Estimation of Atmospheric PM <sub>10</sub> Concentration in China Using an Interpretable Deep Learning Model and Top‐of‐the‐Atmosphere Reflectance Data From China’s New Generation Geostationary Meteorological Satellite, FY‐4A-Reference-Cited by-同舟云学术

Estimation of Atmospheric PM ₁₀ Concentration in China Using an Interpretable Deep Learning Model and Top‐of‐the‐Atmosphere Reflectance Data From China’s New Generation Geostationary Meteorological Satellite, FY‐4A

Published:2022-04-27 Issue:9 Volume:127 Page:
ISSN:2169-897X
Container-title:Journal of Geophysical Research: Atmospheres
language:en
Short-container-title:JGR Atmospheres

Author:

Chen Bin¹²^ORCID,Song Zhihao¹²,Huang Jianping¹²^ORCID,Zhang Peng³,Hu Xiuqing³,Zhang Xingying³,Guan Xiaodan¹²^ORCID,Ge Jinming¹²^ORCID,Zhou Xingzhao¹²

Affiliation:

1. Key Laboratory for Semi‐Arid Climate Change of the Ministry of Education College of Atmospheric Sciences Lanzhou University Lanzhou China

2. Collaborative Innovation Center for Western Ecological Safety Lanzhou China

3. Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites National Satellite Meteorological Center China Meteorological Administration Beijing China

Publisher

American Geophysical Union (AGU)

Subject

Space and Planetary Science,Earth and Planetary Sciences (miscellaneous),Atmospheric Science,Geophysics

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2021JD036393

Reference98 articles.

1. Comparison of Satellite-based PM2.5 Estimation from Aerosol Optical Depth and Top-of-atmosphere Reflectance

2. Particulate Matter Air Pollution and Cardiovascular Disease

3. CCSI. (2022).SRTM3. [Data set].CCSI. Retrieved fromhttps://srtm.csi.cgiar.org/srtmdata/

4. CEMC. (2022).PM10. [Data set].CEMC. Retrieved fromhttp://www.cnemc.cn

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