Retrieving accurate soil moisture over the Tibetan Plateau using multi-source remote sensing data assimilation with simultaneous state and parameter estimations

Abstract

AbstractData assimilation provides a practical way to improve the accuracy of soil moisture simulation by integrating a land surface model and satellite data. This study establishes a multi-source remote sensing data assimilation framework by incorporating a simultaneous state and parameter estimation method to acquire an accurate estimation of the soil moisture over the Tibetan Plateau. The brightness temperature of the Advanced Microwave Scanning Radiometer 2 (AMSR2) is directly assimilated into the coupled system of the Common Land Model (CoLM) and a microwave radiative transfer model (RTM) to improve the soil moisture simulation. The Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature product and the Beijing Normal University (BNU) leaf area index product are employed to not only improve the estimation of temperature and vegetation variables from the CoLM, but also provide more accurate background information for the RTM during the brightness temperature assimilation. In situ measurements from the Naqu network are used to evaluate the results. The model simulation showed an obvious underestimation of soil moisture and overestimation of soil temperature, which was alleviated by the assimilation experiments, particularly in the shallow soil layers. The estimated parameters also showed advantages in the soil moisture simulation when compared with the default parameters. The assimilation experiment presents promising results in the combination of model and multi-source remote sensing data for estimating soil moisture over the complex mountainous region in Tibet.