Comparison of four algorithms to retrieve land surface temperature using Landsat 8 satellite

SONG Ting; DUAN Zheng; LIU Junzhi; SHI Junzhe; YAN Fei; SHENG Shijie; HUANG Jun; WU Wei

doi:10.11834/jrs.20154180

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Comparison of four algorithms to retrieve land surface temperature using Landsat 8 satellite
Vol. 19, Issue 3, Pages: 451-464(2015)
Published：2015
DOI： 10.11834/jrs.20154180
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[1]宋挺,段峥,刘军志,石浚哲,严飞,盛世杰,黄君,吴蔚.Landsat 8数据地表温度反演算法对比[J].遥感学报,2015,19(03):451-464. DOI： 10.11834/jrs.20154180.

SONG Ting, DUAN Zheng, LIU Junzhi, et al. Comparison of four algorithms to retrieve land surface temperature using Landsat 8 satellite[J]. Journal of Remote Sensing, 2015, 19(3): 451-464. DOI： 10.11834/jrs.20154180.

摘要

随着卫星遥感技术的发展

利用遥感反演地表温度的方法不断出现

如劈窗法、双角度法和单通道算法等。Landsat系列卫星的遥感数据是地表温度反演的重要数据之一。本文选择无锡周边区域为研究区

利用Landsat 8卫星遥感数据

对两种劈窗算法（Juan C.Jiménez-Muoz劈窗算法和Offer Rozenstein劈窗算法）和两种单窗算法（Juan C.Jiménez-Muoz单通道算法和覃志豪单窗算法）的地表温度反演精度进行了对比和敏感性分析。采用太湖16个浮标站的实测数据来验证了4种算法的反演精度。结果表明:两种劈窗算法的精度较高且较为接近

误差为0.7 K左右;覃志豪单窗算法和Juan C.Jiménez-Muoz单通道算法精度较低

误差分别为1.3 K和1.4 K左右。Juan C.Jiménez-Muoz劈窗算法对参数的敏感性最低

Juan C.Jiménez-Muoz单通道算法次之

覃志豪单窗算法和Offer Rozenstein劈窗算法敏感性相对最高。其中Juan C.Jiménez-Muoz单通道算法只适用于一定的水汽含量范围

有一定的局限性。

Abstract

Land Surface Temperature（ LST） plays an important role in energy exchange between the land surface and the atmosphere. LST is a key variable in many applications

such as land surface modeling. Many satellite-based algorithms have been proposed to retrieve LST

such as Split-Window（ SW）

dual-angle

and single-channel algorithms. In this study

four satellitebased LST retrieval algorithms

including two SW algorithms（ Juan C. Jiménez-Muoz and Offer Rozenstein SW algorithms） and two mono-window algorithms（ Juan C. Jiménez-Muoz and Qin Zhihao mono-window algorithms）

were compared with Landsat-8satellite data over the region around Wuxi City. The accuracy of the four algorithms was evaluated against the ground measurements from 16 floating stations over Lake Tai. The results showed that the performance of the two SW algorithms

which have an average error of 0. 7 K

was better than that of the SW algorithms

which have an average error of 1. 3—1. 4 K

when compared with ground measurements. The sensitivity analysis of these algorithms showed that the Juan C. Jiménez-Muoz SW algorithm was the least sensitive to key input parameters（ emissivity and water vapor）

whereas the Offer Rozenstein SW algorithm and the Qin Zhihao mono-window algorithm showed high sensitivity to input parameters. The limitations of these four LST retrieving algorithms were also discussed.