A spatially comprehensive water balance model for starch potato from combining multispectral ground station and remote sensing data in precision agriculture

Abstract

The measurement of available water for agricultural plants is a crucial parameter for farmers, particularly to plan irrigation. However, an area-wide measurement is often not trivial as there are several inputs and outputs of water into the system. Here, we present a high-resolution, remote sensing-based water balance model for starch potato cultivation, combining multispectral ground station data with UAV and satellite imagery. Over a three-year period (2021–2023), data from Arable Mark 2 ground stations, DJI Phantom 4 MS drones, PlanetScope satellites, and Sentinel-2 satellites were collected in Mecklenburg–Western Pomerania, Germany. The model utilizes NDVI-based crop coefficients (R2 = 0.999) to estimate evapotranspiration and integrates on-farm irrigation and precipitation data for precise water balance calculations. A correlation with reference NDVI observations by Arable Mark 2 systems can be shown for UAV (R2 = 0.94), PlanetScope satellite data (R2 = 0.94), and Sentinel-2 satellite data (R2 = 0.93). We demonstrate the model’s ability to capture intra-site heterogeneity on a precision farming scale. Our spatially comprehensive model enables farmers to optimize irrigation strategies, reducing water and energy use. Although the results are based on sprinkler irrigation, the model remains adaptable for advanced irrigation methods such as drip and subsurface systems.