Global land mask for satellite ocean color remote sensing
We develop a methodology to derive a global medium resolution (250 m) land mask from several existing data sources. In particular, a number of improved land mask data sets have been developed from satellite measurements recently, though some artifacts and omissions still remain. We show how combining global land mask data from multiple independent data sources can decrease the frequency of artifacts, and improve the data consistency and quality. We use the ocean color product imagery derived from measurements of the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) to evaluate and validate the new global land mask implemented in the Multi-Sensor Level-1 to Level-2 (MSL12) ocean color data processing system, and demonstrate the improvements in the derived global ocean color data coverage. Results show that when using the new proposed land mask the accuracy of global ocean color data coverage is significantly improved over coastal and inland waters. The new land mask more accurately represents the current global land coverage status, providing more complete and consistent global land/water coverage data set for ocean color remote sensing and for various other satellite Earth observing applications.