High-quality remote sensing reflectance products over China and US coast

Abstract

Remote sensing reflectance ( R rs ) is fundamental for deriving bio-optical properties of global surface waters. However, accurate atmospheric correction (AC) to derive R rs in coastal waters remains challenging due to strongly absorbing aerosols and complex water optics. To address this, we developed an improved processing framework that integrates flexible use of global gridded aerosol models better suited for coastal environments and incorporates tailored masking strategies. Based on this framework, we generated a high-quality R rs dataset from Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua observations, spanning 2003–2022 for the coastal waters of China and the United States (US) – two regions where complex water optics and frequent anthropogenic aerosols have long impeded retrieval accuracy and valid data yield. Compared to the NASA standard MODIS Aqua R rs products, the improved framework achieved a significant reduction in systematic bias. Multiple validation metrics support this, with the restoration of the mean regression slope to unity (1.00 ± 0.08 vs. 0.90 ± 0.06) across eight bands being the most prominent. Performance gains are particularly pronounced in the 488–555 nm range, which is most relevant to water color applications. The new dataset also yields significantly more valid retrievals, with regional mean increases of 56 % in the Chinese coastal waters and 18 % in the US coastal waters at 443 nm. Furthermore, it recovers fine-scale features in turbid waters and captures long-term dynamics, demonstrating its robustness for coastal monitoring. As a complementary resource optimized for estuaries, bays, and turbid coastal regions, the new dataset fills a critical gap in the ocean color data portfolio where standard products are less reliable. The dataset is available at https://doi.org/10.5281/zenodo.16413443 (Zhao et al., 2025).