<span style="font-weight: 400;">Accurate sub-daily soil moisture (SM) retrievals from satellite observations remain a major challenge due to sparse temporal sampling and retrieval uncertainties. This study introduces a localized convolutional neural network (CNN-l) framework designed to enhance SM estimates from Advanced SCATterometer (ASCAT) observations by exploiting spatial features and adapting to local conditions. The proposed approach achieves strong agreement with ERA5 reference SM, with total correlation coefficients exceeding 0.9, even at a sub-daily scale. Validation against <em>in situ</em> measurements from 568 monitoring sites across the contiguous United States (CONUS) shows a median temporal correlation of 0.65, compared to 0.59 for the operational ASCAT H120 product. Our CNN-based retrievals also reveal meaningful intraday variability when SM signals exceed retrieval uncertainty, particularly during heavy precipitation events (&gt; 10 mm day</span>^{<span style="font-weight: 400;">&minus;1</span>}<span style="font-weight: 400;">), offering new insight into short-term hydrological responses. Future efforts should prioritize the integration of complementary satellite observations from multiple instruments to enhance retrieval accuracy, robustness, and temporal resolution. Additionally, strategies to improve retrieval of extremes (such as localization strategies or variable augmentation) should be further developed.</span>