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Improvement of Split-Window Algorithm for Land Surface Temperature Retrieval from Sentinel-3A SLSTR Data Over Barren Surfaces Using ASTER GED Product

文献类型：期刊论文

作者Zhang, Shuting1; Duan, Si-Bo1; Li, Zhao-Liang1,2; Huang, Cheng1,3; Wu, Hua2; Han, Xiao-Jing1; Leng, Pei1; Gao, Maofang1
刊名REMOTE SENSING
出版日期2019-12-02
卷号11期号:24页码:19
关键词land surface temperature split-window algorithm temperature validation SLSTR ASTER GED
DOI10.3390/rs11243025
通讯作者Duan, Si-Bo(duansibo@caas.cn)
英文摘要Land surface temperature (LST) is a key variable influencing the energy balance between the land surface and the atmosphere. In this work, a split-window algorithm was used to calculate LST from Sentinel-3A Sea and Land Surface Temperature Radiometer (SLSTR) thermal infrared data. The National Centers for Environmental Prediction (NCEP) reanalysis atmospheric profiles combined with the radiation transport model MODerate resolution atmospheric TRANsmission version 5.2 (MODTRAN 5.2) were utilized to obtain atmospheric water vapor content (WVC). The ASTER Global Emissivity Database Version 3 (ASTER GED v3) product was utilized to estimate surface emissivity in order to improve the accuracy of LST estimation over barren surfaces. Using a simulation database, the coefficients of the algorithm were fitted and the performance of the algorithm was evaluated. The root-mean-square error (RMSE) values of the differences between the estimated LST and the actual LST of the MODTRAN radiative transfer simulation at each WVC subrange of 0-6.5 g/cm(2) were less than 1.0 K. To validate the retrieval accuracy, ground-based LST measurements were collected at two relatively homogeneous desert study sites in Dalad Banner and Wuhai, Inner Mongolia, China. The bias between the retrieved LST and the in situ LST was about 0.2 K and the RMSE was about 1.3 K at the Dalad Banner site, whereas they were approximately -0.4 and 1.0 K at the Wuhai site. As a reference, the retrieved LST was compared with the operational SLSTR LST product in this study. The bias between the SLSTR LST product and the in situ LST was approximately 1 K and the RMSE was approximately 2 K at the Dalad Banner site, whereas they were approximately 1.1 and 1.4 K at the Wuhai site. The results demonstrate that the split-window algorithm combined with improved emissivity estimation based on the ASTER GED product can distinctly obtain better accuracy of LST over barren surfaces.
WOS关键词EMISSIVITY RETRIEVAL ; ARID AREA ; COVER ; AATSR ; VIIRS ; NDVI
资助项目National Natural Science Foundation of China[41871275] ; National Natural Science Foundation of China[41921001]
WOS研究方向Remote Sensing
语种英语
WOS记录号WOS:000507333400139
出版者MDPI
资助机构National Natural Science Foundation of China
源URL[http://ir.igsnrr.ac.cn/handle/311030/131450]  
专题中国科学院地理科学与资源研究所
通讯作者Duan, Si-Bo
作者单位1.Chinese Acad Agr Sci, Key Lab Agr Remote Sensing, Minist Agr, Inst Agr Resources & Reg Planning, Beijing 100081, Peoples R China
2.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
3.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Shuting,Duan, Si-Bo,Li, Zhao-Liang,et al. Improvement of Split-Window Algorithm for Land Surface Temperature Retrieval from Sentinel-3A SLSTR Data Over Barren Surfaces Using ASTER GED Product[J]. REMOTE SENSING,2019,11(24):19.
APA Zhang, Shuting.,Duan, Si-Bo.,Li, Zhao-Liang.,Huang, Cheng.,Wu, Hua.,...&Gao, Maofang.(2019).Improvement of Split-Window Algorithm for Land Surface Temperature Retrieval from Sentinel-3A SLSTR Data Over Barren Surfaces Using ASTER GED Product.REMOTE SENSING,11(24),19.
MLA Zhang, Shuting,et al."Improvement of Split-Window Algorithm for Land Surface Temperature Retrieval from Sentinel-3A SLSTR Data Over Barren Surfaces Using ASTER GED Product".REMOTE SENSING 11.24(2019):19.

入库方式： OAI收割

来源：地理科学与资源研究所

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