Scale matters: Spatial resolution impacts tropical leaf phenology characterized by multi-source satellite remote sensing with an ecological-constrained deep learning model
dc.contributor.author | Song, Guangqin | |
dc.contributor.author | Wang, Jing | |
dc.contributor.author | Zhao, Yingyi | |
dc.contributor.author | Yang, Dedi | |
dc.contributor.author | Lee, Calvin K.F. | |
dc.contributor.author | Guo, Zhengfei | |
dc.contributor.author | Detto, Matteo | |
dc.contributor.author | Alberton, Bruna [UNESP] | |
dc.contributor.author | Morellato, Patricia [UNESP] | |
dc.contributor.author | Nelson, Bruce | |
dc.contributor.author | Wu, Jin | |
dc.contributor.institution | The University of Hong Kong | |
dc.contributor.institution | Shenzhen Campus of Sun Yat-sen University | |
dc.contributor.institution | Brookhaven National Laboratory | |
dc.contributor.institution | Princeton University | |
dc.contributor.institution | Universidade Estadual Paulista (UNESP) | |
dc.contributor.institution | Instituto Tecnológico Vale | |
dc.contributor.institution | National Institute for Amazon Research (INPA) | |
dc.date.accessioned | 2025-04-29T20:08:23Z | |
dc.date.issued | 2024-04-01 | |
dc.description.abstract | Accurate 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. | en |
dc.description.affiliation | Research Area of Ecology and Biodiversity School of Biological Sciences The University of Hong Kong | |
dc.description.affiliation | School of Ecology Shenzhen Campus of Sun Yat-sen University, Guangdong | |
dc.description.affiliation | Department of Environmental and Climate Sciences Brookhaven National Laboratory | |
dc.description.affiliation | Department of Ecology and Evolutionary Biology Princeton University | |
dc.description.affiliation | Department of Biodiversity Bioscience Institute Sao Paulo State University UNESP, Sao Paulo | |
dc.description.affiliation | Biodiversity and Ecosystem Services Instituto Tecnológico Vale | |
dc.description.affiliation | Environmental Dynamics Department National Institute for Amazon Research (INPA) | |
dc.description.affiliationUnesp | Department of Biodiversity Bioscience Institute Sao Paulo State University UNESP, Sao Paulo | |
dc.description.sponsorship | Innovation and Technology Fund | |
dc.description.sponsorship | National Natural Science Foundation of China | |
dc.description.sponsorshipId | National Natural Science Foundation of China: 31922090 | |
dc.identifier | http://dx.doi.org/10.1016/j.rse.2024.114027 | |
dc.identifier.citation | Remote Sensing of Environment, v. 304. | |
dc.identifier.doi | 10.1016/j.rse.2024.114027 | |
dc.identifier.issn | 0034-4257 | |
dc.identifier.scopus | 2-s2.0-85184149192 | |
dc.identifier.uri | https://hdl.handle.net/11449/307089 | |
dc.language.iso | eng | |
dc.relation.ispartof | Remote Sensing of Environment | |
dc.source | Scopus | |
dc.subject | Deep learning | |
dc.subject | Ecosystem deciduousness | |
dc.subject | Leaf phenology | |
dc.subject | Phenological diversity | |
dc.subject | Satellite remote sensing | |
dc.subject | Spatial resolution | |
dc.subject | Spectral unmixing | |
dc.subject | Tropical forest | |
dc.title | Scale matters: Spatial resolution impacts tropical leaf phenology characterized by multi-source satellite remote sensing with an ecological-constrained deep learning model | en |
dc.type | Artigo | pt |
dspace.entity.type | Publication |