Remote sensing inversion of suspended particulate matter in the estuary of the Pinglu Canal in China based on machine learning algorithms - Mo, Jinying; Tian, Yichao; Wang, Jiale; Zhang, Qiang; Zhang, Yali; Tao, Jin; Lin, Junliang (Frontiers in Marine Science, 2024)
Suspended particulate matter (SPM) is a critical indicator of water quality and has a significant impact on the nearshore ecological environment. Consequently, the quantitative evaluation of SPM concentrations is essential for managing nearshore environments and planning marine resources. This study utilized Sentinel-2's single band and water index variables to develop a remote sensing inversion model for oceanic SPM in the estuary of the Pinglu Canal in China. Six machine learning algorithms were employed: K-nearest neighbor regression (KNNR), AdaBoost regression (ABR), random forest (RF), gradient boosting regression (GBR), extreme gradient boosting regression (XGBR), and light generalized boosted regression (LGBM). The model with the optimal performance was then selected for further analysis. This research applied the established model to investigate the spatial-temporal dynamics of SPM from 2021 to 2023. The findings indicated that (1)