引用方式:
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.
| 文献信息 | |
| 标题 |
Assimilation of ASAR data with a hydrologic and semi-empirical backscattering coupled model to estimate soil moisture |
| 年份 | 2010 |
| 出版社 |
Chinese Geographical Science |
| 摘要 |
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. |
| 此文献未收录 PDF(如何提交?) |
联系方式
中国科学院西北生态环境资源研究院
0931-4967287
poles@itpcas.ac.cn
关注我们
时空三极环境大数据平台 © 2018-2020 陇ICP备05000491号 | All Rights Reserved
｜ 
京公网安备11010502040845号
数据中心技术支持: 数云软件
