High-resolution chlorophyll-a ocean color products estimation in turbid estuary and clear open sea waters of the north South China Sea with Landsat-8 OLI

Abstract

Remote-sensing of ocean colour has an advantage over any other biological data source for monitoring long-term global changes in phytoplankton biomass, due to its spatial and temporal sampling capabilities. Chlorophyll-a concentration (Chl-a) provide a proxy for phytoplankton biomass. The Operational Land Imager (OLI) is a multispectral radiometer hosted on the recently launched Landsat8 satellite, which is a potential tool for ocean color radiometry because it includes narrow band, high signal-to-noise ratios (SNRs) and the addition of a band centered at 443 nm, has competitive advantage in phytoplankton pigment Chl-a estimation compare with previous Landsat instruments. The aim of this work was to evaluate the performance of the standard NASA algorithm OC3 type for Landsat-8 OLI in determining Chl-a concentrations in both turbid estuary and clear open sea waters of the north South China Sea, in which the empirical coefficients were tuned by using field data and used 443-, 561-, and 655nm bands instead of 443-, 482 and 561nm bands. The standard OC3-based algorithm for OLI performed well in the Southeast continental shelf of Hainan Island (HNI). While empirical algorithm should be developed in the Pearl River estuary (PRE), and the comparisons between estimated and in situ measured Chl-a produced R2 reaching 0.88 and APD <30%. Furthermore, we assessed Chl-a products by conducting cross-validation with concurrent MODIS-Aqua and NPP VIIRS data, which demonstrate good consistency and minor deviation in HNI waters, while demonstrate good consistency but large deviation in the PRE waters. Our findings demonstrate the potential of high resolution OLI Chl-a products to study short-lasting events and capture fine-scale features in the marine environment in different cases waters. The OLI Chl-a products using standard OC3-based algorithm performed well in the case I waters, while regional algorithm should be developed basing on large field data in the estuary waters.