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The document titled "Remote Sensing of Coastal Waters, Land Use/Cover, Lakes, Rivers, and Watersheds II" explores the significance of remote sensing in monitoring and analyzing coastal regions, land masses, lakes, rivers, and watersheds. It emphasizes the advancements in remote sensing technologies that have improved data collection and understanding of environmental changes. The document includes 22 research papers covering topics such as land cover change, ecological environments, wetlands and habitats, water quality, and marine disasters and environmental impacts. These papers offer valuable insights into the interactions between human and natural factors and aim to inform sustainable resource management efforts. The integration of artificial intelligence with remote sensing data further enhances accuracy and understanding. The authors express their gratitude to the contributors and reviewers for their valuable contributions to this Special Issue. [Extracted from the article]

The upcoming Surface Water and Ocean Topography (SWOT) mission will measure rivers wider than 50–100 m using a 21-day orbit, providing river reach derived discharges that can inform applications like flood forecasting and large-scale hydrologic modelling. However, these discharges will not be uniform in time or coincident with those of neighboring reaches. It is often assumed discharge upstream and downstream of a river location are highly correlated in natural conditions and can be transferred using a scaling factor like the drainage area ratio between locations. Here, the applicability of the drainage area ratio method to integrate, in space and time, SWOT-derived discharges throughout the observable river network of the Mississippi River basin is assessed. In some cases, area ratios ranging from 0.01 to 100 can be used, but cumulative urban area and/or the number of dams/reservoirs between locations decrease the method's applicability. Though the mean number of SWOT observations for a given reach increases by 83% and the number of peak events captured increases by 100%, expanded SWOT sampled time series distributions often underperform compared to the original SWOT sampled time series for significance tests and quantile results. Alternate expansion methods may be more viable for future work. [ABSTRACT FROM AUTHOR]