Characterising the Land Surface Phenology of Europe Using Decadal MERIS Data

Remote Sens. 2015, 7, 9390-9409; doi:10.3390/rs70709390 remote sensing ISSN 2072-4292 www.mdpi.com/journal/remotesensing Article Characterising the Land Surface Phenology of Europe Using Decadal MERIS Data Victor F. Rodriguez-Galiano 1,2,*, Jadunandan Dash 2 and Peter M. Atkinson 2,3,4,5 1 Physical Geography and Regional Geographic Analysis, University of Seville, Seville 41004, Spain 2 Global Environmental Change and Earth Observation Research Group, Geography and Environment, University of Southampton, Southampton SO17 1BJ, UK; E-Mails: [email protected] (J.D.); [email protected] (P.M.A.) 3 Faculty of Science and Technology, Engineering Building, Lancaster University, Lancaster LA1 4YR, UK 4 Faculty of Geosciences, University of Utrecht, Heidelberglaan 2, Utrecht 3584 CS, The Netherlands 5 School of Geography, Archaeology and Palaeoecology, Queen’s University Belfast, Belfast BT7 1NN, Northern Ireland, UK * Author to whom correspondence should be addressed; E-Mail: vr[email protected]; Tel.: +34-954-559-524. Academic Editors: Clement Atzberger and Prasad S. Thenkabail Received: 5 April 2015 / Accepted: 15 July 2015 / Published: 22 July 2015 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 ACCESS Remote 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)). Keywords: land surface phenology; MTCI; biogeographical regions; land covers; spring; autumn 1. Introduction Phenology, the study of the timing of recurring biological cycles, has emerged as an important scientific research focus as vegetation phenological events are regarded as one of the indicators of climate change [1–3]. All vegetation phenological events, such as emergence of the first leaf, flowerin,g or senescence, occur at a specific time depending upon the species, and local environmental and climatic conditions of the current and preceding months. In northern latitude regions such as Europe, temperature is one of the main drivers regulating vegetation phenological events. Thus, changes in temperature as a consequence of global warming may lead to changes in the timing of phenological events [4]. In addition, vegetation phenology is of paramount importance for the understanding of ecosystem functioning, playing an important role in gas and energy fluxes, surface albedo, and species interactions [5–7]. Given the pivotal role of this aspect of vegetation dynamics (phenology responds to climate drivers; phenology drives ecosystem functioning and land-atmosphere exchanges), there is a pressing need for routine monitoring of vegetation phenology and to understand further how these events vary over space and time. Thus, characterisation of the spatial patterns in phenology across extensive regions, as an indicator of the status of ecosystems, is important in understanding existing conditions as well as future changes. Vegetation phenological studies are currently undertaken from two different perspectives: Ground-observed phenology and satellite-estimated phenology, commonly termed land surface phenology (LSP). More recently, observations have been obtained using continuous images from fixed digital cameras looking across the landscape (e.g., Phenocam network [8]). The dates of plant phenophases have been observed traditionally through individual plant observations by individuals (e.g., citizen scientist) and in many countries there are established networks to support and coordinate these observations. Numerous ground phenological studies have been conducted in Europe [2,3,9–14] to investigate vegetation response as short- and/or long-term response to climatic drivers. These phenological studies have the advantage of long temporal coverage with many of these going back to the early 1900s, although the phenological events identified represent individual species rather than communities [15,16]. Alternatively, land surface phenology derived from satellite observation complements ground observations, allowing the mapping of vegetation phenology potentially at the global scale and providing an integrative view at the landscape level [17]. The ability of remotely sensed data to monitor inter-annual changes in phenological events is reliant on the sensitivity of vegetation Remote Sens. 2015, 7 9392 indices (used as a surrogate for monitoring vegetation growth) to changes in both leaf area and chlorophyll concentration [18]. Satellite-based information can be used to construct multi-temporal records of vegetation index data (phenological profiles) to estimate key phenological metrics (such as onset of greenness (OG), end of senescence (EOS), or length of season (LS)) [19]. Since the launch of satellite sensors, such as the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) and National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS), vegetation indices such as the Normalised Difference Vegetation Index (NDVI) have been used extensively in the study of vegetation dynamics at local-to-global scales (Table 