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The OLIVE (On Line Interactive Validation Exercise) platform is dedicated to the validation of global biophysical products such as LAI (Leaf Area Index) and FAPAR (Fraction of Absorbed Photosynthetically Active Radiation). It was developed under the framework of the CEOS (Committee on Earth Observation Satellites) Land Product Validation (LPV) sub-group. OLIVE has three main objectives: (i) to provide a consistent and centralized information on the definition of the biophysical variables, as well as a description of the main available products and their performances (ii) to provide transparency and traceability by an online validation procedure compliant with the CEOS LPV and QA4EO (Quality Assurance for Earth Observation) recommendations (iii) and finally, to provide a tool to benchmark new products, update product validation results and host new ground measurement sites for accuracy assessment. The functionalities and algorithms of OLIVE are described to provide full transparency of its procedures to the community. The validation process and typical results are illustrated for three FAPAR products: GEOV1 (VEGETATION sensor), MGVIo (MERIS sensor) and MODIS collection 5 FPAR. OLIVE is available on the European Space Agency CAL/VAL portal), including full documentation, validation exercise results, and product extracts. [ABSTRACT FROM AUTHOR]

Abstract: Leaf area index (LAI) is a key variable for the understanding of several eco-physiological processes within a vegetation canopy. The LAI could thus provide vital information for the management of the environment and agricultural practices when estimated continuously over time and space thanks to remote sensing sensors. This study proposed a method to estimate LAI spatial and temporal variation based on multi-temporal remote sensing observations processed using a simple semi-mechanistic canopy structure dynamic model (CSDM) coupled with a radiative transfer model (RTM). The CSDM described the temporal evolution of the LAI as function of the accumulated daily air temperature as measured from classical ground meteorological stations. The retrieval performances were evaluated for two different data sets: first, a data set simulated by the RTM but taking into account realistic measurement conditions and uncertainties resulting from different error sources; second, an experimental data set acquired over maize crops the Blue Earth City area (USA) in 1998. Results showed that the proposed approach improved significantly the retrieval performances for LAI mainly by smoothing the residual errors associated to each individual observation. In addition it provides a way to describe in a continuous manner the LAI time course from a limited number of observations during the growth cycle. [Copyright &y& Elsevier]