A Comparison Between Satellite-derived and Physically-based Water Quality Estimates for High Rock Lake, North Carolina

Abstract:

Satellite-derived estimates of water quality could reduce monitoring costs for impaired lakes and provide information at higher spatial and temporal resolutions; however, several factors prevent stakeholders from integrating this information into their existing management strategies (Schaeffer et al., 2013). One of these barriers is communicating the accuracy of remotely sensed estimates relative to the data and information that stakeholders already have access to. Additionally, limited engagement between scientists and local environmental managers has prevented demonstrations of how satellite information can be used to complement existing decision-making tools, as opposed to replacing them. This research presents a first step toward addressing these barriers, by comparing satellite-derived water quality estimates to those produced by a physically-based model that the North Carolina Department of Environmental Quality (NCDEQ) uses to manage High Rock Lake (HRL).

The NCDEQ is currently developing a nutrient load reduction strategy for HRL and has invested in a physical-based model that simulates nutrient and sediment runoff and produces water quality estimates on a gridded scale across the full lake area. However, the calibrated model had substantial errors and stakeholders have not agreed to a nutrient load policy. Satellite-derived estimates of water quality could be integrated into model calibration procedures to improve the model’s accuracy and decision support utility. To assess the feasibility of this initiative, HRL water quality was estimated from Landsat 7 imagery that was acquired between 2008 and 2009, a period that aligns with an intensive in-situ sampling campaign. Previously established semi-empirical models for chlorophyll-a, turbidity, total suspended sediment (TSS), and Secchi disk (SD) depth were calibrated using the in-situ data, and errors associated with these estimates were quantified through cross-validation. The satellite-based errors were compared with those from the physically-based model to demonstrate the utility of remotely sensed water quality estimates using an existing benchmark.

Schaeffer, Blake A., et al. Barriers to adopting satellite remote sensing for water quality management. International Journal of Remote Sensing 34.21 (2013): 7534-7544.