1). However, the spatial resolution of the AVHRR dataset (mostly reported at 8 km) can be too coarse for studying complex landscapes with a highly heterogeneous vegetation cover and relief such as Europe. At this coarse resolution, the satellite sensor integrates different vegetation types and communities across different elevation ranges and with different sensitivities to climate. Therefore, some of the phenological information can be averaged or degraded at this scale [20]. Hence, there is a need for accurate characterization of phenology at a finer spatial resolution. Remote sensing and, more precisely, the design of new satellite sensors, have undergone rapid development over recent years. The new generation of sensors, such as MERIS (Medium Resolution Imaging Spectrometer), greatly increases the potential to study phenology at continental and regional scales. The Envisat MERIS sensor has many virtues [18,21]: (i) good radiometric quality; (ii) well-placed spectral sampling at visible and near-infrared (NIR) wavelengths, coupled with narrow bands that theoretically increase the accuracy of vegetation monitoring: (iii) and a spatial resolution up to 300 m (at nadir). Studying phenological trends requires long time-series, and studies in continental Europe using these more sophisticated satellite sensors are scarce. Although, the continuity of MERIS has been interrupted from 2012, the availability of a time-series of 10 years of Terrestrial Chlorophyll Index (MTCI) data at 1 km spatial resolution provides the potential opportunity for a new characterisation of vegetation phenology across Europe. This spatially continuous phenological information covering the entire European continent is not yet available for the last decade. Several LSP studies that used other satellite sensors such as MODIS or SPOT (Satellite Pour l'Observation de la Terre) VGT (Vegetation) exist at a spatial resolution equal to or finer than 1 km for different regions of Europe (see Table 1), such as: Fennoscandia [22], Ireland [23], Boreal Eurasia [24], and Western Europe [25]. However, at this spatial resolution, studies at a continental level are scarce [26], especially when focused on the last decade [25,27], and none of them address the characterisation of different types of vegetation in different biogeographic regions. The overall objective of this research was to characterise the spatial distribution of the main phenological events (OG, EOS, and LS) for the pan-European continent in the last decade at a spatial resolution of 1 km. A secondary objective was to characterise the phenology of the main natural vegetation types in different biogeographical regions of Europe. This aspect is critical because many previous studies have not stratified phenological characteristics by land cover class and biogeographical region in Europe. Our approach establishes a potential reference for evaluating the susceptibility of vegetation to global climatic changes, and allows comparison of how different regions could respond under climatic conditions. The spatio-temporal reliability of the LSP estimates presented in this study has already been assessed in a separate study [28] using ground phenology observations for a large number of points of different deciduous tree plant species collected across Europe. Remote Sens. 2015, 7 9393 Table 1. Land surface phenology studies in Europe. AVHRR, Advanced Very High Resolution Radiometer; GIMMS, Global Inventory Modelling and Mapping Studies; PAL, Path finder AVHRR Land; MODIS, Moderate Resolution Imaging Spectroradiometer; SPOT, Satellite Pour l'Observation de la Terre. Study Year of Publication Study Area Type Resolution Period Composite Period Vegetation Index Myneni, et al. [29] 1997 Global GIMMS AVHRR 8 km 1981–1991 10 days NDVI Tucker, et al. [30] 2001 Northern Hemisphere AVHRR 8 km 1982–1999 7 days NDVI Zhou, et al. [31] 2001 Eurasia GIMMS AVHRR 9 km 1981–1999 15 days NDVI Stockli and Vidale [32] 2004 Europe PAL AVHRR 8 Km 1982–2001 10 days NDVI Tateishi and Ebata [33] 2004 Global PAL AVHRR 8 Km 1982–2000 10 days NDVI Beck, Atzberger, Høgda, Johansen and Skidmore [22] 2006 Finland Norway and Sweden MODIS 250 m 2000–2004 16 days NDVI Karlsen, et al. [34] 2007 Fennoscandia GIMMS AVHRR 8 Km 1982–2002 15 days NDVI Delbart, Picard, Le Toans, Kergoat, Quegan, Woodward, Dye and Fedotova [24] 2008 Boreal Eurasia SPOT VGT 1 Km 1998–2005 10 days NDVI Maignan, et al. [35] 2008 Europe PAL AVHRR 8 Km 1982–1999 1 day DVI Julien and Sobrino [36] 2009 Global GIMMS AVHRR 8 Km 1981–2003 10 days NDVI De Beurs and Henebry [37] 2010 Eurasia PAL AVHRR 8 km 1981–1999 10 days NDVI Hogda, et al. [38] 2011 Fennoscandia GIMMS AVHRR 8 Km 1982–2011 15 days NDVI Jeong, et al. [39] 2011 Northern Hemisphere GIMMS AVHRR 8 km 1982–2008 15 days NDVI Ivits, et al. [40] 2012 Europe GIMMS AVHRR 8 Km 1982–2006 15 days NDVI O’Connor, Dwyer, Cawkwell and Eklundh [23] 2012 Ireland MERIS 1.2 Km 2003–2009 10 days MGVI Atzberger, et al. [41] 2013 Europe GIMMS AVHRR MODIS 8 Km 2003–2011 