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Evaluation of Three Land Surface Temperature Products From Landsat Series Using in Situ Measurements

文献类型：期刊论文

作者Wang, Mengmeng6,7; He, Can4,5; Zhang, Zhengjia6; Hu, Tian3; Duan, Si-Bo2; Mallick, Kaniska3; Li, Hua1; Liu, Xiuguo7
刊名IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
出版日期2023
卷号61页码:19
关键词Land surface temperature (LST) Landsat single-channel algorithm validation of satellite retrieval
ISSN号0196-2892
DOI10.1109/TGRS.2022.3232624
通讯作者Hu, Tian(hutiangmu@gmail.com) ; Duan, Si-Bo(duansibo@caas.cn)
英文摘要Three operational long-term land surface temperature (LST) products from Landsat series are available to the community until now, i.e., U.S. Geological Survey (USGS) LST, Instituto Portugues do Mar e da Atmosfera (IPMA) LST, and China University of Geosciences (CUG) LST. A comprehensive assessment of these LST products is essential for their subsequent applications (APPs) in energy, water, and carbon cycle modeling. In this study, an evaluation of these three Landsat LST products was performed using in situ LST measurements from five networks [surface radiation budget (SURFRAD), atmospheric radiation measurement (ARM), Heihe watershed allied telemetry experimental research (HiWATER), baseline surface radiation network (BSRN), and National Data Buoy Center (NDBC)] for the period of 2009-2019. Results reveal that the overall accuracies of CUG LST with bias [root-mean-square error (RMSE)] of 0.54 K (2.19 K) and IPMA LST with bias (RMSE) of 0.59 K (2.34 K) are marginally superior to USGS LST with bias (RMSE) of 0.96 K (2.51 K). The RMSE of USGS LST is about 0.3 K less than IPMA/CUG LST at water surface sites and is about 0.4 K higher than IPMA/CUG LST at cropland and shrubland sites. As for tundra, grassland, and forest sites, the RMSEs of three Landsat LST products are similar, and the RMSE difference among three Landsat LST products is < 0.18 K. Considering the close emissivity estimates over water surface in these three LST data, USGS LST has a better performance in atmospheric correction over water surface compared with IPMA/CUG LST. For land surface sites, the RMSE of LST increases initially and then decreases with land surface emissivity (LSE) for three Landsat LST products. This indicates that the emissivity correction has a large uncertainty for moderately vegetated surface with emissivity ranging from 0.970 to 0.980. Underestimated emissivity for USGS LST at vegetated sites leads to overestimation of LST, which could have led to the higher bias and RMSE compared with IPMA/CUG LST. For the LST retrievals for the three different sensors [i.e., Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and thermal infrared sensor (TIRS)] onboard the Landsat satellite series, the accuracies are consistent and comparable, which is beneficial for providing long-term and coherent LST.
WOS关键词SINGLE-CHANNEL ALGORITHM ; RADIATION BUDGET NETWORK ; SPLIT-WINDOW ALGORITHM ; EMISSIVITY RETRIEVAL ; ARIDITY GRADIENT ; LST PRODUCTS ; VALIDATION ; EVAPOTRANSPIRATION ; IMPLEMENTATION ; FRAMEWORK
资助项目Open Research Fund of Key Laboratory of Digital Earth Science, Chinese Academy of Sciences[2022LDE001] ; National Natural Science Foundation of China[61801443] ; National Natural Science Foundation of China[41801348]
WOS研究方向Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:000918315200013
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构Open Research Fund of Key Laboratory of Digital Earth Science, Chinese Academy of Sciences ; National Natural Science Foundation of China
源URL[http://ir.igsnrr.ac.cn/handle/311030/189379]  
专题中国科学院地理科学与资源研究所
通讯作者Hu, Tian; Duan, Si-Bo
作者单位1.Chinese Acad Sci, Aerospace Informat Res Inst, Beijing 100094, Peoples R China
2.Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Key Lab Agr Remote Sensing, Minist Agr & Rural Affairs, Beijing 100081, Peoples R China
3.Luxembourg Inst Sci & Technol LIST, Dept Environm Res & Innovat ERIN, Remote Sensing & Nat Resources Modeling, L-4422 Belvaux, Luxembourg
4.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China
6.Wuchang Univ Technol, Artificial Intelligence Sch, Wuhan 430223, Peoples R China
7.China Univ Geosci, Sch Geog & Informat Engn, Wuhan 430074, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Mengmeng,He, Can,Zhang, Zhengjia,et al. Evaluation of Three Land Surface Temperature Products From Landsat Series Using in Situ Measurements[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2023,61:19.
APA Wang, Mengmeng.,He, Can.,Zhang, Zhengjia.,Hu, Tian.,Duan, Si-Bo.,...&Liu, Xiuguo.(2023).Evaluation of Three Land Surface Temperature Products From Landsat Series Using in Situ Measurements.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,61,19.
MLA Wang, Mengmeng,et al."Evaluation of Three Land Surface Temperature Products From Landsat Series Using in Situ Measurements".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 61(2023):19.

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来源：地理科学与资源研究所

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