предыдущий|следующий

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

  • role： First author第一作者
  • Affiliation：

    Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China

  • Email：duansibo@caas.cn
  • Introduction：段四波，1983年生，男，研究员，研究方向为热红外定量遥感。E-mail： duansibo@caas.cn
DUAN Sibo1,  
  • Affiliation：

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China

RU Chen2,  
  • role： Corresponding author通信作者
  • Affiliation：

    Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China

  • Email：lizhaoliang@caas.cn
  • Introduction：李召良，1964年生，男，研究员，研究方向为热红外遥感。E-mail： lizhaoliang@caas.cn
LI Zhaoliang1*,  
  • Affiliation：

    School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China

WANG Mengmeng3,  
  • Affiliation：

    Institute of Remote Sensing Information Engineering, Fuzhou University, Fuzhou 350108, China

XU Hanqiu4,  
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

LI Hua5,  
  • Affiliation：

    School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China

WU Penghai6,  
  • Affiliation：

    International Institute for Earth System Science, Nanjing University, Nanjing 210023, China

ZHAN Wenfeng7,  
  • Affiliation：

    School of Resource and Environment, University of Electronic and Technology of China, Chengdu 611731, China

ZHOU Ji8,  
  • Affiliation：

    Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China

ZHAO Wei9,  
  • Affiliation：

    Institution of Remote Sensing and Geographical Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China

REN Huazhong10,  
  • Affiliation：

    State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

WU Hua11,  
  • Affiliation：

    Faculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming 650093, China

TANG Bohui12,  
  • Affiliation：

    Hebei International Joint Research Center for Remote Sensing of Agricultural Drought Monitoring, Hebei GEO University, Shijiazhuang 050031, China

ZHANG Xia13,  
  • Affiliation：

    Hebei International Joint Research Center for Remote Sensing of Agricultural Drought Monitoring, Hebei GEO University, Shijiazhuang 050031, China

SHANG Guofei13,  
  • Affiliation：

    Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China

QIN Zhihao1

реферат

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..

ключеви́че слова́

Landsat;thermal infrared data;land surface temperature;land surface emissivity;atmospheric parameter

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