Impact of Spatial Scale on Optical Earth Observation‐Derived Seasonal Surface Water Extents

Abstract Landsat‐derived products are the most prominent, publicly available sources of large‐scale surface water extent data. However, few studies have assessed the limitations of spatial scale on such products. Here, we mapped seasonal surface water extents utilizing high‐resolution (4.77 m) PlanetScope Basemap imagery and machine learning. We conducted a pixel‐wise comparison of these high resolution classifications with a set of classifications from a moderate resolution (30 m) Landsat product. The vast majority (93%) of areas classified as water by the Landsat product were similarly classified by PlanetBasemap; however, only 65%–75% of the PlanetBasemap water area was also classified by the Landsat classes. Of the Landsat classes, only the partial surface water class comparably detects smaller water bodies (widths 50–70 m) with PlanetBasemaps. Our results indicate that higher resolution imagery detects more small water bodies, which are instrumental to better understanding flood dynamics, methane emissions, and downstream water volume and quality.