UAV-Based Thermal Radiation Directionality Capture and Its Evaluation on Kernel-Driven Models

In order to correct the land surface temperature (LST) errors caused by angular effects to normalize the multisource data, various models have been proposed, especially the kernel-driven model shows good prospects for development. However, there are fewer studies on the validation of thermal radiation directionality (TRD) characteristics of features based on real measurements. In this article, a method based on unmanned aerial vehicle (UAV) observation of TRD characteristics of different surfaces is proposed to validate the TRD, which uses the circling flight mode to obtain the thermal radiation characteristics of features in multiple directions. Observations show that there is a significant hotspot effect in the three vegetation scenes, and the TRD and dispersion degree are different due to the differences in vegetation structure. For the realization of further applications of UAV-based methods for measuring TRD, the UAV-based angular observations are evaluated against the simulation results of the current kernel-driven model with high accuracy, and the results show that the correlation coefficients are all greater than 0.78, the coefficients of determination are greater than 0.60, and the root-mean-square error (RMSE) is less than 1. However, the ability of the model to extrapolate forward varies considerably in different vegetation scenes. The present study provides an effective and potential way to observe the TRD, which can lay a practical foundation for the theoretical study and model simulation of the TRD.