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The Greenland Ice Sheet is a leading source of global sea level rise, due to surface meltwater runoff and glacier calving. However, given a scarcity of proglacial river gauge measurements, ice sheet runoff remains poorly quantified. This lack of in situ observations is particularly acute in Northwest Greenland, a remote area releasing significant runoff and where traditional river gauging is exceptionally challenging. Here, we demonstrate that georectified time-lapse camera images accurately retrieve stage fluctuations of the proglacial Minturn River, Inglefield Land, over a 3 year study period. Camera images discern the river’s wetted shoreline position, and a terrestrial LiDAR scanner (TLS) scan of riverbank microtopography enables georectification of these positions to vertical estimates of river stage. This non-contact approach captures seasonal, diurnal, and episodic runoff draining a large (∼2,800 km2) lobe of grounded ice at Inglefield Land with good accuracy relative to traditional in situ bubble-gauge measurements (r2 = 0.81, Root Mean Square Error (RMSE) ±0.185 m for image collection at 3-h frequency; r2 = 0.92, RMSE ±0.109 m for resampled average daily frequency). Furthermore, camera images effectively supplement other instrument data gaps during icy and/or low flow conditions, which challenge bubble-gauges and other contact-based instruments. This benefit alone extends the effective seasonal hydrological monitoring period by ∼2–4 weeks each year for the Minturn River. We conclude that low-cost, non-contact time-lapse camera methods offer good promise for monitoring proglacial meltwater runoff from the Greenland Ice Sheet and other harsh polar environments.

The Surface Water and Ocean Topography (SWOT) satellite and AirSWOT airborne instrument are the first imaging radar-altimeters designed with near-nadir and low incidence, 35.75 GHz Ka-band InSAR for mapping terrestrial water storage variability. Remotely sensed surface water extents are crucial for assessing such variability but are confounded by emergent and inundated vegetation along shorelines. However, because SWOT-like measurements are novel, there remains some uncertainty in the ability to detect certain land and water classes. This study examines the likelihood of misclassification between 15 land cover types and develops the Ka-band Phenomenology Scattering (KaPS) model to simulate changes to Ka-band backscatter as a result of changing surface water fraction and surface structure, including vegetation morphology and water surface waves. Using a separability metric, the study finds that water is five times more distinct compared with dry land classes, but has the potential to be confused with littoral zone and wet soil cover types. The KaPS scattering model simulates AirSWOT backscatter for incidence angles 1–27°, identifying the conditions under which open water is likely to be confused with littoral zone and wet soil cover types. KaPS characterization of the sensitivity of near-nadir and low incidence Ka-band SAR to small changes in both wet area fraction and surface structure enables a more nuanced classification of inundation area. These results provide additional confidence in the ability of SWOT to classify water inundation extent and open the door for novel hydrological and ecological applications of future Ka-band SAR missions.