Retrieval of Fine-Grained PM2.5 Spatiotemporal Resolution Based on Multiple Machine Learning Models
Jan 26, 2022·
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1 min read
Peilong Ma
Fei Tao
Lina Gao
Shaijie Leng
Ke Yang
Tong Zhou
Abstract
This study investigates fine-grained PM2.5 retrieval by combining remote sensing, monitoring, and socioeconomic data with multiple machine learning models, demonstrating strong performance for high-resolution air-quality estimation.
Type
Publication
Remote Sensing, 14(3), 599
This article evaluates multiple machine learning algorithms for PM2.5 estimation and shows how multi-source urban data can improve fine-grained spatiotemporal retrieval.
