Institute: North Dakota
Year Established: 2023 Start Date: 2023-09-01 End Date: 2024-05-15
Total Federal Funds: $3,554 Total Non-Federal Funds: $3,700
Principal Investigators: Yeo Howe Lim
Project Summary: Monitoring nutrient concentrations in water is an important water quality aspect, especially in areas with high amounts of agriculture. One such monitoring technique is remote sensing. Remote sensing of water quality parameters offers an advantage over hand-collected data in that it does not require a researcher to physically collect the sample, saving labor and reducing exposure to potential hazards. But this comes with drawbacks. Commercially available remote sensing methods have difficulty detecting constituents with low optical activity such as nitrates and phosphates. To address this, a state-of-the-art imaging technique, hyperspectral imaging, will be combined with a machine learning algorithm to collect and process a wide variety of spectral data and then use that information to provide an estimate of nitrate and phosphate concentrations in the water. This would result in a Remote sensing technology that could accurately estimate nutrient concentrations that would otherwise be difficult to monitor.