The estimation of hourly PM2.5 concentrations across China based on a Spatial and Temporal Weighted Continuous Deep Neural Network (STWC-DNN)
出版年份 2022 全文链接
标题
The estimation of hourly PM2.5 concentrations across China based on a Spatial and Temporal Weighted Continuous Deep Neural Network (STWC-DNN)
作者
关键词
-
出版物
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
Volume 190, Issue -, Pages 38-55
出版商
Elsevier BV
发表日期
2022-06-05
DOI
10.1016/j.isprsjprs.2022.05.011
参考文献
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注意:仅列出部分参考文献,下载原文获取全部文献信息。- Influence of meteorological conditions on PM2.5 concentrations across China: A review of methodology and mechanism
- (2020) Ziyue Chen et al. ENVIRONMENT INTERNATIONAL
- Geographically and temporally weighted neural networks for satellite-based mapping of ground-level PM2.5
- (2020) Tongwen Li et al. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
- The relationships between PM2.5 and aerosol optical depth (AOD) in mainland China: About and behind the spatio-temporal variations
- (2019) Qianqian Yang et al. ENVIRONMENTAL POLLUTION
- A spatially structured adaptive two-stage model for retrieving ground-level PM2.5 concentrations from VIIRS AOD in China
- (2019) Fei Yao et al. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
- Continual lifelong learning with neural networks: A review
- (2019) German I. Parisi et al. NEURAL NETWORKS
- Estimating 1-km-resolution PM2.5 concentrations across China using the space-time random forest approach
- (2019) Jing Wei et al. REMOTE SENSING OF ENVIRONMENT
- Ground-level PM2.5 estimation over urban agglomerations in China with high spatiotemporal resolution based on Himawari-8
- (2019) Taixin Zhang et al. SCIENCE OF THE TOTAL ENVIRONMENT
- Soybean yield prediction from UAV using multimodal data fusion and deep learning
- (2019) Maitiniyazi Maimaitijiang et al. REMOTE SENSING OF ENVIRONMENT
- Stacking machine learning model for estimating hourly PM2.5 in China based on Himawari 8 aerosol optical depth data
- (2019) Jiangping Chen et al. SCIENCE OF THE TOTAL ENVIRONMENT
- Estimating hourly PM 1 concentrations from Himawari-8 aerosol optical depth in China
- (2018) Lin Zang et al. ENVIRONMENTAL POLLUTION
- Improved Hourly Estimates of Aerosol Optical Thickness Using Spatiotemporal Variability Derived From Himawari-8 Geostationary Satellite
- (2018) Maki Kikuchi et al. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
- Estimation of ultrahigh resolution PM 2.5 concentrations in urban areas using 160 m Gaofen-1 AOD retrievals
- (2018) Tianhao Zhang et al. REMOTE SENSING OF ENVIRONMENT
- Satellite-based mapping of daily high-resolution ground PM 2.5 in China via space-time regression modeling
- (2018) Qingqing He et al. REMOTE SENSING OF ENVIRONMENT
- Spatial-temporal patterns of PM 2.5 concentrations for 338 Chinese cities
- (2018) Wei-Feng Ye et al. SCIENCE OF THE TOTAL ENVIRONMENT
- Effects of land use and landscape pattern on PM 2.5 in Yangtze River Delta, China
- (2018) Debin Lu et al. Atmospheric Pollution Research
- Estimating PM2.5 Concentrations in the Conterminous United States Using the Random Forest Approach
- (2017) Xuefei Hu et al. ENVIRONMENTAL SCIENCE & TECHNOLOGY
- Estimating Ground-Level PM2.5 by Fusing Satellite and Station Observations: A Geo-Intelligent Deep Learning Approach
- (2017) Tongwen Li et al. GEOPHYSICAL RESEARCH LETTERS
- Deriving Hourly PM2.5 Concentrations from Himawari-8 AODs over Beijing–Tianjin–Hebei in China
- (2017) Wei Wang et al. Remote Sensing
- Aerosol data assimilation using data from Himawari-8, a next-generation geostationary meteorological satellite
- (2016) K. Yumimoto et al. GEOPHYSICAL RESEARCH LETTERS
- An Introduction to Himawari-8/9— Japan’s New-Generation Geostationary Meteorological Satellites
- (2016) Kotaro BESSHO et al. JOURNAL OF THE METEOROLOGICAL SOCIETY OF JAPAN
- Satellite-based ground PM 2.5 estimation using timely structure adaptive modeling
- (2016) Xin Fang et al. REMOTE SENSING OF ENVIRONMENT
- VIIRS-based remote sensing estimation of ground-level PM 2.5 concentrations in Beijing–Tianjin–Hebei: A spatiotemporal statistical model
- (2016) Jiansheng Wu et al. REMOTE SENSING OF ENVIRONMENT
- Estimating Ground-Level PM2.5 Using Fine-Resolution Satellite Data in the Megacity of Beijing, China
- (2015) Rong Li et al. Aerosol and Air Quality Research
- Satellite-Based Spatiotemporal Trends in PM2.5 Concentrations: China, 2004–2013
- (2015) Zongwei Ma et al. ENVIRONMENTAL HEALTH PERSPECTIVES
- Estimating PM2.5 in Xi'an, China using aerosol optical depth: A comparison between the MODIS and MISR retrieval models
- (2015) Wei You et al. SCIENCE OF THE TOTAL ENVIRONMENT
- Estimating Ground-Level PM2.5 in China Using Satellite Remote Sensing
- (2014) Zongwei Ma et al. ENVIRONMENTAL SCIENCE & TECHNOLOGY
- A satellite-based geographically weighted regression model for regional PM2.5 estimation over the Pearl River Delta region in China
- (2014) Weize Song et al. REMOTE SENSING OF ENVIRONMENT
- Long- and Short-Term Exposure to PM2.5 and Mortality
- (2013) Itai Kloog et al. EPIDEMIOLOGY
- Estimating ground-level PM2.5 concentrations in the Southeastern United States using MAIAC AOD retrievals and a two-stage model
- (2013) Xuefei Hu et al. REMOTE SENSING OF ENVIRONMENT
- Risk of Nonaccidental and Cardiovascular Mortality in Relation to Long-term Exposure to Low Concentrations of Fine Particulate Matter: A Canadian National-Level Cohort Study
- (2012) Dan L. Crouse et al. ENVIRONMENTAL HEALTH PERSPECTIVES
- Synergy of satellite and ground based observations in estimation of particulate matter in eastern China
- (2012) Yerong Wu et al. SCIENCE OF THE TOTAL ENVIRONMENT
- The analysis of PM2.5 and associated elements and their indoor/outdoor pollution status in an urban area
- (2010) J.-M. Lim et al. INDOOR AIR
- Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices
- (2010) Bo Huang et al. INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
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