Mapping potential surface ponding in agriculture using UAV‐SfM

Among the environmental problems that could affect agriculture, one of most critical is ponding. This may be defined as water storage on surface in concavities and depressions due to soil saturation. Stagnant can seriously crops management agricultural landscapes. It mainly caused by prolonged rainfall events, type, or wrong mechanization practices, which cause compaction. To better understand this problem thus provide adequate solutions reduce related risk, high-resolution topographic information strategically important because it offers an accurate representation morphology. In last decades, new remote sensing techniques interesting opportunities processes Earth's based geomorphic signatures. these, Uncrewed Aerial Vehicles (UAVs), combined with structure-from-motion (SfM) photogrammetry technique, represent a solid, low-cost, rapid, flexible solution for geomorphological analysis. study aims present approach detect potential areas exposed stagnation at farm scale. The digital elevation model (DEM) from UAV-SfM data used do this. depth was calculated DEM using relative attribute algorithm. detection more pronounced convexities allowed estimation mapping ponding conditions. results were assessed observations field measurements are promising, showing Cohen's k(X) accuracy 0.683 planimetric extent phenomena Pearson's rxy coefficient 0.971 pond depth. proposed workflow provides useful indication stakeholders lowland