Large-scale green tides occurring in the Yellow Sea (YS) of China have become a critical eco-environmental problem, causing serious damage to marine and the coastal ecological environment, aquaculture, and tourism since 2007. Green tide biomass is a key parameter for accurate quantification of floating macroalgae, serving as an effective indicator for monitoring changes in the marine ecological environment. Satellite remote sensing technology plays a pivotal role in supporting the monitoring and assessment of green tide. Spaceborne optical sensors, in particular, offer a wealth of data that is indispensable for the fine-scale quantitative monitoring and assessment of green tide. In this study, we have established robust statistical relationships between Biomass Per Area (BPA) and various optical remote sensing indices by modeling the laboratory measurements of U. prolifera biomass (wet weight) per unit area and the corresponding spectral reflectance data. The computational methods of BPA have been carefully designed and validated for different optical data, including Moderate Resolution Imaging Spectroradiometer (MODIS), the Multispectral Instrument (MSI) onboard Sentinel-2 satellites, and the Coastal Zone Imager (CZI) onboard China’s HaiYang-1C/D (HY-1C/D) satellites. These results indicate that BPA can serve as a highly effective parameter in quantifying green tide using remote sensing data. Unlike common parameters such as pixel area or coverage area, BPA can mitigate the scale effects of spatial resolution differences from various observations, minimizing the uncertainty especially when integrating multiple remote sensing data. With the coordinated utilization of CZI and MODIS data in 2021 and the developed BPA models, the detailed intra-annual variations in green tide biomass in the YS of China were quantified. This analysis has revealed the intricate spatial distribution patterns and trends inherent in green tide biomass fluctuations. The utilization of multiple optical remote sensing data sources for the estimation of green tide biomass carries important methodological significance and serves as an accurate data reference for the precise, quantitative, and dynamic monitoring of green tide in the YS of China.

Green tide biomass;optical remote sensing;HY-1C/D;CZI;MODIS