###### Abstract  
To improve productivity, reduce production costs, and minimize the environmental impacts of agriculture, the advancement of nitrogen (N) fertilizer management methods is needed. The objective of this study is to compare the use of Unmanned Aerial Vehicle (UAV) multispectral imagery and PlanetScope satellite imagery, together with plant height, leaf area index (LAI), soil moisture, and field topographic metrics to predict the canopy nitrogen weight (g/m\({}^{2}\)) of wheat fields in southwestern Ontario, Canada. Random Forests (RF) and support vector regression (SVR) models, applied to either UAV imagery or satellite imagery, were evaluated for canopy nitrogen weight prediction. The top-performing UAV imagery-based validation model used SVR with seven selected variables (plant height, LAI, four VIs, and the NIR band) with an R\({}^{2}\) of 0.80 and an RMSE of 2.62 g/m\({}^{2}\). The best satellite imagery-based validation model was RF, which used 17 variables including plant height, LAI, the four PlanetScope bands, and 11 VIs, resulting in an R\({}^{2}\) of 0.92 and an RMSE of 1.75 g/m\({}^{2}\). The model information can be used to improve field nitrogen predictions for the effective management of N fertilizer. Unmanned Aerial Vehicle (UAV); PlanetScope imagery; precision agriculture; nitrogen management; machine learning; Random Forests; Support Vector Regression (SVR); wheat 2022  
31 October 2021 2020 December 2021 23 December  
yield will be low. Water is also key to the transportation of nutrients from the soil to a plant. The availability of water to a plant depends on the weather conditions during the growing season, the soil moisture, and the field micro-topography affecting water flow and accumulation [10; 11]. Understanding a field's characteristics as well as monitoring plant biophysical characteristics including height, leaf area, and leaf colour can provide useful information in nitrogen fertilizer applications. In PA, remote sensing imagery is useful because it does not require physical or destructive contact with plants to gather valuable crop information [12]. Vegetation indices (VIs) can be derived from the spectral information provided by the imager; VIs are mathematical combinations or transformations of spectral bands that have been widely used in agricultural research. VIs allow for the deriving of specific plant properties such as chlorophyll or nutrient content by taking advantage of the differential spectral properties of plants in the visible and near-infrared (NIR) wavelengths [13; 14; 15]. The VI information can then provide timely knowledge of crop conditions, allowing for a suitable rate of application at the right time and location depending on the variations within a field. Optical satellite imagery for crop monitoring has had several decades of research and application [16]. Examples of recently launched optical satellites in operation include RapidEye since 2008, Landsat 8 since 2013, and Sentinel-2 since 2015, all frequently used in studies on crop nutrient, yield, and growth management [3]. RapidEye has five spectral bands with 6.5 m resolution. Depending on the location, the five-satellite constellation revisit time is between one to five days. Landsat 8 Operational Land Imager has nine spectral bands with varying spatial resolutions of 15 to 30 m. It has a 16-day revisit time to the same area and takes over 700 scenes a day. Sentinel-2 has 13 spectral bands with 10 m, 20 m, and 60 m spatial resolutions depending on the band. Sentinel-2 constellation is composed of two satellites allowing for a five-day revisit time over the same area. Limitations in optical satellite imagery include low spatial sensitivity as the spatial resolution may be too coarse for small-scale crop fields [12]. The temporal sensitivity can be rather low, such as that of Landsat 8 with a 16-day revisit time, during which crops would have changed significantly, and thus, valuable information on the different stages of growth would not be obtained.