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
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

1792

Subventions

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

Glob Chang Biol. 2015 Dec;21(12):4673-84
pubmed: 26146813
Front Plant Sci. 2018 Nov 27;9:1752
pubmed: 30542364

Auteurs

Gabriele Candiani (G)

Institute for Electromagnetic Sensing of the Environment, National Research Council, 20133 Milan, Italy.

Giulia Tagliabue (G)

Remote Sensing of Environmental Dynamics Laboratory, University of Milano-Bicocca, 20126 Milan, Italy.

Cinzia Panigada (C)

Remote Sensing of Environmental Dynamics Laboratory, University of Milano-Bicocca, 20126 Milan, Italy.

Jochem Verrelst (J)

Image Processing Laboratory, University of València, 46980 València, Spain.

Valentina Picchi (V)

Research Centre for Engineering and Agro-Food Processing, Council for Agricultural Research and Economics, 20133 Milan, Italy.

Juan Pablo Rivera Caicedo (JPR)

Secretary of Research and Postgraduate, CONACYT-UAN, Tepic 63000, Nayarit, Mexico.

Mirco Boschetti (M)

Institute for Electromagnetic Sensing of the Environment, National Research Council, 20133 Milan, Italy.

Classifications MeSH