气候变化领域集成服务门户(CCPortal): Calibrating vegetation phenology from Sentinel-2 using eddy covariance, PhenoCam, and PEP725 networks across Europe

CCPortal

DOI	10.1016/j.rse.2021.112456
	Calibrating vegetation phenology from Sentinel-2 using eddy covariance, PhenoCam, and PEP725 networks across Europe
	Tian F.; Cai Z.; Jin H.; Hufkens K.; Scheifinger H.; Tagesson T.; Smets B.; Van Hoolst R.; Bonte K.; Ivits E.; Tong X.; Ardö J.; Eklundh L.
发表日期	2021
ISSN	00344257
卷号	260
英文摘要	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. © 2021 Elsevier Inc.
英文关键词	Europe; EVI2; Gross primary production (GPP); NDVI; PEP725; PhenoCam; Plant phenology index (PPI); Sentinel-2; Vegetation phenology
语种	英语
scopus关键词	Distribution functions; Forestry; Reflection; Remote sensing; Systems engineering; Europe; EVI2; Gross primary production; Normalized difference vegetation index; Pep725; Phenocam; Plant phenology; Plant phenology index; Sentinel-2; Vegetation phenology; Vegetation
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/178825
作者单位	School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China; Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden; Department of Environmental Engineering, Technical University of Denmark, Lyngby, Denmark; Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium; INRA, UMR ISPA, Villenave d'Ornon, France; Klima, Zentralanstalt für Meteorologie und Geodynamik (ZAMG), Vienna, Austria; Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark; Remote Sensing Unit, Flemish Institute for Technological Research (VITO), Mol, B-2400, Belgium; European Environment Agency (EEA), Copenhagen, Denmark
推荐引用方式 GB/T 7714	Tian F.,Cai Z.,Jin H.,et al. Calibrating vegetation phenology from Sentinel-2 using eddy covariance, PhenoCam, and PEP725 networks across Europe[J],2021,260.
APA	Tian F..,Cai Z..,Jin H..,Hufkens K..,Scheifinger H..,...&Eklundh L..(2021).Calibrating vegetation phenology from Sentinel-2 using eddy covariance, PhenoCam, and PEP725 networks across Europe.Remote Sensing of Environment,260.
MLA	Tian F.,et al."Calibrating vegetation phenology from Sentinel-2 using eddy covariance, PhenoCam, and PEP725 networks across Europe".Remote Sensing of Environment 260(2021).