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Proceedings Paper

Spatial and temporal soil moisture monitoring in semi-arid and humid areas with high resolution ASAR images
Author(s): C. Notarnicola; S. Paloscia; S. Pettinato; G. Preziosa; E. Santi; B. Ventura
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Paper Abstract

The main aim of the analysis presented in this paper is to cross-compare two retrieval methodologies, one based on Neural Network and the other on Bayesian approach in different types of test areas and verify if they are able to retrieve the same spatial and temporal soil moisture features. The test areas are located in three regions in Italy in order to take into account different soil and meteorological conditions. The comparison of the backscattering coefficients as a function of soil moisture values indicate the same sensitivity to soil moisture variations but with a different bias which may depend on soil characteristics, vegetation presence and roughness effect. The results of the two retrieval methodologies indicate an overall good agreement. Only in one single date, the discrepancy between the results is around 8%. The algorithms are also compared in terms of processing times.

Paper Details

Date Published: 29 September 2009
PDF: 11 pages
Proc. SPIE 7477, Image and Signal Processing for Remote Sensing XV, 74771S (29 September 2009); doi: 10.1117/12.830696
Show Author Affiliations
C. Notarnicola, EURAC research (Italy)
S. Paloscia, Istituto di Fisica Applicata Nello Carrara, CNR (Italy)
S. Pettinato, Istituto di Fisica Applicata Nello Carrara, CNR (Italy)
G. Preziosa, Politecnico di Bari (Italy)
E. Santi, Istituto di Fisica Applicata Nello Carrara, CNR (Italy)
B. Ventura, Univ. degli Studi di Bari (Italy)

Published in SPIE Proceedings Vol. 7477:
Image and Signal Processing for Remote Sensing XV
Lorenzo Bruzzone; Claudia Notarnicola; Francesco Posa, Editor(s)

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