###### Abstract
Sentinel-2 and RapidEye have higher temporal resolutions of one to five days, but it can vary by location and not all images may be useful due to cloud cover obscuring land. Additionally, factors such as cloud cover, geometric distortion, and atmospheric distortion may require advanced processing expertise to ensure sufficient image quality [17]. New satellite systems are improving in spatial and temporal sensitivity, such as the PlanetScope satellite constellation [18]. Designed for collecting information for use in land-change detection, crop monitoring, climate monitoring, and more, the PlanetScope satellite constellation is composed of over 130 satellites called Doves allowing for spatial resolutions of 3 to 5 m and daily revisit. Beginning with the first launch of a group of Doves in March 2016, over 10 more groups have launched since to improve revisit time, as well as spatial and spectral resolutions. PlanetScope imagery products are also available in multiple asset forms with different radiometric processing and rectification, such as the \"surface reflectance\" product imagery downloaded for this study. Currently, a select portion of Planet data is available for free download under an open data access policy. PlanetScope imagery has been used in studies of wheat yield, biomass, and LAI monitoring and modelling with promising results [19; 20; 21]. However, there are few studies focused specifically on nitrogen management using PlanetScope data, a gap that this study aims to fill. With the rapid advancement in UAV technology in recent years, there is much research interest in UAV-based crop canopy nitrogen retrieval [3]. UAV-based remote sensing can provide low cost and higher spatial and temporal resolution data for crop management. Individuals with basic training can operate a UAV using programmed routes and collect images with <10 cm resolutions [22]. They can be flown to capture more frequent image data and offer flexibility in operation for times when weather is most suitable [23]. Compared to satellites, overall, UAV-based systems are often lower in cost for data collection and  
acquired for this study encompassed the wheat crop's growth period from leaf development up to and including inflorescence emergence (BBCH 10 to 59) for nitrogen estimation. The early stages of growth before flowering are especially important as fertilizers applied then have better nitrogen use efficiency and yield response [4, 33]. Fertilizer can be applied during autumn planting, but lower probability of rainfall also decreases the amount of N from moving into the soil. Fertilizer left above soil during winter will not penetrate and may move during spring snowmelt. During later growth stages (BBCH 60+) from fruit development to ripening, root N uptake slows down as the plants translocate N from vegetation to grains and excess fertilizer can leach from the field [34]. Prior to sample point selection, a DJI Phantom 4 Real-Time Kinematics (RTK) UAV was flown over the bare soil of the fields. The UAV connected to an RTK global navigation satellite system (GNSS) base station acquired images with positioning metadata at 1.5 cm vertical and 1 cm horizontal positioning accuracy. These images were mosaicked to create a digital elevation model (DEM) GeToff image. The DEM was imported into Google Earth and 16 sample points were selected for each field based on the variation of elevation and coverage of representative areas (Figure 2). Considering the crop rows were planted northwest-southwest for field W1 and north-south for fields W2 and W3, the sampling pattern followed the row directions for navigation efficiency. The sample points were \begin{table}
\begin{tabular}{c c c} \hline
**Fieldwork Date** & **Air Temperature (\({}^{\circ}\)C)** & **BBCH \({}^{\ast}\) Growth Stage** \\ \hline
5 May & 17 & 22–24 \\
12 May & 12 & 23–26 \\
20 May & 14 & 30–32 \\
27 May & 21 & 39–41 \\
4 June & 23 & 52–58 \\ \hline \end{tabular} \({}^{\ast}\)Biologische Bundesanstalt, Bundessortenamt, and CHemische Industrie Scale.  
\end{table}