Effect of Canopy Geometry on Estimation of Leaf Area Index in Winter Wheat Using Multi-angle Spectrum

This study presents a method for quantitatively estimating leaf area index (LAI) in winter wheat by exploring bi-directional reflectance distribution function (BRDF) data. In BRDF data, near-infrared reflectance (NIR) which is sensitive to crown component, canopy cover and crown shape, is affected by illuminated crown component, while red reflectance is sensitive to canopy gaps and controlled by illuminated ground component. Considering the effect of NIR/red ratio on the reflection of canopy and ground parameters, two new spectral vegetation indices, normalized difference ratio index (NDRI) and enhanced ratio vegetation index (ERVI), have been improved from normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). The efficacy of two new indices in estimation of LAI has been validated using the data sets from multi-angular observations. The results showed that: (a) the LAI estimation models by NDVI or EVI should be established separately for winter wheat with different canopy geometric structures; (b) the NDRI and ERVI at view zenith angle (VZA) of 40° had the highest accuracy for estimating LAI in winter wheat with different crop geometric characteristics, comparison with other commonly used spectral vegetation indices (e.g. NDVI) or the values from other view angles; (c) the NIR/red ratio at VZA of 40°can represent canopy cover and crown shape in the canopy geometry. This study provides a novel method to estimate LAI for a variety of crops with different canopy geometric features using BRDF data in large scale, which can provide reference for developing multi-angle airborne or space-borne sensors in the future. © 2013 Friends Science Publishers