Surrogate Modeling of MODTRAN Physical Radiative Transfer Code Using Deep-Learning Regression-Reference-Cited by-同舟云学术

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Surrogate Modeling of MODTRAN Physical Radiative Transfer Code Using Deep-Learning Regression

Published:2023-11-16 Issue: Volume:10 Page:16
ISSN:
Container-title:ECRS 2023
language:
Short-container-title:

Author:

Aghdami-Nia Mohammad¹,Shah-Hosseini Reza¹^ORCID,Homayouni Saeid²,Rostami Amirhossein¹^ORCID,Ahmadian Nima³

Affiliation:

1. School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran 1417935840, Iran

2. Centre Eau Terre Environnement, Institut National de la Recherche Scientifique, 490 Rue de la Couronne, Quebec City, QC G1K 9A9, Canada

3. Department of Geomatics Engineering, Faculty of Civil Engineering, University of Tabriz, Tabriz 5166616471, Iran

Publisher

MDPI

Link

https://www.mdpi.com/2673-4931/29/1/16/pdf

Reference9 articles.

1. Liang, X., and Liu, Q. (2021). Applying Deep Learning to Clear-Sky Radiance Simulation for VIIRS with Community Radiative Transfer Model—Part 1: Develop AI-Based Clear-Sky Mask. Remote Sens., 13.

2. Effect of transferring pre-trained weights on a Siamese change detection network;Salmani;ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci.,2023

3. Abbasi, M., Shah-Hosseini, R., and Aghdami-Nia, M. (2023). Sentinel-1 Polarization Comparison for Flood Segmentation Using Deep Learning. Proceedings, 87.

4. Using Deep Neural Networks as Cost-Effective Surrogate Models for Super-Parameterized E3SM Radiative Transfer;Pal;Geophys. Res. Lett.,2019

5. Using Deep Learning to Emulate and Accelerate a Radiative Transfer Model;Lagerquist;J. Atmos. Ocean. Technol.,2021

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