Landsat 8 monitoring of multi-depth suspended sediment concentrations in Lake Erie's Maumee River using machine learning

• Published in: International journal of remote sensing Vol. 42; no. 11; pp. 4064 - 4086
• Main Authors: Larson, Matthew D., Simic Milas, Anita, Vincent, Robert K., Evans, James E.
• Format: Journal Article
• Language: English
• Published: London Taylor & Francis 03.06.2021; Taylor & Francis Ltd
• Subjects: Depth; Environmental monitoring; Intervals; Lake sediments; Lakes; Landsat; Landsat satellites; Machine learning; neural network; Neural networks; OTHER INSTRUMENTATION; random forest; regression; Regression analysis; Regression models; Remote sensing; Rivers; Satellite imagery; Satellites; Sediment; Sediment concentration; Sediments; support vector; Support vector machines; Suspended sediments; Water depth; Maumee River
• ISSN: 0143-1161; 1366-5901
• DOI: 10.1080/01431161.2021.1890268

Satellite remote sensing has been widely used to map suspended sediment concentration (SSC) in waterbodies. However, due to the complexity of sediment-water interactions, it has been difficult to derive linear and non-linear regression equations to reliably predict SSC, especially when trying to estimate depth of integrated sediment. This study uses Landsat 8 OLI (Operational Land Imager) sensor to map SSC within the Maumee River in Ohio, USA, at multiple depth intervals (15, 61, 91, and 182 cm). Simple linear least squares regression (LLSR), and three common machine learning models: random forest (RF), support vector regression (SVR), and model averaged neural network (MANN) were used to estimate SSC at the depth intervals. All machine learning models significantly outperformed LLSR while RF performed the best. In both RF and MANN, R 2 (coefficient of determination) increases with depth with a maximum R 2 of 0.89 and 0.83, respectively, at a depth of 0-182 cm. The results show that machine learning models can implement nonlinear relationships that produce better predictions than traditional linear regression methods in estimating depth integrated SSC, especially when samples are limited.