Science Serving Maryland's Coasts

Research Publications: UM-SG-RS-2004-09


Bio-optical model for Chesapeake Bay and the Middle Atlantic Bight.




Magnuson, A; Harding, LW; Mallonee, ME; Adolf, JE


Estuarine Coastal and Shelf Science 61(3):403-424




Retrievals of bio-optical properties from satellite measurements in Case 2 waters depend on algorithms that account for multiple constituents affecting spectral quality of the upwelling light flux. Semi-analytical (SA) models are suitable for this purpose, but must be parameterized with in situ data, particularly in estuarine and coastal waters. We examined spatial and temporal variability of bio-optical properties in Chesapeake Bay and the adjacent Middle Atlantic Bight (CB/MAB) to parameterize and validate the Garver/ Siegel/Maritorena (GSM01) model. Several years (1996-2002) of data on inherent and apparent optical properties confirmed high scattering and strong absorption by dissolved and particulate components that did not co-vary. These data, consisting of the chlorophyll (chl a)-normalized phytoplankton absorption coefficient, a(ph)(*)(lambda) and the spectral slope of absorption due to dissolved p and detrital materials, S-cdm: were used to optimize model parameters of GSM01 and produce a version of the model tuned for CB/ MAB we have designated GSM01-CB. Performance of GSM01-CB was measured against the globally optimized version of GSM01 and the empirical algorithm, OC4v.4, using both in situ and satellite-derived radiances from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) as inputs to the model. GSM01-CB outperformed OC4v.4 in retrieving chl a in CB, and both SA models outperformed OC4v.4 in MAB. GSM01 and GSM01-CB also returned reasonable estimates of other bio-optical products, including the absorption coefficient for dissolved and detrital materials, acd(cdm)(443), and the particulate backscatter coefficient, b(bp)(443). We present alternatives to the parameterization of GSM01-CB to account for interannual variability of a(ph)(*)(lambda) in CB using empirical relationships with key variables that regulate phytoplankton dynamics in the estuary (i.e., freshwater flow and nutrient loading), and to include regional gradients of S-cdm in MAB.

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