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

REMOTE SENSING OF ENVIRONMENT

卷:260

Article

Tian, Feng^1,2  Cai, Zhanzhang²  Jin, Hongxiao^2,3  Hufkens, Koen^4,5  Scheifinger, Helfried⁶  Tagesson, Torbern^2,7  Smets, Bruno⁸  Van Hoolst, Roel⁸  Bonte, Kasper⁸  Ivits, Eva⁹  Tong, Xiaoye⁷  Ardo, Jonas²  Eklundh, Lars² 

[1] Wuhan Univ, Sch Remote Sensing & Informat Engn, Wuhan, Peoples R China

[2] Lund Univ, Dept Phys Geog & Ecosyst Sci, Lund, Sweden

[3] Tech Univ Denmark, Dept Environm Engn, Lyngby, Denmark

[4] Univ Ghent, Fac Biosci Engn, Ghent, Belgium

[5] INRA, UMR ISPA, Villenave Dornon, France

[6] Klima, Zentralanstalt Meteorol & Geodynam ZAMG, Vienna, Austria

[7] Univ Copenhagen, Dept Geosci & Nat Resource Management, Copenhagen, Denmark

[8] Flemish Inst Technol Res VITO, Remote Sensing Unit, B-2400 Mol, Belgium

[9] European Environm Agcy EEA, Copenhagen, Denmark

关键词: Sentinel-2;    Vegetation phenology;    Plant phenology index (PPI);    NDVI;    EVI2;    Gross primary production (GPP);    PhenoCam;    PEP725;    Europe;   

DOI  :  10.1016/j.rse.2021.112456

来源: Elsevier

【 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 similar to 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.

【 授权许可】

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