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

共 23 条

[1] A physical knowledge-based machine learning method for near-real-time dust aerosol properties retrieval from the Himawari-8 satellite data
Li, Jing
Wong, Man Sing
Lee, Kwon Ho
Nichol, Janet Elizabeth
Abbas, Sawaid
Li, Hon
Wang, Jicheng
ATMOSPHERIC ENVIRONMENT, 2022, 280
[2] Tree-Based Machine Learning Estimation of Near-Real-Time Diffuse Solar Irradiance From Himawari-8 Satellite Data
Tan, Yunhui
Wang, Quan
Zhang, Zhaoyang
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2024, 62
[3] Near-real-time estimation of global horizontal irradiance from Himawari-8 satellite data
Tan, Yunhui
Wang, Quan
Zhang, Zhaoyang
RENEWABLE ENERGY, 2023, 215
[4] Near Real-Time Extracting Wildfire Spread Rate from Himawari-8 Satellite Data
Liu, Xiangzhuo
He, Binbin
Quan, Xingwen
Yebra, Marta
Qiu, Shi
Yin, Changming
Liao, Zhanmang
Zhang, Hongguo
REMOTE SENSING, 2018, 10 (10)
[5] Deep-learning-based and near real-time solar irradiance map using Himawari-8 satellite imageries
Suwanwimolkul, Suwichaya
Tongamrak, Natanon
Thungka, Nuttamon
Hoonchareon, Naebboon
Songsiri, Jitkomut
SOLAR ENERGY, 2025, 288
[6] Near-real-time wildfire detection approach with Himawari-8/9 geostationary satellite data integrating multi-scale spatial-temporal feature
Zhang, Lizhi
Zhang, Qiang
Yang, Qianqian
Yue, Linwei
He, Jiang
Jin, Xianyu
Yuan, Qiangqiang
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2025, 137
[7] Near real-time retrieval of lake surface water temperature using Himawari-8 satellite imagery and machine learning techniques: a case study in the Yangtze River Basin
Shi, Kaifang
Han, Jing-Cheng
Wang, Peng
FRONTIERS IN ENVIRONMENTAL SCIENCE, 2024, 11
[8] Satellite Rainfall Estimation from Himawari-8 Multi Channels Observation Based on AWS Data Trained Machine Learning Methods
Lasmono, Farid
Risyanto
Nauval, Fadli
Saufina, Elfira
Trismidianto
Harjana, Teguh
Springer Proceedings in Physics, 2022, 275 : 495 - 506
[9] A Method for Retrieving Cloud-Top Height Based on a Machine Learning Model Using the Himawari-8 Combined with Near Infrared Data
Dong, Yan
Sun, Xuejin
Li, Qinghui
REMOTE SENSING, 2022, 14 (24)
[10] Superior PM2.5 Estimation by Integrating Aerosol Fine Mode Data from the Himawari-8 Satellite in Deep and Classical Machine Learning Models
Zang, Zhou
Li, Dan
Guo, Yushan
Shi, Wenzhong
Yan, Xing
REMOTE SENSING, 2021, 13 (14)

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