In situ estimation of water quality parameters in freshwater aquaculture ponds using hyperspectral imaging system

Knowledge of water quality parameters is integral to sustainability of freshwater aquaculture operations that raise ornamental fish. Our objective in this study is to evaluate the ability of a mobile, ground-based hyperspectral (HS) imaging sensor to determine chlorophyll-a (Chl-a) concentrations in working aquaculture ponds, which represent manipulated, shallow, nutrient-rich systems, and to determine the effect of using submerged reflectance targets on the accuracy of Chl-a estimation. We collected Chl-a measurements from aquaculture ponds ranging from 0.8 to 494 μ g / L . Chl-a measurements showed a strong correlation with two-band and three-band spectral indices computed from the HS image reflectance. Coefficient of determination ( R 2 ) values of 0.975 and 0.982 were obtained for the two- and three-band models, respectively, using spectra captured from the submerged target at 10 cm depth. Using spectra captured from water (no submerged targets), R 2 values were slightly lower at 0.833 and 0.862 for two- and three-band models. Data from the submerged target at 30 cm depth had the lowest correlation with measured chlorophyll-a concentrations, potentially due to variations in water column properties and shadows cast by the platform. Modeling total Phosphorous (P) and Nitrogen (N) concentrations of the collected samples with the spectral indices sensitive to Chl-a concentrations showed a moderate level of correlation. Removing a model outlier (observation with maximum N and P concentrations) led to a significant increase in the models’ coefficient of determination (e.g. from 0.478 to 0.823 for the P model using three-band index values), which highlighted the possibility of using HS imagery to estimate N and P concentrations and the need for more research to model the interrelationships between Chl-a and nutrient concentrations in aquaculture water systems.