Geospatial interpolation of MODIS-derived salinity and temperature in the Chesapeake Bay

Abstract

In dynamic coastal systems such as the Chesapeake Bay, limited coverage and frequency of in situ measurements often makes generalizability of regional forecasting systems difficult. Satellite-derived environmental variables have the potential to address this problem, but satellite datasets suffer from incomplete coverage as well: atmospheric conditions—most notably cloud cover—lead to data gaps that significantly hinder the broad application of satellite-informed predictions. In this study, the Chesapeake Bay estuary was used as a model “test bed” to which we applied the power of near real-time satellite-derived observations to the issue of water quality monitoring. To use remote sensing in support of spatially complete estimates of salinity and temperature in the Bay, we tested geospatial interpolation techniques as a method for filling gaps and minimizing errors in the satellite record. These interpolated values were then compared to the output of a regional hydrodynamic model in order to assess the relative value of each method for generating inputs into various modeling applications. Results show that MODIS-derived salinity and temperature can be interpolated with acceptable accuracy in the Bay, with a mean absolute error of 1.88psu and 0.60°C. These errors differed systematically from ChesROMS errors both spatially and seasonally, with higher errors for salinity and lower errors for temperature at most sampling stations throughout the year. This suggests that the two techniques offer complementary information that can be applied to ecological monitoring systems in complex estuaries like Chesapeake Bay.