Period: 1999 – present
Sensor: SPOT-VGT and PROBA-V
Frequency: 10 days
Data can be accessed here.
Algorithm description: The algorithm is as described for Copernicus BioPAR GEOv2 LAI product. It consists of 3 main steps: (1) neural networking to produce instantaneous biophysical variables, (2) multistep filtering approach to eliminated data affected by atmospheric effects and snow cover, (3) temporal smoothing gap filling and consistent adjustment of climatology to actual observations.
Accuracy: Direct validation with ground-based data over 15 sites during 2003-2007 yielded RMSE = 0.09 and R2 = 0.941. The product is consistent with earlier versions and reference products (MODIS, CYCLOPES). Furthermore, gaps have been reduced significantly compared to GEOv1.
- The product is new and not fully operational at the moment.
- 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).