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Remote Sensing, volume 15, issue 7, pages 1720

Machine Learning Models for Approximating Downward Short-Wave Radiation Flux over the Ocean from All-Sky Optical Imagery Based on DASIO Dataset

Krinitskiy Mikhail 1, 2, 3
, 
Koshkina Vasilisa 2
, 
Borisov Mikhail 2
, 
Anikin Nikita 1
, 
Gulev S. K. 1
, 
Artemeva Maria 1
1
 
2
 
3
 
Moscow Center for Fundamental and Applied Mathematics, Moscow 119991, Russia | 
Publication type: Journal Article
Publication date: 2023-03-23
Journal: Remote Sensing
Quartile SCImago
Q1
Quartile WOS
Q1
Impact factor: 5
ISSN: 20724292
General Earth and Planetary Sciences
Abstract

Downward short-wave (SW) solar radiation is the only essential energy source powering the atmospheric dynamics, ocean dynamics, biochemical processes, and so forth on our planet. Clouds are the main factor limiting the SW flux over the land and the Ocean. For the accurate meteorological measurements of the SW flux one needs expensive equipment-pyranometers. For some cases where one does not need golden-standard quality of measurements, we propose estimating incoming SW radiation flux using all-sky optical RGB imagery which is assumed to incapsulate the whole information about the downward SW flux. We used DASIO all-sky imagery dataset with corresponding SW downward radiation flux measurements registered by an accurate pyranometer. The dataset has been collected in various regions of the World Ocean during several marine campaigns from 2014 to 2021, and it will be updated. We demonstrate the capabilities of several machine learning models in this problem, namely multilinear regression, Random Forests, Gradient Boosting and convolutional neural networks (CNN). We also applied the inverse target frequency (ITF) re-weighting of the training subset in an attempt of improving the SW flux approximation quality. We found that the CNN is capable of approximating downward SW solar radiation with higher accuracy compared to existing empiric parameterizations and known algorithms based on machine learning methods for estimating downward SW flux using remote sensing (MODIS) imagery. The estimates of downward SW radiation flux using all-sky imagery may be of particular use in case of the need for the fast radiative budgets assessment of a site.

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Moscow University Physics Bulletin (English Translation of Vestnik Moskovskogo Universiteta, Fizika)
Moscow University Physics Bulletin (English Translation of Vestnik Moskovskogo Universiteta, Fizika), 1, 100%
Moscow University Physics Bulletin (English Translation of Vestnik Moskovskogo Universiteta, Fizika)
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Allerton Press, 1, 100%
Allerton Press
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Krinitskiy M. et al. Machine Learning Models for Approximating Downward Short-Wave Radiation Flux over the Ocean from All-Sky Optical Imagery Based on DASIO Dataset // Remote Sensing. 2023. Vol. 15. No. 7. p. 1720.
GOST all authors (up to 50) Copy
Krinitskiy M., Koshkina V., Borisov M., Anikin N., Gulev S. K., Artemeva M. Machine Learning Models for Approximating Downward Short-Wave Radiation Flux over the Ocean from All-Sky Optical Imagery Based on DASIO Dataset // Remote Sensing. 2023. Vol. 15. No. 7. p. 1720.
RIS |
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RIS Copy
TY - JOUR
DO - 10.3390/rs15071720
UR - https://doi.org/10.3390%2Frs15071720
TI - Machine Learning Models for Approximating Downward Short-Wave Radiation Flux over the Ocean from All-Sky Optical Imagery Based on DASIO Dataset
T2 - Remote Sensing
AU - Krinitskiy, Mikhail
AU - Koshkina, Vasilisa
AU - Borisov, Mikhail
AU - Anikin, Nikita
AU - Gulev, S. K.
AU - Artemeva, Maria
PY - 2023
DA - 2023/03/23 00:00:00
PB - Multidisciplinary Digital Publishing Institute (MDPI)
SP - 1720
IS - 7
VL - 15
SN - 2072-4292
ER -
BibTex |
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BibTex Copy
@article{2023_Krinitskiy,
author = {Mikhail Krinitskiy and Vasilisa Koshkina and Mikhail Borisov and Nikita Anikin and S. K. Gulev and Maria Artemeva},
title = {Machine Learning Models for Approximating Downward Short-Wave Radiation Flux over the Ocean from All-Sky Optical Imagery Based on DASIO Dataset},
journal = {Remote Sensing},
year = {2023},
volume = {15},
publisher = {Multidisciplinary Digital Publishing Institute (MDPI)},
month = {mar},
url = {https://doi.org/10.3390%2Frs15071720},
number = {7},
pages = {1720},
doi = {10.3390/rs15071720}
}
MLA
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Krinitskiy, Mikhail, et al. “Machine Learning Models for Approximating Downward Short-Wave Radiation Flux over the Ocean from All-Sky Optical Imagery Based on DASIO Dataset.” Remote Sensing, vol. 15, no. 7, Mar. 2023, p. 1720. https://doi.org/10.3390%2Frs15071720.
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