Pixel-Level Projection of PM2.5 Using Landsat Images and Cellular Automata Models in the Yangtze River Delta, China
Author:
Affiliation:
1. College of Surveying and Geo-Informatics, Tongji University, Shanghai, China
Funder
National Key R&D Program of China
National Natural Science Foundation of China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Atmospheric Science,Computers in Earth Sciences
Link
http://xplorestaging.ieee.org/ielx7/4609443/9973430/10184023.pdf?arnumber=10184023
Reference68 articles.
1. The estimated change in the level and distribution of PM2.5-attributable health impacts in the United States: 2005–2014
2. Satellite-based high-resolution PM2.5 estimation over the Beijing-Tianjin-Hebei region of China using an improved geographically and temporally weighted regression model
3. Predicting PM2.5 atmospheric air pollution using deep learning with meteorological data and ground-based observations and remote-sensing satellite big data
4. A review of land-use regression models to assess spatial variation of outdoor air pollution
5. A temporal-spatial interpolation and extrapolation method based on geographic Long Short-Term Memory neural network for PM2.5
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