Abstract
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 (hc) 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 (Tr), leaf stomatal conductance (gs), leaf photosynthesis (A), leaf chlorophyll content and morphological properties (hc and LAI), as well as vegetation cover reflectance and radiometric temperature (TL,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 TL,Rad were able to track these different responses to drought, but only after controlling for confounding changes in phenology. PLS-R modeling of gs, Tr, 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 TL,Rad for soybean and hc 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.
Highlights
Irrigation for agriculture represents the most significant demand for freshwater uptake of around 70% of all water withdrawal [1,2]
We showed that changes in the function of soybean and maize under water stress are expressed in various reflectance and emission traits, according to their different photosynthetic pathways (C3 and C4) and water use strategies
We demonstrated the joint power of optical hyperspectral (VNIR), thermal (TL,Rad), and canopy height, to predict three key physiological variables regulating water and carbon assimilation: leaf stomatal conductance, leaf transpiration (Tr), and leaf photosynthesis (A) during a vegetative growth season
Summary
Irrigation for agriculture represents the most significant demand for freshwater uptake of around 70% of all water withdrawal [1,2]. This demand is growing due to climate change. Increases in the intensity and frequency of droughts and heatwaves will affect soil water demand and supply of crops, hindering crop production [3,4,5]. Crops such as maize and soybean, sensitive to droughts, are already experiencing reductions in yields [6,7,8]. Further information of their responses to droughts at the farm level can improve the sustainability of water use
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