Leaf Chlorophyll a+b and canopy LAI estimation in crops using R-T models and Hyperspectral Reflectance Imagery

In Proceedings of the VII Congress of the European Society for Agronomy, Cordoba, Spain, 15-18th July, 2002.Recent studies have demonstrated the usefulness of optical indices from hyperspectral remote sensing reflectance in the assessment of vegetation biophysical variables both in forestry (Zarco- Tejada et al., 2001) and agriculture (Haboudane et al., 2002). Those indices are, however, the combined response to variations of several vegetation and environmental properties, such as leaf area index (LAI), leaf angle distribution function (LADF), leaf chlorophyll a+b content (Cab), canopy shadows, background reflectance, and illumination-observational conditions. Of particular significance to precision agriculture is Cab, which is related to the nitrogen concentration and serves as a measure of crop response to nitrogen application. We present a modeling approach to predict Cab from hyperspectral remote sensing while minimizing soil reflectance (ρs), shadow effects, and considering LAI variations. This method was developed using simulated data, followed by assessment using hyperspectral airborne imagery. Simulations consisted of leaf and canopy reflectance modeling with PROSPECT and SAILH radiative transfer (RT) models and developing optical indices that minimize bi-directional and soil background effects.Peer reviewe