| 利用单窗算法反演Landsat 8 TIRS数据地表温度 |
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| 引用本文： | 胡德勇,乔琨,王兴玲,赵利民,季国华.利用单窗算法反演Landsat 8 TIRS数据地表温度[J].武汉大学学报(信息科学版),2017,42(7):869-876. |
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| 作者姓名： | 胡德勇 乔琨 王兴玲 赵利民 季国华 |
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| 作者单位： | 1.首都师范大学资源环境与旅游学院, 北京, 100048 |
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| 基金项目： | 国家自然科学基金41671339国防科工局民用航天“十二五”预研项目D030101 |
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| 摘 要： | 以Landsat 8为数据源，并结合地表发射率、大气透过率等参数遥感估算方法，提出了针对TIRS 10数据的单窗算法TIRS10_SC，并开展了研究区的地表温度反演3种单窗算法的对比研究。结果表明，TIRS10_SC算法紧密结合Landsat8 TIRS传感器的特性，通过遥感估算城区下垫面的地表发射率、大气透过率等特征，可以较为准确地估算出地表不同覆被类型的温度；裸土与水泥下垫面等相对均质的下垫面的温度反演效果稍好，TIRS10_SC算法和Q_SC算法其平均误差为0.60℃，JM_SC算法其平均误差为1.01℃；对于植被下垫面，TIRS10_SC算法和Q_SC算法其平均误差为1.48℃，JM_SC算法其平均误差为1.26℃，为了提升城区植被下垫面温度反演精度，应该进一步准确地量化其发射率特性。
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| 关 键 词： | 热红外遥感 地表温度 单窗算法 Landsat 8 TIRS |
| 收稿时间： | 2015-12-09 |
Comparison of Three Single-window Algorithms for Retrieving Land-Surface Temperature with Landsat 8 TIRS Data |
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| Institution: | 1.College of Resource Environment & Tourism, Capital Normal University, Beijing 100048, China2.Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China3.Information Center, Ministry of Civil Affairs, Beijing 100721, China4.Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China |
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| Abstract: | Land surface temperature (LST) is one of the important biophysical variables affecting the exchange of water and energy between land-surface and atmosphere, and it is significant to retrieve LST accurately. The mono-window algorithm is more applied in Landsat TM 6 data, including Jiménez-Mu?oz mono-window algorithm (JM_SC) and Qin Zhihao mono-window algorithm (Q_SC). There are a lot of changes for Landsat8 thermal infrared sensor (TIRS) compared with Landsat TM6. Thus a mono-window algorithm for Landsat 8 data (TIRS10_SC) was proposed first, and then some comparison and analysis of three mono-window algorithms were conducted in this paper. The results show that:(1) The TIRS10_SC algorithm is closely integrated with the characteristics of Landsat8 TIRS sensor and it performs well to retrieve LST of different land-cover types based on the retrieval of atmospheric transmittance and land-surface emissivity. (2) Through the comparative analysis, it is found that the retrieval accuracy with Q_SC and TIRS10_SC is higher than JM_SC algorithm. (3) The retrieval results of homogeneous underlying surfaces such as bare soil land and cement surfaces are more accurate than vegetated surfaces. For bare soil land and cement surfaces, the average error of TIRS10_SC and Q_SC algorithm is 0.60℃, and that of JM_SC is 1.01℃; for vegetated surfaces, the average error of TIRS10_SC and Q_SC algorithm is 1.48℃, and that of JM_SC is 1.26℃.In order to improve the LST retrieval accuracy of vegetated surfaces in urban areas, the land-surface emissivity characteristics of vegetated surfaces need to be quantified more accurately. |
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