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Understanding the impacts of sparse rainstorms on surface erosion and soil moisture changes in hyper-arid deserts offers a unique insight into their dynamical evolution under intensifying hydroclimatic extremes. However, the spatiotemporal distribution of these changes remains poorly identified due to the low spatial resolution of orbital radiometers (a few kilometers) and the scarce temporal acquisition of orbital SAR observations. To mitigate these limitations, we propose an approach integrating three processing techniques, forming a decision-tree combining Sentinel-1 C-band radar interferometric coherence with Sentinel-2 multi-spectral soil moisture indices.