A Global, Gap-Free daily Ocean-Colour dataset (2000–2024) for robust marine ecological monitoring

Satellite ocean-colour data are crucial for monitoring marine ecosystems, yet data gaps caused by limitations such as cloud cover compromise the reliability of derived ecological metrics, particularly phytoplankton phenology indicators. To enable robust marine ecological monitoring, we reconstruct a gap-free global daily ocean-colour data cube at 9 km resolution for 2000–2024 using a GPU-accelerated Discrete Cosine Transform–Penalized Least Squares (DCT–PLS) framework applied to OC-CCI (Ocean Colour-Climate Change Initiative) dataset. The product provides continuous fields of chlorophyll-a (Chl-a), diffuse attenuation coefficients Kd(490), and six-band remote-sensing reflectance (Rrs). Validation against in situ Chl-a data shows high consistency (R2 = 0.75), and comparison with NOAA's DINEOF Level-4 product demonstrates good agreement. Case studies of the Hunga Tonga–Hunga Haʻapai eruption and Hurricane Lorenzo confirm that the reconstruction preserves the spatiotemporal structure of bio-optical anomalies while substantially enhancing data continuity. Using the gap-free Chl-a record, we generated a global climatology of phytoplankton phenology metrics, and quantified the impact of realistic satellite sampling patterns (simulated using MODIS-Aqua masks) on these phenology metrics. Monte Carlo reference–gap analysis reveals that data gaps cause small errors in mean bloom Chl-a (typically <5%), but substantially larger uncertainties in phenological timing: root-mean-square errors for bloom initiation, peak date, and duration often reach 10–30 days or more. Uncertainties peak in frequently clouded and dynamic regions, such as the eastern boundary upwelling systems. These findings establish gap-filling as essential for robust phenology analysis and long-term trend detection from satellite ocean-colour data. The gap-free dataset and associated uncertainty benchmarks provide a foundation for advancing studies of marine ecosystem variability and climate-driven change. The reconstructed dataset and processing code are publicly available on https://doi.org/10.5281/zenodo.18190723.