Period: 1999 – present
Sensor: SPOT-VGT and PROBA-V
Frequency: 10 days
Data can be accessed here.
Algorithm description: The algorithm follows same approach as in for LAI and FAPAR GEOv1 product generation. The algorithm involves training of neural nets using CYCLOPES canopy cover data and SPOT-VGT and PROBA-V reflectance products. As researchers have reported underestimation issues in the CYCLOPES FCOVER product, a simple scaling factor is applied to avoid persistence of underestimations in the GEOv1 product.
Accuracy: Camacho et al (2013) report that GEOV1 products have consistent spatial distribution, smooth temporal profiles and dynamic range of reliable magnitude for different surface types. The comparison against ground data yielded R2 of 0.848 and RMSE of 0.095 with almost no bias and good precision.
- Large spatio-temporal gaps over high latitude and equatorial regions
Baret and M. Weiss, “ALgorithm Theoretical Basis Document: LAI, FAPAR, FCOVE, NDVI-Version 1,” Issue I1.10, Aug. 2014.
F. Baret et al., “GEOV1: LAI and FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part1: Principles of development and production,” Remote Sens. Environ., vol. 137, pp. 299–309, Oct. 2013.
F. Camacho, J. Cernicharo, R. Lacaze, F. Baret, and M. Weiss, “GEOV1: LAI, FAPAR essential climate variables and FCOVER global time series capitalising over existing products. Part 2: Validation and inter-comparison with reference products,” Remote Sens. Environ., vol. 137, pp. 310–329, Oct. 2013.