Accurate satellite-based monitoring of chlorophyll-a (Chla) in optically complex coastal waters remains a key challenge for ocean color remote sensing. This study presents a novel spatial optimization framework to improve Chla retrievals from Sentinel-3 OLCI level 2 products processed with the Case 2 Regional CoastColour (C2RCC) algorithm. This widely used operational satellite product targets complex coastal waters and is readily available for marine monitoring and management purposes, but for Chla showed rather poor performance, so far. Focusing on Danish marine waters in the North and Baltic Seas, we developed an innovative geographically weighted regression (GWR) approach accounting for hydrologically complex coastlines by using cost-distance metrics. Spatially resolved scaling factors linking satellite-derived phytoplankton absorption to in situ Chla concentrations from Denmark's national monitoring program (NOVANA) for the years 2018–2023 were derived. Validation using temporally independent subsets demonstrated that the GWR-derived scaling factors significantly improved agreement with in situ Chla data (R from 0.59 to 0.65), reducing root mean square error (RMSE from 0.29 to 0.27) relative to globally calibrated C2RCC products. The method is transparent, reproducible, operationally feasible, and outperforms the default C2RCC and OC4ME algorithms in both day-precise matchups and long-term averages. The resulting spatially continuous, regionally tuned Chla maps, support enhanced assessments of eutrophication and phytoplankton dynamics. This transferable framework contributes to advancing quantitative remote sensing in coastal environments and supports operational marine monitoring e.g. under EU directives.