Multispectral remote-sensing algorithms for particulate organic carbon (POC): The Gulf of Mexico

To greatly increase the spatial and temporal resolution for studying carbon dynamics in the marine environment, we have developed remote-sensing algorithms for particulate organic carbon (POC) by matching in situ POC measurements in the Gulf of Mexico with matching SeaWiFS remote-sensing reflectance. Data on total particulate matter (PM) as well as POC collected during nine cruises in spring, summer and early winter from 1997-2000 as part of the Northeastern Gulf of Mexico (NEGOM) study were used to test algorithms across a range of environments from low %POC coastal waters to high %POC open-ocean waters. Finding that the remote-sensing reflectance clearly exhibited a peak shift from blue-to-green wavelengths with increasing POC concentration, we developed a Maximum Normalized Difference Carbon Index (MNDCI) algorithm which uses the maximum band ratio of all available blue-to-green wavelengths, and provides a very robust estimate over a wide range of POC and PM concentrations (R² = 0.99, N = 58). The algorithm can be extrapolated throughout the region of shipboard sampling for more detailed coverage and analysis.