Period: 1999 – present


Frequency: 10 days

Resolutions: 1km

Extent: Global

Data can be accessed here.

Algorithm description: The algorithm is essentially similar to GEOV1 with respect to actual retrieval of instantaneous values. Additional features are (1) multistep filtering approach to eliminated data affected by atmospheric effects and snow cover, (2) temporal smoothing, gap filling and consistent adjustment of climatology to actual observations and (3) Compositing step is performed at the parameter level and not at the reflectance level as in GEOV1. This makes use of BRDF corrections redundant and reduces sensitivity to data gaps.

The temporal compositing has been designed such that the historical time series information is incorporated. This not only allows filling up of data gaps and producing short term prediction of the parameter values, but also checks that the retrieved values are within the expected range, thus filters out unrealistic values. The historical time series is calculated using GEOV1 1999-2012 inter-annual averages.

Accuracy: Direct validation of the product with ground-based data over 15 sites during the period 2003-2007 yielded RMSE = 0.69 and R2 = 0.884. The product is consistent with earlier version (GEOv1) and also with other reference products (MODIS, CYCLOPES).


  • The product is not fully operational
  • The validation needs to be extended to other sites and years (such as done with GEOv1)


Verger, A., Baret, F. & Weiss, M. Near Real-Time Vegetation Monitoring at Global Scale. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 7, 3473–3481 (2014).

Verger, A., Baret, F. & Weiss, M. GEOV2/VGT: near real time estimation of global biophysical variables from VEGETATION-P data. in MultiTemp 2013: 7th International Workshop on the Analysis of Multi-temporal Remote Sensing Images 1–4 (IEEE, 2013). doi:10.1109/Multi-Temp.2013.6866023

Verger, A., Baret, F., Weiss, M., Kandasamy, S. & Vermote, E. The CACAO Method for Smoothing, Gap Filling, and Characterizing Seasonal Anomalies in Satellite Time Series. IEEE Trans. Geosci. Remote Sens. 51, 1963–1972 (2013).