Improved retrievals of aerosol optical depth and fine mode fraction from GOCI geostationary satellite data using machine learning over East Asia-Reference-Cited by-同舟云学术

Improved retrievals of aerosol optical depth and fine mode fraction from GOCI geostationary satellite data using machine learning over East Asia

Published:2022-01 Issue: Volume:183 Page:253-268
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:

Kang Yoojin,Kim Miae,Kang Eunjin,Cho Dongjin,Im Jungho

Funder

Korea Environmental Industry and Technology Institute

National Research Foundation of Korea

Ministry of Environment

National Institute of Environmental Research

Ministry of Science, ICT and Future Planning

Publisher

Elsevier BV

Subject

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

Reference64 articles.

1. Intercomparison of MODIS, MISR, OMI, and CALIPSO aerosol optical depth retrievals for four locations on the Indo-Gangetic plains and validation against AERONET data;Bibi;Atmos. Environ.,2015

2. Clouds and aerosols. Climate change 2013: the physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change;Boucher,2013

3. Random forests;Breiman;Mach. Learn.,2001

4. Aerosol optical properties under the condition of heavy haze over an urban site of Beijing, China;Che;Environ. Sci. Pollut. Res.,2015

5. Joint retrieval of the aerosol fine mode fraction and optical depth using MODIS spectral reflectance over northern and eastern China: artificial neural network method;Chen;Remote Sens. Environ.,2020

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