Digital Elevation Models (DEMs) underpin watershed delineation, landform classification, hydrologic/hydrodynamic modelling, and environmental monitoring. While SRTM (~30 m) is a common global baseline, advances in SAR processing (e.g., SNAP) enable locally tailored DEMs from Sentinel-1 interferometry. This study generates a Sentinel-1 InSAR DEM and evaluates it against Copernicus DEM (GLO-30, ~30 m DSM), SRTM (1-arcsec, ~30 m DEM), and very-high-resolution LiDAR patches over the study area. We assess accuracy using RMSE, MAE, and correlation (stratified by land cover, slope, and coherence), and qualitatively via elevation profiles, slope/aspect, and planform inspection. We diagnose error sources—temporal decorrelation, atmospheric delay, and baseline geometry—and examine long-wavelength ramps to gauge the benefit of stacking multiple interferograms. Results: The Sentinel-1 DEM provides enhanced local detail (≈10–30 m posting) and sharper terrain expression than global products on open to moderately vegetated slopes, but its performance degrades in dense vegetation/low-coherence zones. Published comparisons indicate that Copernicus GLO-30 typically outperforms SRTM by ~1–2 m RMSE, with global assessments showing most GLO-30 tiles <~1.2 m RMSE against space-borne lidar references. In contrast, single-pair Sentinel-1 InSAR DEMs in humid, vegetated tropics can show RMSE in the tens of metres (e.g., ~22.5 m), though stacking multiple interferograms improves accuracy relative to single-pair results. SRTM offers a smoother, more conservative surface but misses fine relief; Copernicus generally improves vertical consistency and landform depiction. LiDAR patches show the upper bound on accuracy and reveal local biases across coarse DEMs. We propose a practical selection framework—favouring Sentinel-1 for local detail/recent acquisitions, Copernicus for global consistency, SRTM for baseline coverage, and fusion with LiDAR where available—and outline integration steps for hydrologic/hydrodynamic and geomorphic change applications.