Ordinal METAR Cloud Cover Classification in Sentinel-2 Satellite Data
Accurate cloud cover assessment is crucial in several fields, such as weather forecasting, climate science, agriculture, or energy system planning, precipitation pattern forecasting and aiding in early detection of extreme weather events. Despite the crucial data that weather stations provide about...
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10949748/ |
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| author | Markus Gotz Erik Wessel Ugur Cayoglu Babak Jahani Charlotte Debus Jan Cermak Achim Streit |
| author_facet | Markus Gotz Erik Wessel Ugur Cayoglu Babak Jahani Charlotte Debus Jan Cermak Achim Streit |
| author_sort | Markus Gotz |
| collection | DOAJ |
| description | Accurate cloud cover assessment is crucial in several fields, such as weather forecasting, climate science, agriculture, or energy system planning, precipitation pattern forecasting and aiding in early detection of extreme weather events. Despite the crucial data that weather stations provide about sky cloud coverage, their measurements are geographically localized and thus lack spatial coverage. Meteorological satellites on the other hand offer great potential to address this limitation by continuously scanning large areas in short periods of time. This work proposes a novel approach for predicting cloud cover in global satellite images by leveraging ordinal point labels from ground-based weather stations, rather than relying on spatially resolved cloud masks, and demonstrates the effectiveness of this approach using a rank loss-based convolutional neural network of the EfficientNet family. The model is trained in transfer learning approach on a custom-collected dataset across selected regions in the continental USA. Using station measurements only, we achieve an <inline-formula><tex-math notation="LaTeX">$F_{1}$</tex-math></inline-formula>-score of up to 0.6 and a ranked-within-1-accuracy ranging from 93.5% to 99.1%. Supplementing the data with labels created by visual inspection to correct for station-satellite mismatches improves scores to 0.75 and 98.4% to 100%. The results imply significantly improved cloud cover assessment in regions without weather stations, extending the capabilities to monitor localized cloud patterns. |
| format | Article |
| id | doaj-art-181092b73ce346d4b9e4a613b56d730c |
| institution | DOAJ |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-181092b73ce346d4b9e4a613b56d730c2025-08-20T03:14:01ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118106741068310.1109/JSTARS.2025.355793110949748Ordinal METAR Cloud Cover Classification in Sentinel-2 Satellite DataMarkus Gotz0https://orcid.org/0000-0002-2233-1041Erik Wessel1Ugur Cayoglu2https://orcid.org/0000-0002-9670-3717Babak Jahani3https://orcid.org/0000-0002-7347-4878Charlotte Debus4https://orcid.org/0000-0002-7156-2022Jan Cermak5https://orcid.org/0000-0002-4240-595XAchim Streit6https://orcid.org/0000-0002-5065-469XHelmholtz AI, Neuherberg, GermanyScientific Computing Center (SCC), Eggenstein-Leopoldshafen, GermanyScientific Computing Center (SCC), Eggenstein-Leopoldshafen, GermanyInstitute of Meteorology, Climate Research—Atmospheric Trace Gases, Remote Sensing, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, GermanyScientific Computing Center (SCC), Eggenstein-Leopoldshafen, GermanyInstitute of Meteorology, Climate Research—Atmospheric Trace Gases, Remote Sensing, Karlsruhe Institute of Technology, Karlsruhe, GermanyHelmholtz AI, Neuherberg, GermanyAccurate cloud cover assessment is crucial in several fields, such as weather forecasting, climate science, agriculture, or energy system planning, precipitation pattern forecasting and aiding in early detection of extreme weather events. Despite the crucial data that weather stations provide about sky cloud coverage, their measurements are geographically localized and thus lack spatial coverage. Meteorological satellites on the other hand offer great potential to address this limitation by continuously scanning large areas in short periods of time. This work proposes a novel approach for predicting cloud cover in global satellite images by leveraging ordinal point labels from ground-based weather stations, rather than relying on spatially resolved cloud masks, and demonstrates the effectiveness of this approach using a rank loss-based convolutional neural network of the EfficientNet family. The model is trained in transfer learning approach on a custom-collected dataset across selected regions in the continental USA. Using station measurements only, we achieve an <inline-formula><tex-math notation="LaTeX">$F_{1}$</tex-math></inline-formula>-score of up to 0.6 and a ranked-within-1-accuracy ranging from 93.5% to 99.1%. Supplementing the data with labels created by visual inspection to correct for station-satellite mismatches improves scores to 0.75 and 98.4% to 100%. The results imply significantly improved cloud cover assessment in regions without weather stations, extending the capabilities to monitor localized cloud patterns.https://ieeexplore.ieee.org/document/10949748/Convolution neural networksmeteorological aerodrome report (METAR)machine learningordinal classificationsatellite imagessentinel-2 |
| spellingShingle | Markus Gotz Erik Wessel Ugur Cayoglu Babak Jahani Charlotte Debus Jan Cermak Achim Streit Ordinal METAR Cloud Cover Classification in Sentinel-2 Satellite Data IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Convolution neural networks meteorological aerodrome report (METAR) machine learning ordinal classification satellite images sentinel-2 |
| title | Ordinal METAR Cloud Cover Classification in Sentinel-2 Satellite Data |
| title_full | Ordinal METAR Cloud Cover Classification in Sentinel-2 Satellite Data |
| title_fullStr | Ordinal METAR Cloud Cover Classification in Sentinel-2 Satellite Data |
| title_full_unstemmed | Ordinal METAR Cloud Cover Classification in Sentinel-2 Satellite Data |
| title_short | Ordinal METAR Cloud Cover Classification in Sentinel-2 Satellite Data |
| title_sort | ordinal metar cloud cover classification in sentinel 2 satellite data |
| topic | Convolution neural networks meteorological aerodrome report (METAR) machine learning ordinal classification satellite images sentinel-2 |
| url | https://ieeexplore.ieee.org/document/10949748/ |
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