CIAO: A machine-learning algorithm for mapping Arctic Ocean Chlorophyll-a from space
Ocean color (OC) remote sensing at a Pan-Arctic scale, with over 27 years of continuous daily data, provides critical insights into long-term trends and seasonal variability in phytoplankton abundance, indexed by Chlorophyll-a concentration (Chl-a). However, existing satellite algorithms for retriev...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Science of Remote Sensing |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666017225000185 |
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| author | Maria Laura Zoffoli Vittorio Brando Gianluca Volpe Luis González Vilas Bede Ffinian Rowe Davies Robert Frouin Jaime Pitarch Simon Oiry Jing Tan Simone Colella Christian Marchese |
| author_facet | Maria Laura Zoffoli Vittorio Brando Gianluca Volpe Luis González Vilas Bede Ffinian Rowe Davies Robert Frouin Jaime Pitarch Simon Oiry Jing Tan Simone Colella Christian Marchese |
| author_sort | Maria Laura Zoffoli |
| collection | DOAJ |
| description | Ocean color (OC) remote sensing at a Pan-Arctic scale, with over 27 years of continuous daily data, provides critical insights into long-term trends and seasonal variability in phytoplankton abundance, indexed by Chlorophyll-a concentration (Chl-a). However, existing satellite algorithms for retrieving Chl-a in the Arctic Ocean (AO) exhibit significant limitations, including high uncertainties and inconsistent accuracy across different regions, which propagate errors in primary production estimates and biogeochemical models. In this study, we quantified the uncertainties of seven existing algorithms using harmonized, merged multi-sensor satellite remote sensing reflectance (Rrs) data from the ESA Climate Change Initiative (CCI) spanning 1998–2023. The existing algorithms exhibited varying performance, with Mean Absolute Differences (MAD) ranging from 0.8 to 4.2 mg m−3. To improve these results, we developed CIAO (Chlorophyll In the Arctic Ocean), a machine learning-based algorithm specifically designed for AO waters and trained with satellite Rrs data. The CIAO algorithm uses Rrs at four spectral bands (443, 490, 510 and 560 nm) and Day-Of-Year (DOY) to account for seasonal variations in bio-optical relationships. CIAO significantly outperformed seven existing algorithms, achieving a MAD of 0.5 mg m−3, thereby improving Chl-a retrievals by at least 30%, compared to the best-performing existing algorithm. Furthermore, CIAO produced consistent spatial patterns without artifacts and provided more reliable Chl-a estimates in coastal waters, where other algorithms tend to overestimate. This enhanced the accuracy of seasonal variability tracking at a Pan-Arctic scale. By strengthening the precision of satellite-derived Chl-a estimates, CIAO contributes to more accurate ecological assessments and robust climate projections for the rapidly changing AO. |
| format | Article |
| id | doaj-art-1d9ab37f776a47b5beb3b475bdcdab32 |
| institution | OA Journals |
| issn | 2666-0172 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Science of Remote Sensing |
| spelling | doaj-art-1d9ab37f776a47b5beb3b475bdcdab322025-08-20T02:36:16ZengElsevierScience of Remote Sensing2666-01722025-06-011110021210.1016/j.srs.2025.100212CIAO: A machine-learning algorithm for mapping Arctic Ocean Chlorophyll-a from spaceMaria Laura Zoffoli0Vittorio Brando1Gianluca Volpe2Luis González Vilas3Bede Ffinian Rowe Davies4Robert Frouin5Jaime Pitarch6Simon Oiry7Jing Tan8Simone Colella9Christian Marchese10Istituto di Scienze Marine (ISMAR), Consiglio Nazionale delle Ricerche (CNR), Rome, Italy; Corresponding author.Istituto di Scienze Marine (ISMAR), Consiglio Nazionale delle Ricerche (CNR), Rome, ItalyIstituto di Scienze Marine (ISMAR), Consiglio Nazionale delle Ricerche (CNR), Rome, ItalyIstituto di Scienze Marine (ISMAR), Consiglio Nazionale delle Ricerche (CNR), Rome, ItalyNantes Université, Institut des Substances et Organismes de la Mer, ISOMer, UR2160, Nantes, F-44000, FranceScripps Institution of Oceanography, University California San Diego, La Jolla, CA, USAIstituto di Scienze Marine (ISMAR), Consiglio Nazionale delle Ricerche (CNR), Rome, ItalyNantes Université, Institut des Substances et Organismes de la Mer, ISOMer, UR2160, Nantes, F-44000, FranceScripps Institution of Oceanography, University California San Diego, La Jolla, CA, USAIstituto di Scienze Marine (ISMAR), Consiglio Nazionale delle Ricerche (CNR), Rome, ItalyIstituto di Scienze Marine (ISMAR), Consiglio Nazionale delle Ricerche (CNR), Rome, ItalyOcean color (OC) remote sensing at a Pan-Arctic scale, with over 27 years of continuous daily data, provides critical insights into long-term trends and seasonal variability in phytoplankton abundance, indexed by Chlorophyll-a concentration (Chl-a). However, existing satellite algorithms for retrieving Chl-a in the Arctic Ocean (AO) exhibit significant limitations, including high uncertainties and inconsistent accuracy across different regions, which propagate errors in primary production estimates and biogeochemical models. In this study, we quantified the uncertainties of seven existing algorithms using harmonized, merged multi-sensor satellite remote sensing reflectance (Rrs) data from the ESA Climate Change Initiative (CCI) spanning 1998–2023. The existing algorithms exhibited varying performance, with Mean Absolute Differences (MAD) ranging from 0.8 to 4.2 mg m−3. To improve these results, we developed CIAO (Chlorophyll In the Arctic Ocean), a machine learning-based algorithm specifically designed for AO waters and trained with satellite Rrs data. The CIAO algorithm uses Rrs at four spectral bands (443, 490, 510 and 560 nm) and Day-Of-Year (DOY) to account for seasonal variations in bio-optical relationships. CIAO significantly outperformed seven existing algorithms, achieving a MAD of 0.5 mg m−3, thereby improving Chl-a retrievals by at least 30%, compared to the best-performing existing algorithm. Furthermore, CIAO produced consistent spatial patterns without artifacts and provided more reliable Chl-a estimates in coastal waters, where other algorithms tend to overestimate. This enhanced the accuracy of seasonal variability tracking at a Pan-Arctic scale. By strengthening the precision of satellite-derived Chl-a estimates, CIAO contributes to more accurate ecological assessments and robust climate projections for the rapidly changing AO.http://www.sciencedirect.com/science/article/pii/S2666017225000185Phytoplankton biomassPan-arctic scaleOcean colorSatellite dataTime series |
| spellingShingle | Maria Laura Zoffoli Vittorio Brando Gianluca Volpe Luis González Vilas Bede Ffinian Rowe Davies Robert Frouin Jaime Pitarch Simon Oiry Jing Tan Simone Colella Christian Marchese CIAO: A machine-learning algorithm for mapping Arctic Ocean Chlorophyll-a from space Science of Remote Sensing Phytoplankton biomass Pan-arctic scale Ocean color Satellite data Time series |
| title | CIAO: A machine-learning algorithm for mapping Arctic Ocean Chlorophyll-a from space |
| title_full | CIAO: A machine-learning algorithm for mapping Arctic Ocean Chlorophyll-a from space |
| title_fullStr | CIAO: A machine-learning algorithm for mapping Arctic Ocean Chlorophyll-a from space |
| title_full_unstemmed | CIAO: A machine-learning algorithm for mapping Arctic Ocean Chlorophyll-a from space |
| title_short | CIAO: A machine-learning algorithm for mapping Arctic Ocean Chlorophyll-a from space |
| title_sort | ciao a machine learning algorithm for mapping arctic ocean chlorophyll a from space |
| topic | Phytoplankton biomass Pan-arctic scale Ocean color Satellite data Time series |
| url | http://www.sciencedirect.com/science/article/pii/S2666017225000185 |
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