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Assimilation of ASAR data with a hydrologic and semi-empirical backscattering coupled model to estimate soil moisture

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Liu Q, Wang MY, Zhao YS. Assimilation of ASAR data with a hydrologic and semi-empirical backscattering coupled model to estimate soil moisture. Chinese Geographical Science, 2010, 20(3): 218-225.
Literature information
Type Of Reference JOUR
Title Assimilation of ASAR data with a hydrologic and semi-empirical backscattering coupled model to estimate soil moisture
Authors Liu, Qian| Wang, Mingyu| Zhao, Yingshi|
Secondary Title Chinese Geographical Science
Abstract The most promising approach for studying soil moisture is the assimilation of observation data and computational modeling. However, there is much uncertainty in the assimilation process, which affects the assimilation results. This research developed a one-dimensional soil moisture assimilation scheme based on the Ensemble Kalman Filter (EnKF) and Genetic Algorithm (GA). A two-dimensional hydrologic model—Distributed Hydrology-Soil-Vegetation Model (DHSVM) was coupled with a semi-empirical backscattering model (Oh). The Advanced Synthetic Aperture Radar (ASAR) data were assimilated with this coupled model and the field observation data were used to validate this scheme in the soil moisture assimilation experiment. In order to improve the assimilation results, a cost function was set up based on the distance between the simulated backscattering coefficient from the coupled model and the observed backscattering coefficient from ASAR. The EnKF and GA were used to re-initialize and re-parameterize the simulation process, respectively. The assimilation results were compared with the free-run simulations from hydrologic model and the field observation data. The results obtained indicate that this assimilation scheme is practical and it can improve the accuracy of soil moisture estimation significantly.
Date 2010/06/01/
Year 2010
DOI 10.1007/s11769-010-0218-6
Database Provider link.springer.com
volume 20
Issue Number 3
Start Page 218
Alternate Title1 Chin. Geogr. Sci.
Language en
Issn 1002-0063, 1993-064X
Url http://link.springer.com/article/10.1007/s11769-010-0218-6
Access Date 2013/10/04/05:54:34
Keywords advanced synthetic aperture radar (ASAR)| couple| data assimilation| Distributed Hydrology-Soil-Vegetation Model (DHSVM)| Geography (general)| Oh Model| soil moisture|
file_attachments2 http://link.springer.com/article/10.1007/s11769-010-0218-6
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