This article describes the principles used to generate global gap-free Leaf Area Index (LAI) time series from 2002–2012, based on MERIS (MEdium Resolution Imaging Spectrometer) full-resolution Level1B data. It is produced as a series of 10-day composites in geographic projection at 300-m spatial resolution. The processing chain comprises geometric correction, radiometric correction, pixel identification, LAI calculation with the BEAM (Basic ERS & Envisat (A)ATSR and MERIS Toolbox) MERIS vegetation processor, re-projection to a global grid and temporal aggregation selecting the measurement closest to the mean value. After the LAI pre-processing, we applied time series analysis to fill data gaps and to filter outliers using the technique of harmonic analysis (HA) in combination with mean annual and multiannual phenological data. Data gaps are caused by clouds, sensor limitations due to the solar zenith angle (<10°), topography and intermittent data reception. We applied our technique for the whole period of observation (July 2002–March 2012). Validation, carried out with VALERI (Validation of Land European Remote Sensing Instruments) and BigFoot data, revealed a high degree (R² : 0.88) of agreement on a global scale.

The Advanced Very High Resolution Radiometer (AVHRR) compatible vegetation index derived from data of the MEdium Resolution Imaging Spectrometer (MERIS) is regarded as a continuity index for the well known Normalized Difference Vegetation Index (NDVI) derived from AVHRR with 300 meter resolution. For deriving the AVHRR compatible vegetation index MERIS full resolution (FR) top of atmosphere radiances (ESA L1b data) are used as input. The design of the “AVHRR compatible NDVI” is based on radiative transfer models for simulating the reflectance of the broad spectral channels of the NOAA – AVHRR and the narrow spectral bands of MERIS. Linear combinations of the MERIS reflectances in the red and near‐infrared describe the AVHRR reflectance in channel 1 and 2. The weighting factors for adjusting the reflectances of both sensors are determined from the results of the modelling approach taking into account atmospheric effects by using the Simplified Method for Atmospheric Correction (SMAC), by regarding canopy and soil reflectance and transmission using the Scattering of Arbitrarily Inclined Leaf (SAIL) model and by calculating leaf reflectance and transmittance using the Stochastic model for Leaf Optical Properties Extended for fluorescence (SLOPE). The first validation results show good agreement between the AVHRR NDVI and the “AVHRR compatible NDVI”. The mean difference between the two vegetation indices is of the order of 0.025 units. In 2005 the “AVHRR compatible NDVI” will be available for Europe as maximum value composite (daily, 10‐days and monthly) on a regular basis from the German Aerospace Centre (DLR).

Keywords: Burned Area, Climate Change, MERIS, ATSR, VEGETATION, European Space Agency The European Space Agency (ESA) Climate Change Initiative (CCI) is part of the European contribution to the Global Climate Observing System (GCOS) program. Fire disturbance is one of the Essential Climate Variables (ECV) included in the ESA CCI program. It focus on mapping burned area (BA) using European sensors (ATSR, VEGETATION and MERIS data), and in comparing the performance of the results with other existing datasets. The project aims at developing and validating algorithms to produce consistent, stable, error-characterized global BA information. The project includes as well developing algorithms to generate georeferenced and calibrated reflectances of (A)ATSR, VEGETATION and MERIS data, identifying potential sources of confusion with burned areas (clouds, smoke, cloud shadows, water, snow, topographic shadows). The final product will be a merging of BA information derived from three different sensors . The outputs will be adapted to the needs of the atmospheric and vegetation modelling communities.