## 4 Discussion
### Synergies of Optical, Thermal, and Canopy Height Observations for PLS-R Modeling  
In response to drought severity and duration, crops undertake different adjustments in plant physiology, morphology, and biochemistry. Some of those changes are noticeable from the drought onset (fast changes), while others are noticeable only after some time, also referred as slow changes [10]. For example, stomatal closure occurs practically instantaneous under water stress, while effects on chlorophyll content and structure tend to be visible after longer periods of time [10; 11; 13]. Our study showed that for soybean plants T\({}_{\rm L,Rad}\) was a faster indicator of physiological adjustments than VIS indices such as NDVI and PRI (Figure 6 and Figure S4) as shown also by [45; 100; 101]). Nonetheless, improved detection of water stress can be achieved by combining thermal and optical imaging [37; 46; 47; 102], as the energy balance is affected by drought. The temperature is the result of the balancing of several processes, including transpiration cooling and absorption of radiation. For example, maize plants were warmer than soybean plants (Figure 5), despite presenting higher T\({}_{\rm r}\) for any soil moisture condition (Figure 3). Figure 4A shows how maize wet plants reflected less energy than wet soybean in the VNIR wavelengths, indicating more energy absorption, translated in significantly higher thermal emission. From the point of view of plant physiology, although h\({}_{\rm c}\) is a parameter at the canopy level, it integrates leaf level processes in response to stress: e.g., cell enlargement has been shown very sensitive to water stress [11; 13], and h\({}_{\rm c}\) reduction could be associated with decrease on cell enlargement [7; 17]. For this reason, h\({}_{\rm c}\) was used as predictor for water stress. In addition, h\({}_{\rm c}\) is a growth indicator easily obtained from remote sensing [52], that provided valuable information to model g\({}_{\rm S}\), T\({}_{\rm r}\), and A (Figure 9). PLS-R is not only a tool to model crop physiology parameters, but the VIP scores can inform of the more relevant wavelengths and variables responding to soil drought. In fact, the highest VIP scores for g\({}_{\rm S}\) and T\({}_{\rm r}\) correspond to T\({}_{\rm L,Rad}\) in soybean and h\({}_{\rm c}\) in maize (Figure 9), reflecting the dominant effects of soil drought: stomatal closure on soybean and plant growth on maize plants. Moreover, model improvements were appreciated in soybean g\({}_{\rm S}\) model when including T\({}_{\rm L,Rad}\) predictor, and in maize T\({}_{\rm r}\) model when adding h\({}_{\rm c}\) (Table 5). A curious fact when looking at hyperspectral band contribution was that soybean crops shared importance on similar wavelengths among A, g\({}_{\rm S}\), and T\({}_{\rm r}\), whereas maize response varied between traits with low VIP signal (Figure 8). From this, we can speculate that soybean processes are more interdependent than maize mechanisms. Because part of the information obtained from the new variables (T\({}_{\rm L,Rad}\) and h\({}_{\rm c}\)) could be implicit in some of the 138 bands of the hyperspectral sensor, we looked into the relation between LV1 and LV2 for all predictors (Figure 10). Overall, we found that most of the bands showing high VIP scores (Figure 9) were correlated with T\({}_{\rm L,Rad}\) and h\({}_{\rm c}\) (Figure 10). These correlations could explain why model performance did not improve remarkably by adding the two new predictors (Table 5). For soybean g\({}_{\rm S}\), T\({}_{\rm r}\), and A peaks with VIP scores above one were found in regions that correspond to absorption of chlorophyll b (516 nm), chlorophyll a (691 nm), red edge (780-783 nm), and the NIR plateau (831 and 850 nm) (Figure 9). Most of these bands clearly overlap the ones used in the VIs studied (Table 2), but they are not exactly centered at the same wavelength, highlighting the importance of using hyperspectral remote sensing with contiguous narrowband.