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Calibrating vegetation phenology from Sentinel-2 using eddy covariance, PhenoCam, and PEP725 networks across Europe

Tian, Feng LU ; Cai, Zhanzhang LU ; Jin, Hongxiao LU ; Hufkens, Koen ; Scheifinger, Helfried ; Tagesson, Torbern LU ; Smets, Bruno ; Van Hoolst, Roel ; Bonte, Kasper and Ivits, Eva , et al. (2021) In Remote Sensing of Environment 260.
Abstract

Vegetation phenology obtained from time series of remote sensing data is relevant for a range of ecological applications. The freely available Sentinel-2 imagery at a 10 m spatial resolution with a ~ 5-day repeat cycle provides an opportunity to map vegetation phenology at an unprecedented fine spatial scale. To facilitate the production of a Europe-wide Copernicus Land Monitoring Sentinel-2 based phenology dataset, we design and evaluate a framework based on a comprehensive set of ground observations, including eddy covariance gross primary production (GPP), PhenoCam green chromatic coordinate (GCC), and phenology phases from the Pan-European Phenological database (PEP725). We test three vegetation indices (VI) — the normalized... (More)

Vegetation phenology obtained from time series of remote sensing data is relevant for a range of ecological applications. The freely available Sentinel-2 imagery at a 10 m spatial resolution with a ~ 5-day repeat cycle provides an opportunity to map vegetation phenology at an unprecedented fine spatial scale. To facilitate the production of a Europe-wide Copernicus Land Monitoring Sentinel-2 based phenology dataset, we design and evaluate a framework based on a comprehensive set of ground observations, including eddy covariance gross primary production (GPP), PhenoCam green chromatic coordinate (GCC), and phenology phases from the Pan-European Phenological database (PEP725). We test three vegetation indices (VI) — the normalized difference vegetation index (NDVI), the two-band enhanced vegetation index (EVI2), and the plant phenology index (PPI) — regarding their capability to track the seasonal trajectories of GPP and GCC and their performance in reflecting spatial variabilities of the corresponding GPP and GCC phenometrics, i.e., start of season (SOS) and end of season (EOS). We find that for GPP phenology, PPI performs the best, in particular for evergreen coniferous forest areas where the seasonal variations in leaf area are small and snow is prevalent during wintertime. Results are inconclusive for GCC phenology, for which no index is consistently better than the others. When comparing to PEP725 phenology phases, PPI and EVI2 perform better than NDVI regarding the spatial correlation and consistency (i.e., lower standard deviation). We also link VI phenometrics at various amplitude thresholds to the PEP725 phenophases and find that PPI SOS at 25% and PPI EOS at 15% provide the best matches with the ground-observed phenological stages. Finally, we demonstrate that applying bidirectional reflectance distribution function correction to Sentinel-2 reflectance is a step that can be excluded for phenology mapping in Europe.

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organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Europe, EVI2, Gross primary production (GPP), NDVI, PEP725, PhenoCam, Plant phenology index (PPI), Sentinel-2, Vegetation phenology
in
Remote Sensing of Environment
volume
260
article number
112456
publisher
Elsevier
external identifiers
  • scopus:85105696213
ISSN
0034-4257
DOI
10.1016/j.rse.2021.112456
project
Copernicus High-Resolution Vegetation Phenology and Productivity
language
English
LU publication?
yes
id
dceaa1f1-81da-4164-87aa-07c725407885
date added to LUP
2021-06-01 09:17:59
date last changed
2022-04-27 02:08:59
@article{dceaa1f1-81da-4164-87aa-07c725407885,
  abstract     = {{<p>Vegetation phenology obtained from time series of remote sensing data is relevant for a range of ecological applications. The freely available Sentinel-2 imagery at a 10 m spatial resolution with a ~ 5-day repeat cycle provides an opportunity to map vegetation phenology at an unprecedented fine spatial scale. To facilitate the production of a Europe-wide Copernicus Land Monitoring Sentinel-2 based phenology dataset, we design and evaluate a framework based on a comprehensive set of ground observations, including eddy covariance gross primary production (GPP), PhenoCam green chromatic coordinate (GCC), and phenology phases from the Pan-European Phenological database (PEP725). We test three vegetation indices (VI) — the normalized difference vegetation index (NDVI), the two-band enhanced vegetation index (EVI2), and the plant phenology index (PPI) — regarding their capability to track the seasonal trajectories of GPP and GCC and their performance in reflecting spatial variabilities of the corresponding GPP and GCC phenometrics, i.e., start of season (SOS) and end of season (EOS). We find that for GPP phenology, PPI performs the best, in particular for evergreen coniferous forest areas where the seasonal variations in leaf area are small and snow is prevalent during wintertime. Results are inconclusive for GCC phenology, for which no index is consistently better than the others. When comparing to PEP725 phenology phases, PPI and EVI2 perform better than NDVI regarding the spatial correlation and consistency (i.e., lower standard deviation). We also link VI phenometrics at various amplitude thresholds to the PEP725 phenophases and find that PPI SOS at 25% and PPI EOS at 15% provide the best matches with the ground-observed phenological stages. Finally, we demonstrate that applying bidirectional reflectance distribution function correction to Sentinel-2 reflectance is a step that can be excluded for phenology mapping in Europe.</p>}},
  author       = {{Tian, Feng and Cai, Zhanzhang and Jin, Hongxiao and Hufkens, Koen and Scheifinger, Helfried and Tagesson, Torbern and Smets, Bruno and Van Hoolst, Roel and Bonte, Kasper and Ivits, Eva and Tong, Xiaoye and Ardö, Jonas and Eklundh, Lars}},
  issn         = {{0034-4257}},
  keywords     = {{Europe; EVI2; Gross primary production (GPP); NDVI; PEP725; PhenoCam; Plant phenology index (PPI); Sentinel-2; Vegetation phenology}},
  language     = {{eng}},
  publisher    = {{Elsevier}},
  series       = {{Remote Sensing of Environment}},
  title        = {{Calibrating vegetation phenology from Sentinel-2 using eddy covariance, PhenoCam, and PEP725 networks across Europe}},
  url          = {{http://dx.doi.org/10.1016/j.rse.2021.112456}},
  doi          = {{10.1016/j.rse.2021.112456}},
  volume       = {{260}},
  year         = {{2021}},
}