IGSNRR OpenIR
Nonlinear Split-Window Algorithms for Estimating Land and Sea Surface Temperatures From Simulated Chinese Gaofen-5 Satellite Data
Tang, Bo-Hui1,2
2018-11-01
Source PublicationIEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
ISSN0196-2892
Volume56Issue:11Pages:6280-6289
Corresponding AuthorTang, Bo-Hui(tangbh@igsnrr.ac.cn)
AbstractThis paper proposes a different thermal channel combination split-window (DTCC-SW) method to estimate the land surface temperature (LST) and sea ST (SST) from the Chinese Gaofen-5 (GF-5) satellite thermal infrared (TIR) data. A nonlinear combination of two adjacent channels CH8,20 (centered at 8.20 mu m) and CH8.63 (centered at 8.63 mu m) was proposed to estimate LST for low-emissivity surfaces. A nonlinear combination of two adjacent channels, CH10.80 (centered at 10.80 mu m) and CH11.95 (centered at 11.92 mu m), was developed to estimate LST and SST for high-emissivity surfaces under dry atmospheric conditions, and a nonlinear combination of two channels, CH8.63 and CH11.95, was used to estimate LST and SST for high-emissivity surfaces under wet atmospheric conditions. The numerical values of the DTCC-SW coefficients were obtained using a statistical regression method from synthetic data simulated with an accurate atmospheric radiative transfer model moderate spectral resolution atmospheric transmittance mode 5 over a wide range of atmospheric and surface conditions. The LST (SST), mean emissivity, and atmospheric water vapor content were divided into several tractable subranges to improve the fitting accuracy. The experimental results and the preliminary evaluation results showed that the root-mean-square error between the actual and estimated LSTs (SSTs) is less than 0.7 K (0.3 K), provided that the land surface emissivities are known, which indicates that the proposed DTCC-SW method can accurately estimate the LST and SST from the GF-5 TIR data.
KeywordDifferent thermal channel combination split-window (DTCC-SW) Gaofen-5 (GF-5) land surface temperature (LST) sea surface temperature (SST) thermal infrared (TIR)
DOI10.1109/TGRS.2018.2833859
WOS KeywordEMISSIVITY RETRIEVAL ; MU-M ; ASTER ; VALIDATION ; PRODUCTS ; GOES-8 ; SPACE
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[41571353] ; Innovation Project of LREIS[O88RA801YA] ; National Key Research and Development Program of China[2016YFA0600103]
Funding OrganizationNational Natural Science Foundation of China ; Innovation Project of LREIS ; National Key Research and Development Program of China
WOS Research AreaGeochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
WOS SubjectGeochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000448621000002
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Cited Times:3[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/52621
Collection中国科学院地理科学与资源研究所
Corresponding AuthorTang, Bo-Hui
Affiliation1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
Recommended Citation
GB/T 7714
Tang, Bo-Hui. Nonlinear Split-Window Algorithms for Estimating Land and Sea Surface Temperatures From Simulated Chinese Gaofen-5 Satellite Data[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2018,56(11):6280-6289.
APA Tang, Bo-Hui.(2018).Nonlinear Split-Window Algorithms for Estimating Land and Sea Surface Temperatures From Simulated Chinese Gaofen-5 Satellite Data.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,56(11),6280-6289.
MLA Tang, Bo-Hui."Nonlinear Split-Window Algorithms for Estimating Land and Sea Surface Temperatures From Simulated Chinese Gaofen-5 Satellite Data".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 56.11(2018):6280-6289.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Tang, Bo-Hui]'s Articles
Baidu academic
Similar articles in Baidu academic
[Tang, Bo-Hui]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Tang, Bo-Hui]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.