Hyperspectral and Thermal Sensing of Stomatal Conductance, Transpiration, and Photosynthesis for Soybean and Maize under Drought

Remote Sensing

Andreas Baum¹  Lorant Gulyas¹  Verónica Sobejano-Paz²  ChristianJosef Köppl²  TeisNørgaard Mikkelsen²  Mónica García²  MarkS. Johnson³  Xingguo Mo⁴  Suxia Liu⁴ 

[1] Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark;Department of Environmental Engineering, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark;Institute for Resources, Environment and Sustainability, The University of British Columbia, Vancouver, BC v6t 1z4, Canada;Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;

关键词: crop phenotyping;    hydraulic traits;    leaf conductance;    phenology;    photosynthetic CO₂ assimilation rate;    PLSR;   

DOI  :  10.3390/rs12193182

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown