Improving the estimation of canopy structure using spectral invariants: Theoretical basis and validation

Directional area scattering factor (DASF) is a critical canopy structural parameter for vegetation monitoring as it provides an efficient tool for detection of vegetation functional traits and their dynamics. Current standard approach to estimate DASF from canopy bidirectional reflectance factor (BRF) is based on the assumption that in the weakly absorbing 710 to 790 nm spectral interval, leaf scattering does not change much with the concentration of dry matter and thus its variation can be neglected. This results in biased estimates of DASF and consequently leads to uncertainty in DASF-related applications. This study proposes a new approach to account for variations in concentrations of this biochemical constituent, which additionally uses the canopy BRF at 2260 nm. Analysis of the proposed approach based on radiative transfer models suggests significant decrease in relative root mean square error (rRMSE) by nearly a half for one- and three dimensional dense vegetated scenes, and the improvement is insensitive to the reflectivity of background. Robust refinement is also achieved in a larger actual forest scene. When the new approach is adopted to indoor multi-angular hyperspectral measurements on individual tree crowns, the relative error has also reduced, with more improvement seen in needle-leaf than in broadleaf species. Thus, the proposed DASF estimation approach outperforms the current one and can be used more reliably in DASF-related applications, such as retrieval of vegetation biochemical contents, grasping periodic changes of equatorial forests, and other ecological and environmental vegetation monitoring tasks based on spectral invariants theory.

Automated summary

Directional area scattering factor (DASF) is a critical parameter for vegetation monitoring. The current method to estimate DASF has biases due to neglecting variations in biochemical constituents. This study proposes a new approach that accounts for variations in concentrations and improves DASF estimation.