Statistics – Applications
Scientific paper
Jan 2002
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2002esasp.475..177s&link_type=abstract
In: Proceedings of the Third International Symposium on Retrieval of Bio- and Geophysical Parameters from SAR Data for Land Appl
Statistics
Applications
Soils, Hydrology
Scientific paper
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.
Borgeaud Maurice
Davidson Malcolm
Le Toan Thuy
Mattia Francesco
Pasquariello Guido
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