Applying Deep Learning to Clear-Sky Radiance Simulation for VIIRS with Community Radiative Transfer Model-Part 1: Develop AI-Based Clear-Sky Mask

Liang, Xingming; Liu, Quanhua

doi:10.3390/rs13020222

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Applying Deep Learning to Clear-Sky Radiance Simulation for VIIRS with Community Radiative Transfer Model-Part 1: Develop AI-Based Clear-Sky Mask

2021
By Liang, Xingming ; Liu, Quanhua
Source: Remote Sens. 2021, 13(2), 222

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Journal Title:

Remote Sensing
Personal Author:

Liang, Xingming ; Liu, Quanhua

Liang, Xingming ; Liu, Quanhua Less -
NOAA Program & Office:

NESDIS (National Environmental Satellite, Data, and Information Service) ; STAR (Center for Satellite Applications and Research) ; CISESS (Cooperative Institute for Satellite Earth System Studies)

NESDIS (National Environmental Satellite, Data, and Information Service) ; STAR (Center for Satellite Applications and Research) ; CISESS (Cooperative Institute for Satellite Earth System Studies) Less -
Description:

A fully connected deep neural network (FCDN) clear-sky mask (CSM) algorithm (FCDN_CSM) was developed to assist the FCDN-based Community Radiative Transfer Model (FCDN_CRTM) to reproduce the Visible Infrared Imaging Radiometer Suite (VIIRS) clear-sky radiances in five thermal emission M (TEB/M) bands. The model design was referenced and enhanced from its earlier version (version 1), and was trained and tested in the global ocean clear-sky domain using six dispersion days’ data from 2019 to 2020 as inputs and a modified NOAA Advanced Clear-Sky Processor over Ocean (ACSPO) CSM product as reference labels. The improved FCDN_CSM (version 2) was further enhanced by including daytime data, which was not collected in version 1. The trained model was then employed to predict VIIRS CSM over multiple days in 2020 as an accuracy and stability check. The results were validated against the biases between the sensor observations and CRTM calculations (O-M).
Keywords:

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Advanced Clear-Sky Processor Over Ocean (ACSPO) Artificial Neural Network (ANN) Clear-sky Mask (CSM) Community Radiative Transfer Model (CRTM) Deep Learning Fully Connected “Deep” Neural Network (FCDN) Machine Learning Visible Infrared Imaging Radiometer Suite (VIIRS)
Source:

Remote Sens. 2021, 13(2), 222
DOI:

https://doi.org/10.3390/rs13020222
Document Type:

Journal Article
Funding:

Grant no. NA14NES4320003
Rights Information:

CC BY
Compliance:

Submitted
Main Document Checksum:

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urn:sha256:3d65ff2abd5a9891a9751967398f97e4d33b2b1a0768056107e675372fa96a70
Download URL:

https://repository.library.noaa.gov/view/noaa/41799/noaa_41799_DS1.pdf
File Type:

Erratum

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