inter-comparison of single-sensor and merged multi-sensor ocean color chlorophyll-a products in the shallow turbid waters - case study: persian gulf

Ocean color satellite sensors provide the only long-term essential climate variable (ecv) globally that targets chlorophyll-a concentrations (chl-a) as the most important biological factor in the oceans. it is difficult to develop the long-term and consistent ocean color time-series for climate studies due to the differences in characteristics, atmospheric correction, chl-a retrieval algorithms, and limited lifespans of individual satellite sensors. therefore, the merged multi-sensor ocean color datasets were developed by merging data from different satellite sensor products. the performance of the commonly used single-sensor and multi-sensor merged ocean color datasets is a challenging issue over highly turbid coastal waters and dusty atmospheric conditions. in this study, we compared the common single-sensor [sea-viewing wide field-of-view sensor (seawifs), moderate resolution imaging spectroradiometer (modis), medium resolution imaging spectrometer (meris), visible imager radiometer (viirs), and sentinel-3 ocean and land colour instrument (olci)], and merged multi-sensor [ocean colour climate change initiative (oc-cci), and globcolour weighted average (gc-avw) and garver-siegel-maritorena (gc-gsm)] chl-a datasets over the persian gulf, known as optically complex and highly turbid water bodies in a dusty atmospheric condition. the results indicate that the oc-cci dataset provides more spatial and temporal coverages than the other datasets. temporal consistency between single-sensor and merged datasets was made in two different timespans during the common period of sensors and during the continuous lifespan intersection between individual two-paired of datasets. the statistical metrics were calculated to show the temporal consistency between chl-a datasets during the common and continuous time periods. correlation between oc-cci and the other datasets showed that the relationships between datasets did not change significantly during the proposed time periods. further, it was indicated that the oc-cci product is more constant than the other single-sensor and merged products. it was shown that oc-cci datasets were more consistent with meris and gc-gsm datasets, and seawifs and gc-avw were not significantly correlated to the other datasets. the results revealed that the single sensor products that use polymer atmospheric correction algorithm (e.g. meris), and merged multi-sensor product that performs the gsm blending algorithms (e.g. gc-gsm) are more consistent and stable than the other products over the study area.