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- Publisher Website: 10.1016/j.rse.2024.114027
- Scopus: eid_2-s2.0-85184149192
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Article: Scale matters: Spatial resolution impacts tropical leaf phenology characterized by multi-source satellite remote sensing with an ecological-constrained deep learning model
Title | 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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Authors | |
Keywords | Deep learning Ecosystem deciduousness Leaf phenology Phenological diversity Satellite remote sensing Spatial resolution Spectral unmixing Tropical forest |
Issue Date | 6-Feb-2024 |
Publisher | Elsevier |
Citation | Remote Sensing of Environment, 2024, v. 304 How to Cite? |
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. |
Persistent Identifier | http://hdl.handle.net/10722/344671 |
ISSN | 2023 Impact Factor: 11.1 2023 SCImago Journal Rankings: 4.310 |
DC Field | Value | Language |
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dc.contributor.author | Song, G | - |
dc.contributor.author | Wang, J | - |
dc.contributor.author | Zhao, Y | - |
dc.contributor.author | Yang, D | - |
dc.contributor.author | Lee, CKF | - |
dc.contributor.author | Guo, Z | - |
dc.contributor.author | Detto, M | - |
dc.contributor.author | Alberton, B | - |
dc.contributor.author | Morellato, P | - |
dc.contributor.author | Nelson, B | - |
dc.contributor.author | Wu, J | - |
dc.date.accessioned | 2024-07-31T06:22:55Z | - |
dc.date.available | 2024-07-31T06:22:55Z | - |
dc.date.issued | 2024-02-06 | - |
dc.identifier.citation | Remote Sensing of Environment, 2024, v. 304 | - |
dc.identifier.issn | 0034-4257 | - |
dc.identifier.uri | http://hdl.handle.net/10722/344671 | - |
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. | - |
dc.language | eng | - |
dc.publisher | Elsevier | - |
dc.relation.ispartof | Remote Sensing of Environment | - |
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 | - |
dc.type | Article | - |
dc.identifier.doi | 10.1016/j.rse.2024.114027 | - |
dc.identifier.scopus | eid_2-s2.0-85184149192 | - |
dc.identifier.volume | 304 | - |
dc.identifier.issnl | 0034-4257 | - |