Estimated chlorophyall-a concentration at 60 m spatial resolution (20220307)

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Authors: Bruno Sebastiano Serpico, Gabriele Moser, Abdul Basit, Michela Castellano, Francesco Massa, Tiziana Ciuffardi
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Abstract

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. Full metadata: https://erddap.s4raise.it/erddap/info/unige-diten_chlorophyll_final_output_laspezia_20220307/index.htmlThis 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).

Fields of Study & Subjects

Marine Plankton and Biogeochemistry Remote-Sensing Image Classification Water Quality Monitoring Technologies

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
9 March 2026
Language
EN
Document type
Dataset