Soil moisture change and surface erosion provide unique insight into the evolution of desert ecohydrology and geomorphology in hyper-arid areas under increasing hydroclimatic extremes. However, the spatial and temporal distributions of the transitional changes in soil moisture and surface erosion associated with sparse rainstorm events are not yet well-characterized due to the low spatial resolution (a few kilometers) of the current microwave radiometer and scarce temporal acquisition and SAR orbital observations, respectively. To address this deficiency, we apply three processing techniques to monitor these changes at a spatial resolution of 45-105 m and with a limited dataset covering four precipitation events of different magnitudes over the hyper-arid Qatar peninsula from 2017 to 2019. Two of the techniques are based on radar interferometric coherence using Sentinel1C-band satellite data, as the investigated rainstorm events generate measurable isolated changes in the timeseries coherence associated with variations in transient soil moisture and surface erosion. The third technique employs soil moisture indices derived from Sentinel-2 multi-spectral satellite imagery. Our observations indicate that the timing of satellite acquisitions relative to a rainstorm is the primary factor in selecting the appropriate technique for identifying soil moisture change, surface erosion, or both. As a result, we integrate these three techniques into a decision-tree to guide users in their methodological choices. Moreover, this decision-tree approach can further improve the assessment of erosion hotspots, flood risks and ecohydrological and geomorphological changes of deserts caused by the current fluctuations in rainstorm events over hyper-arid areas due to hydroclimate volatility.