Application of machine learning to hyperspectral radiative transfer simulations-Reference-Cited by-同舟云学术

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Application of machine learning to hyperspectral radiative transfer simulations

Published:2020-05 Issue: Volume:246 Page:106928
ISSN:0022-4073
Container-title:Journal of Quantitative Spectroscopy and Radiative Transfer
language:en
Short-container-title:Journal of Quantitative Spectroscopy and Radiative Transfer

Author:

Le Tianhao,Liu Chao,Yao Bin,Natraj Vijay,Yung Yuk L.

Funder

National Natural Science Foundation of China

Natural Science Foundation of Jiangsu

Young Elite Scientists Sponsorship Program

NASA Earth Science US Participating Investigator program

Publisher

Elsevier BV

Subject

Spectroscopy,Atomic and Molecular Physics, and Optics,Radiation

Reference53 articles.

1. Molecular line absorption in a scattering atmosphere: part I theory;Stephens;J Atmos Sci,2000

2. Development of a fast and accurate PCRTM radiative transfer model in the solar spectral region;Liu;Appl Opt,2016

3. Using passive remote sensing to retrieve the vertical variation of cloud droplet size in marine stratocumulus: an assessment of information content and the potential for improved retrievals from hyperspectral measurements;King;J Geophys Res,2012

4. Deep convective cloud characterizations from both broadband imager and hyperspectral infrared sounder measurements;Ai;J Geophys Res,2017

5. AIRS/AMUS/HSB on the Aqua mission: design, science objectives, data products, and processing systems;Autumn;IEEE Trans Geosci Remote Sens,2003

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3. Professor Richard Mead Goody (1921-2023): A Life in Three Acts;Journal of Quantitative Spectroscopy and Radiative Transfer;2023-12

4. Estimating Full Longwave and Shortwave Radiative Transfer with Neural Networks of Varying Complexity;Journal of Atmospheric and Oceanic Technology;2023-11

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