A Fast Computing Model for the Oxygen A-Band High-Spectral-Resolution Absorption Spectra Based on Artificial Neural Networks
文献类型：期刊论文
| 作者 | Zhou, Jianxi2,3,4; Dai, Congming3,4 ; Wu, Pengfei3,4; Wei, Heli1,3,4
|
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2024-10-01 |
| 卷号 | 16 |
| 关键词 | limb remote sensing oxygen A-band artificial neural network PCA spectral reconstruction technology |
| DOI | 10.3390/rs16193616 |
| 通讯作者 | Wu, Pengfei(wupengfei@aiofm.ac.cn) |
| 英文摘要 | A fast and accurate radiative transfer model is the prerequisite in the field of atmospheric remote sensing for limb atmospheric inversion to tackle the drawback of slow calculation speed of traditional atmospheric radiative transfer models. This paper established a fast computing model (ANN-HASFCM) for high-spectral-resolution absorption spectra by using artificial neural networks and PCA (principal component analysis) spectral reconstruction technology. This paper chose the line-by-line radiative transfer model (LBLRTM) as the comparative model and simulated training spectral data in the oxygen A-band (12,900-13,200 cm-1). Subsequently, ANN-HASFCM was applied to the retrieval of the atmospheric density profile with the data of the Global Ozone Monitoring by an Occultation of Stars (GOMOS) instrument. The results show that the relative error between the optical depth spectra calculated by LBLRTM and ANN-HASFCM is within 0.03-0.65%. In the process of using the global-fitting algorithm to invert GOMOS-measured atmospheric samples, the inversion results using Fast-LBLRTM and ANN-HASFCM as forward models are consistent, and the retrieval speed of ANN-HASFCM is more than 200 times faster than that of Fast-LBLRTM (reduced from 226.7 s to 0.834 s). The analysis shows the brilliant application prospects of ANN-HASFCM in limb remote sensing. |
| WOS关键词 | RETRIEVAL ; PARAMETERS ; PROFILES |
| 资助项目 | National Key Research and Development Program of China ; Youth Innovation Promote Association CAS[2022450] ; [2019YFA0706004] |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001332756700001 |
| 出版者 | MDPI |
| 资助机构 | National Key Research and Development Program of China ; Youth Innovation Promote Association CAS |
| 源URL | [http://ir.hfcas.ac.cn:8080/handle/334002/134624]  |
| 专题 | 中国科学院合肥物质科学研究院 |
| 通讯作者 | Wu, Pengfei |
| 作者单位 | 1.Univ Sci & Technol China, Sch Environm Sci & Optoelect Technol, Hefei 230026, Peoples R China 2.Univ Sci & Technol China, Sci Isl Branch, Grad Sch, Hefei 230026, Peoples R China 3.Adv Laser Technol Lab Anhui Prov, Hefei 230037, Peoples R China 4.Chinese Acad Sci, HFIPS, Anhui Inst Opt & Fine Mech, Key Lab Atmospher Opt, Hefei 230031, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhou, Jianxi,Dai, Congming,Wu, Pengfei,et al. A Fast Computing Model for the Oxygen A-Band High-Spectral-Resolution Absorption Spectra Based on Artificial Neural Networks[J]. REMOTE SENSING,2024,16. |
| APA | Zhou, Jianxi,Dai, Congming,Wu, Pengfei,&Wei, Heli.(2024).A Fast Computing Model for the Oxygen A-Band High-Spectral-Resolution Absorption Spectra Based on Artificial Neural Networks.REMOTE SENSING,16. |
| MLA | Zhou, Jianxi,et al."A Fast Computing Model for the Oxygen A-Band High-Spectral-Resolution Absorption Spectra Based on Artificial Neural Networks".REMOTE SENSING 16(2024). |
入库方式： OAI收割
来源：合肥物质科学研究院
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。