Journal article
Optimization of Soil Hydraulic Model Parameters Using Synthetic Aperture Radar Data: An Integrated Multidisciplinary Approach
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 its, 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 anti 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 basest 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.
Language: | English |
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Publisher: | IEEE |
Year: | 2009 |
Pages: | 455-467 |
ISSN: | 15580644 and 01962892 |
Types: | Journal article |
DOI: | 10.1109/TGRS.2008.2007849 |
ORCIDs: | Skriver, Henning |
Atmosphere Atmospheric modeling Calibration Conductivity Content based retrieval DEMMIN project Durable Environmental Multidisciplinary Monitoring Information Network Information resources Information retrieval Kalman filters Land surface Mecklenburg-Vorpommern North-East Germany Remote sensing SAR data Soil moisture Synthetic aperture radar backscatter biogeophysical parameter extended Kalman filter equation hydrology hydrology application integrated multidisciplinary approach land cover parameter land surface model land surface-atmosphere interaction moisture multitemporal SAR-based soil moisture mapping parameter estimation radar backscatter remote sensing remote sensing by radar soil soil hydraulic conductivity soil hydraulic model parameter soil moisture content soil physical model parameter soil texture synthetic aperture radar synthetic aperture radar (SAR) terrain mapping