Reviews of methods for land surface temperature retrieval from Landsat thermal infrared data

Abstract

Land Surface Temperature (LST) is a pivotal factor in the energy exchange procedure between the land surface and the atmosphere. It plays a critical role in various study fields, including regional and global climate change analysis, environment monitoring, evapotranspiration estimation, and geothermal anomaly exploration. How to accurately capture LST from satellites data is one of the international hot spots and frontier topics in the quantitative remote sensing of surface parameters, and numbers of algorithms and products have been developed since 1960s. Specially, due to the advantage of high-spatial resolution, temporal continuity, and data availability, Landsat thermal infrared (TIR) data is generally used for LST retrieval. Landsat sensors and related LST products are introduced in detail at this paper, involving in Landsat 4-5 TM, Landsat 7 ETM+, and Landsat 8 TIRS. By analyzing the abundant academic papers, this article reviews the related publications and citations from 2000 to 2020 about Landsat LST retrieval by dividing them into two parts: algorithm and application. Furthermore, this paper systematically describes the algorithms for LST retrieved from Landsat TIR data including the Radiative Transfer Equation (RTE)-based algorithm, the mono-window algorithm, the generalized single-channel algorithm, the practical single-channel algorithm, and the split-window algorithm. On this basis, this article introduces the methods to obtain relevant parameters of each algorithm including atmospheric parameters and land surface emissivity. Furthermore, the calculation of atmospheric parameters mainly depends on water vapor and air temperature near the surface and atmospheric profiles, which can be obtained in three ways including ground-based sounding data, satellite inversion and reanalysis data. The methods estimating land surface emissivity depend on surface classification and NDVI images. Additionally, the superiority of high-spatial resolution LST from Landsat products makes them often applied to urban heat island effect, disaster monitoring, the LST impact for land use and land cover, where the studies require high-precision satellite images to facilitate detailed topics. With the development of science and technology, high-resolution data makes current problems in LST retrieval more and more obvious. According to the analysis for academic papers in the past 20 years, the research on the algorithm and application of LST retrieval based on Landsat TIR data shows an overall upward trend, and the Landsat LST retrieval and application will continuously play the important role in the future. Therefore, the prospective research trend and directions are proposed for Landsat TIR data, and this paper pointes out 4 directions for subsequent studies, including LST retrieval at the complex terrain region, LST retrieval under the cloud cover, spatio-temporal fusion of multi-source data, and long-term serial LST products. Finally, this article indicates that the uncertainty of land surface emissivity, real complex land surface, and banding effect causing LST errors. Therefore, more scholars should pay attention to these problems and actively propose new methods to solve the current deficiency. Moreover, it is helpful to further understand the mechanism of LST retrieval from remote sensing, provide inspiration for the establishment of new methods for remote sensing retrieval of LST, and promote the research level of quantitative remote sensing of LST in China..