Retrieval of land surface temperature and emissivity from ASTER1B data using a dynamic learning neural network
文献类型：期刊论文
| 作者 | 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
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| 刊名 | INTERNATIONAL JOURNAL OF REMOTE SENSING
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| 出版日期 | 2011 |
| 卷号 | 32期号:19页码:5413-5423 |
| 文献子类 | Article |
| 英文摘要 | The 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. |
| WOS关键词 | MODIS DATA ; ALGORITHM ; IMAGERY |
| 语种 | 英语 |
| WOS记录号 | WOS:000298369400007 |
| 出版者 | TAYLOR & FRANCIS LTD |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/68065]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Mao, Kebiao |
| 作者单位 | 1.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 |
| 推荐引用方式 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. |
入库方式： OAI收割
来源：地理科学与资源研究所
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