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 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.
Highlights
Vegetation phenology is the timing of repeated biological events, e. g., leaf emergence, flowering, leaf coloration, and leaf fall (Lieth, 1974; Richardson et al, 2013)
Seasonal patterns of the vegetation index (VI) show distinctly different temporal pat terns, to the differences between gross primary production (GPP) and green chromatic coordinate (GCC), which can be attributed to their different functional meanings (GPP expressing pro ductivity vs. GCC expressing canopy colors)
plant phenology index (PPI) is closer to the GPP curve, normalized difference vegetation index (NDVI) is closer to the GCC curve, whereas EVI2 is inbetween (Fig. 4)
Summary
Vegetation phenology is the timing of repeated biological events, e. g., leaf emergence, flowering, leaf coloration, and leaf fall (Lieth, 1974; Richardson et al, 2013). The later MODerate resolution Imaging Spectroradiometer (MODIS) sensor improved vegetation monitoring capabilities with a spatial resolution of 250–500 m and a daily revisit period since the year 2000 (Justice et al, 2002) These two sensors have been widely used for regional to global scale phenology studies due to their long-term records, high temporal resolution, and ready-to-use vegetation index (VI) products, and they have greatly advanced our understanding of vegetation phenology and its responses to global change (Brown et al, 2010; de Beurs and Henebry, 2004; Jeong et al, 2011; Malingreau, 1986; Myneni et al, 1997; Piao et al, 2019; Tong et al, 2019; White et al, 1997; Wu et al, 2018; Zhang et al, 2004). To distinguish remotely sensed phenology from in-situ monitoring of spe cific plant phenophases (e.g., bud-burst, flowering, and leaf-fall), the term land surface phenology is often used (de Beurs and Henebry, 2004; Ganguly et al, 2010; Schwartz and Reed, 1999; White and Nemani, 2006)
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