## 1 Introduction
VIs can be associated to plant structural properties like LAI (e.g., normalized difference vegetation index (NDVI) [38]) or pigment changes such as chlorophyll (e.g., transformed chlorophyll absorption in reflectance index (TCARI) [39]) and carotenoids (e.g., pigment specific normalized difference index (PSNDc) [40]). The photochemical reflectance index (PRI) is a more direct estimate of plant physiology, tracking photosynthetic efficiency [41], but it is also affected by changes in LAI or viewing geometry that might obscure the effects of drought [42; 43]. Because leaf temperatureusually increases rapidly under drought due to less transpiration cooling, thermal imaging has been applied to monitor drought responses [37; 44; 45; 46; 47]. Nonetheless, canopy temperature is also influenced by radiation budgets, atmospheric conditions, and leaf and canopy traits [13; 48], which makes it difficult to isolate the effect of soil water stress. Growth chambers provide the possibility to set and control some of these factors such as air temperature, humidity, radiation, wind speed, and soil moisture [49]. For example, thermal variation was observed between water stress and well-watered maize plants at the seedling stage in a growth chamber [50], showing canopy temperature as a suitable and leading indicators to detect water stress. Combination of different spectral regions provides spatial and temporal data on crop water use [10], which is essential to simulate crop productivity [46; 51]. Synergies between different remote sensing domains (e.g., visible (VIS), near-infrared (NIR), fluorescence, thermal, and microwave) have been proven to better model and estimate water use efficiency (WUE), evapotranspiration (ET), and gross primary productivity (GPP) [52]; detect water stress patterns [37; 45; 46; 47]; and monitor crop yield [53]. Hyperspectral imaging sensors with contiguous narrow bands along the visible and near infrared (VNIR) domain allow to apply spectroscopy and chemometrics techniques at high spatial resolution, improving the accuracy and types of physiological variables retrieved [15; 18; 20; 54]. In addition, it explores new relations between spectral variables and plant function [34]. Machine learning methods such as the partial least squares regression (PLS-R) can handle multiple and correlated spectral bands, being more suitable for large datasets outperforming physiological estimates from spectral indices [15; 20; 54; 55]. Furthermore, from PLS-R models, we can identify the most relevant wavelengths contributing to the model, which might not be necessarily those from commonly used VIs [15]. PLS-R has been successfully used to predict physiological properties such as A [56; 57], \(\mathrm{g_{s}}\)[15; 57], \(\mathrm{T_{r}}\)[57], maximum Rubisco activity (\(\mathrm{V_{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{ \mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{    \mathrm{    }}}}}}}}}}}}}}}\)) [58], and \(\mathrm{V_{L}}\)[15]. PLS-R has been also applied to model biochemical parameters such as chlorophyll content [56; 61], carotenoids [56], and nitrogen concentration [56; 61; 62], and morphological parameters (e.g., leaf mass per unit area [56; 61; 62] and crop yield [53]). These studies have been conducted across species (e.g., wheat, vineyards, soybean, maize, pasture, rice, and boreal trees) over Mediterranean to tropical climates and in greenhouse or external field conditions. Although using remote sensing to assess crop responses to drought is a very active topic of research, most studies to date have focused on estimating drought responses looking at biochemical and structural parameters. In this study, we aim to assess physiological responses to soil drought for two crops with different photosynthetic pathways and water-use strategies, using hyperspectral optical and thermal remote sensing under controlled conditions while separating phenology effects during a vegetative period.