A method for land surface temperature retrieval based on model-data-knowledge-driven and deep learning
文献类型：期刊论文
| 作者 | Wang, Han2; Mao, Kebiao2,3; Yuan, Zijin2; Shi, Jiancheng1; Cao, Mengmeng2; Qin, Zhihao2; Duan, Sibo2; Tang, Bohui4
|
| 刊名 | REMOTE SENSING OF ENVIRONMENT
 |
| 出版日期 | 2021-11-01 |
| 卷号 | 265页码:19 |
| ISSN号 | 0034-4257 |
| 关键词 | Land surface temperature (LST) Model-data-knowledge-driven Deep learning Geophysical logical reasoning Expert knowledge |
| DOI | 10.1016/j.rse.2021.112665 |
| 通讯作者 | Mao, Kebiao(maokebiao@caas.cn) |
| 英文摘要 | Most algorithms for land surface temperature (LST) retrieval depend on acquiring prior knowledge. To overcome this drawback, we propose a novel LST retrieval method based on model-data-knowledge-driven and deep learning, called the MDK-DL method. Based on the expert knowledge and radiation transfer model, we deduce LST retrieval mechanism and determine the best combination of the thermal infrared (TIR) bands of the sensor. Then, we use the radiation transfer model simulation and reliable satellite-ground data to establish a training and test database, and finally use the deep learning neural network for optimal computation. Three typical high-, medium- and low-spatial-resolution TIR remote sensing datasets (from Gaofen, the Moderate Resolution Imaging Spectroradiometer (MODIS), and Fengyun) are used for theoretical simulation and application analysis. The simulation shows that the minimum mean absolute error (MAE) is less than 0.1 K (standard deviation: 0.04 K; correlation coefficient: 1.000) at a small viewing direction (<7.5 degrees) and less than 0.8 K at a large viewing direction (<65 degrees). The in situ validation shows that the minimum MAE obtained by the optimal band combination is approximately 1 K (root mean square error (RMSE) = 1.12 K; coefficient of determination (R-2) = 0.902). The retrieval accuracy is improved by increasing the number of TIR bands in the atmospheric window, and adding accurate atmospheric water vapor information produces better results. In general, four TIR bands in the atmospheric window bands are sufficient to retrieve the LST with high accuracy. Likewise, three TIR bands plus atmospheric water vapor information are sufficient for the retrieval requirements. All analyses indicate that our method is feasible and reliably accurate and can also be used to help design the instrument band to retrieve the LST with high precision. |
| WOS关键词 | SPLIT-WINDOW ALGORITHM ; NEURAL-NETWORK ESTIMATION ; THERMAL INFRARED DATA ; SINGLE-CHANNEL ; EMISSIVITY SEPARATION ; PRECIPITABLE WATER ; MODIS ; VALIDATION ; REPRESENTATIONS ; CLASSIFICATION |
| 资助项目 | Second Tibetan Plateau Scientific Expedition and Research Program (STEP)[2019QZKK0206] ; National Key Project of China[2018YFC1506602] ; National Natural Science Foundation of China[41921001] ; Fundamental Research Funds for Central Nonprofit Scientific Institution[1610132020014] ; Open Fund of State Key Laboratory of Remote Sensing Science[OFSLRSS201910] |
| WOS研究方向 | Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| 出版者 | ELSEVIER SCIENCE INC |
| WOS记录号 | WOS:000697022700003 |
| 资助机构 | Second Tibetan Plateau Scientific Expedition and Research Program (STEP) ; National Key Project of China ; National Natural Science Foundation of China ; Fundamental Research Funds for Central Nonprofit Scientific Institution ; Open Fund of State Key Laboratory of Remote Sensing Science |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/165587]  |
| 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 Sci, Natl Space Sci Ctr, Beijing 100190, Peoples R China 2.Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Beijing 100081, Peoples R China 3.Ningxia Univ, Sch Phys & Elect Engn, Yinchuan 750021, Ningxia, Peoples R China 4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Han,Mao, Kebiao,Yuan, Zijin,et al. A method for land surface temperature retrieval based on model-data-knowledge-driven and deep learning[J]. REMOTE SENSING OF ENVIRONMENT,2021,265:19. |
| APA | Wang, Han.,Mao, Kebiao.,Yuan, Zijin.,Shi, Jiancheng.,Cao, Mengmeng.,...&Tang, Bohui.(2021).A method for land surface temperature retrieval based on model-data-knowledge-driven and deep learning.REMOTE SENSING OF ENVIRONMENT,265,19. |
| MLA | Wang, Han,et al."A method for land surface temperature retrieval based on model-data-knowledge-driven and deep learning".REMOTE SENSING OF ENVIRONMENT 265(2021):19. |
入库方式： OAI收割
来源：地理科学与资源研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。