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A clear understanding of plant transpiration is a crucial step for water cycle and climate modeling, especially for arid ecosystems in which water is one of the major constraints. Traditional field measurements of leaf scale transpiration are always time-consuming and often unfeasible in the context of large spatial and temporal scales. This study focused on a dominant native plant in the arid land of central Asia, Haloxylon ammondendron, with the aim of deriving the leaf-scale transpiration through hyperspectral reflectance using Partial Least Squares Regression (PLSR) analysis. The results revealed that the PLSR model based on the first-order derivative spectra at wavelengths selected through stepwise regression analysis can closely trace leaf transpiration with a high accuracy (R2 = 0.78, RMSE = 1.62 µmol g-1 s-1). The accuracy is also relatively stable even at a spectral resolution of 10 nm, which is very close to the bandwidths of several running satellite-borne hyperspectral sensors such as Hyperion. The results also proved that the first-order derivative spectra within the shortwave infrared (SWIR) domain, especially at 2435, 2440, 2445, and 2470 nm, were critical for PLSR models to predict leaf transpiration. These findings highlight a promising strategy for developing remote sensing methods to potentially characterize transpiration at broad scales.

Leaf transpiration of drought tolerant plant can be captured by hyperspectral reflectance using PLSR analysis