KMS Institute Of Geographic Sciences And Natural Resources Research,CAS
| Retrieving Atmospheric and Land Surface Parameters From At-Sensor Thermal Infrared Hyperspectral Data With Artificial Neural Network | |
Chen, Mengshuo1,2; Ni, Li3; Jiang, Xiaoguang4; Wu, Hua2,4,5
| |
| 2019-07-01 | |
| Source Publication | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
 |
| ISSN | 1939-1404 |
| Volume | 12Issue:7Pages:2409-2416 |
| Corresponding Author | Wu, Hua(wuhua@igsnrr.ac.cn) |
| Abstract | 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. |
| Keyword | Artificial neural network (ANN) atmospheric transmittance hyperspectral thermal infrared (TIR) land surface emissivity (LSE) land surface temperature (LST) |
| DOI | 10.1109/JSTARS.2019.2904992 |
| WOS Keyword | EMISSIVITY RETRIEVAL ; TEMPERATURE ; COMPENSATION ; ALGORITHM ; DESIGN |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | National Key R&D Program of China[2018YFB0504800] ; National Natural Science Foundation of China[41771398] ; National Natural Science Foundation of China[41871267] |
| Funding Organization | National Key R&D Program of China ; National Natural Science Foundation of China |
| WOS Research Area | Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS Subject | Engineering, Electrical & Electronic ; Geography, Physical ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS ID | WOS:000480354800039 |
| Publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.igsnrr.ac.cn/handle/311030/68918 |
| Collection | 中国科学院地理科学与资源研究所 |
| Corresponding Author | Wu, Hua |
| Affiliation | 1.Univ Chinese Acad Sci, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 3.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China 4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 5.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China |
| Recommended Citation 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. |
| Files in This Item: | There are no files associated with this item. | |||||
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Edit Comment