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Analysis of turbulent heat fluxes in urban forests is crucial for understanding structural variations in the urban sub-surface boundary layer. This study used data captured by an unmanned aerial vehicle (UAV) and an improved semi-empirical triangle method to estimate small-scale turbulent heat fluxes in the sub-surface of an urban forest. To improve the estimation accuracy, the surface temperature (TS) of the UAV-based remote sensing inversion was corrected using the hot and cold spot correction method, and the process of calculating ϕmax using the traditional semi-empirical triangle method was improved to simplify the calculation process and reduce the number of parameters in the model. Based on this method, latent heat fluxes (LE) and sensible heat fluxes (H) were obtained with a horizontal resolution of 0.13 m at different time points in the study area. A comparison and validation with the measured values of the eddy covariance (EC) system showed that the absolute error of the LE estimates ranged from 4.43 to 23.11 W/m2, the relative error ranged from 4.57% to 25.33%, the correlation coefficient (r) with the measured values was 0.95, and the root mean square error (RMSE) was 35.96 W/m2, while the absolute error of the H estimates ranged from 3.42 to 15.45 W/m2, the relative error ranged from 7.51% to 28.65%, r was 0.91, and RMSE was 9.77 W/m2. Compared to the traditional triangle method, the r of LE was improved by 0.04, while that of H was improved by 0.06, and the improved triangle method was more accurate in estimating the heat fluxes of urban mixed forest ecosystems in the region. Using this method, it was possible to accurately track the LE and H of individual trees at the leaf level.
Liu et al. (Thu,) studied this question.