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

Weijing Chen,Chunlin Huang,Huanfeng Shen,Xin Li

doi:10.3390/rs9030273

Abstract

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.

Highlights

Accurate soil moisture estimation at the land surface plays a very important role in studies of land surface conditions, natural resource management, and the interactions in the land-atmosphere system [1,2,3]
The spatio-temporal resolution of Common Land Model (CoLM) in this research was set to 0.05 degree and one hour, as well as the output variables
mean bias error (MBE) for soil moisture at the first layer from DA distributed around the 0 value which indicated a large reduction of bias after assimilation

Summary

Introduction

Accurate soil moisture estimation at the land surface plays a very important role in studies of land surface conditions, natural resource management, and the interactions in the land-atmosphere system [1,2,3]. Observation and modeling are two typical methods that are used to acquire soil moisture information, while the former one can be divided into ground station. The unsatisfactory accuracy of modeling, limited coverage area of ground station measurements and coarse spatio-temporal resolution of remote sensing measurements confine their practical applications. Data assimilation effectively controls such shortages by merging the observations into a dynamic model to acquire spatio-temporal continuous state variables [7,8,9,10]. Two of the most common nonlinear filters in sequential data assimilation are ensemble Kalman filter (EnKF) and particle filtering (PF). EnKF hold the assumption of a linear Gaussian state-space model which can be relaxed by the use of sequential Monte Carlo method in the form PF [18,19,20,21]. It is well known that the weights degenerate in high-dimensional problems [22]

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Results

Conclusion