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A physically based algorithm for retrieving land surface temperature under cloudy conditions from AMSR2 passive microwave measurements
Huang, Cheng1,2; Duan, Si-Bo2; Jiang, Xiao-Guang1; Han, Xiao-Jing2; Leng, Pei2; Gao, Mao-Fang2; Li, Zhao-Liang2,3
2019
Source PublicationINTERNATIONAL JOURNAL OF REMOTE SENSING
ISSN0143-1161
Volume40Issue:5-6Pages:1828-1843
Corresponding AuthorDuan, Si-Bo(duansibo@caas.cn) ; Jiang, Xiao-Guang(xgjiang@ucas.ac.cn)
AbstractSatellite remote sensing provides a unique way to measure land surface temperature (LST) at regional and global scales. Algorithms using thermal infrared (TIR) data provide a reliable way to retrieve LST. However, they are limited to clear-sky conditions due to their inability to penetrate clouds. As an alternative for LST retrieval, passive microwave data are much less affected by clouds and water vapour than TIR data. In this study, we presented an improved physically based algorithm for the retrieval of LST under cloudy atmospheric conditions from Advanced Microwave Scanning Radiometer 2 (AMSR2) brightness temperature measurements at 18.7 and 23.8 GHz vertically polarized channels based on the assumption that the emissivity relationship between the two adjacent frequencies is linear. The performance of the algorithm was firstly evaluated using simulation data, with a root mean square error (RMSE) of approximately 2.1K. Moreover, the RMSE value reduces with precipitable water vapour (PWV) increasing. This algorithm was further applied to AMSR2measurements. The retrieved cloudy LST was compared with ground-based air temperature over China in 2016. The bias varies from approximately 2K to 4K and the RMSE from approximately 4K to 6K during daytime and night-time. To eliminate the systematic bias between the retrieved LST and the ground-based air temperature, a linear adjustment was performed to the retrieved LST during daytime and nighttime, respectively. The accuracies for the adjusted LST are nearly the same during daytime and night-time, with an RMSE of approximately 3.6K. The combination of this physically based LST retrieval algorithm with TIR LST algorithm is attractive for generating an all-weather LST product at global scale.
DOI10.1080/01431161.2018.1508920
WOS KeywordBRIGHTNESS TEMPERATURES ; MODEL ; PARAMETERS ; EMISSION ; DROUGHT
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[41231170] ; National Natural Science Foundation of China[41571352] ; National Natural Science Foundation of China[41501406]
Funding OrganizationNational Natural Science Foundation of China
WOS Research AreaRemote Sensing ; Imaging Science & Photographic Technology
WOS SubjectRemote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000464043900015
PublisherTAYLOR & FRANCIS LTD
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/48283
Collection中国科学院地理科学与资源研究所
Corresponding AuthorDuan, Si-Bo; Jiang, Xiao-Guang
Affiliation1.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
2.Chinese Acad Agr Sci, Key Lab Agr Remote Sensing, Minist Agr, Inst Agr Resources & Reg Planning, Beijing 100081, Peoples R China
3.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China
Recommended Citation
GB/T 7714
Huang, Cheng,Duan, Si-Bo,Jiang, Xiao-Guang,et al. A physically based algorithm for retrieving land surface temperature under cloudy conditions from AMSR2 passive microwave measurements[J]. INTERNATIONAL JOURNAL OF REMOTE SENSING,2019,40(5-6):1828-1843.
APA Huang, Cheng.,Duan, Si-Bo.,Jiang, Xiao-Guang.,Han, Xiao-Jing.,Leng, Pei.,...&Li, Zhao-Liang.(2019).A physically based algorithm for retrieving land surface temperature under cloudy conditions from AMSR2 passive microwave measurements.INTERNATIONAL JOURNAL OF REMOTE SENSING,40(5-6),1828-1843.
MLA Huang, Cheng,et al."A physically based algorithm for retrieving land surface temperature under cloudy conditions from AMSR2 passive microwave measurements".INTERNATIONAL JOURNAL OF REMOTE SENSING 40.5-6(2019):1828-1843.
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