The ANN algorithm was formulated specifically for this project by Geiger (unpublished) following the methodology of Ref. 27. The predictors in the ANN algorithm include or ratios of at wavelengths of 412, 443, 412/547, 443/547, and 488/547 nm, as well as longitude, latitude, and sea surface temperature (SST). The eight ANN predictors were chosen based on principle component analysis, in which correlation was determined between in situ surface salinity and a possible set of 17 predictors, including satellite radiometry, position, and environmental parameters (SST, chlorophyll, water depth, river discharge, tides, and year-day). The parameters most correlated with surface salinity were chosen for the neural network training.27 From a matchup data set of 769 matchups between coincident MODIS-Aqua data and in situ surface salinity data, 399 randomly selected matchups were used to train the ANN. The in situ salinity data were collected by underway ships over a 6-year period (2003 to 2008) from Chesapeake Bay waters with salinities ranging from 9.58 to 32.71. The algorithm was evaluated with the remaining 370 matchups not used in the training, by comparing these withheld in situ surface salinities with the coincident algorithm-estimated SSS, which resulted in a mean absolute error (MAE) of 0.82, a root-mean-square error (RMSE) of 1.12, and a correlation coefficient of 0.968.