This invention discloses a method for inverting equivalent water thickness in
plant leaves across species. This method selects equivalent water thickness with more explicit physical meaning as a representative indicator of
plant leaf water content. Based on traditional
vegetation indices, it combines continuous removal of preprocessing parameters to construct absorption feature parameters, thereby reducing
noise in hyperspectral data while deeply mining its inherent spectral characteristics. It innovatively introduces one-heat coding technology to explicitly process species information and employs a Bayesian optimized
machine learning model, effectively improving the accuracy, stability, and computational efficiency of cross-species water inversion. This invention can accurately invert the water status of crops across species, providing a new method for rapidly acquiring
crop water status based on
hyperspectral reflectance characteristics. It can be widely applied in
intercropping and
relay cropping patterns and large-scale survey and analysis scenarios, greatly improving the accuracy and speed of
water stress diagnosis.