Mapping potential surface ponding in agriculture using UAV-SfM

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