Retrieving and Validating Leaf and Canopy Chlorophyll Content at Moderate Resolution: A Multiscale Analysis with the Sentinel-3 OLCI Sensor.

FLEX HyPlant OLCI Sentinel-3 canopy chlorophyll content leaf area index leaf chlorophyll content moderate spatial resolution pixel heterogeneity

Journal

Remote sensing
ISSN: 2072-4292
Titre abrégé: Remote Sens (Basel)
Pays: Switzerland
ID NLM: 101624426

Informations de publication

Date de publication:
07 Apr 2021
Historique:
entrez: 9 9 2022
pubmed: 7 4 2021
medline: 7 4 2021
Statut: ppublish

Résumé

ESA's Eighth Earth Explorer mission "FLuorescence EXplorer" (FLEX) will be dedicated to the global monitoring of the chlorophyll fluorescence emitted by vegetation. In order to properly interpret the measured fluorescence signal, essential vegetation variables need to be retrieved concomitantly. FLEX will fly in tandem formation with Sentinel-3 (S3), which conveys the Ocean and Land Color Instrument (OLCI) that is designed to characterize the atmosphere and the terrestrial vegetation at a spatial resolution of 300 m. In support of FLEX's preparatory activities, this paper presents a first validation exercise of OLCI vegetation products against in situ data coming from the 2018 FLEXSense campaign. During this campaign, leaf chlorophyll content (LCC) and leaf area index (LAI) measurements were collected over croplands, while HyPlant DUAL images of the area were acquired at a 3 m spatial resolution. A multiscale validation strategy was pursued. First, estimates of these two variables, together with the combined canopy chlorophyll content (CCC = LCC × LAI), were obtained at the HyPlant spatial resolution and were compared against the in situ measurements. Second, the fine-scale retrieval maps from HyPlant were coarsened to the S3 spatial scale as a reference to assess the quality of the OLCI vegetation products. As an intermediary step, vegetation products extracted from Sentinel-2 data were used to compare retrievals at the in-between spatial resolution of 20 m. For all spatial scales, CCC delivered the most accurate estimates with the smallest prediction error obtained at the 300 m resolution (R

Identifiants

pubmed: 36082339
doi: 10.3390/rs13081419
pmc: PMC7613399
mid: EMS152670
doi:

Types de publication

Journal Article

Langues

eng

Pagination

1419

Subventions

Organisme : European Research Council
ID : 755617
Pays : International

Déclaration de conflit d'intérêts

Conflicts of Interest: The authors declare no conflict of interest.

Références

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Auteurs

Charlotte De Grave (C)

Image Processing Laboratory (IPL), Parc Científic, Universitat de Valencia, 46980 Paterna, Spain.

Luca Pipia (L)

Institut Cartogrfic i Geològic de Catalunya (ICGC), Parc de Montjüic, 08038 Barcelona, Spain.

Bastian Siegmann (B)

Forschungszentrum Jülich GmbH, Institute of Bio- and Geosciences, Plant Sciences (IBG-2), D-52425 Jülich, Germany.

Pablo Morcillo-Pallarés (P)

Image Processing Laboratory (IPL), Parc Científic, Universitat de Valencia, 46980 Paterna, Spain.
Instituto ITACA, Universidad Politécnica de Valencia, Camino de Vera s/n, 46022 Valencia, Spain.

Juan Pablo Rivera-Caicedo (JP)

CONACyT-UAN, Secretaría de Investigación y Posgrado, Universidad Autónoma de Nayarit, Ciudad de la Cultura Amado Nervo, 63155 Tepic, Nayarit, Mexico.

José Moreno (J)

Image Processing Laboratory (IPL), Parc Científic, Universitat de Valencia, 46980 Paterna, Spain.

Jochem Verrelst (J)

Image Processing Laboratory (IPL), Parc Científic, Universitat de Valencia, 46980 Paterna, Spain.

Classifications MeSH