Retrieval of Water Quality from UAV-Borne Hyperspectral Imagery: A Comparative Study of Machine Learning Algorithms

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2021-10, Vol.13 (19), p.3928
• Main Authors: Lu, Qikai, Si, Wei, Wei, Lifei, Li, Zhongqiang, Xia, Zhihong, Ye, Song, Xia, Yu
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial intelligence; Chemical oxygen demand; Chlorophyll; Comparative studies; Data collection; Eutrophication; Freshwater resources; Groundwater; Heavy metals; Hyperspectral imaging; Laboratories; Learning algorithms; Machine learning; Multilayers; Neural networks; Parameters; Performance prediction; Pollution monitoring; Regression analysis; Regression models; Remote sensing; Solid suspensions; Support vector machines; Suspended solids; UAV-borne hyperspectral data; Unmanned aerial vehicles; Water monitoring; Water pollution; Water quality; water quality mapping; water quality parameters inversion; Water temperature; World population
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs13193928

The rapidly increasing world population and human activities accelerate the crisis of the limited freshwater resources. Water quality must be monitored for the sustainability of freshwater resources. Unmanned aerial vehicle (UAV)-borne hyperspectral data can capture fine features of water bodies, which have been widely used for monitoring water quality. In this study, nine machine learning algorithms are systematically evaluated for the inversion of water quality parameters including chlorophyll-a (Chl-a) and suspended solids (SS) with UAV-borne hyperspectral data. In comparing the experimental results of the machine learning model on the water quality parameters, we can observe that the prediction performance of the Catboost regression (CBR) model is the best. However, the prediction performances of the Multi-layer Perceptron regression (MLPR) and Elastic net (EN) models are very unsatisfactory, indicating that the MLPR and EN models are not suitable for the inversion of water quality parameters. In addition, the water quality distribution map is generated, which can be used to identify polluted areas of water bodies.