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Surface water area estimation is essential for understanding global environmental dynamics, yet it presents significant challenges, particularly when dealing with small water bodies like ponds and narrow width rivers. Surface water areas for these small bodies are often inaccurately represented by existing methods due to the spatial resolution limitations in commonly used remote sensing images. This study introduces DeepWaterFraction (DWF), a deep learning approach, to estimate percent surface water area from Landsat mission imagery. DWF is trained with a self-training method, which creates training data by upscaling remote sensing images and water map labels to a lower resolution, enabling the creation of a large-scale, global coverage training dataset. DWF demonstrates superior accuracy in estimating areas for small water bodies compared to several existing methods for surface water area estimation …