Remote sensing of chlorophyll-a concentrations in coastal oceans of the Greater Bay Area in China: Algorithm development and long-term changes

Abstract

Extensive human activities and climate change in recent decades have triggered severe eutrophication problems in the coastal oceans in the Greater Bay Area (GBA) of China. However, a comprehensive characterization of the spatial and temporal patterns of chlorophyll-a (Chl-a, a major indicator of phytoplankton biomass) in this region is not available. Our study attempts to fill this gap by using long-term satellite observations. With massive in situ datasets from underway sampling systems, we developed a novel hybrid Chl-a retrieval algorithm combining the recalibrated OC3 and line-height-based (BL443) algorithms for waters with different turbidity levels. Satellite-retrieved Chl-a values with the hybrid algorithm agreed well with in situ measurements, with an uncertainty level of 33.8 %. Long-term analysis revealed significant decreasing trends over the inner Pearl River Estuary (averaged at 0.054 μg/L yr−1), while significant increasing trends were found in eastern Daya Bay (averaged at 0.035 μg/L yr−1). The developed algorithm is expected to aid routine Chl-a monitoring in the adjacent oceans of the GBA, and the long-term datasets here can serve as critical information for further coastal conservation and management efforts.