Effects of Chlorophyll Concentration on Green LAI prediction in Crop Canopies: Modelling and Assessment

In Proceedings of the First International Sysmposium on Recent Advances in Quantitative Remote Sensing, Valencia, Spain, 16-20 September, 2002

A growing number of studies have focused on evaluating vegetation indices in terms of their sensitivity to vegetation biophysical parameters as well as to external factors affecting canopy reflectance. In this context, leaf and canopy radiative transfer models have provided a basis for understanding the behaviour of such indices, particularly their resistance to external perturbing effects related to soil background, illumination, and atmospheric conditions. But, so far no studies have thoroughly assessed the impact of leaf chlorophyll concentration changes on the ability of spectral indices to predict green leaf area index (LAI). Because the variables LAI and chlorophyll content have similar effects on canopy reflectance in the visible and red edge portions of the solar spectrum, there is a need to uncouple these effects in order to accurately assess each of these variables. In the present work we used PROSPECT and SAILH models to simulate a wide range of crop canopy reflectances which were used to study the sensitivity of a set of vegetation indices to LAI variability. The aim of the paper was to present a method for minimizing the effect of leaf chlorophyll content on the prediction of vegetation green LAI, and to propose an index that adequately predicts the LAI of crop canopies. Accordingly, we have developed new algorithms that proved to be the best predictor of green LAI with respect to potentially confounding leaf chlorophyll concentration effects. The technique has been validated using CASI hyperspectral reflectance images acquired on different dates (1999, 2000, 2001), over fields with various crops (corn, wheat, and soybean) at different growth stages, containing plots with various fertilization treatments. Maps of predicted LAI were generated and corresponding statistics were compared to ground truth data. Evaluation of predictions revealed good agreement with field measurements

The authors are very grateful for the financial support provided by GEOmatics for Informed Decisions (GEOIDE), the Canadian Space Agency (CSA) and Agriculture and Agri-Food Canada.c

Peer reviewed