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Accurate land-use and land-cover (LULC) mapping is crucial for effective watershed management and hydrological modeling in arid regions. This study examines the use of high-resolution PlanetScope imagery for LULC mapping, change detection, and hydrological modeling in the Wadi Ham watershed, Fujairah, UAE. The authors compared LULC maps derived from Sentinel-2 and PlanetScope imagery using maximum likelihood (ML) and random forest (RF) classifiers. Results indicated that the RF classifier applied to PlanetScope 8-band imagery achieved the highest overall accuracy of 97.27%. Change detection analysis from 2017 to 2022 revealed significant transformations, including a decrease in vegetation from 3.371 km² to 1.557 km² and an increase in built-up areas from 3.634 km² to 6.227 km². Hydrological modeling using the WMS-GSSHA model demonstrated the impact of LULC map accuracy on simulated runoff responses, with the most accurate LULC dataset showing a peak discharge of 1160 CMS at 930 min. In contrast, less accurate maps showed variations in peak discharge timings and magnitudes. The 2022 simulations, reflecting urbanization, exhibited increased runoff and earlier peak flow compared to 2017. These findings emphasize the importance of high-resolution, accurate LULC data for reliable hydrological modeling and effective watershed management. The study supports UAE’s 2030 vision for resilient communities and aligns with UN Sustainability Goals 11 (Sustainable Cities and Communities) and 13 (Climate Action), highlighting its broader relevance and impact.