Bayesian iterative inversion algorithm applied to soil moisture mapping using ground-based and airborne remote sensing data

Claudia Notarnicola; Francesco Posa

doi:10.1117/12.514413

12 January 2004 Bayesian iterative inversion algorithm applied to soil moisture mapping using ground-based and airborne remote sensing data

Claudia Notarnicola, Francesco Posa

Author Affiliations +

Proceedings Volume 5236, SAR Image Analysis, Modeling, and Techniques VI; (2004) https://doi.org/10.1117/12.514413
Event: Remote Sensing, 2003, Barcelona, Spain

Abstract

In this paper, a set of AIRSAR images has been exploited in conjunction with backscatter models of bare soils to develop an algorithm for soil moisture estimation. The algorithm is based on a Bayesian approach and combines prior information on surface parameters with observed data, in order to extract information regarding surface parameters. 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 remote sensed data. In this study, it is obtained by applying the maximum likelihood principle (MLP). The inversion procedure has been applied to C-band images at HH and VV polarisations, to L-band images at HH and VV polarisations and to a merger of C- and L-band images both at HH polarisation. From a comparison with ground truth measurements, the best performances are achieved with two frequencies. Furthermore, it can be noted that the drying phase changes considerably from one part to another of the same field.

Citation Download Citation

Claudia Notarnicola and Francesco Posa "Bayesian iterative inversion algorithm applied to soil moisture mapping using ground-based and airborne remote sensing data", Proc. SPIE 5236, SAR Image Analysis, Modeling, and Techniques VI, (12 January 2004); https://doi.org/10.1117/12.514413

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