| 13 Aug 2025
High-Quality Remote Sensing Reflectance Products over China and US Coast
Abstract. Remote sensing reflectance (Rrs) is fundamental for deriving bio-optical properties of global surface waters. However, accurate atmospheric correction (AC) to derive Rrs 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 Rrs 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 Rrs 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 Rrs 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).
The manuscript aims to construct a high-quality Rrs dataset covering coastal waters of both China and the United States. An AC processing framework that integrates standard NIR-based AC and grided aerosol model AC were developed. Moreover, cloud masking constraint was relaxed to get more effective data. While the topic is important as Rrs products in coastal areas is still have relatively high uncertainty as compared to it in open oceans, the high quality promoted by the title does not match the actual results for the following four major reasons.
1. Relaxing cloud masking constraint of course would enlarge effective data portion, but it would degrade data quality or accuracy of the Rrs products especially in clear waters. Therefore, this contradicts the goal of obtaining high-quality Rrs dataset.
2. The representativeness of grided aerosol models in instantaneous satellite images of coastal or shelf waters is questionable. This issue includes two parts. One is the temporal representativeness, since the grided aerosol models are multi-years and monthly averaged data, it can’t represent the instantaneous aerosol conditions. For example, even for some coastal oceans that are often affected by absorbing aerosols, the aerosol type may still be non or weak-absorbing aerosols in a specific day's image. In this case, if the average absorbing aerosol is still used, the accuracy may decrease. Another one is the spatial representativeness, aerosol types are high spatial heterogeneity, the large grid size (5 degree) makes it difficult to ensure that the grided aerosol models can represent the real aerosol types of each pixel, especially for grids with only one AERONET station far from the coastline.
3. There is no scientific basis for using water turbidity to distinguish between grided aerosol models and traditional AF10 aerosol models. The regulation mechanisms of water turbidity and aerosols are different, and there is no necessary connection between the two.
4. The standard NIR AC algorithm has a high degree of uncertainty in turbid water, which has been widely recognized. Therefore, comparing the results with NIR AC algorithm in this study cannot fully demonstrate the superiority of the proposed method. It is recommended to compare it with existing atmospheric correction results for turbid water, such as OC-SMART, POLYMER, NIR-SWIR, etc.