The Khumbu Icefall lies at an approximate altitude of 5,500 meters on the Nepali side of Mt. Everest and is one of the most dangerous stages of the major South Col route to the summit. In the Khumbu Icefall, seracs, or ridges on the surface of a glacier, are known to collapse unexpectedly, displacing massive volumes of ice down the Khumbu Glacier, estimated to advance 0.9 to 1.2 m down the mountain every day. Satellite Synthetic Aperture Radar (SAR) uniquely enables a next generation of remote sensing capabilities that offer a persistent, all-weather, day/night, complete characterization of the Earth and its dynamic surface changes. Using open C-band SAR data from the Sentinel-1 satellite constellation, ice dynamics can be reliably tracked at an approximately bi-monthly cadence over the past five years. The C-band instrument is nearly unaffected by cloud cover as it is not significantly attenuated by water vapor, making it incredibly useful for time-sensitive and temporal analysis such as tracking ice volumes, especially in regions under heavy cloud cover for roughly half of the year. Also, the Radar signal can be unwrapped to create phase history data to unlock information about the movement of objects in the scene. We demonstrate the use of this complex data to perform very accurate measurements of 3D change across the glacier and to extract two dimensional (azimuth and range directions) glacier surface velocities through the use of SAR speckle tracking. By enhancing 3D static geospatial data in the form of 3D high-resolution surface models with dynamic SAR information in 4D (3D + time) we additionally demonstrate a unique approach for more effective multi-temporal analysis and 3D change detection. We fuse the enhanced SAR open data with commercial 3D surface models to build a temporal stack of imagery over the Khumbu region and track changes in volume over time and their corresponding velocities. We showcase our analysis using 3D renderings of SAR fused data and SAR derived analytics for enhanced interpretability of the results. This data-driven time series coupled with geophysics principles can lead to significant research advances in studying glacier loss or retreat, ice thinning, or forecasting probabilistic ice motion to better prepare expeditions.