[Inversion of Water Quality Parameters Based on UAV Multispectral Images and the OPT-MPP Algorithm].

Area of Science:

• Environmental Science
• Remote Sensing Technology
• Water Quality Monitoring

Background:

• Unmanned aerial vehicle (UAV) multispectral remote sensing offers a viable method for monitoring key water quality parameters.
• Existing matching pixel-by-pixel (MPP) algorithms for UAV imagery face challenges with computational intensity and over-fitting.
• Accurate inversion models are crucial for reliable water quality assessment using remote sensing data.

Purpose of the Study:

• To develop and validate an optimized algorithm (OPT-MPP) for improved water quality parameter inversion from UAV multispectral imagery.
• To establish accurate inversion models for suspended sediment concentration (SS) and turbidity (TU) in Qingshan Lake.
• To demonstrate the capability of the developed models for mapping the spatial distribution of water quality parameters.

Main Methods:

• Development of the optimize-MPP (OPT-MPP) algorithm to address limitations of the traditional MPP algorithm.
• Collection of 45 water samples from Qingshan Lake for model calibration and validation.
• Construction and optimization of inversion models for suspended sediment concentration (SS) and turbidity (TU) using OPT-MPP.

Main Results:

• The optimal SS inversion model achieved a determination coefficient (R²) of 0.7870 and a comprehensive error of 0.1308.
• The optimal TU inversion model demonstrated a R² of 0.8043 and a comprehensive error of 0.1503.
• Successful inversion of spatial distribution for SS and TU across Qingshan Lake was achieved.

Conclusions:

• The OPT-MPP algorithm provides a stable and accurate method for water quality parameter inversion from UAV multispectral data.
• The developed models effectively estimate suspended sediment concentration and turbidity, crucial for water resource management.
• This study validates the utility of UAV-based remote sensing with advanced algorithms for comprehensive lake water quality assessment.