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Award ID contains: 1702697

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  1. ; ; ; ; ; ; ; ; ; ; et al ( , Agricultural and Forest Meteorology)
    Free, publicly-accessible full text available September 1, 2022
  2. ; ; ; ; ; ; ; ; ; ; et al ( , Agricultural and forest meteorology)
    Accepted Manuscript Full Text Available
  3. ; ; ; ( , Remote Sensing of Environment)
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  4. ; ; ; ; ; ; ; ; ; ; et al ( , Global Change Biology)
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  5. ; ; ; ; ; ; ; ; ; ; et al ( , New Phytologist)
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  6. ; ; ; ; ( , Remote Sensing)
    High-quality retrieval of land surface phenology (LSP) is increasingly important for understanding the effects of climate change on ecosystem function and biosphere–atmosphere interactions. We analyzed four state-of-the-art phenology methods: threshold, logistic-function, moving-average and first derivative based approaches, and retrieved LSP in the North Hemisphere for the period 1999–2017 from Copernicus Global Land Service (CGLS) SPOT-VEGETATION and PROBA-V leaf area index (LAI) 1 km V2.0 time series. We validated the LSP estimates with near-surface PhenoCam and eddy covariance FLUXNET data over 80 sites of deciduous forests. Results showed a strong correlation (R2 > 0.7) between the satellite LSP and ground-based observationsmore »from both PhenoCam and FLUXNET for the timing of the start (SoS) and R2 > 0.5 for the end of season (EoS). The threshold-based method performed the best with a root mean square error of ~9 d with PhenoCam and ~7 d with FLUXNET for the timing of SoS (30th percentile of the annual amplitude), and ~12 d and ~10 d, respectively, for the timing of EoS (40th percentile).« less
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  7. ; ; ; ; ; ; ; ; ; ; et al ( , Bulletin of the American Meteorological Society)
    Dunn, Robert J. ; Stanitski, Diane M. ; Gobron, Nadine ; Willett, Kate M. (Ed.)
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  8. ; ; ; ; ( , Agricultural and Forest Meteorology)
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  9. ; ; ; ; ; ( , Remote Sensing of Environment)
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  10. ; ; ; ; ; ; ; ( , Geophysical Research Letters)
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