Nonlinear Split-Window Algorithms for Estimating Land and Sea Surface Temperatures From Simulated Chinese Gaofen-5 Satellite Data

doi:10.1109/TGRS.2018.2833859

	Nonlinear Split-Window Algorithms for Estimating Land and Sea Surface Temperatures From Simulated Chinese Gaofen-5 Satellite Data
	Tang, Bo-Hui 1,2
刊名	IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
	2018-11-01
卷号	56 期号:11 页码:6280-6289
关键词	Different thermal channel combination split-window (DTCC-SW) Gaofen-5 (GF-5) land surface temperature (LST) sea surface temperature (SST) thermal infrared (TIR)
ISSN号	0196-2892
DOI	10.1109/TGRS.2018.2833859
通讯作者	Tang, Bo-Hui(tangbh@igsnrr.ac.cn)
英文摘要	This 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.
资助项目	National Natural Science Foundation of China[41571353] ; Innovation Project of LREIS[O88RA801YA] ; National Key Research and Development Program of China[2016YFA0600103]
WOS关键词	EMISSIVITY RETRIEVAL ; MU-M ; ASTER ; VALIDATION ; PRODUCTS ; GOES-8 ; SPACE
WOS研究方向	Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000448621000002
资助机构	National Natural Science Foundation of China ; Innovation Project of LREIS ; National Key Research and Development Program of China
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/52620]
专题	中国科学院地理科学与资源研究所
通讯作者	Tang, Bo-Hui
作者单位	1.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
推荐引用方式 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.