Retrieval of suspended sediment concentrations using remote sensing and machine learning methods: A case study of the lower Yellow River.

• 24,918 Landsat images focused on the lower Yellow River were used to monitor SSC during the period 1997–2020. • The proposed HGAR method significantly outperforms other retrieval methods in terms of retrieval accuracy. • HGAR is a reliable method for SSC retrieval and can be used for long-term monitoring of SSCs. Monitoring suspended sediment concentrations (SSCs) of channel waters is essential for evaluating important issues such as erosion, sediment transport and deposition, reservoir siltation, and water pollution throughout watersheds. Remote sensing technology provides a new approach to large-scale SSC monitoring, but the accuracy of existing retrieval methods based on remote sensing technology still needs to be improved for complex environments. This study proposes a new hyperparameter and globally adaptive retrieval (HGAR) method for river SSC retrieval that combines a remote sensing technique and machine learning methods. The HGAR method uses the light gradient-boosting machine method as the basic model for SSC retrieval, and the tree-structured Parzen estimator method, coefficient of determination, and residual prediction deviation are combined to optimize the basic model's hyperparameters. The experiment was conducted in the lower Yellow River, where 24,918 Landsat images from 1997 to 2020 were acquired using Google Earth Engine, and 1,772 monthly cloud-free images were obtained in the end. The sequential backward floating selection method was adopted to determine the optimal modeling features. Two regression and three machine learning methods were compared with the proposed method. The results demonstrate that the proposed HGAR method significantly outperforms other retrieval methods in terms of retrieval accuracy (as evidenced by a correlation coefficient of 0.626 and a Nash-Sutcliffe efficiency coefficient of 0.613), generalizability, and robustness. Finally, the model explanation results show that the HGAR method can successfully capture the key information for SSC retrieval and has higher flexibility than traditional statistical models. In addition, we found that the overall fit and the fit of local extreme values are inconsistent in the lower Yellow River, and the proposed improved accuracy indicator balances the two aspects well. In conclusion, the proposed HGAR method can be used effectively to estimate channel SSC and is expected to provide technical support for long-term and large-scale monitoring of the channel surface SSC. [ABSTRACT FROM AUTHOR]