A Two-Step Reconstruction Framework for Mapping Seamless All-Weather Daily Evapotranspiration Using Thermal Infrared Data

Spatio-temporally continuous daily evapotranspiration (ET) is essential for characterizing water and energy exchange and scheduling efficient water management. ET has conventionally been generated using thermal infrared-based models, cloud contamination of satellite data could prohibit accurate estimates of spatial continuous daily ET. Although approaches have been applied to fill these spatial gaps introduced by thermal infrared data, they contain extensive uncertainties and still have gaps. Here, we proposed a two-step reconstruction framework to generate seamless daily ET dataset based on outputs from a soil moisture coupled two-source energy balance (TSEB-SM) model. In these two steps, a deep neural network trained with the outputs of TSEB-SM was used to reconstruct the gaps in daily ET images, which mainly introduced by the missing inputs. Then the remained gaps were filled with reference ET (ETo) directly. The estimated daily ET agrees well with ground measurements across different landcover types with a RMSE of 1.0 mm day<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−1</sup> and a bias of only 0.2 mm day<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−1</sup>. In terms of spatial distributions and temporal dynamics, the generated daily ET has better consistency with its impacting factors, including the landcover map, land surface temperature, downward solar radiation, etc. Our results suggest that this reconstruction framework can generate reliable seamless daily ET dataset, which has high potential for application in crop water consumption monitoring, crop yield prediction and efficient water management.