## 3 Results
### Partial Least Squares Regression (PLS-R) to Predict Leaf Physiology  
Before using PLS-R, a SNV pre-processing was applied to minimize growth and crops differences (Figure 7 compared to Figure 4). Table 5 provides R\({}^{2}\), NRMSE and bias from PLS-R to assess model performance differences when using only hyperspectral data or when including \(\rm T_{L,Rad}\) and \(\rm h_{c}\), pooling data from both crops together or separated. Calibration NRMSE of 14% and below as well as test set NRMSE of 18% and below indicated good model performance with 500 permutations. In addition, all the R\({}^{2}\) values were statistically significant (\(p<\) 0.001). It is notable that the best model performance (both R\({}^{2}\) and NRMSE) for the three physiological variables studied (A, \(\rm g_{s}\), and \(\rm T_{r}\)) was found for joint crop models rather than separate crop models (Table 5). This joint crop model (Figure 8), showed higher correlations between predicted and observed data, and lower calibration and validation errors, especially for A (\(\rm R_{val}\)\({}^{2}\) = 0.92 and \(\rm NRMSE_{val}\) = 8%), followed by \(\rm T_{r}\) (\(\rm R_{val}\)\({}^{2}\) = 0.87 and \(\rm NRMSE_{val}\) = 11%), and \(\rm g_{s}\) (\(\rm R_{val}\)\({}^{2}\) = 0.82, \(\rm NRMSE_{val}\) = 11%). This is most likely due to the larger range of the leaf physiological variables and the increased number of observations (Figure S3 and Figure 8). As previously shown in Figure 3, maize A, \(\rm g_{s}\), and \(\rm T_{r}\) presented higher response values than soybean. [PERSON] et al. (2015) [55] also observed better PLS-R  
Figure 7: Reflectance normalization after applying standard normal variate (SNV) pre-processing. (**A**) Normalization of reflectance from Figure 3A, comparing between soybean (blue continuous line) and maize (red dashed line) for the three WHC groups: control/wet (100% WHC), mid (70% WHC), and dry (40% WHC) shown with different line thickness. (**B**) Normalization of reflectance selecting canopy height as a measured of growth divided in three stages: plants lower than 0.3 m, between 0.3 and 0.6 m, and higher than 0.6 m (shown with different line thickness) for both crops: soybean (blue continuous line) and maize (red dashed line). scores in pasture and rise gross primary productivity (GPP) and net ecosystem exchange (NEE) over Mediterranean climate, using hyperspectral data (400-900 nm). Comparing between crops, maize presented better predictions for \(\mathrm{T_{r}}\) and A while \(\mathrm{g_{s}}\) was better predicted in soybean (Table 5). The main improvement from adding \(\mathrm{T_{L,Rad}}\) to PLS-R models was found in soybean models (Table 5), especially for \(\mathrm{g_{s}}\). Soybean \(\mathrm{g_{s}}\) model including \(\mathrm{T_{L,Rad}}\), increased about 25% \(\mathrm{R^{2}_{val}}\) (13% \(\mathrm{R^{2}_{val}}\)), decreased 13% \(\mathrm{NRMSE_{val}}\) (16% \(\mathrm{NRMSE_{val}}\)), and reduced 10% \(\mathrm{bias_{val}}\) (13% \(\mathrm{bias_{val}}\)) compared to just using hyperspectral data. Adding \(\mathrm{h_{c}}\) was mostly improving maize models, especially \(\mathrm{T_{r}}\), reducing \(\mathrm{NRMSE_{val}}\) about 15% and \(\mathrm{bias_{val}}\) 30%. Maize \(\mathrm{g_{s}}\) and \(\mathrm{T_{r}}\) models showed increased \(\mathrm{R^{2}}\) calibration and validation values of about 10%. VIP scores (Figure 9) higher than 1 indicate important waveband regions to predict A, \(\mathrm{g_{s}}\), and \(\mathrm{T_{r}}\). Overall, we observed the same bands contribution for soybean A, \(\mathrm{g_{s}}\), and \(\mathrm{T_{r}}\), while in maize, the role of specific narrow bands, was not so clear, presenting lower VIP scores and similar influence over wavelengths.