• 论文 •

Assimilating ASAR Data for Estimating Soil Moisture Profile Using an En-semble Kalman Filter

YU Fan, LI Haitao, GU Haiyan, HAN Yanshun

1. Key Laboratory of Geo-Informatics of State Bureau of Surveying and Mapping, Chinese Academy of Surveying and Mapping, Beijing 100830, China
• 收稿日期:2012-07-23 修回日期:2013-01-09 出版日期:2013-11-10 发布日期:2013-11-13
• 通讯作者: YU Fan. E-mail: yufan@casm.ac.cn E-mail:yufan@casm.ac.cn
• 基金资助:

Under the auspices of National Natural Science Foundation for Young Scientists of China (No. 41101321), Major State Basic Research Development Program of China (No. 2007CB714407), Key Projects in the National Science & Technology Pillar Program (No. 2009BAG18B01, 2012BAH28B03)

Assimilating ASAR Data for Estimating Soil Moisture Profile Using an En-semble Kalman Filter

YU Fan, LI Haitao, GU Haiyan, HAN Yanshun

1. Key Laboratory of Geo-Informatics of State Bureau of Surveying and Mapping, Chinese Academy of Surveying and Mapping, Beijing 100830, China
• Received:2012-07-23 Revised:2013-01-09 Online:2013-11-10 Published:2013-11-13
• Contact: YU Fan. E-mail: yufan@casm.ac.cn E-mail:yufan@casm.ac.cn
• Supported by:

Under the auspices of National Natural Science Foundation for Young Scientists of China (No. 41101321), Major State Basic Research Development Program of China (No. 2007CB714407), Key Projects in the National Science & Technology Pillar Program (No. 2009BAG18B01, 2012BAH28B03)

Active microwave remote sensing data were used to calculate the near-surface soil moisture in the vegetated areas. In this study, Advanced Synthetic Aperture Radar (ASAR) observations of surface soil moisture content were used in a data assimilation framework to improve the estimation of the soil moisture profile at the middle reaches of the Heihe River Basin, Northwest China. A one-dimensional soil moisture assimilation system based on the ensemble Kalman filter (EnKF), the forward radiative transfer model, crop model, and the Distributed Hydrology-Soil-Vegetation Model (DHSVM) was developed. The crop model, as a semi-empirical model, was used to estimate the surface backscattering of vegetated areas. The DHSVM is a distributed hydrology-vegetation model that explicitly represents the effects of topography and vegetation on water fluxes through the landscape. Numerical experiments were conducted to assimilate the ASAR data into the DHSVM and in situ soil moisture at the middle reaches of the Heihe River Basin from June 20 to July 15, 2008. The results indicated that EnKF is effective for assimilating ASAR observations into the hydrological model. Compared with the simulation and in situ observations, the assimilated results were significantly improved in the surface layer and root layer, and the soil moisture varied slightly in the deep layer. Additionally, EnKF is an efficient approach to handle the strongly nonlinear problem which is practical and effective for soil moisture estimation by assimilation of remote sensing data. Moreover, to improve the assimilation results, further studies on obtaining more reliable forcing data and model parameters and increasing the efficiency and accuracy of the remote sensing observations are needed, also improving estimation accuracy of model operator is important.

Abstract:

Active microwave remote sensing data were used to calculate the near-surface soil moisture in the vegetated areas. In this study, Advanced Synthetic Aperture Radar (ASAR) observations of surface soil moisture content were used in a data assimilation framework to improve the estimation of the soil moisture profile at the middle reaches of the Heihe River Basin, Northwest China. A one-dimensional soil moisture assimilation system based on the ensemble Kalman filter (EnKF), the forward radiative transfer model, crop model, and the Distributed Hydrology-Soil-Vegetation Model (DHSVM) was developed. The crop model, as a semi-empirical model, was used to estimate the surface backscattering of vegetated areas. The DHSVM is a distributed hydrology-vegetation model that explicitly represents the effects of topography and vegetation on water fluxes through the landscape. Numerical experiments were conducted to assimilate the ASAR data into the DHSVM and in situ soil moisture at the middle reaches of the Heihe River Basin from June 20 to July 15, 2008. The results indicated that EnKF is effective for assimilating ASAR observations into the hydrological model. Compared with the simulation and in situ observations, the assimilated results were significantly improved in the surface layer and root layer, and the soil moisture varied slightly in the deep layer. Additionally, EnKF is an efficient approach to handle the strongly nonlinear problem which is practical and effective for soil moisture estimation by assimilation of remote sensing data. Moreover, to improve the assimilation results, further studies on obtaining more reliable forcing data and model parameters and increasing the efficiency and accuracy of the remote sensing observations are needed, also improving estimation accuracy of model operator is important.