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<p>Human health, agricultural production, and aquatic ecosystem conservation critically rely on water quality, which is increasingly threatened by accelerated eutrophication processes. Artificial reservoirs for irrigation purposes often exhibit hypereutrophic conditions, while remaining largely unmonitored. Although satellite-based remote sensing has improved water quality monitoring, robust spectral indicators tailored to local hypereutrophic conditions are still lacking. In this study, we assess the performance of remote sensing-derived spectral indices for estimating chlorophyll-a concentrations —a widely used proxy for phytoplankton biomass—in two hypereutrophic irrigation lakes in southwestern Uruguay. From October 2020 to January 2024, we conducted monthly field campaigns, collecting hyperspectral reflectance signatures using a handheld JAZ sensor and obtaining water samples for laboratory-based chlorophyll-a analysis. We evaluated the performance of 113 indices reported in the literature, adapted to Sentinel-2 equivalent bands, to identify the most accurate predictors of chlorophyll-a. Seven indices showed strong correlations (Pearson’s <i>r</i> &gt; 0.6), with the B7/B3 ratio emerging as the best-performing index. This index was subsequently used to reconstruct a high-resolution temporal series using Sentinel-2 and generate spatial maps of chlorophyll-a across both lakes. Our results revealed marked seasonal variability, with chlorophyll-a concentrations peaking 932&#xa0;µg/L in summer–autumn 2022 and reaching lows of 43&#xa0;µg/L in winter-spring 2021. These findings highlight the value of integrating remote sensing with in situ data for effective water quality monitoring in agricultural reservoirs. This approach offers a scalable tool for managing water resources and enabling early detection of harmful algal blooms in the context of expanding irrigation infrastructure.</p>

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Performance of spectral indices for detecting chlorophyll-a in hypereutrophic irrigation reservoirs in Uruguay

  • 期刊论文

  • 发表日期：

    2026年1月21日

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  • Claudia Simon-Zinno,
  • Verónica S. Ciganda,
  • Federico Gallego,
  • Lucía Graña-Miraglia,
  • Julieta Mariotta,
  • María Vallejos

摘要

Human health, agricultural production, and aquatic ecosystem conservation critically rely on water quality, which is increasingly threatened by accelerated eutrophication processes. Artificial reservoirs for irrigation purposes often exhibit hypereutrophic conditions, while remaining largely unmonitored. Although satellite-based remote sensing has improved water quality monitoring, robust spectral indicators tailored to local hypereutrophic conditions are still lacking. In this study, we assess the performance of remote sensing-derived spectral indices for estimating chlorophyll-a concentrations —a widely used proxy for phytoplankton biomass—in two hypereutrophic irrigation lakes in southwestern Uruguay. From October 2020 to January 2024, we conducted monthly field campaigns, collecting hyperspectral reflectance signatures using a handheld JAZ sensor and obtaining water samples for laboratory-based chlorophyll-a analysis. We evaluated the performance of 113 indices reported in the literature, adapted to Sentinel-2 equivalent bands, to identify the most accurate predictors of chlorophyll-a. Seven indices showed strong correlations (Pearson’s r > 0.6), with the B7/B3 ratio emerging as the best-performing index. This index was subsequently used to reconstruct a high-resolution temporal series using Sentinel-2 and generate spatial maps of chlorophyll-a across both lakes. Our results revealed marked seasonal variability, with chlorophyll-a concentrations peaking 932 µg/L in summer–autumn 2022 and reaching lows of 43 µg/L in winter-spring 2021. These findings highlight the value of integrating remote sensing with in situ data for effective water quality monitoring in agricultural reservoirs. This approach offers a scalable tool for managing water resources and enabling early detection of harmful algal blooms in the context of expanding irrigation infrastructure.