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.

This study aimed to determine the crop coefficient (Kc) curve for stevia (Stevia rebaudiana Bertoni) in Candelaria, Valle del Cauca, Colombia. The experiment was conducted during the first half of 2015. In a plot planted with stevia, were located three drainage lysimeters arranged randomly and a portable weather station to determine climatic variables necessary for calculating an evapotranspiration reference (ETo), using the Penman Monteith equation. Soil eld capacity moisture was determined and regular monitoring of both, soil moisture and drainage water depth were performed. Irrigation was applied using an exhaustion coefficient of 10% to bring it back to field capacity moisture. Statistical analyzes were performed and Kc was calculated from the ratio of crop evapotranspiration (ETc) to ETo, for each plant in phenological phase. With condidence of 95% Kc for stevia were: growth stage (54 days after transplantation, ddt) 0.86 ± 0.12, mature stage (55-72 ddt) 1.24 ± 0.10 and senescence stage (72-96 ddt) 0.85 ± 0.14. Water consumption of Stevia rebaudiana B., was 4753 m3/ha during its growing cycle of 96 days after transplantation.

In the watershed Centella , located in the upper river basin of the Dagua - Valle del Cauca, in nine farms with association coffee - banana, coffee - guamo, cane panelera and pastures, the water regulation capacity of the soil was studied by means of humidity retention curves obtained in the laboratory and by modeling its hydrophysical properties in Hydrus 2D. Properties such as texture, bulk density, porosity, organic matter, hydraulic conductivity, infiltration and humidity were determined. Subsequently, moistureretention curves were simulated using the hydraulic models of Van Genuchten, Brooks and Corey, Van Genuchten modified and Kosugi, evaluating the average errors and the dispersion of the data. Agreed to the results it is possible to point out that the soil under study has a high capacity for moisture retention ( > 18%), consequence of the high content of clays ( > 41%) and organic matter ( > 5%), characteristics of the Andisols of the Valle del Cauca. Finally, the best fitting model was compared statistically with the data of the curves obtained in the laboratory, finding that Van Genuchten and Van Genuchten models modified, are the most appropriate for obtaining the retention curves from the hydrophysical properties since they presented a lower mean error (ME) with a value not higher than -0.11 cm3 / cm3 and a value of the square root of the mean square error (RMSE) less than 0.11 cm3 / cm3.