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遥感技术与应用  2021, Vol. 36 Issue (1): 33-43    DOI: 10.11873/j.issn.1004-0323.2021.1.0033
综述     
复杂地表地表温度反演研究进展
肖尧1,2,3(),马明国1,2,3(),闻建光4,于文凭1,2,3
1.西南大学 地理科学学院重庆金佛山喀斯特生态系统教育部野外科学观测研究站,重庆 400715
2.西南大学 地理科学学院,重庆 400715
3.西南大学 地理科学学院遥感大数据应用重庆市工程研究中心,重庆 400715
4.中国科学院遥感与数字地球研究所,北京 100101
Progress in Land Surface Temperature Retrieval over Complex Surface
Yao Xiao1,2,3(),Mingguo Ma1,2,3(),Jianguang Wen4,Wenping Yu1,2,3
1.Southwest University,School of Geographical Sciences,Chongqing Jinfo Mountain Field Scientific Observation and Research Station for Karst Ecosystem,Ministry of Education,Chongqing 400715,China
2.School of Geographical Sciences,Southwest University,Chongqing 400715,China
3.Chongqing Engineering Research Center for Remote Sensing Big Data Application,School of Geographical Sciences,Southwest University,Chongqing 400715,China
4.Institute of Remote Sensing and Digital Earth,Chinese Academy of Sciences,Beijing 100101,China
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摘要:

地表温度是陆表过程研究的关键参数,卫星反演地表温度是获取区域及全球尺度辐射平衡、能量收支研究中地表温度参数的有效手段。目前,在平坦地表覆盖均一区域,基于热红外和微波遥感反演的地表温度已经被验证具有较好精度,尤其热红外遥感地表温度产品精度可达1 K以内。但是针对复杂地表的温度反演研究仍面临较大挑战。系统总结了复杂地形区地表温度反演的局限性,包括反演模型病态问题、地形复杂性、水汽云雾厚重造成数据缺失、真实性检验不确定性。并在此基础上,对未来复杂地表温度反演精度提高提出了可能实现的途径。

关键词: 地表温度复杂地表反演方法水汽真实性检验    
Abstract:

Land Surface Temperature (LST) is a crucial input parameter in the study of land surface processes. It’s effective to estimate the radiation balance and energy budget at local and global scales using remotely sensed data. Currently, the fast development of LST retrieval algorithm based on thermal infrared and microwave remote sensing has made a series of progress. Its accuracy can reach within 1K in the uniform area of flat surface coverage derived from thermal infrared remotely sensed data especially. However, it is still a great challenge for their application over complex surface area. This paper systematically summarizes the limitations of LST retrieval in complex topographic areas, including pathological absence of inversion model, terrain complexity, data loss caused by thick vapor cloud, and uncertainty of authenticity test. Furthermore, we present suggestions for the future research to improve the accuracy of LST retrieval over complex surface.

Key words: Land surface temperature    Complex surface    Retrieval algorithm    Water vapor    Validation
收稿日期: 2020-01-13 出版日期: 2021-04-13
ZTFLH:  TP79  
基金资助: 国家自然科学基金项目“西南地区复杂地表陆表过程观测与模拟研究”(41830648);“西南地区表层岩溶生态系统碳通量观测、分析与模拟”(41771453)
通讯作者: 马明国     E-mail: xy10086@email.swu.edu.cn;mmg@swu.edu.cn
作者简介: 肖尧(1996-),女,四川成都人,硕士研究生,主要从事陆面过程遥感研究。E?mail:xy10086@email.swu.edu.cn
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引用本文:

肖尧,马明国,闻建光,于文凭. 复杂地表地表温度反演研究进展[J]. 遥感技术与应用, 2021, 36(1): 33-43.

Yao Xiao,Mingguo Ma,Jianguang Wen,Wenping Yu. Progress in Land Surface Temperature Retrieval over Complex Surface. Remote Sensing Technology and Application, 2021, 36(1): 33-43.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2021.1.0033        http://www.rsta.ac.cn/CN/Y2021/V36/I1/33

