Mapping Reservoir Turbidity Patterns Using SPOT‐HRV Data

Although most orbital remote sensing systems are optimized for viewing terrestrial targets, data from these sensors has frequently been used for detecting and mapping the distribution of suspended sediments in water. In this paper, a linear regression‐based model for mapping turbidity is evaluated in a midwestern U.S. reservoir using October, 1994 SPOT‐HRV data. Results indicate that the red band (SPOT2) is the best predictor of turbidity (r=0.924, R2 = 84.5). Despite relatively low turbidity levels in the water, the radiometric resolution of the SPOT‐HRV sensor was found to be adequate for this application. The high spatial resolution of the sensor permitted observation of very detailed patterns of turbidity.