Variational Estimation of Land–Atmosphere Heat Fluxes and Land Surface Parameters Using MODIS Remote Sensing Data

A variational data assimilation algorithm for assimilating the land surface temperature (LST) into the Common Land Model (CLM) was implemented using the land surface energy balance equation as the adjoint physical constraint. In this data assimilation algorithm, the evaporative fractions of the soil and canopy were adjusted according to the remotely sensed surface temperature observations. This paper developed a very simple analytical algorithm to characterize the errors’ weighting matrices in the cost function. The leaf area index (LAI) retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) was also assimilated into CLM using the direct insertion method. The analysis results from the CLM with the LST assimilation algorithm compare well with MODIS and field observations for the Yucheng site, especially in daytime. On the basis of the histogram of the error of the LST, it can be concluded that after assimilation, the LST was greatly improved in comparison with the MODIS observations, especially in daytime. These results indicate that this surface temperature assimilation method is efficient and effective, even when only one time observational LST data point is available for each day, especially in daytime. The regional spatial patterns of evapotranspiration and soil surface moisture were also compared before assimilation on the basis of LAI data calculated using the empirical formula, before assimilation on the basis of MODIS LAI data, and after assimilation on the basis of MODIS LAI data.