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.