Assessment of maize nitrogen uptake from PRISMA hyperspectral data through hybrid modelling.

Precision farming machine learning regression algorithms plant nitrogen uptake estimation radiative transfer models

Journal

European journal of remote sensing
ISSN: 2279-7254
Titre abrégé: Eur J Remote Sens
Pays: Italy
ID NLM: 101714120

Informations de publication

Date de publication:
31 Dec 2023
Historique:
medline: 5 9 2022
pubmed: 5 9 2022
entrez: 19 1 2024
Statut: epublish

Résumé

The spaceborne imaging spectroscopy mission

Identifiants

pubmed: 38239331
doi: 10.1080/22797254.2022.2117650
pmc: PMC7615541
pii:
doi:

Types de publication

Journal Article

Langues

eng

Déclaration de conflit d'intérêts

Disclosure statement No potential conflict of interest was reported by the authors.

Auteurs

Marina Ranghetti (M)

Institute for Electromagnetic Sensing of the Environment (IREA), National Research Council of Italy, Milano, Italy.

Mirco Boschetti (M)

Institute for Electromagnetic Sensing of the Environment (IREA), National Research Council of Italy, Milano, Italy.

Luigi Ranghetti (L)

Institute for Electromagnetic Sensing of the Environment (IREA), National Research Council of Italy, Milano, Italy.

Giulia Tagliabue (G)

Remote Sensing of Environmental Dynamics Laboratory, Dipartimento di Scienze dell'Ambiente e della Terra, Università degli Studi di Milano - Bicocca, Milano, Italy.

Cinzia Panigada (C)

Remote Sensing of Environmental Dynamics Laboratory, Dipartimento di Scienze dell'Ambiente e della Terra, Università degli Studi di Milano - Bicocca, Milano, Italy.

Marco Gianinetto (M)

Institute for Electromagnetic Sensing of the Environment (IREA), National Research Council of Italy, Milano, Italy.
Department of Architecture, Built Environment and Construction Engineering (DABC), Milano, Italy.

Jochem Verrelst (J)

Image Processing Laboratory (IPL), Parc Científic, University of Valencia, Paterna, Valencia, Spain.

Gabriele Candiani (G)

Institute for Electromagnetic Sensing of the Environment (IREA), National Research Council of Italy, Milano, Italy.

Classifications MeSH