Drivers of deciduous forest near-infrared reflectance: A 3D radiative transfer modeling exercise based on ground lidar

Abstract

The fraction of near infrared (NIR) solar energy reflected by forest canopies has been shown to correlate with leaf nitrogen availability and photosynthesis. But the canopy properties behind this relation are unclear, partly because whole canopy reflectance and albedo results from multiple complex interactions between photons and tree parts, and also because modeling these interactions has been challenging due to difficulties in representing forest canopies with sufficient detail. Ground lidar technology has been applied to this problem in recent years; it is used here to provide highly detailed forest canopy representations as inputs into a ray tracing radiative transfer model. These structural representations are coupled with the best available field data on leaf optical properties to produce simulated images of canopy reflectance in the NIR, as well as the detailed 3D mapping of NIR energy absorption within the canopy. The simulations performed for two deciduous forests in the northeastern USA were first compared to airborne hyperspectral observations to assess their accuracy. The radiative transfer model was then used to quantify the sensitivity of whole canopy reflectance and vertical absorption of NIR to changes in model parametrization in an attempt to identify the main drivers of canopy reflectance. The results point to the arrangement of leaves in the topmost canopy layers, both in terms of leaf angles and leaf clumping within shoots, and to the leaf-level absorptance in all canopy layers as the dominant factors affecting canopy reflectance and albedo. These findings emphasize the need for improving our understanding of leaf arrangement in canopy tops – including what it covaries with – and for including measurements of leaf transmission when measuring leaf spectral properties in the field.