Multiscale and multisource data fusion for full-coverage PM2.5 concentration mapping: Can spatial pattern recognition come with modeling accuracy?-Reference-Cited by-同舟云学术

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Multiscale and multisource data fusion for full-coverage PM2.5 concentration mapping: Can spatial pattern recognition come with modeling accuracy?

Published:2022-02 Issue: Volume:184 Page:31-44
ISSN:0924-2716
Container-title:ISPRS Journal of Photogrammetry and Remote Sensing
language:en
Short-container-title:ISPRS Journal of Photogrammetry and Remote Sensing

Author:

Bai Kaixu,Li Ke,Guo Jianping,Chang Ni-Bin

Funder

Science and Technology Commission of Shanghai Municipality

Shanghai Municipal Natural Science Foundation

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Computers in Earth Sciences,Computer Science Applications,Engineering (miscellaneous),Atomic and Molecular Physics, and Optics

Reference65 articles.

1. Advancing the prediction accuracy of satellite-based PM2.5 concentration mapping: A perspective of data mining through in situ PM2.5 measurements;Bai;Environ. Pollut.,2019

2. Filling the gaps of in situ hourly PM2.5 concentration data with the aid of empirical orthogonal function analysis constrained by diurnal cycles;Bai;Atmos. Meas. Tech.,2020

3. A homogenized daily in situ PM2.5 concentration dataset from national air quality monitoring network in China;Bai;Earth Syst. Sci. Data.,2020

4. Spatiotemporal trend analysis for fine particulate matter concentrations in China using high-resolution satellite-derived and ground-measured PM2.5 data;Bai;J. Environ. Manage.,2019

5. A comparison of statistical and machine learning methods for creating national daily maps of ambient PM2.5 concentration;Berrocal;Atmos. Environ.,2020

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1. Retrieving ground-level PM_2.5 concentrations in China (2013–2021) with a numerical-model-informed testbed to mitigate sample-imbalance-induced biases;Earth System Science Data;2024-08-27

2. A Novel Flexible Geographically Weighted Neural Network for High-Precision PM2.5 Mapping across the Contiguous United States;ISPRS International Journal of Geo-Information;2024-06-22

3. LGHAP v2: a global gap-free aerosol optical depth and PM_2.5 concentration dataset since 2000 derived via big Earth data analytics;Earth System Science Data;2024-05-22

4. State-of-art in modelling particulate matter (PM) concentration: a scoping review of aims and methods;Environment, Development and Sustainability;2024-04-02

5. A review of machine learning for modeling air quality: Overlooked but important issues;Atmospheric Research;2024-04

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