## 2 Materials and Methods
### Remote Sensing Data
#### 2.3.2 UAV Data  
Multi-temporal flights using an Inspire a UAV quadcopter type DJI(r) Inspire 1 (SZ DJI Technology Co., Ltd., Shenzhen, China) were conducted over the experimental site (1080 m\({}^{2}\)) where multispectral images were taken on three different dates (19 January, 22 March and 10 May) during the wheat growing season. Flights were conducted under conditions of low wind speed, partly cloudy skies, and good visibility. Perpendicular flights were made at 50 m height to create a grid covering each plot and its surroundings (90% overlap between flight lines). A multispectral camera was acquired in five spectral bands (Supplementary Material: Figure S1 and Table S2) by MicaSense (MS) RedEdge-M sensor (1.2 Mpx and 5.4 mm focal length) was used to acquire the information attached to the rear from Inspire 1 DJI UAV, using a custom mounting plate and independently powered by an external battery. Images captured by the the RedEdge-M were sorted as 16-bit RAW stiff files. MS images were processed using the Pix4D v4.6.4\({}^{\text{\text{\text@underline{0}}}}\) software. An orthomosaic was generated using Structure from Motion (SfM) techniques, which reconstruct 3D geometry and spatial alignment from overlapping images [55]. This process included feature matching, bundle adjustment, and dense point cloud generation to ensure high spatial accuracy and seamless image stitching. The resulting orthomosaic was georeferenced using real-time kinematic (RTK) GPS points for precise alignment with the experimental site. This software was also utilized to process the MS images, transforming the raw digital data into radiance and reflectance. Calibration of the MS imagery was performed using the camera's standard calibrated white reflectance panel. The radiometric calibration process follows the equation provided by MicaSense [56], shown as (Equation (1)), which is based on their official radiometric conversion model:  
\[L_{sen_{\lambda}}=V_{(x,y)}\times\left(\frac{a_{1}}{g}\right)\times\frac{p-p_{ BL}}{t_{e}-a_{2}y-a_{3}t_{e}y} \tag{1}\]  
where \(L_{sen}\) is the spectral radiance at wavelength \(\lambda\); \(V_{(x,y)}\) is the vignette polynomial function at pixel (\(x\),\(y\)); \(a_{1}\), \(a_{2}\), and \(a_{3}\) are the radiometric calibration coefficients; \(g\) is the sensor gain setting; \(p\) is the normalized DN value; \(p_{BL}\) is the black level offset; and \(t_{e}\) is the image exposure time. All parameters required for radiance calculation are provided  
Figure 2: The rectangles represent each experimental plot while the squares represent PlanetScope pixels. The numbers highlight the proportion of pixel area inside each experimental plot, with a gradient color from red (almost no area in the plot) to green (mostly inside the plot). In the bottom figure, the 21 pixels that fit into the experiment plot (enumerated fields) are highlighted in green. in the image metadata. After determining the radiance, the reflectance is derived using the calibrated white reflectance panel and the function supplied by MicaSense [57]. This computation relies on a standard reflectance panel captured from an image taken at a height of 1 m. The corresponding formula is presented in Equation (2):  
\[F_{i_{\lambda}}=\frac{\rho_{i_{\lambda}}}{\frac{1}{n}\sum_{j=1}^{n}L_{i,j_{ \lambda}}} \tag{2}\]  
where \(\rho_{i,\lambda}\) is the panel's reflectance at wavelength \(\lambda\), and \(L_{i,\lambda}\) represents the radiance measured for the \(j\)-th pixel of the reflectance panel.