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To increase geospatial awareness about local water resources, our team developed learning resources for the 150 km² Lake Sidney Lanier reservoir located in North Georgia, USA. The reservoir is vital for hydroelectric power generation, recreation, tourism, and consumptive uses. Using geospatial analysis in Google Earth Engine (GEE), we analyzed precipitation trends in the watershed using Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data. We also quantified expansion and contraction of reservoir surface area using Landsat-derived Global Surface Water data. As Lake Sidney Lanier is a managed reservoir, surface water extent fluctuations are related to climatic variables, consumptive use, and hydropower generation. Water temperature varies based on seasonality, water depth, water clarity, and lake stratification. Changing temperature dynamics affect ecosystem health and determine other important water quality parameters such as dissolved oxygen concentrations. Landsat 8 Thermal Infrared Sensor (TIRS) data were used to examine temperature trends over multiple years and investigate the timing of lake stratification and mixing. Highly turbid waters are associated with pollutants and lower water quality and can affect ecosystem productivity by minimizing sunlight penetration into the water column. Sentinel 2 MSI data were processed using a turbidity algorithm to analyze temporal trends and spatial correlations with reservoir inflows. Finally, high concentrations of chlorophyll a were used as a proxy to identify harmful algal blooms. The spatial differences in headwaters and near-dam locations were examined and near real-time satellite data were explored for potential development of early-warning systems to protect ecosystem and human health.
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A Machine Learning and Remote Sensing‐Based Model for Algae Pigment and Dissolved Oxygen Retrieval on a Small Inland Lake
Abstract Excessive algae growth can lead to negative consequences for ecosystem function, economic opportunity, and human and animal health. Due to the cost‐effectiveness and temporal availability of satellite imagery, remote sensing has become a powerful tool for water quality monitoring. The use of remotely sensed products to monitor water quality related to algae and cyanobacteria productivity during a bloom event may help inform management strategies for inland waters. To evaluate the ability of satellite imagery to monitor algae pigments and dissolved oxygen conditions in a small inland lake, chlorophyll‐a, phycocyanin, and dissolved oxygen concentrations are measured using a YSI EXO2 sonde during Sentinel‐2 and Sentinel‐3 overpasses from 2019 to 2022 on Lake Mendota, WI. Machine learning methods are implemented with existing algorithms to model chlorophyll‐a, phycocyanin, and Pc:Chla. A novel machine learning‐based dissolved oxygen modeling approach is developed using algae pigment concentrations as predictors. Best model results based on Sentinel‐2 (Sentinel‐3) imagery achieved R2scores of 0.47 (0.42) for chlorophyll‐a, 0.69 (0.22) for phycocyanin, and 0.70 (0.41) for Pc:Chla. Dissolved oxygen models achieved anR2of 0.68 (0.36) when applied to Sentinel‐2 (Sentinel‐3) imagery, and Pc:Chla is found to be the most important predictive feature. Random forest models are better suited to water quality estimations in this system given built in methods for feature selection and a relatively small data set. Use of these approaches for estimation of Pc:Chla and dissolved oxygen can increase the water quality information extracted from satellite imagery and improve characterization of algae conditions among inland waters.
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- PAR ID:
- 10576817
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Water Resources Research
- Volume:
- 60
- Issue:
- 3
- ISSN:
- 0043-1397
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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