Using Simplified Thermal Inertia to Determine the Theoretical Dry Line in Feature Space for Evapotranspiration Retrieval

摘要

With the development of quantitative remote sensing, regional evapotranspiration (ET) modeling based on the feature space has made substantial progress. Among those feature space based evapotranspiration models, accurate determination of the dry/wet lines remains a challenging task. This paper reports the development of a new model, named DDTI (Determination of Dry line by Thermal Inertia), which determines the theoretical dry line based on the relationship between the thermal inertia and the soil moisture. The Simplified Thermal Inertia value estimated in the North China Plain is consistent with the value measured in the laboratory. Three evaluation methods, which are based on the comparison of the locations of the theoretical dry line determined by two models (DDTI model and the heat energy balance model), the comparison of ET results, and the comparison of the evaporative fraction between the estimates from the two models and the in situ measurements, were used to assess the performance of the new model DDTI. The location of the theoretical dry line determined by DDTI is more reasonable than that determined by the heat energy balance model. ET estimated from DDTI has an RMSE (Root Mean Square Error) of 56.77 W/m(2) and a bias of 27.17 W/m(2); while the heat energy balance model estimated ET with an RMSE of 83.36 W/m(2) and a bias of -38.42 W/m(2). When comparing the coeffcient of determination for the two models with the observations from Yucheng, DDTI demonstrated ET with an R-2 of 0.9065; while the heat energy balance model has an R-2 of 0.7729. When compared with the in situ measurements of evaporative fraction (EF) at Yucheng Experimental Station, the ET model based on DDTI reproduces the pixel scale EF with an RMSE of 0.149, much lower than that based on the heat energy balance model which has an RMSE of 0.220. Also, the EF bias between the DDTI model and the in situ measurements is 0.064, lower than the EF bias of the heat energy balance model, which is 0.084.