Period: 2002 – 2011


Frequency: 8 days

Resolutions: 1km, 5km, 25km

Extent: Regional and Global

Data access: Please contact Mathias Disney:

Algorithm description: The algorithm uses the JRC-TIP (1D) radiative transfer model (2 stream) inversion scheme, where TIP refers to Two-stream Inversion Package. LAI is estimated from GlobAlbedo albedo values in the VIS and the NIR/SWIR wavelengths.

The algorithm employs assimilation scheme wherein it uses priori estimates of albedo derived from albedo ‘climatology’. Unlike MODIS LAI/FAPAR algorithm, it is independent of biome specific structural characterisations.

Accuracy: Accuracy was estimated by comparison to ground data and MODIS LAI. The correlation (R2) with MODIS LAI exceeds 0.8 in 13% and 34% of cases in Northern and Southern hemispheres during wintertime, respectively. The correlation (R2) with MODIS LAI exceeds 0.8 in 15% and 20% of cases in North and South hemispheres during summertime, respectively. On average the products are correlated with a mean slope of 1.70 (uncertainty: 1.73) and a mean intercept of 0.15 (uncertainty: 0.58).
The LAI time series is smoother than MODIS LAI due to smooth albedo. Generally, LAI is lower than MODIS LAI (LAI = ~0.6*MODIS LAI, with intercept of 0.2). This difference is more obvious in winter months and in southern hemisphere. These differences are mainly caused by assumptions of the RT model and in the interpretation of LAI itself.


  • The predicted LAI is effective LAI and not ‘real’, but it is consistent with radiation transfer scheme in large scale climate and carbon cycles.
  • During the peak season the values are generally lower than MODIS LAI due to the fact that it does not represent the ‘true’ LAI.
  • Discrepancies are strongly dependant on biome type and season.
  • Disney et al (2016) suggests generating transformation coefficients to apply to the GlobAlbedo-derived LAI.
  • Care should be taken in particular biomes and regions: sparse forest, savanna, tropical region and high latitudes.


Disney, M. et al. A New Global fAPAR and LAI Dataset Derived from Optimal Albedo Estimates: Comparison with MODIS Products. Remote Sens. 8, 275 (2016).