Agriculture
Annual cropland
Crop type mapping
Ethiopia
Field reference data
In-situ crop type observation
Machine learning
Remote sensing
Journal
Data in brief
ISSN: 2352-3409
Titre abrégé: Data Brief
Pays: Netherlands
ID NLM: 101654995
Informations de publication
Date de publication:
Jun 2024
Jun 2024
Historique:
received:
30
01
2024
revised:
10
04
2024
accepted:
11
04
2024
medline:
1
5
2024
pubmed:
1
5
2024
entrez:
1
5
2024
Statut:
epublish
Résumé
Crop type observation is crucial for various environmental and agricultural remote sensing applications including land use and land cover mapping, crop growth monitoring, crop modelling, yield forecasting, disease surveillance, and climate modelling. Quality-controlled georeferenced crop type information is essential for calibrating and validating machine learning algorithms. However, publicly available field data is scarce, particularly in the highly dynamic smallholder farming systems of sub-Saharan Africa. For the 2020/21 main cropping season (
Identifiants
pubmed: 38690323
doi: 10.1016/j.dib.2024.110427
pii: S2352-3409(24)00396-2
pmc: PMC11058092
doi:
Types de publication
Journal Article
Langues
eng
Pagination
110427Informations de copyright
© 2024 The Author(s).