| Landsat卫星热红外数据地表温度遥感反演研究进展 |
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| 引用本文： | 段四波,茹晨,李召良,王猛猛,徐涵秋,历华,吴鹏海,占文凤,周纪,赵伟,任华忠,吴骅,唐伯惠,张霞,尚国琲,覃志豪.Landsat卫星热红外数据地表温度遥感反演研究进展[J].遥感学报,2021,25(8):1591-1617. |
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| 作者姓名： | 段四波 茹晨 李召良 王猛猛 徐涵秋 历华 吴鹏海 占文凤 周纪 赵伟 任华忠 吴骅 唐伯惠 张霞 尚国琲 覃志豪 |
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| 作者单位： | 1.中国农业科学院 农业资源与农业区划研究所, 北京 100081;2.中国科学院大学 资源与环境学院, 北京 100049;3.中国地质大学(武汉) 地理与信息工程学院, 武汉 430074;4.福州大学 遥感信息工程研究所, 福州 350108;5.中国科学院空天信息创新研究院, 北京 100101;6.安徽大学 资源与环境工程学院, 合肥 230601;7.南京大学 国际地球系统科学研究所, 南京 210023;8.电子科技大学 资源与环境学院, 成都 611731;9.中国科学院水利部成都山地灾害与环境研究所, 成都 610041;10.北京大学 地球与空间科学学院 遥感与地理信息系统研究所, 北京 100871;11.中国科学院地理科学与资源研究所 资源与环境信息系统国家重点实验室, 北京 100101;12.昆明理工大学 国土资源工程学院, 昆明 650093;13.河北地质大学 河北省农业干旱遥感监测国际联合研究中心, 石家庄 050031 |
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| 基金项目： | 国家自然科学基金（编号：41871275，42071326，42001300） |
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| 摘 要： | 作为驱动地表与大气之间能量交换的关键物理量，地表温度在众多领域中都发挥着重要作用，包括气候变化、环境监测、蒸散发估算以及地热异常勘探等。Landsat热红外数据因其时间连续性和高空间分辨率等特点被广泛应用于地表温度反演中。本文详细地介绍了Landsat热红外传感器及其可用的数据与产品的现状，梳理了2001年—2020年20年间基于Landsat热红外数据的地表温度遥感反演与应用的相关文献发表及互引情况，系统地综述了基于Landsat热红外数据的地表温度反演算法，包括基于辐射传输方程的算法、单窗算法、普适性单通道算法、实用单通道算法和分裂窗算法等。在此基础上，进一步介绍了每种算法的参数化方案，包括地表比辐射率和大气参数的估算方法。最后针对Landsat热红外数据地表温度遥感反演提出了未来可能的发展趋势与研究方向。
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| 关 键 词： | Landsat 热红外数据 地表温度 地表比辐射率 大气参数 |
| 收稿时间： | 2021/5/12 0:00:00 |
Reviews of methods for land surface temperature retrieval from Landsat thermal infrared data |
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| DUAN Sibo,RU Chen,LI Zhaoliang,WANG Mengmeng,XU Hanqiu,LI Hu,WU Penghai,ZHAN Wenfeng,ZHOU Ji,ZHAO Wei,REN Huazhong,WU Hu,TANG Bohui,ZHANG Xi,SHANG Guofei,QIN Zhihao.Reviews of methods for land surface temperature retrieval from Landsat thermal infrared data[J].Journal of Remote Sensing,2021,25(8):1591-1617. |
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| Authors: | DUAN Sibo RU Chen LI Zhaoliang WANG Mengmeng XU Hanqiu LI Hu WU Penghai ZHAN Wenfeng ZHOU Ji ZHAO Wei REN Huazhong WU Hu TANG Bohui ZHANG Xi SHANG Guofei QIN Zhihao |
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| Institution: | 1.Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China;2.College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China;3.School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China;4.Institute of Remote Sensing Information Engineering, Fuzhou University, Fuzhou 350108, China;5.Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China;6.School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China;7.International Institute for Earth System Science, Nanjing University, Nanjing 210023, China;8.School of Resource and Environment, University of Electronic and Technology of China, Chengdu 611731, China;9.Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China;10.Institution of Remote Sensing and Geographical Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China;11.State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;12.Faculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming 650093, China;13.Hebei International Joint Research Center for Remote Sensing of Agricultural Drought Monitoring, Hebei GEO University, Shijiazhuang 050031, China |
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| 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.. |
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| Keywords: | Landsat thermal infrared data land surface temperature land surface emissivity atmospheric parameter |
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