Observation of the winter regional evaporative fraction using a UAV-based eddy covariance system over wetland area

The observation of regional evaporative fraction (EF) over a heterogeneous area is crucial to surface energy balance modeling and satellite-based evapotranspiration (ET) validation over complex areas. However, regional EF observations are often lacking or not directly acquired because the sparse distribution and inadequate spatial representativeness of ground measurements cannot represent the region's heterogeneity in landscape and land-surface processes. The UAV-based eddy-covariance (EC) system can be used to observe the regional EF at large areas. UAV-based EC measurements were performed in December 2020 at around 90 m above the ground level over Yancheng coastal wetland, which included 7 flights for comparison with ground EC measurements and 4 flights for investigating the regional EF. The results of comparison showed that the sensible and latent heat fluxes from UAV were consistent with those measured from the ground, with the R2 of 0.84, RMSE of 15.7 W/m 2, bias of 50.7 W/m 2 for sensible heat flux, and the R2 of 0.77, RMSE of 13.1 W/m 2, bias of -3.67 W/m 2 for latent heat flux. Then, 53 flux observations were obtained from the 4 regional flights after applying a quality filter. A footprint model in conjunction with a high-resolution land cover map were used to determine the fluxes contribution source areas and to determine the accumulated footprint weight of the various land cover classes within the footprint areas. Thirdly, a multiple linear regression model was used to dis-aggregate the observed fluxes into component land-cover-class-specific fluxes, and to resolve the EF of each land cover class. The dis-aggregation results revealed that cropland had the highest EF (0.69 ± 0.11), followed by Spartina alterniflora (0.3 ± 0.11) and Phragmites australis (0.27 ± 0.09). Lastly, the EF of Phragmites australis was compared with ground measurement and had a relative error of 2.9%, demonstrating that the UAV-based EC system provided a reliable observation of regional EF.