In Situ Hyperspectral Reflectance Sensing for Mixed Water Quality Monitoring: Insights from the RUT Agricultural Irrigation District

Abstract

Water-quality monitoring in agricultural irrigation systems is challenging due to the dynamic and heterogeneous nature of mixed water sources, which complicates traditional and remote sensing-based assessment methods. Traditional water quality monitoring relies on water sampling and laboratory analysis, which can be time-consuming, labor-intensive, and spatially limited. In situ hyperspectral reflectance sensing (HRS) presents a promising alternative, offering high-resolution, non-invasive monitoring capabilities. However, applying HRS in mixed-water environments—where served-water effluent, precipitation, and natural river water converge—presents significant challenges due to variability in water composition and environmental conditions. While HRS has been widely explored in controlled or homogeneous water bodies, its application in highly dynamic agricultural mixed-water systems remains understudied. This study addresses this gap by evaluating the relationships between in situ hyperspectral data (450–900 nm) and key water-quality parameters—pH, turbidity, nitrates, and chlorophyll-a—across three campaigns in a Colombian tropical agricultural irrigation system. A Pearson’s correlation analysis revealed the strongest spectral associations for nitrates, with positive correlations at 500 nm (r ≈ 0.76) and 700 nm (r ≈ 0.85) and negative correlations in the near-infrared (850 nm, r ≈ −0.88). Conversely, the pH exhibited weak and diffuse correlations, with a maximum of r ≈ 0.51. Despite their optical activity, turbidity and chlorophyll-a showed unexpectedly weak correlations, likely due to the optical complexity of the mixed water matrix. Random Forest regression identified key spectral regions for each parameter, yet model performance was limited, with R2 values ranging from 0.51 (pH) to −1.30 (chlorophyll-a), and RMSE values between 0.41 and 1.51, reflecting the challenges of predictive modeling in spatially and temporally heterogeneous wastewater systems. Despite these challenges, this study establishes a baseline for future hyperspectral applications in complex agricultural water monitoring and highlights critical spectral regions for further investigation. To improve the feasibility of HRS in mixed-water assessments, future research should focus on enhancing data-preprocessing techniques, integrating complementary sensing modalities, and refining predictive models to better account for environmental variability.