In- land surface models, which are used to evaluate the role of vegetation in the context of global climate change and variability, LAI and FAPAR play a key role, specifically with respect to the carbon and water cycles. The AVHRR-based LAI/FAPAR dataset offers daily temporal resolution, an improvement over previous products. This climate data record is based on a carefully calibrated and corrected land surface reflectance dataset to provide a high-quality, consistent time-series suitable for climate studies. It spans from mid-1981 to the present. Further, this operational dataset is available in near real-time allowing use for monitoring purposes. The algorithm relies on artificial neural networks calibrated using the MODIS LAI/FAPAR dataset. Evaluation based on cross-comparison with MODIS products and in situ data show the dataset is consistent and reliable with overall uncertainties of 1.03 and 0.15 for LAI and FAPAR, respectively. However, a clear saturation effect is observed in the broadleaf forest biomes with high LAI (>4.5) and FAPAR (>0.8) values.

This paper presents the SPOT-4 (Take 5) experiment, aimed at providing time series of optical images simulating the repetitivity, the resolution and the large swath of Sentinel-2 images. The aim was to help users set up and test their applications and methods, before Sentinel-2 mission data become available. In 2016, when both Sentinel-2 satellites are operational, and for at least fifteen years, users will have access to high resolution time series of images systematically acquired every five days, over the whole Earth land surfaces. Thanks to Sentinel-2’s high revisit frequency, a given surface should be observed without clouds at least once a month, except in the most cloudy periods and regions. In 2013, the Centre National d’Etudes Spatiales (CNES) lowered the orbit altitude of SPOT-4, to place it on a five-day repeat cycle orbit for a duration of five months. This experiment started on 31 January 2013 and lasted until 19 June 2013. SPOT-4 images were acquired every fifth day, over 45 sites scattered in nearly all continents and covering very diverse biomes for various applications. Two ortho-rectified products were delivered for each acquired image that was not fully cloudy, expressed either as top of atmosphere reflectance (Level 1C) or as surface reflectance (Level 2A). An extensive validation campaign was held to check the performances of these products with regard to the multi-temporal registration, the quality of cloud masks, the accuracy of aerosol optical thickness estimates and the quality of surface reflectances. Despite high a priori geo-location errors, it was possible to register the images with an accuracy better than 0.5 pixels in the large majority of cases. Despite the lack of a blue band on the SPOT-4 satellite, the cloud and shadow detection yielded good results, while the aerosol optical thickness was measured with a root mean square error better than 0.06. The surface reflectances after atmospheric correction were compared with in situ data and other satellite data showing little bias and the standard deviation of surface reflectance errors in the range (0.01–0.02). The Take 5 experiment is being repeated in 2015 with the SPOT-5 satellite with an enhanced resolution.