Urban LST Retrieval From the Ultrahigh Spatial Resolution Remote Sensing Data

Urban land surface temperature (ULST) is one of the core parameters in monitoring the urban thermal environment, which has received extensive attention in several study and application areas. Thermal infrared (TIR) remote sensing technology can efficiently observe large-scale land surface thermal radiance information and is a critical approach used to obtain ULST quickly. Traditional LST retrieval algorithms are conducted using the classical radiance transfer equation (RTE) based on the assumption that the land surface is flat, which may be challenging to hold for complex urban landscapes. Moreover, with the improvement of the spatial resolution of remote sensing images, the influence caused by the geometric structure will be more obvious. Various urban thermal radiance transfer (URTE) models have been proposed and successfully applied to TIR remote sensing images with tens of meters spatial resolutions, such as Landsat, ECOSTRESS, and Gaofen-5. Current airborne TIR sensors can observe remote sensing images with ultrahigh spatial resolution (sub-meter). In this paper, using the ensemble learning method based on the ultrahigh spatial resolution urban thermal radiance transfer model (UHURT), a new retrieval algorithm is developed to estimate the LST directly from the observed brightness temperature (BT). The proposed new algorithm applies to ultrahigh spatial resolution remote sensing images. It has the end-to-end advantage of not relying on atmospheric parameters or land surface emissivity, known as in traditional algorithms, thus avoiding the limitations due to the lack of available input data. Validation results based on the simulation dataset showed that the proposed algorithm has higher theoretical accuracy than the traditional split-window (SW) algorithm. As the sky view factor (SVF) decreases, the accuracy advantage becomes more pronounced, growing from 0.149 K (SVF = 1.0) to 1.085 K (SVF = 0.25). The application results in the remote sensing image also indicated that the results of the proposed algorithm (RMSE = 2.093 K) are more accurate than those of the SW algorithm (RMSE = 2.490 K), and the correlation between the resultant error and building density is lower, which can accurately reduce the geometric effect to obtain the ULST better.