Assimilation of remote sensing data products into common land model for evapotranspiration forecasting

Evapotranspiration (ET), the sum of water lost to the atmosphere from the soil surface through evaporation and from plant tissues via transpiration, is a vital component of the water cycle. Accurate measurements of ET are required for the global water and energy cycles. However, ET varies in time and space and is difficult to estimate as it depends on many interacting processes. At the local scale, ET may be accurately estimated from detailed ground observations. At the regional scale sufficient ground observations will never be available and instead spatially. Remote sensing data provide us with spatially continuous information over vegetated surfaces, which supply the frequent lack of ground-measured variables and parameters required to apply the local models at a regional scale. Optical remote sensing data are strongly affected by atmospheric condition, so the uncertainty also exists in the estimation of ET with remote sensing. In this work, we develop a data assimilation scheme to improve the estimation of ET. The common land model (CoLM) is adopted as model operator to simulate the temporal variation of ET. Ensemble Kalman filter algorithm is chosen as data assimilation algorithm. The scheme can dynamically assimilate MODIS land products such as land surface temperature (LST) and leaf area index (LAI). The scheme is tested by automatic weather station (AWS) and flux tower data obtained from Xiaotangshan station in China. The results indicate that assimilating MODIS land products can improve the estimation of ET.