## 4 Discussion
### Physiological, Biochemical, Morphological, and Remote Sensing Responses under Water Stress
In maize, all the leaf physiological variables (g\({}_{\mathrm{s}}\), T\({}_{\mathrm{r}}\), and A) (Figure 3) as well as VPD\({}_{\mathrm{leaf}}\) and intercellular CO\({}_{2}\) concentration (C\({}_{\mathrm{i}}\)) (Figure S1) were very similar among WHC groups. Nonetheless, such differences in T\({}_{\mathrm{r}}\) and A were enough to result in a significantly lower water use efficiency (WUE \(\approx\) 10%) with 60% reduced soil water availability (Figure S1). Therefore, the detected 23% drop of ET under soil water stress (Figure 3) must be mostly driven by a reduction in LAI, as morphological adjustments are an effective mechanism to cope with water stress in maize plants [8]. Our results support this hypothesis, where measurements of LAI performed at the end of the experiment were significantly lower with water stress for maize but not for soybean (Table 3 and Figure S5). Moreover, structural related VIs such as NDVI, rNDVI, OSAVI, EVI, and REIP showed significant effects between soil water levels (Table 4) and significantly positive relations between these VIs with\(\mathrm{g_{s}}\) and \(\mathrm{T_{r}}\) (Figure 6 and Figure S4). In recent studies, structural VIs have also successfully been used to predicted water stress proxies (e.g., \(\mathrm{g_{s}}\), leaf water content, and leaf water potential) in maize plants [91; 92]. In our study, the effect of growth through these VIs is shown by the high sensitivity of phenology (DoM), lack of significance with soil drought (WHC) (Table 4), and the strong positive correlations with \(\mathrm{h_{c}}\) (\(\mathrm{R^{2}}>0.86\)) (Figure 6 and Figure S4). In addition, maize reflectance presented higher reflectance over time than soybean (Figure 4C,D), which indeed could imply more variability due to growth, as changes in LAI can be detected in the NIR region close to the red-edge [56; 75]. In contrast to soybean, maize \(\mathrm{T_{L},Rad}\) was not sensitive to drought (Table 4) and \(\Delta\mathrm{T}\) did not vary with phenology. In addition, \(\Delta\mathrm{T}\) was the only remote sensing variable not related to any maize parameter (\(\mathrm{g_{s}}\), \(\mathrm{T_{r}}\), \(\mathrm{A}\), \(\mathrm{h_{c}}\), and chl) (Figure 6). Unlike our results, others studies of greenhouse maize plants have been successful in detecting drought through thermal imaging, likely due to higher air temperature (27-28 \({}^{\circ}\)C) increasing soil water stress [50; 93]. Despite of such a large mean reduction in soybean \(\mathrm{g_{s}}\) from wet (100%) to dry (40%) WHC, soybean plants significantly increased about 15% their WUE (Figure S1) as they only experienced a small decrease in \(\mathrm{A}\) (\(\approx\)12%) relative to the reduction in transpiration at canopy and leaf levels (Figure 2). These could be explained by a 16% mean increase in top leaves chl concentration, measured with SPAD (Figure S1). [PERSON] et al. (2016) [19] also found increased chlorophyll content as well as decreased \(\mathrm{g_{s}}\), \(\mathrm{T_{r}}\), and \(\mathrm{A}\) in greenhouse soybean plants with soil water reductions of about 40%. However, they reported declines in WUE of about 45%. [PERSON] et al. (2019) [17] have interpreted WUE increases as an adaptive mechanism to drought due to transpiration decline under soil moisture stress in soybean plants. However, in their experiment, they observed a 24% decrease on chlorophyll content of soybean plants with 38% increase in carotenoids. Explanations to differences among studies might be associated to experimental design. For instance, plants in [PERSON] et al. (2019) [17] were exposed to higher radiation and temperatures (29 \({}^{\circ}\)C) than in our experiment, with a longer cycle, which would increase soil water stress. Cultivar types also present different morphological and physiological adjustments to drought [7; 86].