## 5 Conclusions  
In this study, we showed that changes in the function of soybean and maize under water stress are expressed in various reflectance and emission traits, according to their different photosynthetic pathways (C\({}_{3}\) and C\({}_{4}\)) and water use strategies. In a controlled environment, we demonstrated the joint power of optical hyperspectral (VNIR), thermal (T\({}_{\text{L,Rad}}\)), and canopy height (h\({}_{\text{c}}\)), to predict three key physiological variables regulating water and carbon assimilation: leaf stomatal conductance (g\({}_{\text{s}}\)), leaf transpiration (T\({}_{\text{r}}\)), and leaf photosynthesis (A) during a vegetative growth season. Under a 60% reduction in soil water content, canopy transpiration of both soybean and maize decreased significantly (35% and 23%, respectively). In soybean (C\({}_{3}\)), the reduction of canopy transpiration was achieved by a tighter leaf stomatal control, suggesting a more isohydric behavior, while for maize (C\({}_{4}\)), gas exchange parameters were not significantly affected, which could indicate a more anisohydric tendency. For maize, the reduction of canopy transpiration was caused by structural changes, with leaf area index (LAI) and h\({}_{\text{c}}\) significantly reduced by the end of the season. These very different responses were readily picked by some narrowband vegetation indices (VIs) and the leaf-air temperature difference (\(\Delta\)T), but only after drought responses were separated from variations in phenology. Although well-known VIs are useful to point out some of the plants potential mechanisms to cope with water stress, almost none of the studied VIs just responded to water stress. In addition, the VI variance explained individually in relation to water stress was not enough to estimate leaf gas exchange processes. Machine learning methods such PLS-R enable identification and exploitation of other wavelengths and variables without any prior assumption about the exact mechanisms involved in order to predict the studied physiological parameters. However, PLS-R requires a large number of samples and range of variation, which could explain why a joint crop model (R\({}^{2}\)val \(\approx\) 0.77-0.92, NRMSEval \(<\) 13%) performed better than individual crop models (R\({}^{2}\)val \(\approx\) 0.54-0.81, NRMSEval \(<\) 18%), even though maize and soybean presented quite distinct functioning for the sole purpose of predicting leaf gas exchange. Nonetheless, separated PLS-R crop models were useful to identify the most relevant variables in each crop, such as T\({}_{\text{L,Rad}}\) for soybean and h\({}_{\text{c}}\) for maize according to our experimental conditions. Interestingly, the most important spectral bands sensitive to drought, derived from PLS-R analysis, were not exactly centered at the same wavelengths of the studied VIs sensitive to drought, highlighting the benefit of having contiguous narrow spectral bands to predict leaf physiology and suggesting different wavelengths combinations based on crop type. Additionally, PLS-R VIP results showed that soybean relevant wavelengths were similar for A, g\({}_{\text{s}}\), and T\({}_{\text{r}}\), whereas maize response varied between physiological parameters with lower VIP scores, suggesting higher interdependence of processes in soybean than maize. The results from this study are only a first but a promising step towards larger scale applications, using airborne or satellite remote sensing. PLS-R estimates of leaf-level parameters such as g\({}_{\text{s}}\) could help to parameterize canopy level GPP and ET models, recognize different photosynthetic paths, or identify the degree of stomatal closure in response to drought, which could be linked to isohydric/anisohydric behavior. **Supplementary Materials:** The following are available online at [[http://www.mdpi.com/2072-4292/12/19/3182/s1](http://www.mdpi.com/2072-4292/12/19/3182/s1)]([http://www.mdpi.com/2072-4292/12/19/3182/s1](http://www.mdpi.com/2072-4292/12/19/3182/s1)), Figure S1.