### Canopy Nitrogen Weight Modelling  
CNW was calculated using the following method [58]:  
\[CNW=LNC\times W_{d} \tag{1}\] \begin{table}
\begin{tabular}{c c c} \hline \hline
**Index 1** & **Formula 2** & **Authors** \\ \hline BNDVI & (NIR \(-\) BLUE)/(NIR \(+\) BLUE) & [PERSON] et al. [44] \\ CI\_RE & (NIR/ REDEDGE) \(-\) 1 & [PERSON] et al. [45] \\ ISR & RED/NIR & [PERSON] et al. [46] \\ MSR & (NIR/ RED) \(-\)1 & [PERSON] [47] \\ MTVI2 & \(\frac{1.8\text{(NIR-Green)}-3.75\text{(RED-Green)}}{\sqrt{(2\text{NIR}+1)^{ 2}-6\text{(NIR-5}\text{(RED)}-0.5}}\) & [PERSON] et al. [48] \\ NDRE & (NIR \(-\) REDEDGE)/(NIR \(+\) REDEDGE) & [PERSON] and [PERSON] [49] \\ NDVI & (NIR \(-\) RED)/(NIR \(+\) RED) & [PERSON] et al. [50] \\ OSAVI & 1.6(\(\text{NIR - RED}\))/(NIR \(+\) RED \(+\) 0.16) & [PERSON], [PERSON], and [PERSON] [51] \\ RDVI & \(\frac{\text{NIR-RED}}{\sqrt{\text{NIR}+\text{RED}}}\) & [PERSON] and [PERSON] [52] \\ RGBVI & \(\left(\text{GreenN}^{2}-\text{ BLUE}*\text{ RED}\right)/\left(\text{GreenN}^{2}+ \text{BLUE}*\text{ RED}\right)\) & [PERSON] et al. [53] \\ RVI & NIR/RED & [PERSON] [54] \\ RVI2 & NIR/REDEDGE & [PERSON] et al. [55] \\ WDRVI & \(\left(0.2*\text{NIR}-\text{ RED}\right)/\left(0.2*\text{NIR}+\text{RED}\right)\) & [PERSON] [56] \\ VDVI & \(\left(2*\text{GreenN}-\text{RED}-\text{BLUE}\right)\) & [PERSON] et al. [57] \\ \hline \hline \end{tabular}  
* Vegetation index abbreviations: BNDVI = blue normalized difference vegetation index; CI_RE = chlorophyll index red edge; ISR = infrared simple ratio; MSR = modified simple ratio; MTVI2 = modified triangular vegetation index; NDRE = normalized difference vegetation index; NDVI = normalized difference vegetation index; OSAVI = optimized soil adjusted vegetation index; RDVI = renormalized difference vegetation index; RGBVI = red green blue vegetation index; RVI = ratio vegetation index; WDRVI = wide dynamic range vegetation index; VDVI = visible band difference vegetation index. 2 Formula variables: BLUE = blue reflectance; GREEN = green reflectance; RED = red reflectance; REDEDGE = red edge reflectance; NIR = near-infrared reflectance.  
\end{table} 
Table 4: Vegetation indices used in the study. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Band** & **Name** & **Band Range (nm)** & **Centre Wavelength (nm)** & **Bandwidth (nm)** \\ \hline
1 & Blue & 464–517 & 491 & 53 \\
2 & Green & 547–585 & 566 & 38 \\
3 & Red & 650–682 & 667 & 32 \\
4 & NIR & 846–888 & 867 & 42 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Spectral characteristics of the PlanetScope Dove-R sensors. where \(CNW\) is the canopy nitrogen weight (g/m\({}^{2}\)), \(LNC\) is the leaf nitrogen content (%), and \(W_{d}\) is the dry biomass weight (g/m\({}^{2}\)). \(CNW\) assumes the plants collected for biomass around the sample point, within the 0.25 m\({}^{2}\) block, have the same amount of nitrogen. The total biomass per area was used as the dry biomass of wheat plants at early growth stages (BBCH < 60) since the leaves constitute most of the plant weight. Compared to other biophysical parameters such as plant nitrate content and plant nitrogen concentration (%), CNW has been found to have greater correlation with spectral data [28]. RF is an ensemble learning method suited for the classification or regression of large, nonparametric datasets. Training data are randomly selected from the dataset based on a user-defined percentage; commonly, 70% of a dataset is used to train a model. From the training data, many decision trees are generated by the algorithm to determine the variables' importance in the regression. Decision trees split at nodes depending on the independent variable that contributes most to the dependent variable. The validation data are the remainder of the dataset that is not used in training, and the average output from decision trees is used to evaluate the model's performance. Advantages of RF include quick computation, no overfitting from training data, and high performance in studies [29].