Estimation of fAPAR, LAI, and vegetation fractional cover from ATSR-2 imagery

Abstract

We examine methodologies for estimation of vegetation cover, leaf area index (LAI), and fraction of absorbed photosynthetically active radiation ( f APAR), considering the spectral sampling and dual-view capability of the ATSR-2 sensor. A set of simulated ATSR-2 reflectance measurements and corresponding vegetation parameters is defined using a Monte Carlo ray-tracing model. The case of semiarid vegetation is considered allowing for varying fractional cover, structure, and presence of standing litter. The error in estimation of vegetation properties using vegetation indices, linear spectral unmixing, and model inversion is compared over this dataset, quantified by a measure of signal to noise (S/N). For the estimation of f APAR, the NDVI gave best S/N among vegetation indices (S/N 4.5). Linear mixture modelling based on library spectra showed considerable improvement over vegetation indices for estimation of total vegetation cover. LAI is not retrieved with much accuracy by any method in the presence of standing litter and variable fractional cover. Model inversion has potential to be the most accurate method for retrieving all parameters, but only if the model approximates reality within 15%. Overall, the S/N in estimating parameters by any method is considerably lower than the S/N in instrument calibration (20/1). Use of the dual-view showed potential to improve estimates, but requires accurate registration.