Plant phenology, the annually recurring sequence of plant developmental stages, is important for plant functioning and ecosystem services and their biophysical and biogeochemical feedbacks to the ...climate system. Plant phenology depends on temperature, and the current rapid climate change has revived interest in understanding and modeling the responses of plant phenology to the warming trend and the consequences thereof for ecosystems. Here, we review recent progresses in plant phenology and its interactions with climate change. Focusing on the start (leaf unfolding) and end (leaf coloring) of plant growing seasons, we show that the recent rapid expansion in ground‐ and remote sensing‐ based phenology data acquisition has been highly beneficial and has supported major advances in plant phenology research. Studies using multiple data sources and methods generally agree on the trends of advanced leaf unfolding and delayed leaf coloring due to climate change, yet these trends appear to have decelerated or even reversed in recent years. Our understanding of the mechanisms underlying the plant phenology responses to climate warming is still limited. The interactions between multiple drivers complicate the modeling and prediction of plant phenology changes. Furthermore, changes in plant phenology have important implications for ecosystem carbon cycles and ecosystem feedbacks to climate, yet the quantification of such impacts remains challenging. We suggest that future studies should primarily focus on using new observation tools to improve the understanding of tropical plant phenology, on improving process‐based phenology modeling, and on the scaling of phenology from species to landscape‐level.
This review examines the recent progresses in plant phenology and its interactions with climate change. Over its long history, phenology has grown from an empirical subject of observing and recording the timing of annual natural events for specific species to a comprehensive field that involves expanded observations, experiments, modeling, as well as the ecological consequences and climatic feedbacks of phenological changes. This review also emphasizes the need for future studies on the understanding of tropical plant phenology with new tools, on improving process‐based modeling, and on the scaling of phenology from species level to landscape level.
Land Surface Phenology (LSP) is the most direct representation of intra‐annual dynamics of vegetated land surfaces as observed from satellite imagery. LSP plays a key role in characterizing ...land‐surface fluxes, and is central to accurately parameterizing terrestrial biosphere–atmosphere interactions, as well as climate models. In this article, we present an evaluation of Pan‐European LSP and its changes over the past 30 years, using the longest continuous record of Normalized Difference Vegetation Index (NDVI) available to date in combination with a landscape‐based aggregation scheme. We used indicators of Start‐Of‐Season, End‐Of‐Season and Growing Season Length (SOS, EOS and GSL, respectively) for the period 1982–2011 to test for temporal trends in activity of terrestrial vegetation and their spatial distribution. We aggregated pixels into ecologically representative spatial units using the European Landscape Classification (LANMAP) and assessed the relative contribution of spring and autumn phenology. GSL increased significantly by 18–24 days decade⁻¹ over 18–30% of the land area of Europe, depending on methodology. This trend varied extensively within and between climatic zones and landscape classes. The areas of greatest growing‐season lengthening were the Continental and Boreal zones, with hotspots concentrated in southern Fennoscandia, Western Russia and pockets of continental Europe. For the Atlantic and Steppic zones, we found an average shortening of the growing season with hotspots in Western France, the Po valley, and around the Caspian Sea. In many zones, changes in the NDVI‐derived end‐of‐season contributed more to the GSL trend than changes in spring green‐up, resulting in asymmetric trends. This underlines the importance of investigating senescence and its underlying processes more closely as a driver of LSP and global change.
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.
•Several aspects of producing a new Sentinel-2 phenology product are studied.•NDVI, EVI2, and PPI data are compared to GPP and GCC data for phenology mapping.•Phenometrics from various amplitude thresholds are linked to PEP725 phenophases.•PPI shows an overall advantage over NDVI and EVI2 for phenology mapping in Europe.•Impact of BRDF correction on Sentinel-2 phenology mapping is found to be minimal.
Vegetation dynamics and phenology play an important role in inter-annual vegetation changes in terrestrial ecosystems and are key indicators of climate-vegetation interactions, land use/land cover ...changes and variation in year-to-year vegetation productivity. Satellite remote sensing data have been widely used for vegetation phenology monitoring over large geographic domains using various types of observations and methods over the past several decades. The goal of this paper is to present a detailed review of existing methods for phenology detection and emerging new techniques based on the analysis of time-series, multispectral remote sensing imagery. This paper summarizes the objective and applications of detecting general vegetation phenology stages (e.g., green onset, time or peak greenness and growing season length) often termed ‘land surface phenology’, as well as more advanced methods that estimate species-specific phenological stages (e.g., silking stage of maize). Common data processing methods, such as data smoothing, applied to prepare the time-series remote sensing observations to be applied to phenological detection methods are presented. Specific land surface phenology detection methods as well as species-specific phenology detection methods based on multispectral satellite data are then discussed. The impact of different error sources in the data on remote-sensing based phenology detection are also discussed in detail, as well as ways to reduce these uncertainties and errors. Joint analysis of multi-scale observations ranging from satellite to more recent ground-based sensors is helpful for us to understand satellite-based phenology detection mechanism and extent phenology detection to regional scale in the future. Finally, emerging opportunities to further advance remote sensing of phenology is presented that includes observations from Cubesats, near-surface observations such as PhenoCams and image data fusion techniques to improve the spatial resolution of time-series image data sets needed for phenological characterization.
•Review of satellite remote sensing-based land surface phenology detection methods.•Discussion of advantages and drawbacks of phenological metrics extraction methods.•Review of error sources and methods to reduce their effects on phenology detection.•Opportunities and challenges related to improve phonological metrics extraction.
