during the 1st SIR-C/X-SAR mission and results are discussed.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
