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Proceedings Paper

Assimilation of soil moisture using Ensemble Kalman Filter
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Paper Abstract

In this work, a soil moisture data assimilation scheme was developed based on the Community Land Model Version 3.0 (hereafter CLM) and Ensemble Kalman Filter. Soil moisture in the 1st soil layer was assimilated into CLM to evaluate the improvements of land surface process simulation. The results indicated that the assimilation system could improve the model accuracy effectively. It can transfer the variations of shallow soil layer’s moisture to the deep soil and make great improvements to the soil water and heat status in an overall level. The system could improve the soil moisture accuracy from the 1st soil layer to the 6th soil layer by 50%. According to this experiment, the transfer depth of soil moisture was from 40 cm to 60 cm. After assimilation, the correlation coefficient of latent heat flux observation and simulation increased from 0.68 to 0.91 and the RMSE dropped from 86.7 W/m2 to 45.7 W/m2. For the sensible heat flux, the correlation coefficient increased from 0.69 to 0.80 and the RMSE reduced from 105.1 W/m2 to 71.3 W/m2. It was feasible and significant to assimilate soil moisture remote sensing products.

Paper Details

Date Published: 2 October 2014
PDF: 10 pages
Proc. SPIE 9221, Remote Sensing and Modeling of Ecosystems for Sustainability XI, 92210N (2 October 2014); doi: 10.1117/12.2058852
Show Author Affiliations
Juan Du, East China Normal Univ. (China)
Joint Lab. for Environmental Remote Sensing and Data Assimilation, ECNU and CEODE (China)
Chaoshun Liu, East China Normal Univ. (China)
Joint Lab. for Environmental Remote Sensing and Data Assimilation, ECNU and CEODE (China)
Wei Gao, East China Normal Univ. (China)
Joint Lab. for Environmental Remote Sensing and Data Assimilation, ECNU and CEODE (China)
Colorado State Univ. (United States)


Published in SPIE Proceedings Vol. 9221:
Remote Sensing and Modeling of Ecosystems for Sustainability XI
Wei Gao; Ni-Bin Chang; Jinnian Wang, Editor(s)

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