Continental aerosol properties and absorption retrieval using random forest machine learning method specific to geostationary remote sensing-Reference-Cited by-同舟云学术

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Continental aerosol properties and absorption retrieval using random forest machine learning method specific to geostationary remote sensing

Published:2024-09 Issue: Volume:311 Page:114275
ISSN:0034-4257
Container-title:Remote Sensing of Environment
language:en
Short-container-title:Remote Sensing of Environment

Author:

Bao Fangwen,Wu Shengbiao,Gao Jinhui,Yuan Shuyun,Liu Yiwen,Huang Kai

Publisher

Elsevier BV

Reference87 articles.

1. On the atmospheric transmission of sun radiation and on dust in the air;Ångström;Geogr. Ann.,1929

2. Single scattering albedo of high loading aerosol estimated across East Asia from S-NPP VIIRS;Bao;IEEE Trans. Geosci. Remote Sens.,2021

3. The retrieval of aerosol optical properties based on a random forest machine learning approach: exploration of geostationary satellite images;Bao;Remote Sens. Environ.,2023

4. Carbonaceous aerosols remote sensing from geostationary satellite observation, part I: algorithm development using critical reflectance;Bao;Remote Sens. Environ.,2023

5. An introduction to Himawari-8/9—Japan’s new-generation geostationary meteorological satellites;Bessho;J. Meteorol. Soc. Japan. Ser. II,2016

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