Publications

Cui, YK; Song, LS; Fan, WJ (2021). Generation of spatio-temporally continuous evapotranspiration and its components by coupling a two-source energy balance model and a deep neural network over the Heihe River Basin. JOURNAL OF HYDROLOGY, 597, 126176.

Abstract
Evapotranspiration (ET) and its components of soil evaporation (E) and vegetation transpiration (T), as key variables for the water-energy exchange between the land surface and the atmosphere, are widely used in hydrological and agricultural applications. The land surface temperature based two-source energy balance (TSEB) model can provide high accuracy E, T and ET, which are spatio-temporally discontinuous, whereas the spatio-temporally continuous daily ET is more helpful in water resources management. In this study, to improve the continuity of estimates from the TSEB model, we developed a new combined model coupling the TSEB model and deep neural network (DNN) (TSEB_DNN). First, spatio-temporally continuous reference data was prepared based on the remote sensing and meteorological data as input, and E from soil and T from vegetation were obtained from the TSEB model under clear-sky condition as outputs. Then, the DNN was trained under clear-sky condition to obtain the relationship between E and T estimates from TSEB and reference data. Finally, the trained DNN was driven by the spatio-temporally continuous reference data to obtain spatio-temporally continuous E, T and ET. Compared with the ET estimates from the original TSEB model, the continuity was significantly improved for the TSEB_DNN model. The TSEB_DNN model was well consistent with the in situ measurements and had the overall correlation coefficient (R), root-mean-square-error (RMSE), and bias values of 0.88, 0.88 mm d(-1), and 0.37 mm d(-1), respectively. The ratio of T/ET estimates from the TSEB_DNN model had high accuracy against in situ measurements with RMSE and bias values of 7.49% and -2.22%, respectively. The combined model and the maps of E, T and ET will help improve water resource management.

DOI:
10.1016/j.jhydrol.2021.126176

ISSN:
0022-1694