Optimal Inversion of Conversion Parameters from Satellite AOD to Ground Aerosol Extinction Coefficient Using Automatic Differentiation
文献类型：期刊论文
| 作者 | Li, Lianfa1,2
|
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2020-02-01 |
| 卷号 | 12期号:3页码:20 |
| 关键词 | parameter inversion aerosol optical depth PBLH ground-based AOD PM2 5 automatic differentiation |
| DOI | 10.3390/rs12030492 |
| 通讯作者 | Li, Lianfa(lilf@lreis.ac.cn) |
| 英文摘要 | Satellite aerosol optical depth (AOD) plays an important role for high spatiotemporal-resolution estimation of fine particulate matter with diameters <= 2.5 mu m (PM2.5). However, the MODIS sensors aboard the Terra and Aqua satellites mainly measure column (integrated) AOD using the aerosol (extinction) coefficient integrated over all altitudes in the atmosphere, and column AOD is less related to PM2.5 than low-level or ground-based aerosol (extinction) coefficient (GAC). With recent development of automatic differentiation (AD) that has been widely applied in deep learning, a method using AD to find optimal solution of conversion parameters from column AOD to the simulated GAC is presented. Based on the computational graph, AD has considerably improved the efficiency in applying gradient descent to find the optimal solution for complex problems involving multiple parameters and spatiotemporal factors. In a case study of the Jing-Jin-Ji region of China for the estimation of PM2.5 in 2015 using the Multiangle Implementation of Atmospheric Correction AOD, the optimal solution of the conversion parameters was obtained using AD and the loss function of mean square error. This solution fairly modestly improved the Pearson's correlation between simulated GAC and PM2.5 up to 0.58 (test R-2: 0.33), in comparison with three existing methods. In the downstream validation, the simulated GACs were used to reliably estimate PM2.5, considerably improving test R-2 up to 0.90 and achieving consistent match for GAC and PM2.5 in their spatial distribution and seasonal variations. With the availability of the AD tool, the proposed method can be generalized to the inversion of other similar conversion parameters in remote sensing. |
| WOS关键词 | PARTICULATE MATTER ; PM2.5 CONCENTRATIONS ; POLLUTION ; DISTRIBUTIONS ; VARIABILITY ; TRANSPORT ; CHINA ; HAZE ; NO2 |
| 资助项目 | Strategic Priority Research Program of Chinese Academy of Sciences[XDA19040501] ; National Natural Science Foundation of China[41471376] |
| WOS研究方向 | Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:000515393800151 |
| 出版者 | MDPI |
| 资助机构 | Strategic Priority Research Program of Chinese Academy of Sciences ; National Natural Science Foundation of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/132859]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Li, Lianfa |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Datun Rd, Beijing 100101, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Lianfa. Optimal Inversion of Conversion Parameters from Satellite AOD to Ground Aerosol Extinction Coefficient Using Automatic Differentiation[J]. REMOTE SENSING,2020,12(3):20. |
| APA | Li, Lianfa.(2020).Optimal Inversion of Conversion Parameters from Satellite AOD to Ground Aerosol Extinction Coefficient Using Automatic Differentiation.REMOTE SENSING,12(3),20. |
| MLA | Li, Lianfa."Optimal Inversion of Conversion Parameters from Satellite AOD to Ground Aerosol Extinction Coefficient Using Automatic Differentiation".REMOTE SENSING 12.3(2020):20. |
入库方式： OAI收割
来源：地理科学与资源研究所
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