Spatiotemporal Analysis of Water Quality Indicators in Small Lakes Using Sentinel-2 Satellite Data: Lake Bloomington and Evergreen Lake, Central Illinois, USA

Lake water quality issue related to algal blooms is a serious problem in basins with abundant agricultural lands, causing harmful effects to freshwater ecosystems and decreasing water quality for human uses. Remote sensing methods have been used to monitor water quality of large lakes and coastal areas; however, few studies have been conducted to understand spectral properties of small lakes. This research explores applicability of Sentinel-2 satellite in understanding spatiotemporal patterns of water quality of two small-sized reservoirs, Lake Bloomington and Evergreen Lake, Central Illinois. We tested the feasibility of Sentinel-2 satellite data for monitoring algal blooms by comparing and calibrating multiple satellite algorithms (e.g., Bottom-of-Atmosphere (BOA) reflectance, Maximum Chlorophyll Index (MCI), and band ratios) against lake water quality indicator variables (e.g., chlorophyll-a, turbidity, secchi depth) obtained from water sample analysis in the laboratory or field measurements. The regression models performed better for Evergreen Lake than Lake Bloomington. Comparison of chlorophyll-a and turbidity with satellite reflectance values showed that suspended sediments contribute to the turbidity in the case of Lake Bloomington, while the algae contribute to the turbidity in Evergreen Lake. Using regression models, water quality indicator maps were created, showing the spatial pattern of algae in the lakes, generally, showing heterogeneous chlorophyll-a distribution, lower in downstream and higher in upstream areas. The study confirmed suitability of Sentinel-2 data for monitoring water quality of less turbid small-sized lake.