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Particulate organic carbon (POC) plays an important role in coastal carbon cycling and the formation of hypoxia. Yet, coastal POC dynamics are often poorly understood due to a lack of long-term POC observations and the complexity of coastal hydrodynamic and biogeochemical processes that influence POC sources and sinks. Using field observations and satellite ocean color products, we developed a new multiple regression algorithm to estimate POC on the Louisiana Continental Shelf (LCS) from satellite observations. The algorithm had reliable performance with mean relative error (MRE) of approximate to 40% and root mean square error (RMSE) of approximate to 50% for MODIS and SeaWiFS images for POC ranging between approximate to 80 and approximate to 1200 mg m(-3), and showed similar performance for a large estuary (Mobile Bay). Substantial spatiotemporal variability in the satellite-derived POC was observed on the LCS, with high POC found on the inner shelf (<10 m depth) and lower POC on the middle (10-50 m depth) and outer shelf (50-200 m depth), and with high POC found in winter (January-March) and lower POC in summer to fall (August-October). Correlation analysis between long-term POC time series and several potential influencing factors indicated that river discharge played a dominant role in POC dynamics on the LCS, while wind and surface currents also affected POC spatial patterns on short time scales. This study adds another example where satellite data with carefully developed algorithms can greatly increase the spatial and temporal observations of important biogeochemical variables on continental shelf and estuaries.