Remote Sensing Retrieval of Turbidity in Alpine Rivers based on high Spatial Resolution Satellites

UAV Multispectral Image-Based Urban River Water Quality Monitoring Using Stacked Ensemble Machine Learning Algorithms—A Case Study of the Zhanghe River, China

Timely monitoring of inland water quality using unmanned aerial vehicle (UAV) remote sensing is critical for water environmental conservation and management. In this study, two UAV flights were conducted (one in February and the other in December 2021) to acquire images of the Zhanghe River (China), and a total of 45 water samples were collected concurrently with the image acquisition. Machine learning (ML) methods comprising Multiple Linear Regression, the Least Absolute Shrinkage and Selection Operator, a Backpropagation Neural Network (BP), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) were applied to retrieve four water quality parameters: chlorophyll-a (Chl-a), total nitrogen (TN), total phosphors (TP), and permanganate index (CODMn). Then, ML models based on the stacking approach were developed. Results show that stacked ML models could achieve higher accuracy than a single ML model; the optimal methods for Chl-a, TN, TP, and CODMn were RF-XGB, BP-RF, RF, and BP-RF, respectively. For the testing dataset, the R2 values of the best inversion models for Chl-a, TN, TP, and CODMn were 0.504, 0.839, 0.432, and 0.272, the root mean square errors were 1.770 μg L−1, 0.189 mg L−1, 0.053 mg L−1, and 0.767 mg L−1, and the mean absolute errors were 1.272 μg L−1, 0.632 mg L−1, 0.045 mg L−1, and 0.674 mg L−1, respectively. This study demonstrated the great potential of combined UAV remote sensing and stacked ML algorithms for water quality monitoring.

The changes in physicochemical and stable isotope compositions in the lower Yellow River of China due to artificial flooding

Increasingly, modern hydrological technologies are dynamically altering river water flow and drastically affecting river hydrogeochemical cycle regimes globally. The present study focused on the reservoir discharges of artificial floodwaters that influence spatiotemporal variations in the physicochemical and stable isotope compositions in the lower Yellow River (LYR) of China. The surface water samples were collected at the nine sites along the LYR during the pre-, inter-and post-flood periods. Then, the collected samples were analysed with the following standard method. The δD and δ¹⁸O slopes of the waterline clearly indicated that the prolonged reservoir water and different water flows impacted the hydrological cycle in the LYR regions compared to GMWL (global meteoric water line) and LMWL (local meteoric water line). The thermal stratification processes of the water in the largest reservoir slightly enriched the heavy isotopes, and physicochemical alteration was neglected. Statistical analysis of two-way ANOVA revealed that the p-values (p < 0.01, p < 0.05) were very strong for most of the variables between the periods, and the linear regression exhibited weak values (R² = 0.253, R² = 0.150) at the surface water temperature variations and suggested no significant influence of isotope composition. Overall, the Xiaolangdi reservoir water prolonged time rates, and artificial floodwater flow had a very small effect on the isotope composition; in particular, a large high turbidity concentration in the discharged artificial floodwaters was the only considerable ecological risk condition in the LYR. This kind of proper monitoring work is immensely important and prevents reservoirs from causing hydrological cycle impacts in the LYR and the adjacent coastal ecosystems.