Retrieving soil moisture and agricultural variables by microwave
radiometry using neural networks

F. Del Frate, P. Ferrazzoli, G. Schiavon

Abstract

Two neural network algorithms trained by a physical vegetation model are used to retrieve soil moisture and vegetation variables of wheat canopies during the whole crop cycle. The first algorithm retrieves soil moisture using L band, two polarizations and multiangular radiometric data, for each single date of radiometric acquisition. The algorithm includes roughness and vegetation effects, but does not require a priori knowledge of roughness and vegetation parameters for the specific field. The second algorithm retrieves vegetation variables using dual band, V polarization and multiangular radiometric data. This algorithm operates over the whole multitemporal data set. Previously retrieved soil moisture values are also used as a priori information. The algorithms have been tested considering measurements carried out in 1993 and 1996 over wheat fields at the INRA Avignon test site.

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