This study provides detailed information about the use of a hyperspectral imaging system mounted on a motor-driven multipurpose floating platform (MFP) for water quality sensing and water sampling, including the spatial and spectral calibration for the camera, image acquisition and correction procedures. To evaluate chlorophyll-a concentrations in an irrigation pond, visible/near-infrared hyperspectral images of the water were acquired as the MFP traveled to ten water sampling locations along the length of the pond, and dimensionality reduction with correlation analysis was performed to relate the image data to the measured chlorophyll-a data. About 80,000 sample images were acquired by the line-scan method. Image processing was used to remove sun-glint areas present in the raw hyperspectral images before further analysis was conducted by principal component analysis (PCA) to extract three key wavelengths (662 nm, 702 nm, and 752 nm) for detecting chlorophyll-a in irrigation water. Spectral intensities at the key wavelengths were used as inputs to two near-infrared (NIR)-red models. The determination coefficients (R²) of the two models were found to be about 0.83 and 0.81. The results show that hyperspectral imagery from low heights can provide valuable information about water quality in a fresh water source.

Phytoplankton community composition has been utilized for water quality assessments of various freshwater sources, but studies are lacking on agricultural irrigation ponds. This work evaluated the performance of the random forest algorithm in estimating phytoplankton community structure from in situ water quality measurements at two agricultural ponds. Sampling was performed between 2017 and 2019 and measurements of three phytoplankton groups (green algae, diatoms, and cyanobacteria) and three sets of water quality parameters (physicochemical, organic constituents, and nutrients) were obtained to train and test mathematical models. Models predicting green algae populations had superior performance to the diatom and cyanobacteria models. Spatial models revealed that water in the ponds’ interior sections had lower root mean square errors (RMSEs) compared to nearshore waters. Furthermore, model performance did not change when input datasets were compounded. Models based on physicochemical parameters, which can be obtained in real time, outperformed models based on organic constituent and nutrient parameters. However, the use of nutrient parameters improved model performance when examining cyanobacteria data at the ordinal level. Overall, the random forest algorithm was useful for predicting major phytoplankton taxonomic groups in agricultural irrigation ponds, and this may help resource managers mitigate the use of cyanobacteria bloom-laden waters in agricultural applications.

Irrigation waters may facilitate the spread of antibiotic-resistant bacteria or genes to humans and animals. Monitoring of resistance in irrigated waters has become common; however, many studies do not incorporate a spatial component into sampling designs. The objective of this work was to assess spatiotemporal variations in tetracycline-resistant E. coli in an irrigation pond. Water samples were collected at 10 locations and two different water depths, and in situ and laboratory water quality measurements were performed. The percentage of E. coli resistant to the low (4 μg mL⁻¹) and high (16 μg mL⁻¹) tetracycline doses varied by date and location but were observed to be as high as 12.7% and 6.3% of the total population throughout the study, respectively. While significant differences were not observed between resistance levels measured at different depths, on one date resistant E. coli were only detected in samples collected at depth. Nitrate, fluorescent dissolved organic matter, and dissolved oxygen concentrations were found to be the leading control variables for the percentage of resistant E. coli. This work demonstrates that there may be substantial spatial variability in concentrations of antibiotic-resistant E. coli in irrigation ponds which should be accounted for in the design of monitoring programs.