In Situ Hyperspectral Reflectance Sensing for Mixed Water Quality Monitoring: Insights from the RUT Agricultural Irrigation District DOI Open Access
Jhony Armando Benavides-Bolaños, Andrés Echeverri-Sánchez, Aldemar Reyes Trujillo

et al.

Water, Journal Year: 2025, Volume and Issue: 17(9), P. 1353 - 1353

Published: April 30, 2025

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

Language: Английский

Mapping reservoir water quality from Sentinel-2 satellite data based on a new approach of weighted averaging: Application of Bayesian maximum entropy DOI Creative Commons
Mohammad Reza Nikoo, Mohammad Zamani,

Mahshid Mohammad Zadeh

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: July 16, 2024

Abstract In regions like Oman, which are characterized by aridity, enhancing the water quality discharged from reservoirs poses considerable challenges. This predicament is notably pronounced at Wadi Dayqah Dam (WDD), where meeting demand for ample, superior downstream proves to be a formidable task. Thus, accurately estimating and mapping indicators (WQIs) paramount sustainable planning of inland in study area. Since traditional procedures collect data time-consuming, labor-intensive, costly, resources management has shifted gathering field measurement utilizing remote sensing (RS) data. WDD been threatened various driving forces recent years, such as contamination different sources, sedimentation, nutrient runoff, salinity intrusion, temperature fluctuations, microbial contamination. Therefore, this aimed retrieve map WQIs, namely dissolved oxygen (DO) chlorophyll-a (Chl-a) (WDD) reservoir Sentinel-2 (S2) satellite using new procedure weighted averaging, Bayesian Maximum Entropy-based Fusion (BMEF). To do so, outputs four Machine Learning (ML) algorithms, Multilayer Regression (MLR), Random Forest (RFR), Support Vector (SVRs), XGBoost, were combined approach together, considering uncertainty. Water samples 254 systematic plots obtained (T), electrical conductivity (EC), (Chl-a), pH, oxidation–reduction potential (ORP), WDD. The findings indicated that, throughout both training testing phases, BMEF model outperformed individual machine learning models. Considering Chl-a, WQI, R-squared, evaluation indices, MLR, SVR, RFR, XGBoost 6%, 9%, 2%, 7%, respectively. Furthermore, results significantly enhanced when best combination spectral bands was considered estimate specific WQIs instead all S2 input variables ML algorithms.

Language: Английский

Citations

5
In Situ Hyperspectral Reflectance Sensing for Mixed Water Quality Monitoring: Insights from the RUT Agricultural Irrigation District DOI Open Access
Jhony Armando Benavides-Bolaños, Andrés Echeverri-Sánchez, Aldemar Reyes Trujillo

et al.

Water, Journal Year: 2025, Volume and Issue: 17(9), P. 1353 - 1353

Published: April 30, 2025

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

Language: Английский

Citations

0