Relating Seasonal Patterns of the AVHRR Vegetation to Simulated Photosynthesis and Transpiration of Forests in Different Climates

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Remote Sensing of Environment

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Recent research has suggested that the Normalized Difference Vegetation Index (NDVI) calculated from the AVHRR sensor is directly related to photosynthesis (PSN), transpiration (TRAN), and aboveground net primary production (ANPP) of terrestrial vegetation. Weekly NDVI data for 1983–1984 were retrieved for seven sites of diverse climate in North America. The sites were Fairbanks, AK, Seattle, WA, Missoula, MT, Madison, WI, Knoxville, TN, Jacksonville, FL, and Tucson, AZ. Meteorological data from ground stations were retrieved to drive an ecosystem simulation model (FOREST-BGC) calculating daily canopy PSN and TRAN and annual ANPP of a hypothetical forest stand for the corresponding period at each site. Correlations of annual integrated NDVI across all sites for both years were: annual PSN, R2 = 0.87; annual TRAN, R2 = 0.77; annual ANPP, R2 = 0.72. Correlation between weekly NDVI and PSN was variable; with high latitude wet sites, R2 = 0.77–0.84. On sites with less seasonal amplitude of NDVI and PSN, or on sites with substantial seasonal water stress correlations ranged from R2 = 0.08 to 0.64. Correlations of weekly NDVI with TRAN followed the same pattern as PSN, but were slightly lower. The tendency of raw NDVI data to overpredict PSN and TRAN on water limited sites was partially corrected using an “aridity index” of annual radiation/annual precipitation that could be computed from general climatological data for improving large scale NDVI maps of PSN and TRAN. The spatial subsampling done for the global vegetation index (GVI) precludes following specific study sites through the growing season. We conclude that estimates of vegetation productivity using the GVI should only be done as annual integrations until unsubsampled local area coverage (LAC) NDVI data can be tested against forest PSN, TRAN, and ANPP, measured at shorter time intervals.




© 1988 Elsevier

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