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Accurate and timely inland waterbody extent and location data are foundational information to support a variety of hydrological applications and water resources management. Recently, the Cyclone Global Navigation Satellite System (CYGNSS) has emerged as a promising tool for delineating inland water due to distinct surface reflectivity characteristics over dry versus wet land which are observable by CYGNSS’s eight microsatellites with passive bistatic radars that acquire reflected L-band signals from the Global Positioning System (GPS) (i.e., signals of opportunity). This study conducts a baseline 1-km comparison of water masks for the contiguous United States between latitudes of 24°N-37°N for 2019 using three Earth observation systems: CYGNSS (i.e., our baseline water mask data), the Moderate Resolution Imaging Spectroradiometer (MODIS) (i.e., land water mask data), and the Landsat Global Surface Water product (i.e., Pekel data). Spatial performance of the 1-km comparison water mask was assessed using confusion matrix statistics and optical high-resolution commercial satellite imagery. When a mosaic of binary thresholds for 8 sub-basins for CYGNSS data were employed, confusion matrix statistics were improved such as up to a 34% increase in F1-score. Further, a performance metric of ratio of inland water to catchment area showed that inland water area estimates from CYGNSS, MODIS, and Landsat were within 2.3% of each other regardless of the sub-basin observed. Overall, this study provides valuable insight into the spatial similarities and discrepancies of inland water masks derived from optical (visible) versus radar (Global Navigation Satellite System Reflectometry, GNSS-R) based satellite Earth observations.

 

Intermittent floodplain channels are low‐relief conduits etched into the floodplain surface and remain dry much of the year. These channels comprise expansive systems and are important because during low‐level inundation they facilitate lateral hydraulic connectivity throughout the floodplain. Nevertheless, few studies have focused on these floodplain channels due to uncertainty in how to identify and characterize these systems in digital elevation models (DEMs). In particular, their automatic extraction from widely available DEMs is challenging due to the characteristically low‐relief and low‐gradient topography of floodplains. We applied three channel extraction approaches to the Congaree River floodplain DEM and compared the results to a channel reference map created through numerous field excursions over the past 30 years. The methods that we tested are based on flow accumulation area, topographic curvature, and mathematical morphology, or the D8, Laplacian, and bottom‐hat transform (BHT), respectively. Of the 198 km of reference channels the BHT, Laplacian, and D8 extracted 83%, 71%, and 23%, respectively, and the BHT consistently had the highest agreement with the reference network at the local (5 m) and regional (10 km) scales. The extraction results also include commission “error”, augmenting the reference map with about 100 km of channel length. Overall, the BHT method provided the best results for channel extraction, giving over 298 km in 69 km2 with a detrended regional relief of 1.9 m. Further, these analyses allow us to shed light on the meaning and use of the term “low‐relief landscapes”. 

Groundwater withdrawals are not actively monitored in most places of the world at a scale necessary to implement sustainable solutions

Various multitemporal remote sensing data are integrated into a machine learning framework to effectively predict groundwater withdrawals

The results over the High Plains Aquifer, Kansas, USA, show that this approach is applicable to similar regions having sparse in situ data