## Materials and Methods
### Data Analysis  
#### 2.5.1 Development of Leaf N Predictive Model  
Field measured reflectance spectral data were resampled to Sentinel-2 wavelength configuration using the respective spectral response function (Figure 2). Several indices were computed including the red edge based indices. The normalized difference vegetation indices (NDVI) = (NIR \(-\) R)/(NIR \(+\) R) and the simple ratio (SR) = NIR/R [33] were computed, where NIR was near-infrared and R was the red region of the spectrum. The red edge based indices for both NDVI and SR were computed as follows (NIR vs. R); RE1 (\(R_{740}\) vs. \(R_{665\,\mathrm{nm}}\)), RE2 (\(R_{705}\) vs. \(R_{665\,\mathrm{nm}}\)), RE3 (\(R_{783}\) vs. \(R_{705\,\mathrm{nm}}\)) [34; 35], RE4 (\(R_{783}\) vs. \(R_{740\,\mathrm{nm}}\)), RE5 (\(R_{842}\) vs. \(R_{705\,\mathrm{nm}}\)), RE6 (\(R_{842}\) vs. \(R_{740\,\mathrm{nm}}\)) and RE7 (\(R_{740}\) vs. \(R_{705\,\mathrm{nm}}\)) [36; 37; 35]. Chlorophyll--red edge based index (CI\({}_{\text{red-edge}}\)) = (\(R_{783}/R_{705})-1\)[35; 38; 39] and the CI\({}_{\text{green}}\) = (\(R_{783}/R_{560})-1\)[35; 38; 39]. The MERIS Terrestrial Chlorophyll Index (MTCI) was also computed based on the bands centred at 705 nm, 740 nm and 665 nm (\(R_{740}-R_{705}/R_{705}-R_{665}\)) [40]. The red edge position (REP) based on a linear four-point interpolation method, was computed and Sentinel-2 bands centred at 663, 705, 740 and 783 nm were used; see [PERSON] and [PERSON] [41]. Simple linear regression with bootstrapping was used to validate leaf N models based on various remote sensing variables (indices and bands), and was implemented in R programming language (\"Boot function package\"). Bootstraping was used because of the small sample size and it is an unbiased means of evaluating the performance of various estimation models [42; 43; 44]. Bootstrapping technique subsets -70% of data for calibration of the regression model and validates with the remainder, iteratively, and 1000 interactions were used in this study. [PERSON] et al. [16; 17; 18] and [PERSON] and [PERSON] [19] are examples of successful validation of regression models using this technique (bootstrapping). Statistical accuracy and precision metrics such as the coefficient of determination (\(R^{2}\)), root mean square error (RMSE) and relative RMSE (%) were used. The relative RMSE was computed by dividing the RMSE by the observed mean and multiplying by 100.  
Additional independent validation was undertaken using the leaf N and RapidEye data collected in 2010, from [PERSON] et al. [16]. Firstly, the red edge-based vegetation index was derived using RapidEye data. Leaf N will be predicted using Sentinel-2 derived model applied to the corresponding and selected RapidEye based vegetation index. Finally, the statistical accuracy will be computed based on the predicted and measured leaf N values. The assumption here is that Sentinel-2 red edge based model should be able to estimate leaf N using RapidEye's red edge based indices--to confirm the transferability and stability of the red edge based models. The red edge bands for Sentinel-2 and RapidEye are comparable, 705 nm and 710 nm, respectively. The first red edge band (705 nm) of Sentinel-2 has been found to be more important in estimating leaf N using machine learning techniques [18] and it is expected to perform better in estimating leaf N when transformed to indices.