IGSNRR OpenIR
Retrieval of land surface temperature and emissivity from ASTER1B data using a dynamic learning neural network
Mao, Kebiao1,2,3,4,5,6; Li, Sanmei7; Wang, Daolong1,2,3; Zhang, Lixin8; Wang, Xiufeng9; Tang, Huajun1,2,3; Li, Zhao-Liang10,11
2011
Source PublicationINTERNATIONAL JOURNAL OF REMOTE SENSING
Volume32Issue:19Pages:5413-5423
AbstractThe accuracy of a radiance transfer model neural network (RM-NN) for separating land surface temperature (LST) and emissivity from AST09 (the Advanced Spaceborne and Thermal Emission and Reflection Radiometer (ASTER) Standard Data Product, surface leaving radiance) is very high, but it is limited by the accuracy of the atmospheric correction. This article uses a neural network and radiance transfer model (MODTRAN4) to directly retrieve the LST and emissivity from ASTER1B data, which overcomes the difficulty of atmospheric correction in previous methods. The retrieval average accuracy of LST is about 1.1 K, and the average accuracy of emissivity in bands 11-14 is under 0.016 for simulated data when the input nodes are a combination of brightness temperature in bands 11-14. The average accuracy of LST is under 0.8 K when the input nodes are a combination of water vapour content and brightness temperature in bands 11-14. Finally, the comparison of retrieval results with ground measurement data indicates that the RM-NN can be used to accurately retrieve LST and emissivity from ASTER1B data.
SubtypeArticle
WOS HeadingsScience & Technology ; Technology
WOS Subject ExtendedRemote Sensing ; Imaging Science & Photographic Technology
WOS KeywordMODIS DATA ; ALGORITHM ; IMAGERY
Indexed BySCI
Language英语
WOS SubjectRemote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000298369400007
PublisherTAYLOR & FRANCIS LTD
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/68066
Collection中国科学院地理科学与资源研究所
Corresponding AuthorMao, Kebiao
Affiliation1.Chinese Acad Agr Sci, Key Lab Resources Remote Sensing & Digital Agr, Minist Agr, Beijing 100081, Peoples R China
2.Chinese Acad Agr Sci, Key Lab Agrometeorol Safeguard & Appl Tech, China Meteorol Assoc, Beijing 100081, Peoples R China
3.Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Beijing 100081, Peoples R China
4.Chinese Acad Sci, State Key Lab Remote Sensing Sci, Inst Remote Sensing Applicat, Beijing 100101, Peoples R China
5.Beijing Normal Univ, Beijing 100101, Peoples R China
6.Lanzhou Univ, Minist Educ, Key Lab Semiarid Climate Change, Lanzhou 730000, Peoples R China
7.China Meteorol Assoc, Natl Satellite Meteorol Ctr, Beijing 100081, Peoples R China
8.Beijing Normal Univ, Sch Geog, Beijing 100875, Peoples R China
9.Hokkaido Univ, Grad Sch Agr, Kita Ku, Sapporo, Hokkaido 0608589, Japan
10.Ecole Natl Super Phys Strasbourg, UMR 7005, Lab Sci Image Informat & Teledetect, F-67412 Illkirch Graffenstaden, France
11.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
Recommended Citation
GB/T 7714
Mao, Kebiao,Li, Sanmei,Wang, Daolong,et al. Retrieval of land surface temperature and emissivity from ASTER1B data using a dynamic learning neural network[J]. INTERNATIONAL JOURNAL OF REMOTE SENSING,2011,32(19):5413-5423.
APA Mao, Kebiao.,Li, Sanmei.,Wang, Daolong.,Zhang, Lixin.,Wang, Xiufeng.,...&Li, Zhao-Liang.(2011).Retrieval of land surface temperature and emissivity from ASTER1B data using a dynamic learning neural network.INTERNATIONAL JOURNAL OF REMOTE SENSING,32(19),5413-5423.
MLA Mao, Kebiao,et al."Retrieval of land surface temperature and emissivity from ASTER1B data using a dynamic learning neural network".INTERNATIONAL JOURNAL OF REMOTE SENSING 32.19(2011):5413-5423.
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