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dc.contributor.authorJantol, Nela
dc.contributor.authorPrikaziuk, Egor
dc.contributor.authorCelesti, Marco
dc.contributor.authorHernandez-Sequeira, Itza
dc.contributor.authorTomelleri, Enrico
dc.contributor.authorPacheco-Labrador, Javier
dc.contributor.authorVan Wittenberghe, Shari
dc.contributor.authorPla, Filiberto
dc.contributor.authorBandopadhyay, Subhajit
dc.contributor.authorKoren, Gerbrand
dc.contributor.authorSiegmann, Bastian
dc.contributor.authorLEGOVIĆ, TARZAN
dc.contributor.authorKutnjak, Hrvoje
dc.contributor.authorCendrero-Mateo, MaPi
dc.date.accessioned2023-11-13T17:08:27Z
dc.date.available2023-11-13T17:08:27Z
dc.date.issued2023
dc.identifier.citationJANTOL, Nela, et al. Using Sentinel-2-Based Metrics to Characterize the Spatial Heterogeneity of FLEX Sun-Induced Chlorophyll Fluorescence on Sub-Pixel Scale. Remote Sensing, 2023, 15.19: 4835ca_CA
dc.identifier.issn2072-4292
dc.identifier.urihttp://hdl.handle.net/10234/204887
dc.description.abstractCurrent and upcoming Sun-Induced chlorophyll Fluorescence (SIF) satellite products (e.g., GOME, TROPOMI, OCO, FLEX) have medium-to-coarse spatial resolutions (i.e., 0.3–80 km) and integrate radiances from different sources into a single ground surface unit (i.e., pixel). However, intrapixel heterogeneity, i.e., different soil and vegetation fractional cover and/or different chlorophyll content or vegetation structure in a fluorescence pixel, increases the challenge in retrieving and quantifying SIF. High spatial resolution Sentinel-2 (S2) data (20 m) can be used to better characterize the intrapixel heterogeneity of SIF and potentially extend the application of satellite-derived SIF to heterogeneous areas. In the context of the COST Action Optical synergies for spatiotemporal SENsing of Scalable ECOphysiological traits (SENSECO), in which this study was conducted, we proposed direct (i.e., spatial heterogeneity coefficient, standard deviation, normalized entropy, ensemble decision trees) and patch mosaic (i.e., local Moran’s I) approaches to characterize the spatial heterogeneity of SIF collected at 760 and 687 nm (SIF760 and SIF687, respectively) and to correlate it with the spatial heterogeneity of selected S2 derivatives. We used HyPlant airborne imagery acquired over an agricultural area in Braccagni (Italy) to emulate S2-like top-of-the-canopy reflectance and SIF imagery at different spatial resolutions (i.e., 300, 20, and 5 m). The ensemble decision trees method characterized FLEX intrapixel heterogeneity best (R2 > 0.9 for all predictors with respect to SIF760 and SIF687). Nevertheless, the standard deviation and spatial heterogeneity coefficient using k-means clustering scene classification also provided acceptable results. In particular, the near-infrared reflectance of terrestrial vegetation (NIRv) index accounted for most of the spatial heterogeneity of SIF760 in all applied methods (R2 = 0.76 with the standard deviation method; R2 = 0.63 with the spatial heterogeneity coefficient method using a scene classification map with 15 classes). The models developed for SIF687 did not perform as well as those for SIF760, possibly due to the uncertainties in fluorescence retrieval at 687 nm and the low signal-to-noise ratio in the red spectral region. Our study shows the potential of the proposed methods to be implemented as part of the FLEX ground segment processing chain to quantify the intrapixel heterogeneity of a FLEX pixel and/or as a quality flag to determine the reliability of the retrieved fluorescence.ca_CA
dc.format.extent25 p.ca_CA
dc.format.mimetypeapplication/pdfca_CA
dc.language.isoengca_CA
dc.publisherMDPIca_CA
dc.relationProyecto SENSECOca_CA
dc.relationProyecto ERC‑2021‑STG PHOTOFLUXca_CA
dc.relation‘Integrated Remote Sens. for Biodiversity‑Ecosystem Function’ca_CA
dc.relation.isPartOfRemote Sensing, 2023, 15.19: 4835ca_CA
dc.rights© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).ca_CA
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/ca_CA
dc.subjectspatial heterogeneityca_CA
dc.subjectvegetation indicesca_CA
dc.subjectbiophysical traitsca_CA
dc.subjectSIFca_CA
dc.subjecthyperspectral sensorca_CA
dc.subjectSentinel-2ca_CA
dc.subjectFLEXca_CA
dc.subjectBraccagnica_CA
dc.titleUsing Sentinel-2-Based Metrics to Characterize the Spatial Heterogeneity of FLEX Sun-Induced Chlorophyll Fluorescence on Sub-Pixel Scaleca_CA
dc.typeinfo:eu-repo/semantics/articleca_CA
dc.identifier.doihttps://doi.org/10.3390/rs15194835
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca_CA
dc.relation.publisherVersionhttps://www.mdpi.com/2072-4292/15/19/4835ca_CA
dc.type.versioninfo:eu-repo/semantics/publishedVersionca_CA
project.funder.nameEuropean Cooperation in Science and Technology - COSTca_CA
project.funder.nameEuropean Space Agency - ESAca_CA
project.funder.nameCroatian Science Foundationca_CA
project.funder.nameConsejo Europeo de Investigación - ERCca_CA
project.funder.nameMinisterio de Ciencia e Innovación del Gobierno de Españaca_CA
project.funder.nameESA Living Planet Fellowship IRS4BEFca_CA
oaire.awardNumberCA17134ca_CA
oaire.awardNumber4000125402/18/NL/NAca_CA
oaire.awardNumberDOK‑2020‑01‑9841ca_CA
oaire.awardNumber101041768ca_CA
oaire.awardNumberIJC/2018/038039/Ica_CA
oaire.awardNumber4000125442/18/I-NSca_CA
oaire.awardNumber4000140028/22/I-DT-lrca_CA


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© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Excepto si se señala otra cosa, la licencia del ítem se describe como: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).