Regular monitoring of water quality is increasingly necessary to keep pace with rapid environmental change and protect human health and well-being. Remote sensing has been suggested as a potential solution for monitoring certain water quality parameters without the need for in situ sampling, but universal methods and tools are lacking. While many studies have developed predictive relationships between remotely sensed surface reflectance and water parameters, these relationships are often unique to a particular geographic region and have little applicability in other areas. In order to remotely monitor water quality, these relationships must be developed on a region by region basis. This paper presents an automated method for processing remotely sensed images from Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) and extracting corrected reflectance measurements around known sample locations to allow rapid development of predictive water quality relationships to improve remote monitoring. Using open Python scripting, this study (1) provides an openly accessible and simple method for processing publicly available remote sensing data; and (2) allows determination of relationships between sampled water quality parameters and reflectance values to ultimately allow predictive monitoring. The method is demonstrated through a case study of the Ozark/Ouchita-Appalachian ecoregion in eastern Oklahoma using data collected for the Beneficial Use Monitoring Program (BUMP).
- CleanWater Act
ASJC Scopus subject areas
- Geography, Planning and Development
- Aquatic Science
- Water Science and Technology