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| Main Authors: | , , , , , |
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| Format: | Recurso digital |
| Language: | English |
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Zenodo
2026
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| Online Access: | https://doi.org/10.5281/zenodo.18934756 |
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| _version_ | 1866901062515949568 |
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| author | Serpico, Bruno Sebastiano Moser, Gabriele Basit, Abdul Castellano, Michela Massa, Francesco Ciuffardi, Tiziana |
| author_facet | Serpico, Bruno Sebastiano Moser, Gabriele Basit, Abdul Castellano, Michela Massa, Francesco Ciuffardi, Tiziana |
| contents | The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework. <br><br>Full metadata: <a href="https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_portofino_20220307/index.html" target="_blank">https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_portofino_20220307/index.html</a><br><br>This dataset is part of the RAISE Spoke 3 project outcomes. RAISE is an innovation ecosystem funded by the Ministry of University and Research under the National Recovery and Resilience Plan (NRRP, Mission 4, Component 2, Investment 1.5). |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18934756 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Estimated chlorophyall-a concentration at 60 m spatial resolution (20220307) Serpico, Bruno Sebastiano Moser, Gabriele Basit, Abdul Castellano, Michela Massa, Francesco Ciuffardi, Tiziana Chlorophyll-a Copernicus Programme Earth Science > Oceans > Ocean Optics > Chlorophyll (15Cc550B-068C-49F4-B082-Bc2A43675606) Earth Science Services > Models > Machine Learning Models (fe4392b0-13a9-43ff-bacc-f44a65aed4fa) Earth Science Services > Models > Machine Learning Models > Ensemble Models > Random Forest (A68048F4-181C-4C6C-9Bfa-9E4171E9F237) Instruments > Earth Remote Sensing Instruments (6015ef7b-f3bd-49e1-9193-cc23db566b69) Ligurian Sea Machine Learning Marine Environment Mondrian Forest Random Forest Remote Sensing Satellite Oceanography Sentinel-2 Space-based Platforms > Earth Observation Satellites (3466eed1-2fbb-49bf-ab0b-dc08731d502b) Thermal Infrared The dataset consists of estimated chlorophyll-a (CHL-a) concentration maps obtained as the output of a machine learning model. Multispectral data from twelve bands of the Sentinel-2 mission of the Copernicus Programme of the European Union, together with in situ CHL-a measurements provided by colleagues at UNIGE-DISTAV and ENEA, are used to train the model within a supervised machine learning framework. <br><br>Full metadata: <a href="https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_portofino_20220307/index.html" target="_blank">https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_portofino_20220307/index.html</a><br><br>This dataset is part of the RAISE Spoke 3 project outcomes. RAISE is an innovation ecosystem funded by the Ministry of University and Research under the National Recovery and Resilience Plan (NRRP, Mission 4, Component 2, Investment 1.5). |
| title | Estimated chlorophyall-a concentration at 60 m spatial resolution (20220307) |
| topic | Chlorophyll-a Copernicus Programme Earth Science > Oceans > Ocean Optics > Chlorophyll (15Cc550B-068C-49F4-B082-Bc2A43675606) Earth Science Services > Models > Machine Learning Models (fe4392b0-13a9-43ff-bacc-f44a65aed4fa) Earth Science Services > Models > Machine Learning Models > Ensemble Models > Random Forest (A68048F4-181C-4C6C-9Bfa-9E4171E9F237) Instruments > Earth Remote Sensing Instruments (6015ef7b-f3bd-49e1-9193-cc23db566b69) Ligurian Sea Machine Learning Marine Environment Mondrian Forest Random Forest Remote Sensing Satellite Oceanography Sentinel-2 Space-based Platforms > Earth Observation Satellites (3466eed1-2fbb-49bf-ab0b-dc08731d502b) Thermal Infrared |
| url | https://doi.org/10.5281/zenodo.18934756 |