Increased loads of land-based pollutants are a major threat to coastal-marine ecosystems. Identifying the affected marine areas and the scale of influence on marine ecosystems is critical to assess the ecological impacts of degraded water quality and to inform planning for catchment management and marine conservation. Studies using remotely sensed data have contributed to our understanding of the occurrence and influence of river plumes, and to our ability to assess exposure of marine ecosystems to land-based pollutants. However, refinement of plume modeling techniques is required to improve risk assessments. We developed a novel approach to model exposure of coastal-marine ecosystems to land-based pollutants. We used supervised classification of MODIS-Aqua true-color satellite imagery to map the extent of plumes and to qualitatively assess the dispersal of pollutants in plumes. We used the Great Barrier Reef (GBR), the world's largest coral reef system, to test our approach. We combined frequency of plume occurrence with spatially distributed loads (based on a cost-distance function) to create maps of exposure to suspended sediment and dissolved inorganic nitrogen. We then compared annual exposure maps (2007-2011) to assess inter-annual variability in the exposure of coral reefs and seagrass beds to these pollutants. Our findings indicate that classification of true-color satellite images is useful to map plumes and to qualitatively assess exposure to land-based pollutants. This approach should be considered complementary to remote sensing methods based on ocean color products used to characterize surface water in plumes. Observed inter-annual variation in exposure of ecosystems to pollutants stresses the need to incorporate this temporal component into plume exposure/risk models. Our study contributes to our understanding of plume spatial-temporal dynamics of the GBR and offers a method that can improve plume exposure models. Our method can also be applied to monitor exposure of coastal-marine ecosystems to plumes and explore their ecological influences.