CIAO: A Machine-Learning Algorithm for Mapping Arctic Ocean Chlorophyll-a from Space
Link here: Zoffoli et al., 2025
Ocean color (OC) remote sensing at a Pan-Arctic scale, with over 26 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