Evaluation of Hybrid Models to Estimate Chlorophyll and Nitrogen Content of Maize Crops in the Framework of the Future CHIME Mission.
Gaussian process regression
active learning
chlorophyll content
machine learning regression algorithm
nitrogen content
radiative transfer modeling
spaceborne imaging spectroscopy
Journal
Remote sensing
ISSN: 2072-4292
Titre abrégé: Remote Sens (Basel)
Pays: Switzerland
ID NLM: 101624426
Informations de publication
Date de publication:
08 Apr 2022
08 Apr 2022
Historique:
entrez:
9
9
2022
pubmed:
10
9
2022
medline:
10
9
2022
Statut:
ppublish
Résumé
In the next few years, the new Copernicus Hyperspectral Imaging Mission (CHIME) is foreseen to be launched by the European Space Agency (ESA). This missions will provide an unprecedented amount of hyperspectral data, enabling new research possibilities within several fields of natural resources, including the "agriculture and food security" domain. In order to efficiently exploit this upcoming hyperspectral data stream, new processing methods and techniques need to be studied and implemented. In this work, the hybrid approach (HYB) and its variant, featuring sampling dimensionality reduction through active learning heuristics (HAL), were applied to CHIME-like data to evaluate the retrieval of crop traits, such as chlorophyll and nitrogen content at both leaf (LCC and LNC) and canopy level (CCC and CNC). The results showed that HYB was able to provide reliable estimations at canopy level (R
Identifiants
pubmed: 36081596
doi: 10.3390/rs14081792
pmc: PMC7613389
mid: EMS152685
doi:
Types de publication
Journal Article
Langues
eng
Pagination
1792Subventions
Organisme : European Research Council
ID : 755617
Pays : International
Déclaration de conflit d'intérêts
Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
Références
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pubmed: 26146813
Front Plant Sci. 2018 Nov 27;9:1752
pubmed: 30542364