Monitoring and mapping inland water quality is critical for sustainability of water supplies for human consumption, agriculture, industry, and other economic activities. In previous studies, empirical models have been widely used to map water quality parameters for inland waters using remotely sensed imagery. Due to the complex optical properties of inland waters, empirical models often have limited performances and they cannot be extended or reused across space and time.
Researchers at the University of Alabama have developed a method of utilizing a set of heterogeneous empirical models for different parts of a water body having different characteristics (opposed to single model for all sections). This technology efficiently measures and monitors water quality parameters weekly, seasonally and annually at regional, national and continental scales using satellite and aerial multispectral or hyperspectral imagery.
·Significantly improved prediction accuracy. (50-100%) ·Spatial extensibility. (regional, national, and continental scales) ·Temporal extensibility. (weekly, seasonal, annual, and decadal) ·Significant reduction of the cost and time for water quality remote sensing.