  This paper presents the algorithm developed in LSA-SAF (Satellite Application
Facility for Land Surface Analysis) for the derivation of global vegetation
parameters from the AVHRR (Advanced Very High-Resolution Radiometer) sensor
onboard MetOp (Meteorological-Operational) satellites forming the EUMETSAT
(European Organization for the Exploitation of Meteorological Satellites) Polar
System (EPS). The suite of LSA-SAF EPS vegetation products includes the leaf
area index (LAI), the fractional vegetation cover (FVC), and the fraction of
absorbed photosynthetically active radiation (FAPAR). LAI, FAPAR, and FVC
characterize the structure and the functioning of vegetation and are key
parameters for a wide range of land-biosphere applications. The algorithm is
based on a hybrid approach that blends the generalization capabilities offered
by physical radiative transfer models with the accuracy and computational
efficiency of machine learning methods. One major feature is the implementation
of multi-output retrieval methods able to jointly and more consistently
estimate all the biophysical parameters at the same time. We propose a
multi-output Gaussian process regression (GPRmulti), which outperforms other
considered methods over PROSAIL (coupling of PROSPECT and SAIL (Scattering by
Arbitrary Inclined Leaves) radiative transfer models) EPS simulations. The
global EPS products include uncertainty estimates taking into account the
uncertainty captured by the retrieval method and input error propagation. The
consistent generation and distribution of the EPS vegetation products will
constitute a valuable tool for monitoring of earth surface dynamic processes.
