Derivation and Evaluation of Global 1-km Fractional Vegetation Cover Data for Land Modeling
Open Access
- 1 June 2000
- journal article
- Published by American Meteorological Society in Journal of Applied Meteorology and Climatology
- Vol. 39 (6) , 826-839
- https://doi.org/10.1175/1520-0450(2000)039<0826:daeogk>2.0.co;2
Abstract
Fractional vegetation cover (συ) is needed in the modeling of the land–atmosphere exchanges of momentum, energy, water, and trace gases. From global 1-km, 10-day composite Advanced Very High Resolution Radiometer normalized difference vegetation index (NDVI) data from April 1992 to March 1993, global 1-km συ is derived based on the annual maximum NDVI value for each pixel in comparison with the NDVI value that corresponds to 100% vegetation cover for each International Geosphere–Biosphere Program land cover type. This dataset is pixel dependent but season independent, with the seasonal variation of vegetation greenness in a pixel accounted for by the leaf area index. The authors’ algorithm is found to be insensitive to the use of a specific land cover classification. In comparison with an independent dataset derived by DeFries et al. by using a more sophisticated statistical approach, the current dataset has a similar spatial distribution but systematically smaller συ (particularly over shrubland... Abstract Fractional vegetation cover (συ) is needed in the modeling of the land–atmosphere exchanges of momentum, energy, water, and trace gases. From global 1-km, 10-day composite Advanced Very High Resolution Radiometer normalized difference vegetation index (NDVI) data from April 1992 to March 1993, global 1-km συ is derived based on the annual maximum NDVI value for each pixel in comparison with the NDVI value that corresponds to 100% vegetation cover for each International Geosphere–Biosphere Program land cover type. This dataset is pixel dependent but season independent, with the seasonal variation of vegetation greenness in a pixel accounted for by the leaf area index. The authors’ algorithm is found to be insensitive to the use of a specific land cover classification. In comparison with an independent dataset derived by DeFries et al. by using a more sophisticated statistical approach, the current dataset has a similar spatial distribution but systematically smaller συ (particularly over shrubland...Keywords
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