数字农科院2.0

Satellite Remote Sensing of Global Land Surface Temperature: Definition, Methods, Products, and Applications

文献类型： 外文期刊

作者： Zhao Liang Li;Hua Wu;Si Bo Duan;Wei Zhao;Huazhong Ren;Xiangyang Liu;Pei Leng;Ronglin Tang;Xin Ye;Jinshun Zhu;Yingwei Sun;Menglin Si;Meng Liu;Jiahao Li;Xia Zhang;Guofei Shang;Bo Hui Tang;Guangjian Yan;Chenghu Zhou

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期刊名称： Reviews of Geophysics

ISSN： 8755-1209

年卷期： 2023 年 61 卷 1 期

页码：

收录情况： SCIE（2023版） ; ; EI（2023版）

摘要： 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.

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