Assessment of a global leaf area index product from SPOT-4 VEGETATION data over selected sites in Canada

Leaf area index (LAI) is a fundamental land surface parameter for various earth science applications. A new set of recently developed LAI algorithms has been employed for producing a global LAI dataset at 1 km resolution and in time steps of 10 days, using the Satellite pour l'observation de la terre (SPOT) VEGETATION sensor data. This paper presents the results of a regional validation of the new product at seven reference sites in Canada. Differences in land cover classifications and quality of the input sensor data were identified as the largest sources of scene-wide bias errors; the aggregation of the images from 1 km to 4 km resolution led to a reduction of errors in the order of 12%. Systematic errors were observed over mountainous areas where terrain shading, clumping, and bidirectional reflectance distribution function (BRDF) normalization are problematic. Overall, the new global LAI dataset was found to be reasonably accurate and proved to have the potential to become a sound alternative to the global moderate-resolution imaging spectroradiometer (MODIS) LAI product.