Predicting spring phenology in deciduous broadleaf forests: NEON phenology forecasting community challenge (Englisch)
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- Neue Suche nach: Zachmann, Luke
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- Neue Suche nach: Wheeler, Kathryn I.
- Neue Suche nach: Dietze, Michael C.
- Neue Suche nach: LeBauer, David
- Neue Suche nach: Peters, Jody A.
- Neue Suche nach: Richardson, Andrew D.
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- Neue Suche nach: Floreani Buzbee, Raphaela
- Neue Suche nach: Chen, Min
- Neue Suche nach: Goldstein, Benjamin
- Neue Suche nach: Guo, Jessica
- Neue Suche nach: Hao, Dalei
- Neue Suche nach: Jones, Chris
- Neue Suche nach: Kelly-Fair, Mira
- Neue Suche nach: Liu, Haoran
- Neue Suche nach: Malmborg, Charlotte
- Neue Suche nach: Neupane, Naresh
- Neue Suche nach: Pal, Debasmita
- Neue Suche nach: Shirey, Vaughn
- Neue Suche nach: Song, Yiluan
- Neue Suche nach: Steen, McKalee
- Neue Suche nach: Vance, Eric A.
- Neue Suche nach: Woelmer, Whitney M.
- Neue Suche nach: Wynne, Jacob H.
- Neue Suche nach: Zachmann, Luke
In:
Agricultural and Forest Meteorology
;
345
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2023
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ISSN:
- Aufsatz (Zeitschrift) / Elektronische Ressource
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Titel:Predicting spring phenology in deciduous broadleaf forests: NEON phenology forecasting community challenge
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Beteiligte:Wheeler, Kathryn I. ( Autor:in ) / Dietze, Michael C. ( Autor:in ) / LeBauer, David ( Autor:in ) / Peters, Jody A. ( Autor:in ) / Richardson, Andrew D. ( Autor:in ) / Ross, Arun A. ( Autor:in ) / Thomas, R. Quinn ( Autor:in ) / Zhu, Kai ( Autor:in ) / Bhat, Uttam ( Autor:in ) / Munch, Stephan ( Autor:in )
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Erschienen in:
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Erscheinungsdatum:08.11.2023
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ISSN:
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DOI:
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Medientyp:Aufsatz (Zeitschrift)
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Format:Elektronische Ressource
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Sprache:Englisch
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Schlagwörter:Phenology , Ecological forecasting , Deciduous broadleaf , Budburst , Community challenge , Forests , CRPS , Continuous ranked probability Score , DB , DN , Digital number , DOY , Day of year , EFI , Ecological forecasting initiative , GAM , Generalized additive model , GCC , Green chromatic coordinate , GDD , Growing degree day , GEFS , Global ensemble forecast system , MAT , Mean annual temperature , ML , Machine learning , MODIS , Moderate resolution imaging spectroradiometer , NEON , National ecological observatory network , ROI , Region of interest
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