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• Upscale airborne heat flux to region with machine learning and remote sensing. • Support vector regression performed best compared to other machine learning models. • The upscaled heat flux maps captured the spatiotemporal variation of surface flux. Acquiring the relative truth values of land surface sensible (H) and latent (LE) heat fluxes at the regional scale is important for monitoring and simulating evapotranspiration at a global scale using satellite data or process-based models. However, it is difficult to directly obtain these values with fine spatial representation at regional scales using conventional ground-based measurement methods. Therefore, airborne measurements of turbulent flux are highly advantageous. By leveraging airborne measurements of H and LE fluxes collected in the Netherlands in August 2008, in this study we evaluated five machine learning methods for the construction of a regional gridded heat flux map, including artificial neural networks, boosted regression trees, random forest regression, deep neural networks, and support vector regression. The models were trained and tested using a dataset compiled from observations of H and LE, the digital elevation model, MODIS land surface temperature, enhanced vegetation index, and albedo data for each flux footprint. The best performing model was used to construct a regional gridded heat flux map over a case region located in the center of the Netherlands. The results shown that the support vector regression performed better than other models, with R 2 = 0.91 , RMSE = 9.6 W / m 2 for H, and R 2 = 0.89 , RMSE = 26.32 W / m 2 for LE. The constructed heat flux maps achieved a relative prediction error of ≤ 32.8 % ( R 2 > 0.71) for H and ≤ 43.5 % ( R 2 > 0.53) for LE compared to aircraft measurements. The constructed heat flux maps were then aggregated for each land cover type, providing individual estimates of source strength (52< H< 66 W/m2 and 108< LE< 218 W/m2) and spatial variability (52< σ H < 66 W/m2 and 108< σ L E < 218 W/m2) with an ensemble precision of < 6% (1 σ). Finally, the limitations and future prospects of the current study were summarized and discussed. [ABSTRACT FROM AUTHOR]

• Evaporative fraction over wetland is measured by UAV-based eddy-covariance system. • Sensible and latent heat fluxes from UAV are consistent with these from ground. • Disaggregation of UAV fluxes is feasible over heterogeneous areas. 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/m2, bias of 50.7 W/m2 for sensible heat flux, and the R2 of 0.77, RMSE of 13.1 W/m2, bias of -3.67 W/m2 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. [ABSTRACT FROM AUTHOR]