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 (mean regression slope: 0.90 ± 0.06), the improved framework achieves higher accuracy and reduced overcorrection biases (slope: 1.00 ± 0.08) across eight bands, especially in the 488–555 nm range. 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. Regional image analyses confirm its superior capability in preserving valid retrievals and resolving fine-scale spatial features in turbid nearshore waters. Preliminary spatiotemporal analyses further demonstrate its effectiveness in capturing long-term R_rs dynamics and trends. These results highlight the robustness of the improved framework and the practical utility of the new dataset for long-term monitoring of coastal water quality and ecosystem variability. The dataset is available at https://doi.org/10.5281/zenodo.16413443 (Zhao et al., 2025).