Land surface temperature (LST) is a crucial parameter that reflects land-atmosphere interaction and has thus attracted wide interest from geoscientists. Owing to the rapid development of Earth observation technologies, remotely sensed LST is playing an increasingly essential role in various fields. This review aims to summarize the progress in LST estimation algorithms and accelerate its further applications. Thus, we briefly review the most-used thermal infrared (TIR) LST estimation algorithms. More importantly, this review provides a comprehensive collection of the widely used TIR-based LST products and offers important insights into the uncertainties in these products with respect to different land cover conditions via a systematic intercomparison analysis of several representative products. In addition to the discussion on product accuracy, we address problems related to the spatial discontinuity, spatiotemporal incomparability, and short time span of current LST products by introducing the most effective methods. With the aim of overcoming these challenges in available LST products, much progress has been made in developing spatiotemporal seamless LST data, which significantly promotes the successful applications of these products in the field of surface evapotranspiration and soil moisture estimation, agriculture drought monitoring, thermal environment monitoring, thermal anomaly monitoring, and climate change. Overall, this review encompasses the most recent advances in TIR-based LST and the state-of-the-art of applications of LST products at various spatial and temporal scales, identifies critical further research needs and directions to advance and optimize retrieval methods, and promotes the application of LST to improve the understanding of surface thermal dynamics and exchanges.Plain Language Summary Land surface temperature (LST) is a crucial geophysical parameter related to surface energy and water balance of the land-atmosphere system. Satellite remote sensing provides the best way to measure LST and generate various LST products at regional and global scales. In this review, to facilitate the application of LST products in different fields, we first present the physical meaning of satellite-derived LST. Subsequently, we summarize recent advances in LST retrieval and validation methods, with a special focus on the state-of-the-art product collections, product accuracies and intercomparisons, and main problems in current LST products as well as their possible solutions. Additionally, we also review the major applications of LST products in agricultural drought monitoring, thermal environment monitoring, thermal anomaly monitoring, and climate change. Finally, we offer recommendations or perspectives to promote LST retrieval methods and their applications. This review will aid the user in gaining a thorough comprehensive understanding of satellite-derived LST products and promoting their appropriate applications. LI ET AL.

The derivation of widely used geometric optical (GO) kernels in bidirectional reflectance distribution function models, that is, LiSparseReciprocal kernel (K GOLSR ) and LiDenseReciprocal kernel, was based on two important assumptions: (1) The shaded components are perfect black and (2) the contributions of two sunlit components are identical. Different from the bidirectional reflectance, thermal radiation directionality effect mainly results from component temperature differences, suggesting the above assumptions are not applicable in most situations. Therefore, this study derived GO kernels for thermal radiation based on temperature differences rather than illumination differences. Specifically, four GO kernels, that is, K GO4 with considering sunlit/shaded vegetation and sunlit/shaded soil, K GO3 with considering sunlit/shaded soil and vegetation, K GO2 with considering vegetation and soil, and K GOg only considering the hottest sunlit soil, have been developed. By using a comprehensive simulated data set, their performances have been thoroughly evaluated and the comparison with K GOLSR has also been analyzed in depth. Results showed that (1) K GO4 had the highest accuracy and K GO3 was the second; in the case of only two available angles, K GOg performed best.(2) Variables such as component temperature, component emissivity, solar zenith angle, and the percentage of tree crown cover mainly affected the comparison result between K GOLSR and K GO2 ; K GOLSR would have a better performance for a scene with a stronger vegetation effect. Moreover, the best values of two structure characteristics, that is, the crown shape parameter b/r and relative height h/b, for these five kernels have been determined, which can provide instruction for practical application. Plain Language SummaryLand surface temperature is a key variable for a variety of geoscientific studies. However, because of the heterogeneity of land surface, this important parameter has a significant angular effect. Previous studies mainly learned from directional reflectance models. Therefore, in this study, we developed four geometric optical kernels for correcting directional effect of land surface temperature from the inherent characteristic of thermal radiation. These new kernels not only own clearer physical meaning but also have higher accuracies. Moreover, we also determined the best values of two structure characteristics to provide instruction for practical application.