NRT Product available since Jan 2013
Fractional Vegetation Cover (FVC) defines an important structural property of a plant canopy, which corresponds to the complement to unity of the gap fraction at nadir direction, accounting for the amount of vegetation distributed in a horizontal perspective. FVC is related with the partition between soil and vegetation contribution for emissivity and temperature. This property is necessary for describing land surface processes and surface parameterisation schemes used for climate and weather forecasting. Besides, the FVC is relevant for a wide range of Land Biosphere Applications such us agriculture and forestry, environmental management and land use, hydrology, natural hazards monitoring and management, vegetation-soil dynamics monitoring, drought conditions and fire scar extent.
This operational product is documented in the following documents:
Please see Product Peer-Review publications in References.
The use of LSA SAF products in publications is kindly requested to be duly acknowledged:
FVC and MTFVC were provided by the EUMETSAT Satellite Application Facility on Land Surface Analysis (LSA SAF; Trigo et al., 2011)
Trigo, I. F., C. C. DaCamara, P. Viterbo, J.-L. Roujean, F. Olesen, C. Barroso, F. Camacho-de Coca, D. Carrer, S. C. Freitas, J. García-Haro, B. Geiger, F. Gellens-Meulenberghs, N. Ghilain, J. Meliá, L. Pessanha, N. Siljamo, and A. Arboleda, 2011: The Satellite Application Facility on Land Surface Analysis. Int. J. Remote Sens., 32, 2725-2744, doi: 10.1080/01431161003743199
The FVC product is currently generated daily at the full spatial resolution of the MSG/SEVIRI instrument, and will be later provided on a 10-days and monthly basis. The product is based on the three short-wave channels (VIS 0.6 µm, NIR 0.8 µm, SWIR 1.6 µm) using as input the k0 parameter of a parametric BRDF (Bi-directional Reflectance Distribution Function) model (Roujean et al. 1992). The k0 parameter (normalized reflectance) provides cloud-free observations over the SEVIRI disk based on an iterative scheme with a characteristic time scale of five days. The FVC product is expressed in the range from 0 % to 100 %. It is corrected from uncertainty derived of the view/sun angles and also the anisotropy effects of surface's reflectance in the SEVIRI image. The FVC product includes routine quality check and error estimates. The product will be validated in order to define the product uncertainties over a range of global conditions.
The algorithm relies on an optimised Spectral Mixture Analysis (SMA) technique based on the Bayesian Theorem, along with the use of standardised SMA, to improve understanding of the impact of endmember variability on the derivation of subpixel vegetation fractions at a global scale (García-Haro et al. 2005a, 2005b). In a first step, an exhaustive training set for the soil and vegetation components is defined. Second, a Gaussian Mixture Model is fit to the training data. Third, a Bayesian model selection is used to compute the relative likelihood of membership in each soil/vegetation single-model. FVC is then estimated using a linear-weighted combination single-model estimate. The unmixing is performed using standardised signatures in order to reduce the influence of external factors such as surface roughness, terrain illumination and canopy shade. The overall FVC error combines uncertainty due to the model selection and uncertainties based on the errors of the input data.
The FVC product is computed within the area covered by the MSG disk, over 4 specific geographical regions (Europe, Africa - N_Africa and S_Africa- and South America). For each day and geographical region, the FVC product, its error estimate and the processing flag are disseminated in HDF5 format. The relevant information concerning the data fields is included in the HDF5 attributes.
Automatic Quality Control (QC) is performed on FVC product and the quality information is provided on a pixel basis. QC contains general information about input data quality, and specific information related with the limits of application. The error estimate define the confidence level of the different products on the basis of the theoretical model assumption and considering the statistical uncertainties of the observations and model parameters. The quality of FVC product depends on input quality (signal-to-noise ratio, accuracy of atmospheric corrections, number of cloud-free observations), accuracy of cloudy pixels identification, and spectral variation in reflectance of different land-surface elements.
Validation will include the assessment of the product in a systematic and statistically robust way representing global conditions. The specification for the overall accuracy of the FVC product is 10%. Results of detailed validation studies will be given in the Validation Report and later included in the Product User Manual.
Roujean, J.L., M. Leroy and P.Y. Dechamps, (1992). A bidirectional reflectance model of the earth's surface for the correction of remote sensing data. Journal of Geophysical Research, 97 (D18), pp. 20455-20468.
García-Haro, F.J, S. Sommer, T. Kemper (2005), Variable multiple endmember spectral mixture analysis (VMESMA), International Journal of Remote Sensing, 26:2135-2162.
García-Haro, F.J., F. Camacho-de Coca, J. Meliá, B. Martínez, Operational derivation of vegetation products in the framework of the LSA SAF project, (2005), EUMETSAT Meteorological Satellite Conference. Dubrovnik (Croatia). 19-23 Septiembre, in press.