This article describes the algorithmic principles used to generate LAI, fAPAR and fCover estimates from VEGETATION observations. These biophysical variables are produced globally at 10 days temporal sampling interval under lat-lon projection at 1/112°spatial resolution. After a brief description of the VEGETATION sensors, radiometric calibration process, based on vicarious desertic targets is first presented. The cloud screening algorithm was then fine tuned using a global network of cloudiness observations. Atmospheric correction is then achieved using the SMAC code with inputs coming from meteorological values of pressure, ozone and water vapour. Aerosol optical thickness is derived from MODIS climatology assuming continental aerosol type. The Roujean BRDF model is then adjusted for red, near infrared and short wave infrared bands used to the remaining cloud free observations collected over a time window of ± 15 days. Outliers due to possible cloud contamination or residual atmospheric correction are iteratively eliminated and prior information is used to get more robust estimates of the three BRDF kernel coefficients. Nadir viewing top of canopy reflectance in the three bands is input to the biophysical algorithm to compute the products at 10 days sampling interval. This algorithm is based on training neural networks over SAIL + PROPSPECT radiative transfer model simulations for each biophysical variable. Details on the way the training data base was generated and the neural network designed and calibrated are presented. Finally, theoretical performances are discussed. Validation over ground measurement data sets and inter-comparison with other similar biophysical products are presented and discussed in a companion paper. The CYCLOPES products and associated detailed documentation are available at

Vicarious calibration approaches using in situ measurements saw first use in the early 1980s and have since improved to keep pace with the evolution of the radiometric requirements of the sensors that are being calibrated. The advantage of in situ measurements for vicarious calibration is that they can be carried out with traceable and quantifiable accuracy, making them ideal for interconsistency studies of on-orbit sensors. The recent development of automated sites to collect the in situ data has led to an increase in the available number of datasets for sensor calibration. The current work describes the Radiometric Calibration Network (RadCalNet) that is an effort to provide automated surface and atmosphere in situ data as part of a network including multiple sites for the purpose of optical imager radiometric calibration in the visible to shortwave infrared spectral range. The key goals of RadCalNet are to standardize protocols for collecting data, process to top-of-atmosphere reflectance, and provide uncertainty budgets for automated sites traceable to the international system of units. RadCalNet is the result of efforts by the RadCalNet Working Group under the umbrella of the Committee on Earth Observation Satellites (CEOS) Working Group on Calibration and Validation (WGCV) and the Infrared Visible Optical Sensors (IVOS). Four radiometric calibration instrumented sites located in the USA, France, China, and Namibia are presented here that were used as initial sites for prototyping and demonstrating RadCalNet. All four sites rely on collection of data for assessing the surface reflectance as well as atmospheric data over that site. The data are converted to top-of-atmosphere reflectance within RadCalNet and provided through a web portal to allow users to either radiometrically calibrate or verify the calibration of their sensors of interest. Top-of-atmosphere reflectance data with associated uncertainties are available at 10 nm intervals over the 400 nm to 1000 nm spectral range at 30 min intervals for a nadir-viewing geometry. An example is shown demonstrating how top-of-atmosphere data from RadCalNet can be used to determine the interconsistency between two sensors.

[1] Current increase in atmospheric CO 2 is expected to modify both climate and plant function, thereby impacting plant structure and gas exchange. We investigate the effects of doubled CO 2 on leaf area index (LAI) and evapotranspiration (ETR) using a global vegetation model for present-day and doubled-CO 2 conditions. The model assumes that adaptation of plants to the local climate leads to an equilibrium LAI, which depends on resource availability (minimizing water stress, canopy carbon cost and self-shading). The model compares reasonably well with remote sensing estimates of LAI. Four doubled-CO 2 simulations are designed to investigate the role of climate, CO 2 -induced stomatal closure, enhanced photosynthesis, and a combination of these effects. These simulations show that plant physiological responses to doubled CO 2 are potentially more important than climate changes for LAI, and equally important for ETR. In addition, even the sign of the simulated changes in LAI and ETR varies with the assumptions on plant responsiveness to CO 2 . A reduction of stomatal conductance moderates or cancels the water losses caused by a warmer and drier climate, but photosynthesis stimulation counteracts this stomatal effect, especially in the mid-to-high latitudes, because of enhanced LAI. Experimental evidence of LAI and ETR response to CO 2 has been reviewed and compared to the different simulations. On the basis of this confrontation we argue that plant response to CO 2 doubling may have a relatively small net impact on global ETR and may cause a moderate increase of LAI. Tree stomata may be less responsive to CO 2 than was previously assumed, and stimulated plant growth partly cancels the water savings caused by stomatal closure. Understanding the responses of plant canopies to CO 2 is therefore critical for land surface hydrology in a CO 2 rich world.

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