Estimating Water and Energy Fluxes By Simultaneously Assimilation of Optical and Thermal Infrared Remote Sensing Data in Irrigated Fields

Energy and water exchanges are among the most important processes in land-atmosphere interactions. Furthermore, accurate estimations of turbulent fluxes are critical for climate change research, planning, and management of water resources. This study developed a data assimilation scheme based on ensemble Kalman filter and common land model (CoLM).

Apparent thermal inertia (ATI) characterizes the resistance to changes of surface temperature, which can indicate changes in soil moisture. Based on MODIS surface temperature products and albedo products, we try to retrieve ATI data, and furthermore, retrieve soil moisture of sites according to these MODIS ATI data. Based on Ensemble Kalman filter (EnKF) algorithm, we separately assimilated MODIS ATI and soil moisture data which is retrieved by MODIS ATI into the Common Land Model (CoLM), expecting to improve the estimation accuracy of surface water and heat fluxes. We select three sites which are all farmlands of different features, and two assimilation strategies to test the results. These three sites are Daman Site of Heihe River Basin (an irrigated farmland), Guantao site (a small amount of irrigated farmland) and Huailai Site (a rain fed farmland) of Haihe River Basin. The results show that the ATI and ATI-based soil moisture can improve the modeling accuracy of turbulent fluxes and soil moisture with the developed data assimilation scheme.