Multispectral Remote Sensing for Estimating Water Quality Parameters: A Comparative Study of Inversion Methods Using Unmanned Aerial Vehicles (UAVs)

Yong Yan, Ying Wang (), Cheng Yu and Zhimin Zhang
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Yong Yan: School of Geographic Science and Geomatics Engineering, Suzhou University of Science and Technology, Suzhou 215009, China
Ying Wang: School of Geographic Science and Geomatics Engineering, Suzhou University of Science and Technology, Suzhou 215009, China
Cheng Yu: School of Geographic Science and Geomatics Engineering, Suzhou University of Science and Technology, Suzhou 215009, China
Zhimin Zhang: School of Geographic Science and Geomatics Engineering, Suzhou University of Science and Technology, Suzhou 215009, China

Sustainability, 2023, vol. 15, issue 13, 1-18

Abstract: Multispectral remote sensing technology using unmanned aerial vehicles (UAVs) is able to provide fast, large-scale, and dynamic monitoring and management of water environments. We here select multiple water-body indices based on their spectral reflection characteristics, analyze correlations between the reflectance values of water body indices and the water quality parameters of synchronous measured sampling points, and obtain an optimal water body index. A representative selection, such as statistical analysis methods, neural networks, random forest, XGBoost and other schemes are then used to build water-quality parameter inversion models. Results show that the XGBoost model has the highest accuracy for dissolved oxygen parameters (R 2 = 0.812, RMSE = 0.414 mg L −1 , MRE = 0.057) and the random forest model has the highest accuracy for turbidity parameters (R 2 = 0.753, RMSE = 0.732 NTU, MRE = 0.065). Finally, spatial distribution maps of dissolved oxygen and turbidity of water bodies in the experimental domain are drawn to visualize water-quality parameters. This study provides a detailed comparative analysis of multiple inversion methods, including parameter quantity, processing speed, algorithm rigor, solution accuracy, robustness, and generalization, and further evaluates the technical characteristics and applicability of several inversion methods. Our results can provide guidance for improved small- and medium-sized surface-water quality monitoring, and provide an intuitive data analysis basis for urban water environment management.

Keywords: UAV multispectral monitoring; waterbody index; machine learning; dissolved oxygen; turbidity (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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