Improving Estimations of Inherent Optical Properties in Coastal Ocean Color Modeling.
Drennan, Craig M.
2018
- Synoptic, satellite-derived estimates of phytoplankton biomass from ocean color have significantly advanced our understanding of biological oceanography. However, the algorithms that translate ocean color to phytoplankton biomass and other water quality metrics have severely diminished accuracy in coastal and estuarine ecosystems due to high and variable concentrations of suspended sediment and ... read moredissolved organic matter. Field sampling within the Chesapeake Bay indicates particulate backscattering and total absorption significantly covary as a function of salinity. This field data includes water quality, optical, and in situ remote sensing measurements previously collected from the Choptank River, a Chesapeake Bay tributary, along with the Bay itself to sample a broad range of optical and estuarine conditions. Improvements in the estimation of absorbance and backscattering will significantly improve the performance of current ocean color models in the estimation of phytoplankton absorption from satellite imagery in the optically complex waters of the Chesapeake Bay. Towards this end, empirical correlations between these two optical parameters were derived as a function of salinity, to reflect how these parameters change along the Chesapeake Bay estuary. These relationships were then incorporated with spatial salinity data to build a steady-state geospatial model to predict these parameters within the Choptank River during summer conditions. This model sets the groundwork for a real-time predictive model to incorporate into current remote sensing algorithms, improving day-to-day satellite measurements. In addition, this model could be used to investigate the impacts of larger temporal processes, such as El-Nino Oscillations and climate change, on these optical properties and the field of coastal remote sensing as a whole.read less
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