Improving the estimation of directional area scattering factor (DASF) from canopy reflectance: theoretical basis and validation

TL;DR

This paper introduces a new method for estimating the directional area scattering factor (DASF) from canopy reflectance data, significantly improving accuracy by accounting for biochemical variations and using additional spectral information.

Contribution

A novel approach to estimate DASF that incorporates biochemical variations and spectral data, reducing bias and increasing reliability over standard methods.

Findings

rRMSE reduced by 49% and 34% in simulations

68% reduction in mean absolute error for needle leaf canopies

20% reduction in mean absolute error for broadleaf canopies

Abstract

Directional area scattering factor (DASF) is a critical canopy structural parameter for vegetation monitoring. It provides an efficient tool for decoupling of canopy structure and leaf optics from canopy reflectance. 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. In silico analysis of the proposed approach suggests significant increase in accuracy over the standard technique by a relative root mean square error (rRMSE) of 49% and 34% for one- and three dimensional scenes, respectively. When compared with indoor multi-angular hyperspectral measurements reported in literature, the mean absolute error has reduced by 68% for needle leaf and 20% for broadleaf canopies. Thus, the proposed DASF estimation approach outperforms the current one and can be used more reliably in DASF-related applications, such as vegetation monitoring of functional traits, dynamics, and radiation budget.