LAUSR

Satellite ocean color products derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) and the National Oceanic and Atmospheric Administration (NOAA)-20, and the Ocean and Land Colour Instrument (OLCI) on the Sentinel-3A (S3A) and Sentinel-3B (S3B) have been widely used for surveillance of the ocean environment and research on ocean physical, biological, biogeochemical, and ecological processes. However, either VIIRS or OLCI daily ocean color images are often incomplete in spatial coverage due to cloud cover, contamination of high sun glint, narrow swath width, high sensor-zenith angle, high solar-zenith angle, and/or other unfavorable retrieval conditions. Although merging daily ocean color images from multiple satellite sensors can help reduce the number of invalid pixels, gap-filling methods, such as the data interpolating empirical orthogonal function (DINEOF), are often used to reconstruct invalid pixels and generate gap-free images. The 9-km spatial resolution global gap-free ocean color data have been routinely produced by the NOAA Ocean Color Science Team and distributed through NOAA CoastWatch (https://coastwatch.noaa.gov/cw/index.html). In this study, we aim to develop and produce improved spatial resolution gap-free products, including chlorophyll-a (Chl-a) concentration, diffuse attenuation coefficient at the wavelength of 490 nm [ $K_{d}$ (490)], and suspended particulate matter (SPM) concentration for spatial resolutions of 0.5-, 1-, and 2-km. Two-sensor (VIIRS-SNPP and VIIRS-NOAA-20), three-sensor (two-sensor + OLCI-S3A), and four-sensor (three-sensor + OLCI-S3B) daily merged global Level-3 ocean color data are created and compared. It is found that, by merging data from the two VIIRS sensors, ~38% more valid ocean product data are retrieved compared with a single sensor from either SNPP or NOAA-20. Adding OLCI-S3A to the two-sensor merged data can increase the number of valid pixels by ~12%, and adding OLCI-S3B to the three-sensor merged data can further increase the number of valid pixels by ~8%. The DINEOF method is applied to daily two-, three-, and four-sensor merged data to generate global 2-km resolution gap-free images. Results show that 2-km resolution gap-free images are able to resolve fine ocean features, such as coastal eddies and filaments, which are not available in the 9-km resolution images. While adding OLCI-S3A data significantly improves the three-sensor-derived gap-free images over the two-sensor images, no significant enhancement is found in the four-sensor-derived gap-free images by adding OLCI-S3B data. The DINEOF method is also applied to 1- and 0.5-km resolution four-sensor merged Chl-a, $K_{d}$ (490), and SPM images in the Gulf of Mexico and U.S. West Coast region. It is found that both 0.5- and 1-km resolution images show more detailed ocean structures and features in coastal regions.