Hyperspectral determination of eutrophication for a water supply source via genetic algorithm–partial least squares (GA–PLS) modeling

Morse Reservoir (MR), a major source of the water supply for the Indianapolis metropolitan region, is now experiencing nuisance cyanobacterial blooms. These blooms cause water quality degradation, as well as reducing the aesthetic quality of water by producing toxins, scums, and foul odors. Hyperspectral remote sensing data from both in situ and airborne AISA measurements were applied to GA–PLS by relating the spectral signal with measured water eutrophication parameters, e.g., chlorophyll-a (Chl-a), phycocyanin (PC), total suspended matter (TSM), and Secchi disk depth (SDD). Our results indicate that GA–PLS relating field sensor acquired spectral reflectance to the above-mentioned four parameters yielded low root mean square error between measured and estimated Chl-a (RMSE = 10.4; Range (R): 1.8–215.8 μg/L), PC (RMSE = 18.6; R: 1.4–371.0 μg/L), TSM (RMSE = 3.8; R: 3.6–81.4 mg/L), SDD (RMSE = 5.8; R: 25–135 cm) for MR. The GA–PLS model also yielded high performance with AISA image spectra, and the RMSEs were 12.1 μg/L, 25.3 μg/L, 5.9 mg/L and 5.7 cm, respectively for Chl-a, PC, TSM, and SDD. Four water quality parameters were mapped with GA–PLS using AISA hyperspectral image. Based on these results, in situ and airborne hyperspectral remote sensors can provide both quantitative and qualitative information on the distribution and concentration of cyanobacteria, suspended matter, and transparency in MR.

► We estimate chlorophyll-a, phycocyanin, and total suspended matter using hyperspectral data.
► The hybrid GA–PLS model is an effective approach for water quality estimation.
► The GA–PLS coupled with hyperspectral data is an effective tool for water resource managers.