反演算法作者及发表时间特点
热红外反演单通道算法Price, 1983[11]; Susskind, 1984[12]; Qin, et al, 2001[14]; Mu?oz & Sobrino, 2003[15]; Jiménez-Mu?oz, et al, 2009[4]; Cristobal, et al, 2018[16]对单个红外通道进行建模,需要输入地表发射率、大气传输模型、精确大气廓线。
多通道算法McMillin, 1975[3]; Deschamps & Phulpin, 1980[18]; Becker & Li, 1990[19]; Sobrino, et al, 1996[20]; Franca & Carvalho, 2004[21]; Qian, et al, 2016[22]; Tang, 2018[23]对多个红外通道进行建模,无需大气廓线数据,反演精度较高,地表发射率具有不确定性,对算法结果影响较大。
多角度算法Chedin, et al, 1982[24]; Prata, 1993[25]; Sobrino, et al, 1996[20]; Li, et al, 2001[26]; Ren, 2015[27]根据特定通道在不同角度的亮温差异来消除大气的影响,无需大气廓线数据,数据源稀缺。
多时相算法Watson, 1992[28]; Wan & Li, 1997[29]; Wan, 2008[30]基于地表发射率不随时间变化的假设,无需输入地表发射率,结果对传感器噪声、大气校正误差和影像配准精度敏感,现在主要应用于MODIS地表温度反演。
高光谱反演算法Kanani, et al, 2007[31]; Borel, 2008[32]; Ouyang, et al, 2010[33]; Wang, et al, 2011[34]; Zhong, et al, 2016[35]; Chen, et al, 2019[36]利用地表发射率固有的光谱特征,无需输入地表发射率,需要精确大气校正。
被动微波反演统计模型单通道回归

Mcfarland, 1990[37]; Njoku, 1999[38]; 毛克彪, et al, 2006[39]; Han, et al, 2018[40];

Zhou, et al, 2019[41]

算法简单,模型回归需要大量实验数据,模型系数具有较强局地性。
多通道回归
物理模型微波辐射传输方程Xiang, et al, 1997[42]; Basist, et al, 1998[43]; Fily, et al, 2003[44]; Kohn, et al, 2010[45]; Zhang, et al,2019[46]; Huang, et al, 2019[47]具有物理意义,且反演精度较经验模型高,过于依赖输入参数和假设条件的准确性。
基于发射率求解
基于发射率不变
神经网络模型Zurk, et al, 1992[48]; Aires, et al, 2001[49]; Prigent, et al, 2016[50]; Ermida, et al, 2017[53]; Jiménez, et al, 2017[54]; Mao, et al,2018[55]方法简单易行,但不具有实际物理意义,通过输入有代表性的训练样本建立数学公式推导结果,对训练样本的依赖性较大。
表1  地表温度反演算法及特点
传感器算法时间范围时空分辨率查询及下载地址
MODIS劈窗算法/温度发射率分离算法2000~5min、1 day、8 day、monthly/1 kmhttps:∥modis.gsfc.nasa.gov/
VIIRS劈窗算法/温度发射率分离算法2011~1 day/750 mhttps:∥ladsweb.modaps.eosdis.nasa.gov/
AVHRR劈窗算法2007~1 day/1 kmhttps:∥noaasis.noaa.gov/
AATSR劈窗算法2002~20123 day/ 1 kmhttps:∥earth.esa.int/web/guest/home
ASTER温度发射率分离算法2000~16 day/90 mhttps:∥ims.aster.ersdac.jspacesystems.or.jp/
FY-2 S-VISSR劈窗算法2012~1 hour/5 kmhttp:∥satellite.nsmc.org.cn/
FY-3 VIRR劈窗算法2009~1day/1 kmhttp:∥satellite.nsmc.org.cn/
FY-4A AGRI劈窗算法2019~15 min/4 kmhttp:∥satellite.nsmc.org.cn/
GOES-16 ABI劈窗算法2018~15 min/2 kmhttps:∥www.ospo.noaa.gov/
Landsat系列单通道算法1984~16 day/~100 mhttps:∥earthexplorer.usgs.gov/
MSG-SEVIRI劈窗算法2005~15 min/3 kmhttps:∥www.eumetsat.int/website/
SLSTR劈窗算法2016~1 day/1 kmhttps:∥www.asf.alaska.edu/
表2  常用地表温度产品
检验方法参考对象适用范围特点
基于温度检验地表实测温度下垫面均一,地势平坦地区简单直接,对站点数据质量要求高,不适用于无站点和地物破碎区域
基于辐射检验辐射传输方程模拟辐射值无地面监测站点地区需要输入实测大气廓线和地表发射率,复杂地表区域参数获取困难
交叉验证设为真值的温度产品无实测温度和模拟参数情况对参考产品要求高,山区和云覆盖区产品精度无保证;产品匹配问题影响验证结果
时间序列验证目标物长序列变化传感器本身监测传感器运行时间较长,对异常值敏感,不适用于地表温度检验
表3  地表温度真实性检验
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