Characterising the Land Surface Phenology of Europe Using Decadal MERIS Data

Abstract

Land surface phenology (LSP), the study of the timing of recurring cycles of changes in the land surface using time-series of satellite sensor-derived vegetation indices, is a valuable tool for monitoring vegetation at global and continental scales. Characterisation of LSP and its spatial variation is required to reveal and predict ongoing changes in Earth system dynamics. This study presents and analyses the LSP of the pan-European continent for the last decade, considering three phenological metrics: onset of greenness (OG), end of senescence (EOS), and length of season (LS). The whole time-series of Multi-temporal Medium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI) data at 1 km spatial resolution was used to estimate the phenological metrics. Results show a progressive pattern in phenophases from low to high latitudes. OG dates are distributed widely from the end of December to the end of May. EOS dates range from the end of May to the end of January and the spatial distribution is generally the inverse of that of the OG. Shorter growing seasons (approximately three months) are associated with rainfed croplands in Western Europe, and forests in boreal and mountainous areas. Maximum LS values appear OPEN ACCESSRemote Sens. 2015, 7 9391 in the Atlantic basin associated with grasslands. The LSP maps presented in this study are supported by the findings of a previous study where OG and EOS estimates were compared to those of the pan-European phenological network at certain locations corresponding to numerous observations of deciduous tree plant species. Moreover, the spatio-temporal pattern of the OG and EOS produced close agreement with the dates of deciduous tree leaf unfolding and autumnal colouring, respectively (pseudo R-squared equal to 0.70 and 0.71 and root mean square error of six days (over 365 days)).

Mapping Periodic Patterns of Global Vegetation Based on Spectral Analysis of NDVI Time Series

“…In these areas, vegetation dynamics show highly stable intra-and inter-annual patterns specially in the "very cold" and "cold" climates ( Figure 3b-c), resulting probably from the low inter-annual variability of temperature and illumination as main climatic constraints for vegetation growth in these regions [77,78]. Other studies based on phenometric approach have shown similar results with low variability in greenness onset and/or end of senescence [79] and length of growing season [80].…”

Spatiotemporal Variability of Land Surface Phenology in China from 2001–2014

“…Field observations have been widely and successfully conducted, providing detailed information at the species level based on individual plant observations across many countries [8], and observation networks have been established to support these ongoing observations [9,10]. A large number of studies have been conducted to observe vegetation phenology at short-and/or long-term scales across many regions [11], but these observations have focused mainly on developmental switches within individual species [12] and cannot reveal integrative phenology patterns on the biome or broader scale, and this research is often time inefficient and labor intensive [1,13]. Alternatively, satellite-based methods, which rely on vegetation indexes (e.g., NDVI) provide a powerful, integrative, and objective tool for monitoring and characterizing key phenological metrics (such as the start of the season (SOS), end of the season (EOS), and length of the season (LOS)) at regional and global scales across long time-series, serving as an excellent complement to the shortage of field observations [14].…”

Regional detection of canopy nitrogen in Mediterranean forests using the spaceborne MERIS Terrestrial Chlorophyll Index

“…Furthermore, MTCI has been used to discriminate between C3 and C4 grasses (Foody and Dash, 2007) and to monitor vegetation phenology at the sub-regional (Boyd et al, 2011) and continental scales (Rodriguez-Galiano et al, 2015;Crabbe et al, 2016). Regarding canopy N detection, most applications were aimed at agricultural crops using MTCI values computed from in situ hyperspectral reflectance data (Tian et al, 2011;Clevers and Gitelson, 2013;Li et al, 2014).…”