Bayesian approach to retrieve soil parameters from SAR data

MATÍAS BARBER; MARTIN MAAS; PABLO PERNA; FRANCISCO GRINGS; HAYDEE KARSZENBAUM

Buenos Aires

Encuentro; XIV J.J.Giambiagi Winter School ? Applied and Environmental Geophysics; 2012

Depto de Física, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires.

Orbiting microwave synthetic aperture radar (SAR) systems offer the opportunity of monitoring soil moisture content at different scales and under any kind of weather conditions, through the known sensitivity that backscattered signal exhibits to soil parameters, including soil moisture. In this framework, soil parameter retrieval can be considered an inference problem, where one essentially wants to infer soil condition given a set of measured backscatter coefficients and ancillary information.A wide range of forward models, ranging from experimental fittings to physically-based models have been developed in order to assess the dependency of soil parameters to backscattered signal. One of the main problems of SAR-based soil moisture retrieval is the limited performance of inverse models, mostly related to measurement errors, the heterogeneity of the target?s surface, the difficulty to parameterize the models in terms of the soil parameters [1, 2, 3] and the speckle noise [4]. These issues are addressed by a Bayesian retrieval methodology [5], which only requires a forward model along with prior information to estimate soil parameters, thus avoiding the use of an inverse model. The Bayesian approach itself is the inversion procedure applied to forward model data. Within this Bayesian approach, a numerical investigation is carried out to assess the performance of two widely used forward models: the semiempirical Oh?s model [6] and the physically-based Integral Equation Model (IEM) [7]. The performance of each model is put on a quantitative basis by analyzing the accuracy of the retrieved parameters. This work is framed within the CONAE?s current SAOCOM SAR mission and through the AO project "Monitoring soil hydrologic condition in agricultural fields: fieldwork & simulations for SARAT-SAOCOM soil moisture validation", currently in progress.REFERENCES[1] N. E. C Verhoest, H. Lievens, W. Wagner, J. Álvarez-Mozos, M. S. Moran, and F. Mattia, ?On the Soil Roughness Parameterization Problem in Soil Moisture Retrieval of Bare Surfaces From Synthetic Aperture Radar?, Sensors, pp. 4213-4248, July 2008.[2] M. Callens, N. E. C Verhoest, and M. W. J. Davidson, ?Parameterization of tillage-induced single-scale soil roughness from 4-m profiles?, IEEE Transactions on Geoscience and Remote Sensing, pp. 878-888, April 2006.[3] Z.S. Haddad, P.D. Dubois, and J.J. van Zyl, ?Bayesian Estimation of Soil Parameters from Radar Backscatter Data?, IEEE Transactions on Geoscience and Remote Sensing, pp. 76-82, January 1996.[4]  J.S. Lee, K. W. Hoppel, S. A. Mango, and A. R. Miller, ?Intensity and Phase Statistics of Multilook Polarimetric and Interferometric SAR Imagery?, IEEE Transactions on Geoscience and Remote Sensing, pp. 1017-1028, September 1994.[5] M. Barber, F. Grings, P. Perna, M. Piscitelli, M. Maas, C. Bruscantini, J. Jacobo Berlles, H. Karszenbaum, ?Speckle noise and soil heterogeneities as error sources in a Bayesian soil moisture retrieval scheme for SAR data?, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2012. (In press).[6] Y. Oh, ?Quantitative retrieval of soil moisture content and surface roughness from multipolarized radar observations of bare soil surfaces,? IEEE Transactions on Geoscience and Remote Sensing, vol. 42, no. 3, pp. 596 ? 601, Mar. 2004.[7] A. K. Fung, Microwave Scattering and Emission Models and Their Applications, Artech House Publishers, 1994.