单窗算法结合landsat8热红外数据反演地表温度.doc

单窗算法结合 Landsat8 热红外数据反演地表温度 胡德勇 乔琨 王兴玲 赵利民 季国华 首都师范大学资源环境与旅游学院 民政部国家减灾中心 中国科学院遥感与数字地球研究所 摘 要： Landsat 热红外系列数据一直是地表温度反演重要的遥感数据源,目前用于地表温度反演的单窗算法主要针对 Landsat TM/ETM+第 6 波段数据(TM 6)建立的,Landsat 8 热红外传感器(TIRS)与 TM 6 相比有很多变化,因而其单窗算法也需要改进本文以 Landsat 8 TIRS 第 10 波段(TIRS 10)为数据源,提出了针对TIRS 10 的单窗算法(TIRS10_SC),并对研究区地表温度进行反演研究,确定了研究区不同类型地表的温度值研究结果表明:(1)TIRS10_SC 算法可以较好地应用于 Landsat 8 数据的地表温度反演,平均反演误差为 0.83℃,相关系数为0.805,反演温度与模拟数据和实测数据都具有较好的一致性;(2)通过对单窗算法中的地表发射率、大气水汽含量和大气平均作用温度等参数敏感性分析发现,TIRS10 SC 算法能够获得较为可靠的反演结果;同时,TIRS10 SC 算法对大气水汽含量和地表发射率敏感性较高,对大气平均作用温度敏感性稍弱。

该算法对于利用 Landsat 8 TIRS 数据快速反演地表温度具有应用价值关键词： 热红外遥感; 地表温度反演; 单窗算法; Landsat 8 TIRS; MODIS; 作者简介：胡德勇(1974—),男,副教授,主要研究领域为资源环境遥感、自然灾害遥感监测与评估E-mail:deyonghu@作者简介：乔琨(1989—),女,硕士,主要从事环境遥感研究E-mail:qiaoyingying2009@收稿日期：2015-03-17基金：国防科工局民用航天“十二五”预研项目(编号:D030101)Land surface temperature retrieval from Landsat 8 thermal infrared data using mono-window algorithmHU Deyong QIAO Kun WANG Xingling ZHAO Limin JI Guohua College of Resource Environment & Tourism,Capital Normal University; National Disaster Reduction Center of China,Ministry of Civil Affairs; Institute of Remote Sensing and Digital Earth,Chinese Academy of Sciences; Abstract： Land Surface Temperature( LST) is a significant surface biophysical variable. This parameter is also important in vari-ous fields such as urban thermal environment,agricultural monitoring,surface radiation,and energy balance. Data from Landsat satellites are vital remote sensing data for LST retrieval since the 1980 s. The present Landsat 8 Thermal Infrared Sensor( TIRS)imagery provides a new data source for LST retrieval. Landsat 8 TIRS is improved compared with the Landsat 6 Thematic Mapper.Landsat 8 data are extensively applied,so the mono-window algorithm should be updated with new sensor characteristics. Therefore,we aim to explore an adaptive method with more reliable accuracy to retrieve LST using Landsat 8 TIRS data.In this paper,a relation model( TIRS10_ SC) was established between LST and several parameters,namely,brightness temperature,mean atmospheric temperature,atmospheric transmittance,and land surface emissivity. The model was based on the radiative transfer equation and characteristics of Landsat 8 TIRS10. The LSTs of the study area were retrieved by initially deriving the atmospheric transmittance from MODIS data and MODTRAN simulation results. Then,the mean atmospheric temperature was obtained using empirical formulas,and land surface emissivity was retrieved from the Landsat 8 OLI data using image classification-based method. Finally,the LSTs of the study area were retrieved from the processed data. The algorithm and retrieval results were assessed by simulated and measured data. Meanwhile,the sensitivity of variables in themono-window algorithm was analyzed.Results show that the mono-window algorithm can perform well for Landsat8 TIRS data for LST retrieval. The LSTs of different land-cover types in study area varied. The LSTs of bare soil and cements were evidently higher than those of the vegetated areas.The LST of the former varied between 24. 12 ℃ and 32. 25 ℃,whereas that of latter ranged from 10. 72 ℃ to 19. 79 ℃. Furthermore,compared with the measured data,the average error and correlation coefficient of retrieved LSTs were 0. 83 ℃ and 0. 805,respectively. The accuracy of the algorithm was also assessed using simulated data,which showed that the error in the LST data in the majority of cases ranged between 0. 2 ℃ and 0. 3 ℃. The retrieval results agree with the assessed temperature data. Results from the analysis of the sensitivities of land surface emissivity,atmospheric water vapor content,and average temperature showed that the TIRS10_SC algorithm can obtain more reliable results with higher sensitivities for the former two para meters and lower sensitivity for the latter one. The proposed algorithm can be applied for the fast retrieval of LST using Landsat 8 TIRS data.Keyword： thermal remote sensing; land surface temperature retrieval; mono-window algorithm; Landsat 8 TIRS; MODIS; Received： 2015-03-171 引言地表温度是常见的地表生物物理参量之一,在城市热环境、地表辐射能量平衡、全球气候变化等应用领域都有重要研究价值。

热红外遥感探测技术能够获取地表热红外谱段的辐射能量,并基于地表物体的发射率特性反演其热力学温度,因而成为了获取大区域温度值及其时空分布特征的重要途径( 覃志豪等,2005; Sobrino 等,2005; 宋挺等, 2015) 目前地表温度的遥感反演算法包括辐射传输方程法( Sobrino 等,2004; Li 等,2004; 毛克彪等, 2007) 、单窗算法( Qin 等,2001; Jiménez-Muoz 和Sobrino,2003; 周纪等,2011 ) 、分裂窗算法( Wan 和 Dozier,1996; Ri 等,2013 ) 以及多通道多角度算法( Sobrino 等,1996; Gillespie 等,1998; 毛克彪等, 2006) 等,不同的算法适用于不同的遥感传感器的热红外数据( 罗菊花等,2010) Landsat 卫星的热红外系列数据一直是地表温度反演最重要的遥感数据之一,从 Landsat TM、Landsat ETM + 、到 2013 年 3 月发射成功的 Landsat 8 热红外传感器 TIRS( Ther- mal Infrared Sensor) ,Landsat 为遥感用户提供了可供长期、连续观测的热红外遥感图像。

对于最新的 Landsat 8 TIRS 数据,已有研究者通过正演模拟方法,构建模拟数据开展了 TIRS 数据地表温度反演算法研究,如 Jiménez-Muoz 等人( 2014) 对单通道算法和分裂窗算法的反演精度和敏感性进行了对比分析,结果表明随着大气水汽含量的增加,分裂窗算法的精度略高于单通道算法; Rozenstein 等( 2014) 探讨了分裂窗算法反演地表温度的可行性以及相关参数的敏感性; Yu 等( 2014) 对辐射传输方程法、分裂窗算法以及单通道算法 3 种算法的反演精度进行了定量对比分析,结果表明辐射传输方程法精度最高,其次是分裂窗算法、单通道算法 但是,上述提到。