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Using multispectral drones to predict water quality in a subtropical estuary.

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Drone multispectral imagery effectively predicts water quality parameters like chlorophyll in intermittently closed and opened lakes. However, temporal variations require dynamic, site-specific models for accurate monitoring.

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Area of Science:

  • Environmental Science
  • Remote Sensing
  • Water Resource Management

Background:

  • Drones are increasingly vital for high-resolution earth system observations.
  • Monitoring water quality in intermittently closed and opened lakes (ICOLs) presents unique challenges due to dynamic conditions.

Purpose of the Study:

  • To assess the efficacy of drone-based multispectral imagery in predicting key water quality parameters in an ICOLL.
  • To identify specific spectral bands and ratios correlated with water quality indicators.

Main Methods:

  • Conducted three water quality sampling campaigns measuring parameters like chlorophyll, dissolved oxygen, and turbidity.
  • Utilized DistilM statistical analyses to identify key spectral bands and performed linear correlations with water quality data.

Main Results:

  • Significant correlations (R² > 0.4) were found for eleven water quality parameters, with chlorophyll showing the strongest link to the green band (R² = 0.72).
  • The most influential spectral bands varied monthly, highlighting temporal dynamics.
  • Predictive band importance differed significantly across campaigns and water quality variables.

Conclusions:

  • Drone-based multispectral sensors provide valuable 'snapshot' predictions for parameters like chlorophyll, bluegreen algae, and dissolved oxygen.
  • A single predictive model is insufficient for dynamic estuarine systems due to temporal variations in water parameters.