Description
LST DSSF DSLF ALBEDO SC
Surface Albedo

Product Documentation
   Documents:
           (Product User Manual)
           (Product Output Format)
           (Validation Report)
Introduction
Product Description
Algorithm Description
Data Characteristics
Product Uncertainties
References
Example of Product
User comments for this product
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Product Time Line Versions
  Year: 2010
Date:  
Vers:  
6.2
Today
Product Documentation
This product is pre-operational status. Its documentation are the product user manual document (PUM) and the product output format document (POF) The validation documentation for this product is available in the (VR) document.
 
Introduction
Land surface albedo is a key variable for characterising the energy balance in the coupled soil-vegetation-atmosphere system. The albedo quantifies the part of the energy that is absorbed and transformed into heat and latent fluxes. Owing to strong feedback effects the knowledge of albedo is important for determining weather conditions at the atmospheric boundary layer. Climate sensitivity studies with Global Circulation Models have confirmed the unsteady nature of the energy balance with respect to small changes in surface albedo. Other domains of applications are in hydro-meteorology, agro-meteorology and environment-related studies.
 
Product Description
The Albedo product is generated each day at the full spatial resolution of the MSG/SEVIRI instrument. An iterative scheme allows the composition of the information with a characteristic time scale of five days. The product is based on the three short-wave channels (VIS 0.6µm, NIR 0.8µm, SWIR 1.6µm). In addition to the corresponding narrowband estimates, broadband albedo is derived for the visible, near-infrared and total short-wave wavelength ranges. Information on cloud cover is obtained from the output of the Nowcasting and Very Short Range Forecasting Satellite Application Facility (NWC SAF) software. Dynamic information on the atmospheric pressure and water vapour content comes from the ECMWF numerical weather prediction model. Climatologic values are currently used for ozone concentration and aerosol optical thickness.
 
Algorithm Description
In a first step the cloud-free reflectance observations of each slot are corrected for atmospheric effects using the simplified radiative transfer code SMAC (Rahman and Dedieu, 1994). Afterwards, the linear kernel-driven BRDF model from Roujean et al. (1992) is fitted to a daily time series of the resulting top-of-canopy reflectance factor values. In the applied model the reflectance properties are split into two kernels describing the dominant light scattering processes in separating geometric and volumetric scattering effects, either interpreted as a transposition of scaling effects, a separation between the soil and vegetation, or the conjunction between thin and thick optical media. Albedo estimates are then obtained by suitably integrating the BRDF model functions. Generated products are the directional-hemispherical (or ?black-sky?) albedo at local solar noon and for the narrowband and total broadband range also the bi-hemispherical (or ?white-sky?) albedo.
 
Data Characteristics
The Albedo product is computed on a daily basis within the area covered by the MSG disk, over four specific geographical regions (Europe, Africa - N_Africa and S_Africa- and South America). For each day and each geographical region the albedo quantities, their respective error estimates, and a processing flag are disseminated in HDF5 format. The relevant information concerning the data fields is included in the HDF5 attributes.
 
Product Uncertainties
For each albedo quantity the algorithm delivers theoretical estimates for the non-correlated (random) error contribution by propagating estimates for the error of the top-of-canopy reflectance factors through the linear model inversion. In practice non-detected clouds and systematic errors in the aerosol optical thickness values used for the atmospheric correction are the most important error sources. The specification for the overall accuracy of the albedo product is 10%. Results of detailed validation studies will be given in the Validation Report and later included in the Product User Manual.
 
References

Barnsley M.J., Strahler A.H., Morris K.P. and J.P. Muller, 1994, Sampling the surface bidirectional reflectance distribution function (BRDF): Evaluation of current and future satellite sensors, Remote Sens. Rev., 8, 271-311.

Lucht W. and J.L. Roujean, 2000, Considerations in the parametric modeling of BRDF and albedo from multiangular satellite sensor observations, Remote Sens. Rev., 18, 343-379.

Roujean J.-L., M. Leroy and P.-Y. Deschamps, 1992, A bidirectional reflectance model of the Earth's surface for the correction of remote sensing data, J. Geophys. Res., 97(D18), 20455-20468.

Wanner, W., Li, X. and A.H. Strahler, 1995, On the derivation of kernels for kernel-driven models of bidirectional reflectance, J. Geophys. Res., vol. 100, p. 21077-21090.

Example of Product
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