Multi-scale remote sensing for sustainable citrus farming: Predicting canopy nitrogen content using UAV-satellite data fusion

• Canopy Nitrogen Content (CNC) was estimated using a combination of UAV and Sentinel-2 data. • Developed models that utilize vegetation indices and tree structural data for CNC estimation. • Created spatial and temporal heat maps to visualize nitrogen content distribution in citrus trees. • Demonstrated a strong correlation between CNC and yield across three growing seasons. • Highlighted the potential for CNC applications to guide the development of site-specific nitrogen management strategies. Accurate monitoring of nitrogen (N) levels, while accounting for spatiotemporal variability is crucial for optimizing fertilization in citrus orchards. Traditional methods, such as frequent leaf and soil sampling followed by laboratory analysis, are costly, labor-intensive, and prone to human error. Remote sensing (RS) technologies, including unmanned aerial vehicles (UAVs) and satellite platforms, offer scalable and precise alternatives for N management. However, integrating these platforms poses challenges due to significant differences in spatial, temporal, and spectral resolution. This study presents a novel approach incorporating multispectral and temporal data from UAVs and Sentinel-2 satellites to estimate canopy N content (CNC) in citrus orchards. This method captures spatiotemporal variability across multiple citrus cultivars, aiming to enhance nitrogen use efficiency (NUE) while reducing environmental impact, ultimately promoting sustainable orchard management practices. The study was conducted in commercial citrus plots in the Hefer Valley, Israel, and spanned two phases. The first phase (May 2019 to April 2022) focused on four plots of the 'Newhall' cultivar, while the second phase expanded to twelve additional plots featuring five different citrus cultivars. The methodology consisted of six key steps: (1) Leaf samples from the study area were collected for laboratory nitrogen (N) analysis. (2) Acquiring and preprocessing bimonthly UAV multispectral images and Sentinel-2 satellite images to ensure data quality and consistency. (3) Segmenting individual trees using UAV imagery and extracting structural features through Structure-from-Motion (SfM) photogrammetry. (4) Processing images and extracting spectral and structural features relevant to N estimation. (5) Developing Random Forest (RF) models to estimate CNC using UAV-derived vegetation indices (VIs) and SfM data and combining these with Sentinel-2 VIs to generate canopy-scale CNC heatmaps. (6) Analyzing the relationship between CNC and yield to understand nitrogen dynamics and their impact on productivity. The integrated RF model, which combined UAV-VIs, Sentinel-2 VIs, and SfM-derived structural data, achieved superior performance (R² = 0.80, RMSE = 0.17 kg/m²) compared to models relying solely on UAV-VIs (R² = 0.68, RMSE = 0.23 kg/m²) or Sentinel-2 VIs (R² = 0.48, RMSE = 0.30 kg/m²). Additionally, CNC expressed as mass per tree demonstrated a strong positive correlation with yield (R² = 0.66), highlighting the relationship between nitrogen dynamics and orchard productivity. These results underscore the robustness of the integrated model and the clear advantage of multi-platform data fusion over single-source approaches. The study provides compelling evidence for the potential of combining UAV and Sentinel-2 data to improve CNC estimation and its correlation with yield in citrus orchards. The findings contribute to advancements in precision agriculture by offering a scalable, data-driven framework to enhance nutrient management and support sustainable orchard practices.