Evaluation of merged multi-sensor ocean-color chlorophyll products in the Northern Persian Gulf

Abstract

Persian Gulf is a highly turbid and optically complex marginal sea. Satellite remotely sensed chlorophyll-a (Chl-a) products have been widely used in this marine area, despite uncertainties due to complex oceanic and atmospheric optical properties. In this study, accuracy of daily merged multi-sensor Ocean Color Climate Change Initiative (OC–CCI), Copernicus Marine Environmental Monitoring Service (CMEMS) GlobColour, and OC5 single-sensor products of SeaWiFS, MERIS, MODIS, and VIIRS datasets were evaluated using in situ chlorophyll concentrations collected from 2008 to 2018 in the Iranian territorial waters. The results showed that the MAPE, RMSE, bias(δ) and R2 values between in situ and satellite-4 derived Chl-a vary in the range of 131–273%, 0.38–0.69, 0.27–0.43, and 0.27–0.44, respectively. Satellite-derived Chl-a concentrations overestimated the field observations by 131–232% in the northern parts of the middle deeper areas and up to 173–273% in Iranian coastal areas. The OC-CCI and GlobColour merged datasets overestimate the Chl-a concentrations by 19% more than the average of OC5 single-sensor products. Systematic errors were observed in the log-normal distributions of difference between in situ and satellite-derived Chl-a. A bootstrapping-like assessment was performed to eliminate the systematic errors, and to reduce the bias from satellite datasets. The results of statistical adjustment were applied on daily matchup data-pairs and time-series datasets. Furthermore, an inter-comparison was made between merged multi-sensor (OC–CCI, GlobColour CHL2) and OC5 single-sensor (SeaWiFS, MERIS, MODIS, and VIIRS) Chl-a products using 8-day time-series datasets during the years 2000–2020. The results showed that the OC5 single-sensor Chl-a datasets were more consistent with OC-CCI than GlobColour merged CHL2 in the deep and shallow regions of the study area. In contrast, the merged multi-sensor products were more similar to each other than the OC5 single-sensor datasets in the river plume zone. After performing statistical adjustment of time-series datasets, the bias values between OC-CCI, GlobColour CHL2, and OC5 single-sensor datasets decreased by 14–22%, and the single-sensor datasets showed more similarity to OC-CCI datasets.