Crop Nitrogen Retrieval Methods for Simulated Sentinel-2 Data Using In-Field Spectrometer Data.
ARTMO toolbox
agro-ecosystem monitoring
chlorophyll
gaussian processes regression
leaf area index
nitrogen
random forest
spectral indices
Journal
Remote sensing
ISSN: 2072-4292
Titre abrégé: Remote Sens (Basel)
Pays: Switzerland
ID NLM: 101624426
Informations de publication
Date de publication:
19 Jun 2021
19 Jun 2021
Historique:
entrez:
9
9
2022
pubmed:
19
6
2021
medline:
19
6
2021
Statut:
epublish
Résumé
Nitrogen (N) is one of the key nutrients supplied in agricultural production worldwide. Over-fertilization can have negative influences on the field and the regional level (e.g., agro-ecosystems). Remote sensing of the plant N of field crops presents a valuable tool for the monitoring of N flows in agro-ecosystems. Available data for validation of satellite-based remote sensing of N is scarce. Therefore, in this study, field spectrometer measurements were used to simulate data of the Sentinel-2 (S2) satellites developed for vegetation monitoring by the ESA. The prediction performance of normalized ratio indices (NRIs), random forest regression (RFR) and Gaussian processes regression (GPR) for plant-N-related traits was assessed on a diverse real-world dataset including multiple crops, field sites and years. The plant N traits included the mass-based N measure, N concentration in the biomass (N
Identifiants
pubmed: 36082363
doi: 10.3390/rs13122404
pmc: PMC7613346
mid: EMS152668
doi:
Types de publication
Journal Article
Langues
eng
Pagination
2404Subventions
Organisme : European Research Council
ID : 755617
Pays : International
Déclaration de conflit d'intérêts
Conflicts of Interest: The authors declare no conflict of interest.
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