OLCI-based NIR-red models for estimating chlorophyll- <i>a</i> concentration in productive coastal waters—a preliminary evaluation.

Abstract

We present here results that demonstrate the potential of the recently launched Ocean and Land Colour Instrument (OLCI) onboard the satellite Sentinel-3A to deliver accurate estimates of chlorophyll-a (chl-a) concentration in coastal waters using reflectances in the red and near-infrared (NIR) spectral regions. Two-band and three-band NIR-red models that were previously used for data from the MEdium Resolution Imaging Spectrometer (MERIS) were applied to OLCI data from the Sea of Azov and the Taganrog Bay, Russia. Atmospherically corrected reflectance data from OLCI were compared to in situ reflectance data collected concurrently with a field spectrometer. Results show that the default atmospheric correction procedure currently applied to OLCI data performs well in preserving the spectral shape of chl-a-specific reflectance features in the red and NIR regions. Similar to what was achieved with MERIS data, the NIR-red models yield accurate estimates of chl-a concentration, with accuracies on the order of 90%, though the parameters of the NIR-red algorithms based on OLCI data are slightly different from what was obtained with MERIS data. More data, from various geographical locations, need to be analyzed to establish robust NIR-red algorithms for OLCI data.