Abstract
Gap-free global chlorophyll-a (Chl-a) concentration images derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) and NOAA-20 are now available online in near-real-time for the ocean color community. With the data fusion of Chl-a from the two satellite sources, missing pixels caused by cloud, sun glint contamination, high satellite viewing angles, and other unfavorable conditions are completely filled using the Data Interpolating Empirical Orthogonal Function (DINEOF) method. Our further studies show that adding more data from additional satellite sensors can remarkably improve the spatial coverage of the merged ocean color products, provide more ocean features over coastal and inland waters, and enhance the accuracy of the gap-free data. Since the launch of the Sentinel-3A and Sentinel-3B satellites, the onboard Ocean and Land Colour Instrument (OLCI) sensors have been providing global ocean color product data. In this work, we recruit the OLCI-Sentinel-3A to join the two VIIRS sensors to produce a three-sensor merged ocean color dataset, and further derive global gap-free images using the DINEOF method. It is found that adding OLCI data as the input source significantly enhances the spatial features of high Chl-a by ∼10–20% in productive coastal regions and inland lakes. Additionally, the first-ever dataset of global gap-free suspended particulate matter (SPM) and water diffuse attenuation coefficient at 490 nm (Kd(490)) is also generated using the three-sensor merged data. Results show that Kd(490) and SPM have similar spatial patterns as that of Chl-a in the major ocean basins, and that SPM can provide more details of the spatial variations than those of Kd(490) in the center of the ocean basin and coastal oceans. Global gap-free Chl-a, Kd(490), and SPM products provide important and useful water quality information particularly over global productive ocean/water regions.
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