## 3 Results
### Partial Least Squares Regression (PLS-R) to Predict Leaf Physiology
These metrics are calculated from bootstrapped data for the same crop, water group, date and set. LV indicates the average of latent variables over 500 iterations, after using 10-fold cross validation for each iteration. Based on VIP threshold score and loading weights (Figure 9), the most influential regions for soybean A, g\({}_{s}\), and T\({}_{r}\) were the green band in the VIS (peak around 516 nm), at the beginning (691 nm) and end (780-783 nm) of the red-edge slope and in the NIR plateau region (peaks at 831 and 850 nm). Furthermore, g\({}_{s}\) and T\({}_{r}\) showed a spike in another green band (540 nm), especially important in g\({}_{s}\) VIP curve, presenting the second highest value. In soybean g\({}_{s}\) and T\({}_{r}\), the highest VIP score was given by T\({}_{L,Rad}\) contribution. In maize, however, h\({}_{c}\) played more essential role as shown in Table 5. For A of both crops, these two added predictors did not seem to add much information. Note that h\({}_{c}\) was also important for predicting soybean T\({}_{r}\). Figure 8: Measured vs. predicted values grouped by crop and water holding capacity (WHC) groups after applying the 500 bootstrapped PLR-S model to predict leaf photosynthetic CO\({}_{2}\) assimilation rate (A in umol CO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\)), leaf stomatal conductance (g\({}_{s}\) in mol H\({}_{2}\)O m\({}^{-2}\) s\({}^{-1}\)), and leaf transpiration rate (T\({}_{r}\) in umol CO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\)). Left figures show the values used for calibration (70%) with red dots and validation (30%) with blue crosses. R\({}^{2}\), normalized root mean square error (NRMSE) and bias are shown in the left upper corner for calibration (cal) and in the right lower corner for validation (cal). Right figures show calibration data grouped by crop (soybean in blueish colors and maize in reddish colors) and WHC. Error bars present the variability of model performance pooling the 500 PLR-S iterations and replicates: the \(x\)-axis shows the standard deviation across iterations and \(y\)-axis for the six replicates. The diagonal is the 1:1 line. In maize A, g\({}_{\rm s}\), and T\({}_{\rm r}\), we could highlight some important regions around the red edge and NIR, more specifically, at the beginning and end of the red edge slope with peaks around 700 and 770 nm. In the NIR of maize A VIP, some displaced regions were found compared to g\({}_{\rm s}\) and T\({}_{\rm r}\), which showed a peak at 843 nm. In addition, further in the NIR, different wavelengths were prominent for physiology, with some relations between A with g\({}_{\rm s}\) and T\({}_{\rm r}\). Maize g\({}_{\rm s}\) and T\({}_{\rm r}\) VIP scores were also influenced by green bands (520 and 560 nm). For A, g\({}_{\rm s}\), and T\({}_{\rm r}\) in the studied crops, unimportant regions were observed in blue and yellow-red bands from 450 to 500 nm and between 580 and 680 nm, respectively. In addition, VIPs for soybean and maize g\({}_{\rm s}\) and T\({}_{\rm r}\) were very low around 900 nm. To assess the correlation between hyperspectral, thermal, and h\({}_{\rm c}\) data, we plotted the predictors PLS-R mean loadings of the 500 bootstrapped iterations for the first and second LVs (Figure 10), highlighting important wavelengths from the VIP analysis (Figure 9). From Figure 10, we observed that most of the noteworthy bands from VIP analysis were positively or negatively correlated with h\({}_{\rm c}\) and T\({}_{\rm L,Rad}\) for both crops, especially for soybean plants. From Figure 10, In soybean A, g\({}_{\rm s}\), and T\({}_{\rm r}\), we found that wavelengths corresponding to absorption of chlorophyll b (516 nm) and NIR plateau (850 nm) were the closest to T\({}_{\rm L,Rad}\) and in the same quadrant. Opposite trends to T\({}_{\rm L,Rad}\) were shown by red-edge (780-783 nm) and NIR canopy moisture (831 nm) bands. Similarly occurred with the green band at 540 nm for soybean g\({}_{\rm s}\) and T\({}_{\rm r}\).