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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 ϕ m a x 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. [ABSTRACT FROM AUTHOR]

The refractive index structure constant C n 2 near the ocean surface is an important parameter for studying atmospheric optical turbulence over the ocean. The measured refractive index structure constant and meteorological parameters, such as temperature and three-dimensional wind speed, near the sea surface on the South China Sea during the period from January to November 2020 were analyzed. On the basis of Monin–Obukhov similarity theory, the dimensionless temperature structure parameter function f T near the sea surface was established, and a new parameterized model of the near-sea surface was proposed. The new model improved the error of the widely used model proposed by Wyngaard in 1973 (W73) and better reproduced the daily variation in the measured C n 2 . Further analysis of the seasonal applicability of the new model indicated that the correlation coefficients between the estimated and measured C n 2 in the spring, summer, autumn, and winter were 0.94, 0.94, 0.95, and 0.89, respectively, and the root mean square errors were 0.32, 0.41, 0.46, and 0.40 m−2/3, respectively. Compared with the C n 2 estimated by the W73 model, the correlation coefficient of C n 2 estimated by the new model and measured by the micro-thermometer increased by 0.05–0.27 and the root mean square error decreased by 0.04–0.56. The improved f T demonstrated higher accuracy than the existing models, which can lay a foundation for estimating turbulence parameters in different sea areas. [ABSTRACT FROM AUTHOR]