Utility of an image-based canopy reflectance modeling tool for remote estimation of LAI and leaf chlorophyll content at regional scales

Abstract

This paper describes a novel physically-based approach for estimating leaf area index (LAI) and leaf chlorophyll content (Cab) at regional scales that relies on radiance data acquirable from a suite of aircraft and operational satellite sensors. The REGularized canopy reFLECtance (REGFLEC) modeling tool integrates leaf optics (PROSPECT), canopy reflectance (ACRM), and atmospheric radiative transfer (6SV1) model components, facilitating the direct use of at-sensor radiances in green, red and near-infrared wavelengths. REGFLEC adopts a multi-step LUT-based inversion approach and incorporates image-based techniques to reduce the confounding effects of land cover specific vegetation parameters and soil reflectance. REGFLEC was applied to agricultural and natural vegetation areas using 10 m and 20 m resolution SPOT imagery, and variable environmental and plant development conditions allowed for model validation over a wide range in LAI (0 – 6) and Cab (20 – 75 μg cm-2). Validation against in-situ measurements yielded relative root-mean-square deviations on the order of 13% (0.4) for LAI and between 11 – 19% (4.9 – 9.1 μg cm-2) for Cab. REGFLEC demonstrated good utility in detecting spatial and temporal variations in LAI and Cab without requiring site-specific data for calibration. The physical approach presented here can quite easily be applied to other regions and has the potential of being more universally applicable than traditional empirical approaches for retrieving LAI and Cab