Optimization of Soil Hydraulic Model Parameters Using Synthetic Aperture Radar Data: An Integrated Multidisciplinary Approach

• Published in: IEEE transactions on geoscience and remote sensing 2009-02, Vol.47 (2), p.455-467
• Main Authors: Pauwels, V.R.N., Balenzano, A., Satalino, G., Skriver, H., Verhoest, N.E.C., Mattia, F.
• Format: Article
• Language: English
• Subjects: Applied geophysics; Atmosphere; Atmospheric modeling; Calibration; Conductivity; Content based retrieval; Earth sciences; Earth, ocean, space; Exact sciences and technology; Hydraulics; hydrology; Information resources; Information retrieval; Internal geophysics; Land; Land surface; Mathematical models; Parameter estimation; Remote sensing; Soil (material); Soil moisture; Studies; Synthetic aperture radar; synthetic aperture radar (SAR); Texture
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2008.2007849

It is widely recognized that synthetic aperture radar (SAR) data are a very valuable source of information for the modeling of the interactions between the land surface and the atmosphere. During the last couple of decades, most of the research on the use of SAR data in hydrologic applications has been focused on the retrieval of land and biogeophysical parameters (e.g., soil moisture contents). One relatively unexplored issue consists of the optimization of soil hydraulic model parameters, such as, for example, hydraulic conductivity values, through remote sensing. This is due to the fact that no direct relationships between the remote-sensing observations, more specifically radar backscatter values, and the parameter values can be derived. However, land surface models can provide these relationships. The objective of this paper is to retrieve a number of soil physical model parameters through a combination of remote sensing and land surface modeling. Spatially distributed and multitemporal SAR-based soil moisture maps are the basis of the study. The surface soil moisture values are used in a parameter estimation procedure based on the extended Kalman filter equations. In fact, the land surface model is, thus, used to determine the relationship between the soil physical parameters and the remote-sensing data. An analysis is then performed, relating the retrieved soil parameters to the soil texture data available over the study area. The results of the study show that there is a potential to retrieve soil physical model parameters through a combination of land surface modeling and remote sensing.