matlab code of TSEB_DNN
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'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' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["Evaporation and transpiration","Two-source energy balance","Deep neural network","Water resources management","Remote sensing"]} <|eot_id|>
 Here is the Description of a dataset. Description:'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' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
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
Evaporation and transpiration; Two-source energy balance; Deep neural network; Water resources management; Remote sensing