A physical knowledge-based machine learning method for near-real-time dust aerosol properties retrieval from the Himawari-8 satellite data

被引：0

作者：

Li, Jing ^{[1
]}

Wong, Man Sing ^{[1
]}

Lee, Kwon Ho ^{[2
]}

Nichol, Janet Elizabeth ^{[3
]}

Abbas, Sawaid ^{[1
,5
]}

Li, Hon ^{[1
]}

Wang, Jicheng ^{[4
]}

机构：

[1] Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong

[2] Department of Atmospheric & Environmental Sciences, Gangneung-Wonju National University, Korea, Republic of

[3] Department of Geography, School of Global Studies, University of Sussex, UK, United Kingdom

[4] Key Laboratory of Ministry of Education on Land Resources Evaluation and Monitoring in Southwest China, Sichuan Normal University, China

[5] Remote Sensing, GIS and Climatic Research Lab (RSGCRL), National Center of GIS and Space Applications, University of the Punjab, Lahore, Pakistan

来源：

Atmospheric Environment | 2022年 / 280卷

关键词：

D O I：

119098

中图分类号：

学科分类号：

摘要：

Aerosol optical thickness - Aerosol properties - Artificial neural network modeling - Dust aerosols - Machine learning methods - Natural dust aerosol - Near-real time - Third generation - Third-generation geostationary satellite - Xgboost

引用