Derivation of high-resolution leaf area index maps in support of validation activities: Application to the cropland Barrax site

The validation of coarse satellite-derived products from field measurements generally relies on up-scaling the field data to the corresponding satellite products. This question is commonly addressed through the generation of a reference high-resolution map of an area covering several moderate resolution pixels. This paper describes a method by which reference leaf area index (LAI) maps can be generated from ground-truth LAI measurements. The method is based on a multivariate ordinary least squares (OLS) algorithm which uses an iteratively re-weighted least squares (IRLS) algorithm. It provides an empirical relationship (i.e. a transfer function) between in situ measurements and concomitant radiance values from high-resolution HRVIR2/SPOT4 imagery. The band combination composed of green (G), red (R) and near infrared (NIR) proved to be appropriate for LAI prediction, with a cross-validation error lower than 0.7. The convex hull technique has been proposed to assess the extrapolation error of the up-scaling model. Although the model showed to be sensitive to pixels outside the convex hull, the error associated with extrapolated pixels was moderate (i.e. root mean square error (RMSE) approximate to 0.3). A close agreement was found between LAI maps derived from different field based datasets despite the notable differences in the sampling constraints imposed by the use of different instruments. It confirms the validity of a stratified random sampling strategy and suggests the suitability of using around 50 elementary sampling units (ESUs) to characterize the study area. A sensitivity analysis has confirmed that this method is suited to capture the variability across the site extent and, at the same time, minimize field efforts. Knowledge gained from this work provides guidance for an efficient sampling strategy of LAI in a cropland area.(c) 2008 Elsevier B.V. All rights reserved.