Enhancing satellite chlorophyll estimates using in situ environmental data in the freshwater-influenced Canadian Arctic Archipelago

Estimating chlorophyll-a (Chl-a) concentrations from satellite ocean color data remains challenging in the Arctic, where freshwater inputs, colored dissolved organic matter (CDOM), suspended particles, and low sun elevation alter optical properties and influence blue–green reflectance. Here, we combine satellite and in situ observations to examine how freshwater-driven optical variability shapes satellite-derived Chl-a across the Canadian Arctic Archipelago (CAA). Continuous underway observations were collected by a FerryBox system aboard the MS Roald Amundsen during August–September 2022 and matched with MODIS-OC3M Level-3 Chl-a (4 km, ± 2 days, 0.1° bins; n = 758). Satellite-derived Chl-a showed large differences relative to in situ observations, with a mean positive bias of 0.69 log10 units and a root-mean-square error of 0.73 log10 units, corresponding to an approximate 4.9-fold difference. These differences were strongly structured by environmental gradients, with the largest discrepancies occurring in low-salinity, CDOM-rich waters influenced by the Mackenzie River and decreasing eastward toward clearer, marine-dominated regions of Lancaster Sound. Previously-developed Arctic-tuned algorithms were applied to examine how regional models represent these gradients with the CAA. These approaches reduced overall bias and also resulted in substantial spatial variability linked to freshwater and optical gradients. To further account for these nonlinear environmental effects, a generalized additive model (GAM) incorporating salinity, CDOM, and temperature was applied, resulting in closer agreement between satellite-derived and in situ Chl-a, particularly in the Kitikmeot Sea. These findings demonstrate that freshwater-driven optical variability is a primary control on the calculation of satellite-derived Chl-a in Arctic shelf systems and that integrating environmental predictors into observational frameworks improves the interpretation of ocean color data in optically complex regions.