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Proceedings Paper

Feasibility of soil moisture and roughness retrieval using microwave data
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Paper Abstract

An extensive data set, made up of different remote sensing experiments, six carried with a ground-based instrument, a scatterometer, and two with the AIRSAR sensor, Washita '92 and SMEX '02, has been investigated. The aim was to study the feasibility of soil parameters extractions in different environmental conditions and with different sensors. The extraction algorithm is a combination of Bayesian methodology with theoretical models. The chosen theoretical model is the Integral Equation Model because its range of applicability covers most of experiment surface conditions. Bayesian methodology allows meaningful and rigorous incorporations of all information sources into the inverse problem solution. The key point is the evaluation of a joint posterior probability density function based on the contemporary knowledge of data sets consisting of soil parameters measurements and the corresponding remotely sensed data. In this study, it is obtained by applying the maximum likelihood principle (MLP). The inversion procedure has been applied to bare and vegetated fields. The correlation coefficient between measured and estimated dielectric constant values are R = 0.41 and R = 0.81 for bare fields and for C and L band respectively. In the case of the vegetated soils, the correlation coefficients are variable between 0.34 and 0.94, according to the different level of vegetation. It can be noted that the drying phase changes considerably from one part to another of the same field. The in-homogeneity of the fields introduces further errors in the inversion procedure.

Paper Details

Date Published: 22 October 2004
PDF: 11 pages
Proc. SPIE 5574, Remote Sensing for Environmental Monitoring, GIS Applications, and Geology IV, (22 October 2004); doi: 10.1117/12.568185
Show Author Affiliations
Claudia Notarnicola, INFM (Italy)
Univ. degli Studi di Bari (Italy)
Francesco Posa, INFM (Italy)
Univ. degli Studi di Bari (Italy)

Published in SPIE Proceedings Vol. 5574:
Remote Sensing for Environmental Monitoring, GIS Applications, and Geology IV
Manfred Ehlers; Francesco Posa, Editor(s)

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