太阳成8722(中国)有限公司-GREEN NO.1

标题： Improving Soil Moisture Estimation with a Dual Ensemble Kalman Smoother by Jointly Assimilating AMSR-E Brightness Temperature and MODIS LST作者：Chen, WJ (Chen, Weijing); Shen, HF (Shen, Huanfeng); Huang, CL (Huang, Chunlin); Li, X (Li, Xin)

来源出版物：REMOTE SENSING卷：9 期：3 文献编号：273 DOI：10.3390/rs9030273 出版年：MAR 2017

摘要：Uncertainties in model parameters can easily result in systematic differences between model states and observations, which significantly affect the accuracy of soil moisture estimation in data assimilation systems. In this research, a soil moisture assimilation scheme is developed to jointly assimilate AMSR-E (Advanced Microwave Scanning Radiometer-Earth Observing System) brightness temperature (TB) and MODIS (Moderate Resolution Imaging Spectroradiometer) Land Surface Temperature (LST) products, which also corrects model bias by simultaneously updating model states and parameters with a dual ensemble Kalman filter (DEnKS). Common Land Model (CoLM) and a Radiative Transfer Model (RTM) are adopted as model and observation operator, respectively. The assimilation experiment was conducted in Naqu on the Tibet Plateau from 31 May to 27 September 2011. The updated soil temperature at surface obtained by assimilating MODIS LST serving as inputs of RTM is to reduce the differences between the simulated and observed TB, then AMSR-E TB is assimilated to update soil moisture and model parameters. Compared with in situ measurements, the accuracy of soil moisture estimation derived from the assimilation experiment has been tremendously improved at a variety of scales. The updated parameters effectively reduce the states bias of CoLM. The results demonstrate the potential of assimilating AMSR-E TB and MODIS LST to improve the estimation of soil moisture and related parameters. Furthermore, this study indicates that the developed scheme is an effective way to retrieve downscaled soil moisture when assimilating the coarse-scale microwave TB.

入藏号： WOS:000398720100087

文献类型：Article

语种：English

作者关键词： data assimilation; soil moisture; state-parameter estimation; AMSR-E; MODIS; Common Land Model

扩展关键词： LAND-SURFACE MODEL; HYDROLOGIC DATA ASSIMILATION; RADIATIVE-TRANSFER MODEL; MONTE-CARLO METHODS; PARAMETER-ESTIMATION; MICROWAVE EMISSION; PARTICLE FILTER; TIBETAN PLATEAU; CALIBRATION; SYSTEM

通讯作者地址： Shen, HF (reprint author), Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

Huang, CL (reprint author), Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Key Lab Remote Sensing Gansu Prov, Heihe Remote Sensing Expt Res Stn, Lanzhou 730000, Peoples R China.

电子邮件地址：chenweijinghn@whu.edu.cn; shenhf@whu.edu.cn; huangcl@lzb.ac.cn; lixin@lzb.ac.cn

地址：

[Chen, Weijing; Shen, Huanfeng] Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

[Huang, Chunlin; Li, Xin] Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Key Lab Remote Sensing Gansu Prov, Heihe Remote Sensing Expt Res Stn, Lanzhou 730000, Peoples R China.

[Li, Xin] Chinese Acad Sci, CAS Ctr Excellence Tibetan Plateau Earth Sci, Beijing 100101, Peoples R China.

研究方向： Remote Sensing

ISSN：2072-4292

影响因子：3.036