Period: 1999 – present
Sensor: SPOT-VGT and PROBA-V
Frequency: 10 days
Data can be accessed here.
Algorithm description: The algorithm is based on neural network and is same as described for Copernicus BioPAR GEOv1 LAI product.
Accuracy: Direct validation with ground-based data showed RMSE and R2 equal to 0.078 and 0.889 respectively. For comparison, direct validation for other available products resulted in: RMSE = 0.108 and R2 = 0.779 (Camacho et al, 2013).
- This accuracy tests were calculated using SPOT-VGT time series. However, since 2014 data come from PROBA-V. Products derived from PROBA-V have shown less accurate results than those from SPOT-VGT.
- The GEOV1 product is not gap-filled.
- Large gaps in products over northern latitudes in wintertime and equatorial areas.
- Larger fraction of missing values on PROBA-V products than on SPOT-VGT products due to stricter cloud masking algorithm.
Baret, F. et al. LAI, fAPAR and fCover CYCLOPES global products derived from VEGETATION Part 1: Principles of the algorithm. Remote Sens. Environ. 110, 275–286 (2007).
Baret, F. & Weiss, M. Algorithm Theorethical Basis Document: LAI, FAPAR, FCOVE, NDVI-Version 1. 1–51 (2014).
Camacho, F., Cernicharo, J., Lacaze, R., Baret, F. & Weiss, M. GEOV1: LAI, FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part 2: Validation and intercomparison with reference products. Remote Sens. Environ. 137, 310–329 (2013).
Verger, A., Baret, F. & Weiss, M. A multisensor fusion approach to improve LAI time series. Remote Sens. Environ. 115, 2460–2470 (2011).