A High-Frequency and Real-Time Ground Remote Sensing System for Obtaining Water Quality Based on a Micro...

Area of Science:

• Environmental Science
• Remote Sensing Technology
• Analytical Chemistry

Background:

• Continuous water quality monitoring is vital for managing scarce water resources.
• Traditional monitoring methods face limitations in cost-efficiency and data frequency.
• There is a need for advanced, autonomous systems for real-time water parameter assessment.

Purpose of the Study:

• To develop and evaluate a ground-based hyperspectral remote sensing system for high-frequency water quality monitoring.
• To assess the system's capability in accurately measuring key water quality indicators.
• To provide a practical and reliable solution for autonomous, long-term water quality assessment.

Main Methods:

• A ground-based hyperspectral remote sensing system with a compact spectrometer (350-950 nm, 4 nm resolution) was deployed.
• The system integrates solar power, Wi-Fi, and microcomputers for autonomous operation.
• Reflective spectra were correlated with water quality parameters using empirical methods and machine learning (SVM, linear models).

Main Results:

• The band ratio algorithm achieved high accuracy for chlorophyll a (Chla) concentration (R-squared = 0.95).
• Machine learning models demonstrated strong performance for total phosphorus (TP), total nitrogen (TN), and chemical oxygen demand (COD) (R-squared values of 0.93, 0.92, and 0.88, respectively).
• The system captured water reflective spectra rapidly (within 3 seconds).

Conclusions:

• The developed hyperspectral water quality monitoring system is practical and reliable.
• This innovative system offers a new solution for effective, high-frequency water quality assessment.
• The system supports autonomous, long-term monitoring crucial for water resource management.