A Multiband Model With Successive Projections Algorithm for Bathymetry Estimation Based on Remotely Sensed Hyperspectral Data in Qinghai Lake

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing 2021, Vol.14, p.6871-6881
• Main Authors: Zhang, Dianjun, Guo, Quan, Cao, Lingjuan, Zhou, Guoqing, Zhang, Guangyun, Zhan, Jie
• Format: Article
• Language: English
• Subjects: Algorithms; Atmospheric modeling; Bathymeters; Bathymetry; Correlation coefficient; Correlation coefficients; Data; Data models; Decision making; Ecological monitoring; Environmental management; Environmental monitoring; Hyperspectral imaging; hyperspectral remote sensing; Imagery; Inland waters; Lakes; Least squares method; multiband model; Reflectivity; Remote sensing; Saline water; Spectral bands; successive projections algorithm (SPA); Surveys; Water; Water depth; Water quality
• ISSN: 1939-1404; 2151-1535
• DOI: 10.1109/JSTARS.2021.3093624

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.