During water stress, crops undertake adjustments in functional, structural, and biochemical traits. Hyperspectral data and machine learning techniques (PLS-R) can be used to assess water stress responses in plant physiology. In this study, we investigated the potential of hyperspectral optical (VNIR) measurements supplemented with thermal remote sensing and canopy height (h_c) to detect changes in leaf physiology of soybean (C₃) and maize (C₄) plants under three levels of soil moisture in controlled environmental conditions. We measured canopy evapotranspiration (ET), leaf transpiration (T_r), leaf stomatal conductance (g_s), leaf photosynthesis (A), leaf chlorophyll content and morphological properties (h_c and LAI), as well as vegetation cover reflectance and radiometric temperature (T_L,Rad). Our results showed that water stress caused significant ET decreases in both crops. This reduction was linked to tighter stomatal control for soybean plants, whereas LAI changes were the primary control on maize ET. Spectral vegetation indices (VIs) and T_L,Rad were able to track these different responses to drought, but only after controlling for confounding changes in phenology. PLS-R modeling of g_s, T_r, and A using hyperspectral data was more accurate when pooling data from both crops together rather than individually. Nonetheless, separated PLS-R crop models are useful to identify the most relevant variables in each crop such as T_L,Rad for soybean and h_c for maize under 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 wavelength combinations based on crop type. Our results are only a first but a promising step towards larger scale remote sensing applications (e.g., airborne and satellite). PLS-R estimates of leaf physiology could help to parameterize canopy level GPP or ET models and to identify different photosynthetic paths or the degree of stomatal closure in response to drought.

Using satellite observations of Normalized Difference Vegetation Index (NDVI) from NOAA-AVHRR and Terra-MODIS, together with climatic data in a physical evapotranspiration (ET) model, the spatio-temporal variability of ET is investigated in terrestrial China from 1981 to 2010. The model predictions of actual ET (ET_a) are validated with ET values from in situ eddy covariance flux measurements and from basin water balance calculations. The national averaged crop reference ET (ET_p) and ET_a values are 916 ± 21 and 415 ± 12 mm year^-1, respectively. The annual ET_a pattern is closely associated with vegetation conditions in the eastern part of China, whereas ET_a is low in the sparsely-vegetated areas and deserts in the northwestern region, corresponding to scarce rainfall events and amounts. The trends of ET_p and ET_a are remarkably different over the country, and the complementary relationship between ET_p and ET_a is revealed for the study period. Averaged over the whole country, ET_a showed an increasing trend from the 1980s to the mid-1990s, followed by a decreasing trend, consistent with the precipitation anomaly. Across the main vegetation types, annual ET_a amounts are found to correspond clearly with the bands of precipitation and ET_p.