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Observations of satellite land surface phenology suggest that maximum leaf greenness affects global vegetation productivity more than growing season length
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  • Xiaojie Gao,
  • Ian McGregor,
  • Josh Gray,
  • Mark Friedl,
  • Minkyu Moon
Xiaojie Gao
Center for Geospatial Analytics, North Carolina State University

Corresponding Author:[email protected]

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Ian McGregor
Center for Geospatial Analytics
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Josh Gray
Center for Geospatial Analytics
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Mark Friedl
Boston University
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Minkyu Moon
Boston University
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Abstract

Vegetation green leaf phenology directly impacts gross primary productivity (GPP) of terrestrial ecosystems. Satellite observations of land surface phenology (LSP) provide an important means to monitor the key timing of vegetation green leaf development. However, differences between satellite-derived LSP proxies and in-situ measurements of GPP make it difficult to quantify the impact of climate-induced changes in green leaf phenology on annual GPP. Here we used 1,110 site-years of GPP measurements from eddy-covariance towers in association with time series of satellite LSP observations from 2000-2014 to show that while satellite LSP explains a large proportion of variation in annual GPP, changes in green-leaf-based growing season length (GSL) had less impact on annual GPP by ~30% than GSL changes in GPP-based photosynthetic duration. Further, maximum leaf greenness explained substantially more variance in annual GPP than green leaf GSL, highlighting the role of future vegetation greening trends on large-scale carbon budgets. We conclude that satellite LSP-based inferences regarding large-scale dynamics in GPP need to consider changes in both green leaf GSL and maximum greenness.