## 4 Discussion
### Physiological, Biochemical, Morphological, and Remote Sensing Responses under Water Stress
Moreover, VIs used to detect changes in chlorophyll concentration (TCARI, TCARI/OSAVI, and \(\mathrm{R_{700}}\)/670) can track responses to soil drought across different dates, especially for \(\mathrm{R_{700}}\)/670 (Table 4, Figure 6 and Figure S4). Unlike soybean, maize leaf chl did not change across soil water levels (Table 3), reflected also in the lack of significance in TCARI and TCARI/OSAVI indices. However, this was not the case for \(\mathrm{R_{700}}\)/670 (\(p\)-value \(<0.01\)) (Table 4), likely because this VI does not minimize LAI variations as TCARI and TCARI/OSAVI [39]. Differences on chl response between crops can be related to decreased soybean reflectance in the VIS with soil drought (up to around 640 nm), whereas maize reflectance increased (Figure 4A,B). Similarly, [PERSON] et al. (2013) [94] found greater maize reflectance under soil water stress in this region. VIs linked to photosynthetic efficiency like PRI are often used to detect soil moisture changes [37; 41; 43; 45; 76]. However, PRI is also quite sensitive to structural changes, pigment levels, soil background, illumination effects, and viewing angles [42; 43]. As the experiment was conducted in a growth chamber with high-pressure mercury and halogen lamps (peak at 538 nm), some of the PRI results might not be like in field conditions (Figure 3). However, variations of PRI calculated with other bands as sPRI (510 nm and 560 nm) and PRI\(\mathrm{g_{570}}\)-515 were significantly reduced presenting less photosynthetic activity with drought (Table 4), especially for soybean plants (C\({}_{3}\)) as they are less efficient than C\({}_{4}\) species (maize) [21; 22]. Note that soybean sPRI as well as \(\mathrm{R_{700}}\)/670 were not influenced by changes across time (Table 4), making them attractive to detect drought in soybean plants, likely subtracting growing effects. Unlike soybean, not a single maize VI was only significant across water groups (Table 4). One area of interest is the possibility to use remote sensing to detect isohydric and anisohydric behavior, which is concept related to leaf water potential (\(\mathrm{\Psi_{L}}\)) [95]. Leaf water content can be retrieved from optical (between 800 nm and 2500 nm) and microwave radiation [10]. In our experiment, because our hyperspectral camera only covers the VNIR region (400-900 nm), we did not measure \(\mathrm{\Psi_{L}}\). However, if reductions in \(\mathrm{g_{s}}\) are linked to reductions in \(\mathrm{\Psi_{L}}\) as shown by [PERSON] et al. (2019) [17], it could be inferred that soybean plants behaved more as isohydric crop compared to our type of maize plants. This could also be associated with the type of photosynthetic pathway as C\({}_{3}\) species such as soybean tend to exhibit more considerable reductions in g\({}_{\rm S}\) than their C\({}_{4}\) relatives under drought [23]. Nevertheless, according to [PERSON] et al. (2013) [48], g\({}_{\rm S}\) is better indicator of soil water stress in isohydric plants, enabling thermal imaging to better detect drought, as we observed in our results (Figure 6). In addition, [PERSON] et al. (2017) [25] also identified a maize cultivar as anisohydric. Still, the concept of iso/anisohydric appears more complex than can be represented as a binary factor, and could instead occur as a response to environmental thresholds or occur along a continuum [95; 96]. Therefore, discrepancies in this classification are found for soybean [48; 97] and maize crops [16; 48; 97; 98].