15 days NDVI Hamunyela, Verbesselt, Roerink and Herold [25] 2013 Western Europe MODIS 250 m 2001–2011 16 days NDVI Han, Luo and Li [26] 2013 Europe SPOT VGT 1 Km 1999–2005 10 days NDVI Klisch, Atzberger and Luminari [27] 2014 Europe MODIS 250 m 2003–2011 16 days NDVI Zhang, et al. [42] 2014 Global AVHRR and MODIS 5 Km 1982–2010 3 days EVI Remote Sens. 2015, 7 9394 2. Data and Methods 2.1. Dataset Three data sources were used for this research, they include: (i) temporal composites of MERIS MTCI; (ii) GlobCover 2009 land cover map; and (iii) a classification of the European biogeographical regions. The MERIS MTCI is a ratio of the difference in reflectance between band 10 and band 9 and the difference in reflectance between band 9 and band 8 of the MERIS standard band setting. Bands 10, 9, and 8 correspond to wavelengths 753.75 nm, 708.75 nm, and 681.25 nm, respectively [21]. The MTCI is a surrogate of canopy chlorophyll content, and therefore provides a good estimation of photosynthetically active period of vegetation growth. We used composites of MERIS MTCI data at 1 km spatial resolution from 2002 to 2012. Composite periods were equal to eight days for the period from 2002–2007 and ten days for 2008–2012. This dataset was supplied by the European Space Agency and processed by Airbus Defence and Space. MTCI data were composited from the MERIS L2 product using an arithmetic mean and flux conservation resampling algorithm. The arithmetic mean is less sensitive to temporal biases and produces consistent images in both the spatial and temporal domains. Land cover information was acquired from the Global Land Cover Classification (GlobCover2009) dataset at 300 m spatial resolution [43]. Currently, the GlobCover2009 dataset, derived from MERIS data, is the most detailed and recent global land cover map available with a hierarchical thematic legend consisting of 22 classes. Globcover2009 was selected for its greater consistency with MERIS MTCI time-series and its high geolocational accuracy (<150 m) [44]. Homogeneous biogeographical regions were used to stratify the European landscape at an aggregate level of ecological complexity. The map of biogeographical regions was based on the official delineations used in the Habitats Directive (92/43/EEC) and for the EMERALD Network set up under the Convention on the Conservation of European Wildlife and Natural Habitats (Bern Convention). This map categorizes Europe into 11 relatively homogeneous regions at a map scale of 1:1,000,000. 2.2. Phenology Estimates The time-series MTCI data for every estimation period (i.e., year) used 1.5 years of data (from October in the previous year to the month of July in the next year) to estimate both the OG and EOS for every pixel. This length was used because the annual pattern spans across calendar years and hence complete information about LSP would not be obtained using single year data. The yearly values of OG and EOS were estimated for each image pixel of the study area using the methodology described in Dash, Jeganathan and Atkinson [18]. This methodology consists of two major procedures: data smoothing and extraction of phenological metrics. The MERIS MTCI is a ratio of the difference in reflectance between band 10 and band 9 and the difference in reflectance between band 9 and band 8 of the MERIS standard band setting. Bands 10, 9 and 8 correspond to wavelengths 753.75 nm, 708.75 nm, and 681.25 nm, respectively. The MTCI time-series was smoothed using the Fourier method [45,46]. Although, there is a lack of general consensus regarding the robustness of smoothing methods and its performance can vary depending on data, Atkinson, et al. [47] showed that Fourier methods have a competitive performance when applied to Remote Sens. 2015, 7 9395 MERIS MTCI data and require the specification of one parameter only, the number of harmonics for reconstruction. The first six Fourier components (mean + five harmonics) were used because they reproduced satisfactorily the original annual growth profile, obtaining a good fit between raw and smoothed MTCI time-series. Figure 1 shows some examples of raw MTCI and smoothed time-series as well as the extracted phenology metrics for different land cover types. Figure 1. Inter-annual variation in MTCI values and phenology metrics for major natural vegetation types. (a) Broadleaved deciduous forests; (b) needleleaved evergreen forests; (c) grasslands; (d) shrublands. The first derivative (difference between one composite value and its immediate preceding neighbour) of MTCI time-series was computed to locate the annual maximum. This peak was defined as a point on the phenological profile where the curvature changes the sign before and after a negative and positive trend of five successive composites, respectively. Next, the derived data were searched backward and Remote Sens. 2015, 7 9396 forward departing at the maximum annual peak to estimate the OG and EOS, respectively. OG was defined as a valley at the beginning of the growing season point (a change in derivative value from positive to negative) and EOS was defined as a valley point occurring at the decaying end of a phenology cycle (a change in derivative value from negative to positive) [18]. The searching function stops when a valley peak greater than one-fifth of the annual maximum peak is reached. The composite number identified during the phenology estimation procedure was then converted to Julian days, assigning the date of the last day of the composite period. Figure 1 shows some examples of MTCI time-series for different land covers and the estimated phenology metrics. The length of the season was equal to the number of days between OG and EOS. The phenological information over homogeneous pixels was grouped and analysed in terms of each natural vegetation land cover and different biogeographical regions. The statistical significance of the differences in OG and EOS between different biogeographical regions was assessed through a two-tailed t-test. A median phenological map was produced from the 10 years of time-series data to characterise the spatial variation of phenology across Europe. The 10-year time-series analysed overcomes some limitations in the satellite sensor data quality for a specific year and minimises the inter-annual variation in LSP metrics. 3. Results 3.1. Spatial Variation in Phenological Metrics Figure 2 shows the OG, EOS, and LS of the vegetation of the pan-European continent. OG dates are widely distributed from -10 days (approximately 21 December) to 150 days (30 May): later OG dates were observed at higher latitudes and vice versa. Earlier OG is associated mainly with rainfed croplands in the south of Europe and grasslands in Ireland and the southwest of England and Wales. On the other hand, later OG occurs in Fennoscandia and the north of Russia and in mountainous areas such as the Carpathians or the Caucasus. The progressive patterns in phenophases from low to high latitudes are attributed mainly to temperature variation [48], but also to the photoperiod and winter chilling requirements for some temperate tree species [5]. In terms of longitudinal variation, the OG of the Atlantic and Mediterranean basin regions is earlier than the OG of the Continental region. This is likely due to different vegetation responses to climatic conditions (i.e., temperature and precipitation) [39]. This large variability in phenophases could be explained not only by the difference in climatic conditions between coastal and interior continental regions, but also by the effect of changes in altitude associated with the mountain ridges. The EOS dates range from 150 days (30 May) to 390 days (25 January), and their spatial distribution is generally the reverse of that of the OG dates for natural vegetation (Figure 2b). The later OG is generally coincident with a delayed EOS. However, this is only applicable to regions of natural or semi-natural vegetation, whereas cropland areas are characterised by earlier OG and EOS. Hence, regions with a later EOS exhibit longer length than regions with an earlier OG (Figure 2c). Note that there exist areas in Western Europe mainly associated with crops which present earlier dates for both OG and EOS. Remote Sens. 2015, 7 9397 Figure 2. Phenology maps of Europe. (a) Onset of greenness (OG); (b) end of senescence (EOS); (c) length of the season (LS). Remote Sens. 2015, 7 9398 Figure 3. Standard deviation of phenology estimates. (a) Spatial distribution of different land cover types (DF); (b) onset of greenness (OG); (c) end of senescence (EOS). Remote Sens. 2015, 7 9405 defined in terms of vegetation type and biogeographical region. In line with expectations, incremental latitudinal variation was observed for OG, whereas the opposite pattern was found for EOS, when considering natural vegetation. However, this latitudinal gradient was interrupted in mountainous regions. In terms of longitude, coastal regions experienced an earlier OG and more delayed EOS than interior regions. Statistically significant differences existed between biogeographical regions in terms of the dates of both OG and EOS for all land cover types considered in this study. The phenology maps produced here represent one of the most comprehensive and recent assessments of the land surface phenology of continental Europe at a 1 km spatial resolution. The spatial variability of phenology can be used for tuning models of carbon emissions and can play an important role in monitoring programmes for the management of natural vegetation. Further research is needed to study and understand the main drivers of LSP in Europe among different land covers and biogeographic regions. Acknowledgments The first author is a Marie Curie Grant holder (Ref. FP7-PEOPLE-2012-IEF- 331667). The authors are grateful for the financial support given by the European Commission under the 7th Framework Programme, the Spanish MINECO (project BIA2013-43462-P), and Junta de Andalucía (Group RNM122). 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