Improving Soil Moisture Estimation with a Dual Ensemble Kalman Smoother by Jointly Assimilating AMSR-E Brightness Temperature and MODIS LST

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 assimil...

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Main Authors: Weijing Chen, Huanfeng Shen, Chunlin Huang, Xin Li
Format: Article
Language:English
Published: MDPI AG 2017-03-01
Series:Remote Sensing
Subjects:
Online Access:http://www.mdpi.com/2072-4292/9/3/273
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spelling doaj-60878488c02a42fba811c79390f96f682020-11-24T20:45:58ZengMDPI AGRemote Sensing2072-42922017-03-019327310.3390/rs9030273rs9030273Improving Soil Moisture Estimation with a Dual Ensemble Kalman Smoother by Jointly Assimilating AMSR-E Brightness Temperature and MODIS LSTWeijing Chen0Huanfeng Shen1Chunlin Huang2Xin Li3School of Resource and Environmental Science, Wuhan University, Wuhan 430079, ChinaSchool of Resource and Environmental Science, Wuhan University, Wuhan 430079, ChinaKey Laboratory of Remote Sensing of Gansu Province, Heihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaKey Laboratory of Remote Sensing of Gansu Province, Heihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaUncertainties 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.http://www.mdpi.com/2072-4292/9/3/273data assimilationsoil moisturestate-parameter estimationAMSR-EMODISCommon Land Model
collection DOAJ
language English
format Article
sources DOAJ
author Weijing Chen
Huanfeng Shen
Chunlin Huang
Xin Li
spellingShingle Weijing Chen
Huanfeng Shen
Chunlin Huang
Xin Li
Improving Soil Moisture Estimation with a Dual Ensemble Kalman Smoother by Jointly Assimilating AMSR-E Brightness Temperature and MODIS LST
Remote Sensing
data assimilation
soil moisture
state-parameter estimation
AMSR-E
MODIS
Common Land Model
author_facet Weijing Chen
Huanfeng Shen
Chunlin Huang
Xin Li
author_sort Weijing Chen
title Improving Soil Moisture Estimation with a Dual Ensemble Kalman Smoother by Jointly Assimilating AMSR-E Brightness Temperature and MODIS LST
title_short Improving Soil Moisture Estimation with a Dual Ensemble Kalman Smoother by Jointly Assimilating AMSR-E Brightness Temperature and MODIS LST
title_full Improving Soil Moisture Estimation with a Dual Ensemble Kalman Smoother by Jointly Assimilating AMSR-E Brightness Temperature and MODIS LST
title_fullStr Improving Soil Moisture Estimation with a Dual Ensemble Kalman Smoother by Jointly Assimilating AMSR-E Brightness Temperature and MODIS LST
title_full_unstemmed Improving Soil Moisture Estimation with a Dual Ensemble Kalman Smoother by Jointly Assimilating AMSR-E Brightness Temperature and MODIS LST
title_sort improving soil moisture estimation with a dual ensemble kalman smoother by jointly assimilating amsr-e brightness temperature and modis lst
publisher MDPI AG
series Remote Sensing
issn 2072-4292
publishDate 2017-03-01
description 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.
topic data assimilation
soil moisture
state-parameter estimation
AMSR-E
MODIS
Common Land Model
url http://www.mdpi.com/2072-4292/9/3/273
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