Period: 2000 – present
Sensor: MODIS Terra+Aqua
Frequency: 4 days
Data can be accessed here.
Algorithm description: The algorithm is similar to that used for NASA MODIS LAI product and consists of a main Look-up-Table (LUT) based procedure that exploits the spectral information content of the MODIS red (648 nm) and near-infrared (NIR, 858 nm) surface reflectances. The LUT was generated using 3D radiative transfer equation. In case of algorithm failure, a back-up empirical method based on relationships between NDVI and canopy FPAR is used. Compared to the v5, biomes classification used in v6 was improved.
Accuracy: The consistency of V6 products was estimated by comparison with the v5 products. Global and seasonal comparison between v5 and v6 indicates good continuity and consistency for all biome types. For areas with same biome between v5 and v6, average RMSE = 0.091 was achieved. For areas with disagreement in biomes between v5 and v6, average RMSE = 0.102 was estimated.
Comparison with ground measurements showed that v6 performs better than v5: RMSE similar (0.15) and R2=0.74 vs R2=0.68, respectively.
- Errors in biome classification can propagate into FAPAR product and lead to incorrect FAPAR values
- Significant overestimation of v5 and v6 at low FAPAR
- Systematic overestimation of FAPAR over sparsely-vegetated areas.
Knyazikhin, Y. et al. MODIS Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation Absorbed by Vegetation (FPAR) Product (MOD15): Algorithm Theoretical Basis Document Verision 4.0. 1–130 (1999).
Knyazikhin, Y., Martonchik, J. V., Myneni, R. B., Diner, D. J. & Running, S. W. Synergistic algorithm for estimating vegetation canopy leaf area index and fraction of absorbed photosynthetically active radiation from MODIS and MISR data. J. Geophys. Res. Atmospheres103, 32257–32275 (1998).
Yan, K. et al. Evaluation of MODIS LAI/FPAR Product Collection 6. Part 1: Consistency and Improvements. Remote Sens. 8, 359 (2016).
Yan, K. et al. Evaluation of MODIS LAI/FPAR Product Collection 6. Part 2: Validation and Intercomparison. Remote Sens. 8, 460 (2016).