Optimal sample size and composition for crop classification with Sen2-Agri’s random forest classifier
Type:
Title:
Optimal sample size and composition for crop classification with Sen2-Agri’s random forest classifier
Creator:
Schulthess, U.;
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.

Rodrigues, F.;

Rodrigues, F.




View
Taymans, M.;
Bellemans, N.;
Bontemps, S.;
Ortiz-Monasterio, I.;

Gerard, B.;

Defourny, P.
Year:
2023
Copyright:
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Journal:
Remote Sensing
Journal volume:
15
Journal issue:
3
Article number:
608
DOI:
Place of Publication:
Basel (Switzerland)
Publisher:
MDPI
Citation:
Optimal sample size and composition for crop classification with Sen2-Agri’s random forest classifier. 2023. 15 (3) DOI: 10.3390/rs15030608 MDPI.
Related Datasets
CGIAR Initiatives
Initiative:
Digital Innovation
Impact Area:
Nutrition, health & food security
Poverty reduction, livelihoods & jobs
Poverty reduction, livelihoods & jobs
Action Area:
Systems Transformation
Resilient Agrifood Systems
Resilient Agrifood Systems
Donor or Funder:
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
CGSpace URL: