Early Detection of Plant Physiological Responses to Different Levels of Water Stress Using Reflectance Spectroscopy

Early detection of water stress is critical for precision farming for improving crop productivity and fruit quality. To investigate varying rootstock and irrigation interactions in an open agricultural ecosystem, different irrigation treatments were implemented in a vineyard experimental site either: (i) nonirrigated (NIR); (ii) with full replacement of evapotranspiration (FIR); or (iii) intermediate irrigation (INT, 50% replacement of evapotranspiration). In the summers 2014 and 2015, we collected leaf reflectance factor spectra of the vineyard using field spectroscopy along with grapevine physiological parameters. To comprehensively analyze the field-collected hyperspectral data, various band combinations were used to calculate the normalized difference spectral index (NDSI) along with 26 various indices from the literature. Then, the relationship between the indices and plant physiological parameters were examined and the strongest relationships were determined. We found that newly-identified NDSIs always performed better than the indices from the literature, and stomatal conductance (G(s)) was the plant physiological parameter that showed the highest correlation with NDSI(R-603, R-558) calculated using leaf reflectance factor spectra (R-2 = 0.720). Additionally, the best NDSI(R-685, R-415) for non-photochemical quenching (NPQ) was determined (R-2 = 0.681). Gs resulted in being a proxy of water stress. Therefore, the partial least squares regression (PLSR) method was utilized to develop a predictive model for G(s). Our results showed that the PLSR model was inferior to the NDSI in Gs estimation (R-2 = 0.680). The variable importance in the projection (VIP) was then employed to investigate the most important wavelengths that were most effective in determining G(s). The VIP analysis confirmed that the yellow band improves the prediction ability of hyperspectral reflectance factor data in G(s) estimation. The findings of this study demonstrate the potential of hyperspectral spectroscopy data in motoring plant stress response.