@inproceedings{21b4878c16b44686bde45c9bfd1f2990,
title = "Assimilation of soil moisture using Ensemble Kalman Filter",
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.",
keywords = "Community land model, Data assimilation, Ensemble kalman filter, Soil heat flux, Soil moisture",
author = "Juan Du and Chaoshun Liu and Wei Gao",
note = "Publisher Copyright: {\textcopyright} 2014 SPIE.; Remote Sensing and Modeling of Ecosystems for Sustainability XI ; Conference date: 18-08-2014 Through 20-08-2014",
year = "2014",
doi = "10.1117/12.2058852",
language = "英语",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Jinnian Wang and Ni-Bin Chang and Wei Gao",
booktitle = "Remote Sensing and Modeling of Ecosystems for Sustainability XI",
address = "美国",
}