Enhancing WorldCereal crop calendars with land surface phenology and machine learning

Accurate crop calendars are critical for understanding global agricultural phenology, mapping crop types, improving yield forecasts, and addressing food security challenges in the context of climate variability. This study presents significant advancements in crop calendar modeling, building upon th...

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Autors principals:	Italo Moletto-Lobos, Belen Franch, Andreu Guillem-Valls, Katarzyna Cyran, Natacha Kalecinski, Kristof Van Tricht, Eric Vermote, Inbal Becker-Reshef, Shabarinath Nair, Jeroen Degerickx, Christina Butsko, Koen de Vos, Alyssa Whitcraft, Zoltan Szantoi
Format:	Artigo
Idioma:	Inglês
Publicat:	Elsevier 2026-05-01
Col·lecció:	Ecological Informatics
Matèries:	Crop calendars Land surface phenology Machine learning WorldCereal Remote sensing ERA5
Accés en línia:	http://www.sciencedirect.com/science/article/pii/S1574954126001482
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