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Applied and Environmental Soil Science
Volume 2011 (2011), Article ID 175473, 12 pages
http://dx.doi.org/10.1155/2011/175473
Research Article

Estimation of Soil Moisture in an Alpine Catchment with RADARSAT2 Images

1Department of Information Engineering and Computer Science, University of Trento, Via Sommarive, 14, 38123 Trento, Italy
2EURAC-Institute for Applied Remote Sensing, Viale Druso, 1, 39100 Bolzano, Italy
3EURAC-Institute for Alpine Environment, Viale Druso, 1, 39100 Bolzano, Italy
4Institute of Ecology, University of Innsbruck, Sternwartestr. 15, 6020 Innsbruck, Austria
5Department of Computer Science, Systems and Production Engineering, Tor Vergata University, Via del Politecnico, 1, 00133 Rome, Italy

Received 15 December 2010; Accepted 22 February 2011

Academic Editor: Mehrez Zribi

Copyright © 2011 L. Pasolli et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Soil moisture retrieval is one of the most challenging problems in the context of biophysical parameter estimation from remotely sensed data. Typically, microwave signals are used thanks to their sensitivity to variations in the water content of soil. However, especially in the Alps, the presence of vegetation and the heterogeneity of topography may significantly affect the microwave signal, thus increasing the complexity of the retrieval. In this paper, the effectiveness of RADARSAT2 SAR images for the estimation of soil moisture in an alpine catchment is investigated. We first carry out a sensitivity analysis of the SAR signal to the moisture content of soil and other target properties (e.g., topography and vegetation). Then we propose a technique for estimating soil moisture based on the Support Vector Regression algorithm and the integration of ancillary data. Preliminary results are discussed both in terms of accuracy over point measurements and effectiveness in handling spatially distributed data.