Hyperspectral Response of Agronomic Variables of Wheat-Like Canopies to Background Optical Variability

Understanding how biophysical and biochemical variables contribute to the spectral characteristics of vegetation canopies is critical for their monitoring. Quantifying these contributions, however, remains difficult due to extraneous factors such as the spectral variability of canopy background materials including soil/crop-residue moisture, soil-type, and non-photosynthetic vegetation (NPV). This study focused on exploring the spectral response of two important agronomic variables (leaf chlorophyll content (Cab) and leaf area index (LAI)) under various canopy backgrounds through global sensitivity analysis algorithm for wheat-like canopy spectra simulated using the physically-based PROSAIL radiative transfer model. Our results reveal the following general findings: (1) the contribution of each agronomic variable to the simulated canopy spectral signature varies considerably with respect to the background optical properties; (2) the influence of the soil-type and NPV on the spectral response of canopy to Cab and LAI is more significant than that caused by soil/crop-residue moisture; (3) individual spectral bands that remain sensitive to Cab while being least affected by the impacts of variations in the NPV, soil-type and moisture are at 560 and 704 nm; (4) the near-infrared (NIR) spectral bands exhibit higher sensitivity to LAI and lower background effects only in the cases of soil/crop-residue moisture, but are relatively strongly affected by soil-type and NPV. Comparative analyses of the correlations of twelve widely used vegetation indices with Cab and LAI indicate good performance of LICI (LAI-insensitive chlorophyll index), Macc01 (Maccioni index), OSAVI (optimized soil adjusted vegetation index) and MCARI2 (modified chlorophyll absorption ratio index 2), respectively. Our results highlight that background reflectance variability introduces considerable differences in the spectral response of agronomic variables and therefore lead to inconsistencies in the VI- Cab /-LAI relationship. This naturally impedes their estimation accuracy from remote sensing measurements. Further studies should integrate these results of spectral responsivity to develop trait-specific hyperspectral inversion models.