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Evaluating Remote Sensing Metrics for Land Surface Phenology in Peatlands

cris.virtual.author-orcid0000-0002-0953-7045
cris.virtual.author-orcid0000-0003-0901-9894
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cris.virtual.author-orcid0000-0002-5212-7383
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cris.virtualsource.author-orcid0af80967-45b1-40e8-a0bf-9989e4e639c2
cris.virtualsource.author-orcidfaa187d8-53df-4536-8acf-ac523f3e8a05
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cris.virtualsource.author-orcid039c5639-27fb-49d1-97b9-e20f4c473688
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dc.abstract.enVegetation phenology is an important indicator of climate change and ecosystem productivity. However, the monitoring of vegetation generative phenology through remote sensing techniques does not allow for species-specific retrieval in mixed ecosystems; hence, land surface phenology (LSP) is used instead of traditional plant phenology based on plant organ emergence and development observations. Despite the estimated timing of the LSP parameters being dependent on the vegetation index (VI) used, inadequate attention was paid to the evaluation of the commonly used VIs for LSP of different vegetation covers. We used two years of data from the experimental site in central European peatland, where plots of two peatland vegetation communities are under a climate manipulation experiment. We assessed the accuracy of LSP retrieval by simple remote sensing metrics against LSP derived from gross primary production and canopy chlorophyll content time series. The product of Near-Infrared Reflectance of Vegetation and Photosynthetically Active Radiation (NIRvP) and Green Chromatic Coordinates (GCC) was identified as the best-performing remote sensing metrics for peatland physiological and structural phenology, respectively. Our results suggest that the changes in the physiological phenology due to increased temperature are more prominent than the changes in the structural phenology. This may mean that despite a rather accurate assessment of the structural LSP of peatland by remote sensing, the changes in the functioning of the ecosystem can be underestimated by simple VIs. This ground-based phenological study on peatlands provides the base for more accurate monitoring of interannual variation of carbon sink strength through remote sensing.
dc.affiliationWydział Inżynierii Środowiska i Inżynierii Mechanicznej
dc.affiliation.instituteKatedra Ekologii i Ochrony Środowiska
dc.contributor.authorAntala, Michal
dc.contributor.authorRastogi, Anshu
dc.contributor.authorStróżecki, Marcin Grzegorz
dc.contributor.authorAlbert-Saiz, Mar
dc.contributor.authorBandopadhyay, Subhajit
dc.contributor.authorJuszczak, Radosław
dc.date.access2025-03-31
dc.date.accessioned2025-03-31T09:50:50Z
dc.date.available2025-03-31T09:50:50Z
dc.date.copyright2024-12-26
dc.date.issued2025
dc.description.abstract<jats:p>Vegetation phenology is an important indicator of climate change and ecosystem productivity. However, the monitoring of vegetation generative phenology through remote sensing techniques does not allow for species-specific retrieval in mixed ecosystems; hence, land surface phenology (LSP) is used instead of traditional plant phenology based on plant organ emergence and development observations. Despite the estimated timing of the LSP parameters being dependent on the vegetation index (VI) used, inadequate attention was paid to the evaluation of the commonly used VIs for LSP of different vegetation covers. We used two years of data from the experimental site in central European peatland, where plots of two peatland vegetation communities are under a climate manipulation experiment. We assessed the accuracy of LSP retrieval by simple remote sensing metrics against LSP derived from gross primary production and canopy chlorophyll content time series. The product of Near-Infrared Reflectance of Vegetation and Photosynthetically Active Radiation (NIRvP) and Green Chromatic Coordinates (GCC) was identified as the best-performing remote sensing metrics for peatland physiological and structural phenology, respectively. Our results suggest that the changes in the physiological phenology due to increased temperature are more prominent than the changes in the structural phenology. This may mean that despite a rather accurate assessment of the structural LSP of peatland by remote sensing, the changes in the functioning of the ecosystem can be underestimated by simple VIs. This ground-based phenological study on peatlands provides the base for more accurate monitoring of interannual variation of carbon sink strength through remote sensing.</jats:p>
dc.description.accesstimeat_publication
dc.description.bibliographyil., bibliogr.
dc.description.financepublication_nocost
dc.description.financecost0,00
dc.description.if4,2
dc.description.number1
dc.description.points100
dc.description.versionfinal_published
dc.description.volume17
dc.identifier.doi10.3390/rs17010032
dc.identifier.issn2072-4292
dc.identifier.urihttps://sciencerep.up.poznan.pl/handle/item/2655
dc.identifier.weblinkhttps://www.mdpi.com/2072-4292/17/1/32
dc.languageen
dc.pbn.affiliationenvironmental engineering, mining and energy
dc.relation.ispartofRemote Sensing
dc.relation.pagesart. 32
dc.rightsCC-BY
dc.sciencecloudsend
dc.share.typeOPEN_JOURNAL
dc.subject.enclimate change
dc.subject.engross primary production
dc.subject.enland surface phenology
dc.subject.enpeatland
dc.subject.envegetation indices
dc.subject.plzmiany klimatu
dc.subject.plproukcja pierwotna brutto
dc.subject.plfenologia powierzchni ziemi
dc.subject.pltorfowisko
dc.subject.plindeksy roślinne
dc.titleEvaluating Remote Sensing Metrics for Land Surface Phenology in Peatlands
dc.typeJournalArticle
dspace.entity.typePublication
oaire.citation.issue1
oaire.citation.volume17