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.

Land surface temperature (LST) is one of the key parameters in the process of energy exchange between the land surface and atmosphere, and thermal infrared (TIR) remote sensing is an important approach to efficiently obtain LST over a large area. Algorithms for retrieval of LST from TIR remote sensing data have been studied for decades, and the split-window (SW) algorithm can directly eliminate atmospheric effects by using the brightness temperature (BT) at the top of the atmosphere in two adjacent TIR channels and, thus, is widely applied. Landsat-9, the latest launch in the Landsat series of satellites, provides two-channel TIR images with the same 100-m spatial resolution as Landsat-8, and it is meaningful to develop the SW algorithm for LST retrieval using Landsat-9 data. In this letter, four SW algorithms were developed, and the accuracy and noise sensitivity of the results under different observation conditions were compared based on the simulation dataset to select the algorithm with the best performance. The ground measurement data under different land cover types and the global Landsat-9 LST products, produced by the single-channel algorithm, were selected to verify the accuracy of the proposed algorithm. The results show that the ground validation accuracy is about 1.574 K, better than the Landsat-9 existing LST product. Moreover, the retrieved LST images have similar spatial distribution to the Landsat-9 LST products, with RMSEs from 0.31 to 2.87 K in various regions.

Land surface temperature (LST) is an important parameter affecting ground-air energy exchange, and can be applied in many fields such as drought monitoring, evapotranspiration estimation, and crop yield assessment. The single-channel (SC) algorithm only needs thermal infrared (TIR) data with only one channel to retrieve the LST, but requires accurate atmospheric correction, which is difficult to guarantee in many cases. The split-window (SW) algorithm exploits the difference in atmospheric absorption of adjacent channels and can accurately obtain the LST without atmospheric parameters, but the input requirement of two TIR channels makes the application limited. Some previous studies found that there is a strong correlation between the top-of-atmosphere thermal radiance in adjacient TIR channels with similar wavelength ranges, which makes it possible to decompose a single TIR channel into two TIR channels with close wavelengths using empirical relationships with errors smaller than those caused by the uncertainty of atmospheric correction. In this article, Landsat-7 ETM+ TIR data was decomposed into two virtual TIR channels by fusing with the simultaneously observed Terra moderate resolution imaging spectroradiometer (MODIS) data, and the channel decomposed SW (TCD-SW) based on the generalized nonlinear SW algorithm was developed and applied to retrieve the LST to eliminate the dependence on atmospheric parameters. The validation results using the simulation dataset, ground-measured site data, and well-validated MODIS sea surface temperature product showed that the proposed TCD-SW algorithm achieved more accurate results than the SC algorithm, being more advantageous in the humid atmosphere. The TCD-SW algorithm can be used as a potential new method for LST retrieval from remote sensing data with only one TIR channel under complex atmospheric conditions.

Thermal infrared (TIR) remote sensing is an important technological tool for observing large-scale land surface thermal radiance and can obtain the spatially continuous land surface temperature (LST), a critical land surface physical parameter of great interest in several fields. After decades of development, various LST retrieval algorithms have been proposed. However, current studies indicated that the commonly used retrieval algorithms show a decrease in the accuracy of the results under humid atmospheric conditions, and the theoretical analysis of the phenomenon needs to be developed. This study derives the LST error as a function of atmospheric parameters (transmittance, upward radiance, and downward radiance) directly based on the TIR radiative transfer equation. Compared with the TIR channel, the mid-infrared (MIR) channel has less water vapor absorption, is more insensitive to water vapor, and has a larger transmittance, which is expected to improve the accuracy of LST retrieval under humid atmospheric conditions. In this study, a typical simulation dataset under various atmospheric and land surface conditions is constructed based on the MIR channels of moderate resolution imaging spectroradiometer (MODIS) remote sensing data. Analysis of the retrieval results based on the simulation datasets shows that the MIR channels have more minor errors for the same level of atmospheric errors. With the growth of column water vapor (CWV), the error of the split-window (SW) algorithm constructed based on the TIR channel increases. In contrast, the accuracy of the algorithm developed by MIR channels is more stable, and the advantage of the accuracy in humid atmospheric conditions is more prominent. Two SW algorithms are applied to nighttime TIR and MIR remote sensing images observed by Aqua MODIS, and the validation results obtained based on surface radiation budget network (SURFRAD) ground sites also showed that the two MIR SW algorithms achieved the accuracy advantage of 0.425 K (SW1_TIR: 2.582 K/SW1_MIR: 2.157 K) and 0.525 K (SW2_TIR: 2.624 K/SW2_MIR: 2.099 K), which indicated that the MIR-SW algorithms can more accurately retrieve the LST under humid atmospheric conditions.