Scale matters: Spatial resolution impacts tropical leaf phenology characterized by multi-source satellite remote sensing with an ecological-constrained deep learning model

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorThe University of Hong Kong-
Autor(es): dc.contributorShenzhen Campus of Sun Yat-sen University-
Autor(es): dc.contributorBrookhaven National Laboratory-
Autor(es): dc.contributorPrinceton University-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorInstituto Tecnológico Vale-
Autor(es): dc.contributorNational Institute for Amazon Research (INPA)-
Autor(es): dc.creatorSong, Guangqin-
Autor(es): dc.creatorWang, Jing-
Autor(es): dc.creatorZhao, Yingyi-
Autor(es): dc.creatorYang, Dedi-
Autor(es): dc.creatorLee, Calvin K.F.-
Autor(es): dc.creatorGuo, Zhengfei-
Autor(es): dc.creatorDetto, Matteo-
Autor(es): dc.creatorAlberton, Bruna-
Autor(es): dc.creatorMorellato, Patricia-
Autor(es): dc.creatorNelson, Bruce-
Autor(es): dc.creatorWu, Jin-
Data de aceite: dc.date.accessioned2025-08-21T18:41:23Z-
Data de disponibilização: dc.date.available2025-08-21T18:41:23Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2024-04-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/j.rse.2024.114027-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/307089-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/307089-
Descrição: dc.descriptionAccurate monitoring of tropical leaf phenology, such as the leaf-on/off status, at both individual and ecosystem scales is essential for understanding and modelling tropical forest carbon and water cycles, and their sensitivity to climate change. The discrepancy between tree-crown size and pixel size (i.e., spatial resolution) across orbital sensors can affect the capability of cross-scale phenology monitoring, an aspect that remains understudied. To examine the impact of spatial resolution on tropical leaf phenology monitoring, we applied a spectral index-guided, ecologically constrained autoencoder (IG-ECAE) to automatically generate a deciduousness metric (i.e., percentage of upper canopy area that is leaf-off status within an image pixel) from simulated VIS-NIR PlanetScope data at a range of resolutions from 3 m to 30 m, as well as from VIS-NIR data of three satellite platforms with the same range of spatial resolutions (3 m PlanetScope, 10 m Sentinel-2, and 30 m Landsat-8). We compared the deciduousness metrics derived from the simulated and satellite data to corresponding measurements derived from WorldView-2 (three sites) and local phenocams (four sites) at five tropical forest sites. Our results revealed that: (1) the IG-ECAE model captured the amount of deciduousness across spatial scales, with the highest accuracy obtained from PlanetScope, followed by Sentinel-2 and Landsat-8; (2) coarser spatial resolutions led to lower accuracies in tropical deciduousness monitoring, as demonstrated by both simulated PlanetScope data across various spatial resolutions and real satellite data; and (3) while not as accurate in capturing fine-scale tropical phenological diversity as PlanetScope, Sentinel-2 provided satisfactory monitoring of deciduousness seasonality at the ecosystem level consistently across all phenocam sites, whereas Landsat-8 failed to do so. Collectively, this study provides a robust assessment for advancing cross-scale tropical leaf phenology monitoring with potential for extension to pan-tropical regions and highlights the impact of spatial resolution on such monitoring efforts.-
Descrição: dc.descriptionInnovation and Technology Fund-
Descrição: dc.descriptionNational Natural Science Foundation of China-
Descrição: dc.descriptionResearch Area of Ecology and Biodiversity School of Biological Sciences The University of Hong Kong-
Descrição: dc.descriptionSchool of Ecology Shenzhen Campus of Sun Yat-sen University, Guangdong-
Descrição: dc.descriptionDepartment of Environmental and Climate Sciences Brookhaven National Laboratory-
Descrição: dc.descriptionDepartment of Ecology and Evolutionary Biology Princeton University-
Descrição: dc.descriptionDepartment of Biodiversity Bioscience Institute Sao Paulo State University UNESP, Sao Paulo-
Descrição: dc.descriptionBiodiversity and Ecosystem Services Instituto Tecnológico Vale-
Descrição: dc.descriptionEnvironmental Dynamics Department National Institute for Amazon Research (INPA)-
Descrição: dc.descriptionDepartment of Biodiversity Bioscience Institute Sao Paulo State University UNESP, Sao Paulo-
Descrição: dc.descriptionNational Natural Science Foundation of China: 31922090-
Idioma: dc.languageen-
Relação: dc.relationRemote Sensing of Environment-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectDeep learning-
Palavras-chave: dc.subjectEcosystem deciduousness-
Palavras-chave: dc.subjectLeaf phenology-
Palavras-chave: dc.subjectPhenological diversity-
Palavras-chave: dc.subjectSatellite remote sensing-
Palavras-chave: dc.subjectSpatial resolution-
Palavras-chave: dc.subjectSpectral unmixing-
Palavras-chave: dc.subjectTropical forest-
Título: dc.titleScale matters: Spatial resolution impacts tropical leaf phenology characterized by multi-source satellite remote sensing with an ecological-constrained deep learning model-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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