Nitrogen variability assessment of pasture fields under an integrated crop-livestock system using UAV, PlanetScope, and Sentinel-2 data

Abstract

In agricultural production, nitrogen (N) deficiency reduces yield, while overapplication may have an unwanted impact on the natural environment and farm finances. Frequent field N monitoring is impractical due to the time and cost required for traditional laboratory analysis. However, remotely sensed data are an alternative to evaluate and monitor crop nutrition status throughout the growing season. This study evaluates the spatial distribution of N in pasture fields cultivated under an integrated crop-livestock system (ICLS) using unmanned aerial vehicle (UAV) and satellites data. We assessed the performance of UAV, PlanetScope, and Sentinel-2A platforms to predict the N parameters: plant N content (PNC), aboveground biomass (AGB), and nutritional nitrogen index (NNI). Moreover, we also simulated a UAV device with a visible light sensor (i.e., red–green-blue (RGB)) as a costly alternative to near-infrared (NIR) sensors to monitor N status. Finally, we assessed whether combining the information from these platforms would improve the N predictions in our study area. The UAV, PlanetScope, and Sentinel-2A data were able to estimate N parameters in the studied pasture fields using the random forest regression algorithm. The UAV multispectral data resulted in the best prediction accuracies (R2: 0.84-PNC, 0.70-AGB, and 0.84-NNI). The combination of UAV_RGB with either PlanetScope (R2: 0.79-PNC, 0.67-AGB, 0.77-NNI) or Sentinel-2A (R2: 0.76-PNC, 0.57-AGB, 0.69-NNI) improved the performance of the three platforms individually (UAV_RGB, PlanetScope or Sentinel-2A). The association between high spatial and spectral resolutions contributes to the highest prediction accuracy in estimating N variability in pasture fields using remote sensing data. Our results suggest that remote sensing techniques are a reliable approach for N monitoring in commercial pasture fields under ICLS.