Optimal sample size and composition for crop classification with Sen2-Agri’s random forest classifier
Tipo:
Título:
Optimal sample size and composition for crop classification with Sen2-Agri’s random forest classifier
Creador/a:
Schulthess, U.;
Rodrigues, F.;
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Taymans, M.;
Bellemans, N.;
Bontemps, S.;
Ortiz-Monasterio, I.;
Gerard, B.;
Defourny, P.
Rodrigues, F.;
Rodrigues, F.
https://orcid.org/0000-0001-7273-2217
Scopus ID
Researcher ID
Mendeley
Items in this Repository
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Taymans, M.;
Bellemans, N.;
Bontemps, S.;
Ortiz-Monasterio, I.;
Gerard, B.;
Defourny, P.
Año:
2023
Copyright:
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Revista:
Remote Sensing
Volumen de la Revista:
15
No de Revista:
3
Número de artículo:
608
DOI:
Lugar de publicación:
Basel (Switzerland)
Editor:
MDPI
Cita:
Optimal sample size and composition for crop classification with Sen2-Agri’s random forest classifier. 2023. 15 (3) DOI: 10.3390/rs15030608 MDPI.
Datasets relacionados
Iniciativas del CGIAR
Iniciativa:
Digital Innovation
Área de impacto:
Nutrition, health & food security
Poverty reduction, livelihoods & jobs
Poverty reduction, livelihoods & jobs
Área de acción:
Systems Transformation
Resilient Agrifood Systems
Resilient Agrifood Systems
Donante o financiador:
CGIAR Research Program on Maize
CGIAR Research Program on Wheat
Henan Agricultural University
CGIAR Trust Fund
CGIAR Research Program on Wheat
Henan Agricultural University
CGIAR Trust Fund
URL en CGSpace: