Estimation of leaf water content in cotton by means of hyperspectral indices

摘要

The knowledge of vegetation water conditions can contribute to drought assessment. Remote sensing has a proven ability to assess vegetation properties. In this study, all two-band combinations (350-2500 nm) in the ratio type of vegetation index (RVI) and the normalized difference type of vegetation index (NDVI) were performed on cotton leaf raw spectral reflectance (R) and the first derivative reflectance (DR). The correlation coefficient (r) between all two-band combinations and two leaf water parameters (EWT: equivalent water thickness, and FMC: fuel moisture content) were determined, and the results of this comprehensive analysis were presented by matrix plots. Band centers (lambda(1) and lambda(2)) and band widths (Delta lambda(1) and Delta lambda(2)) that combine to form the best indices were identified for EWT and FMC through matrix plots. Then the evaluation of the predictive power of three predictors, i.e. single narrow band reflectance, the widely used published water indices and the best band combination indices, were performed. The results shown that the new indices DR1647/DR1133 and DR1653/DR1687, proposed by two-band combinations, were considered as the optimal indices for EWT and FMC estimation, respectively. The models based on these two best combination indices could explain 58% and 67% variability in EWT and FMC, respectively. Besides, bands with center wavelengths in region from 950 nm to 1100 nm, and 1650 nm to 1750 nm were represented almost all selected bands. The study should further our understanding of the relationships between leaf water content and hyperspectral reflectance