| 热红外地表温度遥感反演方法研究进展 |
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| 引用本文： | 李召良,段四波,唐伯惠,吴骅,任华忠,阎广建,唐荣林,冷佩.热红外地表温度遥感反演方法研究进展[J].遥感学报,2016,20(5):899-920. |
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| 作者姓名： | 李召良 段四波 唐伯惠 吴骅 任华忠 阎广建 唐荣林 冷佩 |
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| 作者单位： | 中国农业科学院 农业资源与农业区划研究所 农业部农业信息技术重点实验室, 北京 100081;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,中国农业科学院 农业资源与农业区划研究所 农业部农业信息技术重点实验室, 北京 100081,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,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,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,北京师范大学 遥感科学国家重点实验室, 地理学与遥感科学学院, 北京 100875,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,中国农业科学院 农业资源与农业区划研究所 农业部农业信息技术重点实验室, 北京 100081 |
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| 基金项目： | 国家自然科学基金项目（编号：41231170，41501406） |
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| 摘 要： | 地表温度是表征地表过程变化的一个非常重要的特征物理量,是地表—大气能量交换的直接驱动因子,广泛地用于地表能量平衡、气候变化和资源环境监测等研究领域。本文系统地评述了热红外地表温度遥感反演方法,包括单通道算法、多通道算法、多角度算法、多时相算法和高光谱反演算法。回顾了地表温度反演的基础理论和方法;并在此基础上,进一步综述了地表温度遥感反演的验证方法,以及地表温度的时间和角度归一化方法;最后对未来提高地表温度反演精度的研究方向提出了建议。
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| 关 键 词： | 地表温度 热红外数据 反演 验证 |
| 收稿时间： | 2016/5/15 0:00:00 |
| 修稿时间： | 2016/7/1 0:00:00 |
Review of methods for land surface temperature derived from thermal infrared remotely sensed data |
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| LI Zhaoliang,DUAN Sibo,TANG Bohui,WU Hu,REN Huazhong,YAN Guangjian,TANG Ronglin and LENG Pei.Review of methods for land surface temperature derived from thermal infrared remotely sensed data[J].Journal of Remote Sensing,2016,20(5):899-920. |
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| Authors: | LI Zhaoliang DUAN Sibo TANG Bohui WU Hu REN Huazhong YAN Guangjian TANG Ronglin and LENG Pei |
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| Institution: | Key Laboratory of Agri-informatics, Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China;State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China,Key Laboratory of Agri-informatics, Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China,State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China,State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China,Institute of Remote Sensing and Geographic Information System, Peking University, Beijing 100871, China,School of Geography, State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China,State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China and Key Laboratory of Agri-informatics, Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China |
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| Abstract: | Land Surface Temperature (LST) is a key parameter in the physical processes of surface energy and water balance at local and global scales. Knowledge of LST provides information on the temporal and spatial variations of the surface equilibrium state and is of fundamental importance in many applications. This paper systematically surveys the methods for LST derived from thermal infrared remotely sensed data. These methods include single-channel, multi-channel, multi-angle, multi-temporal, and hyperspectral retrieval methods. To provide potential LST users with reliable information regarding the quality of the LST product and to provide feedback to the developers of LST retrieval algorithms for future improvement, assessing the accuracy of the retrieved LST is necessary. We review the methods used to validate LST derived from thermal infrared remotely sensed data, including temperature-based, radiance-based, and inter-comparison methods. The advantages and disadvantages of these methods are discussed. Furthermore, we review the temporal and angular normalization methods of satellite-derived LST. Finally, we present suggestions for future research to improve the accuracy of satellite-derived LST. |
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| Keywords: | land surface temperature thermal infrared data retrieval validation |
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