Wheat growth monitoring and yield estimation based on remote sensing data assimilation into the SAFY crop growth model.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
31 03 2022
31 03 2022
Historique:
received:
08
10
2021
accepted:
22
03
2022
entrez:
1
4
2022
pubmed:
2
4
2022
medline:
5
4
2022
Statut:
epublish
Résumé
Crop growth monitoring and yield estimate information can be obtained via appropriate metrics such as the leaf area index (LAI) and biomass. Such information is crucial for guiding agricultural production, ensuring food security, and maintaining sustainable agricultural development. Traditional methods of field measurement and monitoring typically have low efficiency and can only give limited untimely information. Alternatively, methods based on remote sensing technologies are fast, objective, and nondestructive. Indeed, remote sensing data assimilation and crop growth modeling represent an important trend in crop growth monitoring and yield estimation. In this study, we assimilate the leaf area index retrieved from Sentinel-2 remote sensing data for crop growth model of the simple algorithm for yield estimation (SAFY) in wheat. The SP-UCI optimization algorithm is used for fine-tuning for several SAFY parameters, namely the emergence date (D
Identifiants
pubmed: 35361910
doi: 10.1038/s41598-022-09535-9
pii: 10.1038/s41598-022-09535-9
pmc: PMC8971471
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
5473Informations de copyright
© 2022. The Author(s).
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