An Efficient and Accurate Model Coupled With Spatiotemporal Kalman Filter and Linear Mixed Effect for Hourly PM<sub>2.5</sub> Mapping-Reference-Cited by-同舟云学术

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An Efficient and Accurate Model Coupled With Spatiotemporal Kalman Filter and Linear Mixed Effect for Hourly PM_2.5 Mapping

Published:2023 Issue: Volume:61 Page:1-17
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Liu Ning¹^ORCID,Zou Bin²^ORCID,Li Yi³,Li Sha¹,Li Shenxin¹,Wu Jian²,Chen Jun²

Affiliation:

1. School of Geosciences and Info-Physics, Central South University, Changsha, China

2. Changsha Environmental Monitoring Center of Hunan Province, Changsha, China

3. College of Meteorology and Oceanography, National University of Defense Technology, Changsha, China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Independent Innovation Science Fund of the National University of Defense Technology

Natural Science Foundation of Hunan Province, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/10006360/10288213.pdf?arnumber=10288213

Reference66 articles.

1. Identifying the spatiotemporal dynamic of PM2.5 concentrations at multiple scales using geographically and temporally weighted regression model across China during 2015–2018

2. A Validation Approach Considering the Uneven Distribution of Ground Stations for Satellite-Based PM2.5 Estimation

3. Satellite-based mapping of daily high-resolution ground PM2.5 in China via space-time regression modeling

4. The Computation of Equivalent Potential Temperature

5. Satellite-based ground PM2.5 estimation using timely structure adaptive modeling

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. An Accurate and Efficient Forecast Framework for Fine PM2.5 Maps Using Spatiotemporal Recurrent Neural Networks;Journal of Cleaner Production;2024-09

2. Generating Hourly Fine Seamless Aerosol Optical Depth Products by Fusing Multiple Satellite and Numerical Model Data;IEEE Transactions on Geoscience and Remote Sensing;2024

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