A Multiband Model With Successive Projections Algorithm for Bathymetry Estimation Based on Remotely Sensed Hyperspectral Data in Qinghai Lake
Dianjun Zhang, Quan Guo, Lingjuan Cao, Guoqing Zhou, Guangyun Zhang, Jie Zhan
Abstract
Lake bathymetry plays a pivotal role in environmental monitoring, ecological management, water quality protection, etc. Hyperspectral remote sensing technology can provide large-scale coverage and more detailed spectral information for bathymetry estimation than traditional measurements or multispectral imagery techniques. In this study, a multiband linear model with successive projections algorithm (SPA-MLM) was developed to retrieve the bathymetry of Qinghai Lake, which is the largest inland saltwater lake in China. The three most sensitive spectral bands were first selected by the SPA, and a multiband linear model was established by the least squares method combined with the in situ measured water depth. Zhuhai-1 hyperspectral remotely sensed imagery is employed as the data source. In all, 98 in situ bathymetry measurements matched with the obtained images were obtained during three surveys performed in May, September, and October 2020. The results demonstrated that the established retrieval model can be used to accurately estimate the water depth in the study area, with an accuracy exceeding approximately 90%, which suggests that the proposed model performs better than those used in previous studies employing hyperspectral imagery. The correlation coefficient reaches 0.92, and the root-mean-square error is approximately 1.26 m. This demonstrates that bathymetry estimation obtained using remotely sensed hyperspectral data is an effective detection method and can provide large-scale, rapid monitoring data to the relevant decision-making departments.