Sentinel-1 SAR Monitoring of River Water Surface Elevation: Effects of Wind, Waves, River Morphology and Vegetation

Accurate monitoring of river water surface elevation (WSE) is important for hydrologic and flood applications, particularly where in situ observations are limited. This study evaluates Sentinel-1 Synthetic Aperture Radar (SAR) for estimating river WSE using a river inundation area–water surface elevation (RIA–WSE) relationship derived from high-resolution topobathymetric elevation data and investigates environmental factors affecting estimation accuracy. A total of 265 Sentinel-1 SAR images acquired during 2015–2026 covering the USGS Thebes gauging station along the Mississippi River were analyzed using six VV- and VH-based backscatter metrics, yielding 1590 WSE estimates. Comparison with a conventional 1-m digital elevation model (DEM) revealed that its representation of river water-surface elevations at the time of the elevation survey, rather than submerged channel-bed elevations, limits its applicability for estimating WSE at low river stages. Among the six metrics, log10(VV) produced the best overall performance, with an RMSE of 1.61 m, mean error of −0.96 m, R2 of 0.81, and percentage of lowest absolute error (PLE) of 44.5%. A sensitivity analysis using SAR image grid spacings of 10, 5, 2, and 1 m showed that log10(VV) maintained relatively stable estimation accuracy, whereas finer resampling did not consistently improve WSE estimates. Errors were smallest under low-flow conditions (WSE < 97.0 m), largest under medium- to high-flow conditions (97.0–102.5 m) and decreased again during flooding (WSE > 102.5 m). Analysis of 10 National Agricultural Imagery Program (NAIP) images revealed increasing vegetation along the inundation boundary above approximately 100 m WSE. Under medium- to high-flow conditions, calm conditions generally produced smaller errors than non-calm conditions, although errors did not increase monotonically with wind speed. E–W and SE–NW wind orientations generally produced larger errors than N–S and NE–SW orientations. These results indicate that WSE estimation accuracy reflects combined effects of vegetation, wind-generated water surface roughness, wind orientation, effective fetch, water depth, and river morphology. Although log10(VV) performed best overall, it provided the lowest absolute error for only 44.5% of acquisitions, highlighting the potential for condition-dependent backscatter metric selection to improve SAR-based river WSE retrieval.