Abstract :
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.
Keywords :
Remote sensing , Phytoplankton , Spatial coverage , Complex waters , Dusty atmosphere
