29 September 2009 Spatial and temporal soil moisture monitoring in semi-arid and humid areas with high resolution ASAR images
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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.
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C. Notarnicola, S. Paloscia, S. Pettinato, G. Preziosa, E. Santi, B. Ventura, "Spatial and temporal soil moisture monitoring in semi-arid and humid areas with high resolution ASAR images", Proc. SPIE 7477, Image and Signal Processing for Remote Sensing XV, 74771S (29 September 2009); doi: 10.1117/12.830696; https://doi.org/10.1117/12.830696
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