Retrieving Atmospheric and Land Surface Parameters From At-Sensor Thermal Infrared Hyperspectral Data With Artificial Neural Network

doi:10.1109/JSTARS.2019.2904992

	Retrieving Atmospheric and Land Surface Parameters From At-Sensor Thermal Infrared Hyperspectral Data With Artificial Neural Network
	Chen, Mengshuo 1,4; Ni, Li 5; Jiang, Xiaoguang 3; Wu, Hua 1,2,3
刊名	IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
	2019-07-01
卷号	12 期号:7 页码:2409-2416
关键词	Artificial neural network (ANN) atmospheric transmittance hyperspectral thermal infrared (TIR) land surface emissivity (LSE) land surface temperature (LST)
ISSN号	1939-1404
DOI	10.1109/JSTARS.2019.2904992
通讯作者	Wu, Hua(wuhua@igsnrr.ac.cn)
英文摘要	The radiances observed by satellites are influenced by both land surface and atmospheric parameters, and it is difficult to retrieve these parameters simultaneously from multispectral measurements with high accuracies. Even though several methods have been proposed, they focus on the retrieval of land surface or atmospheric parameters separately. Generally, these atmospheric parameters are atmospheric water vapor and temperature profiles. Thus, this study aims to establish a back propagation (BP) artificial neural network (ANN) to retrieve land surface emissivity (LSE), land surface temperature (LST), atmospheric transmittance, upward radiance, and downward radiance simultaneously from the hyperspectral thermal infrared (TIR) data, suitable for various air mass types and surface conditions. The principle component analysis technique is first used to compress and remove noise from the data. The evaluation of the ANN using the simulated data without noise indicated that the root mean square error (RMSE) of LST is approximately 0.643 K; the RMSEs of emissivity, transmittance, upward, and downward radiance are approximately 0.0046, 0.005, 0.72, and 2.95 K, respectively. When applied on the simulated data containing noise, the errors of LST, LSE, transmittance, upward, and downward radiance are 1.26, 0.01, 0.01, 1.54, and 4.57 K, respectively. When applied on the real atmospheric infrared sounder data, the retrieved accuracies become worse because of various unstudied reasons. However, the results show that the proposed ANN is promising in retrieving the land surface and atmospheric parameters simultaneously. Because of its simplicity, the proposed ANN can be used to produce preliminary results employed as the first estimates for physics-based retrieval methods.
资助项目	National Key R&D Program of China[2018YFB0504800] ; National Natural Science Foundation of China[41771398] ; National Natural Science Foundation of China[41871267]
WOS关键词	EMISSIVITY RETRIEVAL ; TEMPERATURE ; COMPENSATION ; ALGORITHM ; DESIGN
WOS研究方向	Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000480354800039
资助机构	National Key R&D Program of China ; National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/68917]
专题	中国科学院地理科学与资源研究所
通讯作者	Wu, Hua
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.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 4.Univ Chinese Acad Sci, Beijing 100190, Peoples R China 5.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China
推荐引用方式 GB/T 7714	Chen, Mengshuo,Ni, Li,Jiang, Xiaoguang,et al. Retrieving Atmospheric and Land Surface Parameters From At-Sensor Thermal Infrared Hyperspectral Data With Artificial Neural Network[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2019,12(7):2409-2416.
APA	Chen, Mengshuo,Ni, Li,Jiang, Xiaoguang,&Wu, Hua.(2019).Retrieving Atmospheric and Land Surface Parameters From At-Sensor Thermal Infrared Hyperspectral Data With Artificial Neural Network.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,12(7),2409-2416.
MLA	Chen, Mengshuo,et al."Retrieving Atmospheric and Land Surface Parameters From At-Sensor Thermal Infrared Hyperspectral Data With Artificial Neural Network".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 12.7(2019):2409-2416.