A novel big data mining framework for reconstructing large-scale daily MAIAC AOD data across China from 2000 to 2020-Reference-Cited by-同舟云学术

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A novel big data mining framework for reconstructing large-scale daily MAIAC AOD data across China from 2000 to 2020

Published:2022-03-21 Issue:1 Volume:59 Page:670-685
ISSN:1548-1603
Container-title:GIScience & Remote Sensing
language:en
Short-container-title:GIScience & Remote Sensing

Author:

Chen Binjie¹,Ye Yang¹,Tong Cheng¹,Deng Jinsong¹²,Wang Ke¹,Hong Yang³

Affiliation:

1. College of Environment and Resource Sciences, Zhejiang University, Hangzhou, Zhejiang, China

2. Zhejiang Ecological Civilization Academy, Anji, Zhejiang, China

3. School of Civil Engineering and Environmental Sciences, University of Oklahoma, Norman, OK, USA

Funder

National Key Research and Development Program of China

Key Research and Development Program of Zhejiang Province

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/15481603.2022.2051382

Reference52 articles.

1. Spatiotemporal trend analysis for fine particulate matter concentrations in China using high-resolution satellite-derived and ground-measured PM2.5 data

2. Evaluation of Aqua MODIS Collection 6 AOD Parameters for Air Quality Research over the Continental United States

3. Comparison of Different Missing-Imputation Methods for MAIAC (Multiangle Implementation of Atmospheric Correction) AOD in Estimating Daily PM2.5 Levels

4. Extreme gradient boosting model to estimate PM2.5 concentrations with missing-filled satellite data in China

5. Handling Missing Data in Large-Scale MODIS AOD Products Using a Two-Step Model

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2. Research progress, challenges, and prospects of PM_2.5 concentration estimation using satellite data;Environmental Reviews;2023-12-01

3. An improved deep learning network for AOD retrieving from remote sensing imagery focusing on sub-pixel cloud;GIScience & Remote Sensing;2023-10-14

4. Retrieval of Hourly Aerosol Optical Depth Using Top-of-Atmosphere Reflectance from GOCI-II and Machine Learning over South Korea;KOREAN J REMOTE SENS;2023

5. Retrieval of hourly PM2.5 using top-of-atmosphere reflectance from geostationary ocean color imagers I and II;Environmental Pollution;2023-04

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