Quantitative mapping of chlorophyll a distributions in coastal zones by remote sensing

Results of experiments conducted in the James River, Virginia and the New York Bight indicate that concurrently collected sea-truth measurements may be used to calibrate remotely sensed multispectral scanner data collected over each of these environmentally different scenes. Statistical stepwise regression analysis was used in both experiments to incorporate significant bands of MSS data into regression equations that quantitatively relate remotely sensed data to water quality parameters, such as chlorophyll a and suspended sediment. These regression equations are used to map synoptic distributions of chlorophyll a in the remotely sensed scenes.