Transparency Estimation of Narrow Rivers by UAV-Borne Hyperspectral Remote Sensing Imagery

Urban rivers are often narrow, and general remote sensing data cannot meet the needs of water quality monitoring. In the process of monitoring of river water quality by remote sensing, the spectral and spatial dimension of satellite-borne images cannot be taken into consideration at the same time, making fine pollution monitoring of urban rivers difficult. Transparency is one of the core indicators for evaluating water quality, and hyperspectral remote sensing data are rich in spectral information and can be used for quantitative transparency estimation. The application of unmanned aerial vehicles (UAV)remote sensing effectively makes up for the deficiencies in satellite remote sensing monitoring. Aiming at this problem, this paper proposed the use of the eXtreme Gradient Boosting (XGBoost) regression algorithm for the quantitative inversion of urban river transparency. The spatial resolution of the collected imagery is 18.5 cm, which is suitable for urban rivers that are almost ten meters wide. Compared with five traditional empirical models, integrated algorithms such as gradient regression and random forest get much better results. Moreover, the accuracy of transparency estimation using the XGBoost regression algorithm was significantly improved, and the inversion model R
²
in both study areas reached over 0.97. Finally, the established transparency inversion models were used to generate transparency distribution maps of the two study areas. The results showed that the distribution of the water transparency was consistent with the results of the field monitoring, indicating that it is feasible to use the XGBoost algorithm for the inversion of urban river transparency in UAV-borne hyperspectral imagery.