The timing of the end of the vegetation growing season (EOS) plays a key role in terrestrial ecosystem carbon and nutrient cycles. Autumn phenology is, however, still poorly understood, and previous ...studies generally focused on few species or were very limited in scale. In this study, we applied four methods to extract EOS dates from NDVI records between 1982 and 2011 for the Northern Hemisphere, and determined the temporal correlations between EOS and environmental factors (i.e., temperature, precipitation and insolation), as well as the correlation between spring and autumn phenology, using partial correlation analyses. Overall, we observed a trend toward later EOS in ~70% of the pixels in Northern Hemisphere, with a mean rate of 0.18 ± 0.38 days yr−1. Warming preseason temperature was positively associated with the rate of EOS in most of our study area, except for arid/semi‐arid regions, where the precipitation sum played a dominant positive role. Interestingly, increased preseason insolation sum might also lead to a later date of EOS. In addition to the climatic effects on EOS, we found an influence of spring vegetation green‐up dates on EOS, albeit biome dependent. Our study, therefore, suggests that both environmental factors and spring phenology should be included in the modeling of EOS to improve the predictions of autumn phenology as well as our understanding of the global carbon and nutrient balances.
Climatic warming has lengthened the photosynthetically active season in recent decades, thus affecting the functioning and biogeochemistry of ecosystems, the global carbon cycle and climate. ...Temperature response of carbon uptake phenology varies spatially and temporally, even within species, and daily total intensity of radiation may play a role. We empirically modelled the thresholds of temperature and radiation under which daily carbon uptake is constrained in the temperate and cold regions of the Northern Hemisphere, which include temperate forests, boreal forests, alpine and tundra biomes. The two‐dimensionality of the temperature‐radiation constraint was reduced to one single variable, θ, which represents the angle in a polar coordinate system for the temperature‐radiation observations during the start and end of the growing season. We found that radiation will constrain the trend towards longer growing seasons with future warming but differently during the start and end of season and depending on the biome type and region. We revealed that radiation is a major factor limiting photosynthetic activity that constrains the phenology response to temperature during the end‐of‐season. In contrast, the start of the carbon uptake is overall highly sensitive to temperature but not constrained by radiation at the hemispheric scale. This study thus revealed that while at the end‐of‐season the phenology response to warming is constrained at the hemispheric scale, at the start‐of‐season the advance of spring onset may continue, even if it is at a slower pace.
The constraint of temperature and radiation on vegetation productivity was estimated with the variable θ. Values of θ differ during the start and end of season (SoS and EoS), indicating that radiation constraints the trend towards longer growing seasons but differently during spring and autumn. While the phenology response to future warming is overall constrained at the end of season, the advance of spring onset may continue.
Проанализированы особенности сезонного развития яблони на коллекционном участ-ке Ботанического сада им. Вс.М. Крутовского. Сопоставлены фено фазы, характеризующиеся наи-большей межсортовой ...изменчивостью. Выделены деревья и сорта с ранним и поздним вступлением в основные фенологические фазы.
Changes in phenology induced by climate change occur across the globe with important implications for ecosystem functioning and services, species performance and trophic interactions. Much of the ...work on phenology, especially leaf out and flowering, has been conducted on woody plant species. Less is known about the responses in phenology of herbaceous species induced by global change even though they represent a large and important part of biodiversity worldwide. A globally coordinated research effort is needed to understand the drivers and implications of such changes and to predict effects of global change on plant species phenology and related ecosystem processes.
Here, we present the rationale of the PhenObs initiative—botanical gardens as a global phenological observation network. The initiative aims to collect data on plant phenology in botanical gardens which will be used alongside information on plant traits and site conditions to answer questions related to the consequences of global change:
What is the variation in plant phenology in herbaceous species across the growing season and in response to changes in climate?
How can plant phenology be predicted from species’ trait composition, provenance, position and extent of the distribution range and species’ phylogeny?
What are the implications of this variation with respect to species performance and assembly, biotic interactions (e.g. plant–pollinator interactions) as well as ecosystem processes and services under changing land use and climate?
Here, we lay out the development of a straightforward protocol that is appropriate for monitoring phenology across a vast diversity of growth forms of herbaceous species from various habitats and geographical regions.
To focus on a key number of stages necessary to capture all aspects of plant species phenology, we analysed associations between 14 phenological stages. These data were derived from a 2‐year study on 199 species in four German botanical gardens.
Based on the relationships of the phenological stages, we propose to monitor three vegetative stages (‘initial growth’, ‘leaves unfolding’ and ‘senescence’) and two reproductive stages (‘flowers open’ and ‘ripe fruits’) to fully capture herbaceous species phenology.
A free Plain Language Summary can be found within the Supporting Information of this article.
A free Plain Language Summary can be found within the Supporting Information of this article.
The frequent acquisitions of fine spatial resolution imagery (10 m) offered by recent multispectral satellite missions, including Sentinel-2, can resolve single agricultural fields and thus provide ...crop-specific phenology metrics, a crucial information for crop monitoring. However, effective phenology retrieval may still be hampered by significant cloud cover. Synthetic aperture radar (SAR) observations are not restricted by weather conditions, and Sentinel-1 thus ensures more frequent observations of the land surface. However, these data have not been systematically exploited for phenology retrieval so far. In this study, we extracted crop-specific land surface phenology (LSP) from Sentinel-1 and Sentinel-2 of major European crops (common and durum wheat, barley, maize, oats, rape and turnip rape, sugar beet, sunflower, and dry pulses) using ground-truth information from the “Copernicus module” of the Land Use/Cover Area frame statistical Survey (LUCAS) of 2018. We consistently used a single model-fit approach to retrieve LSP metrics on temporal profiles of CR (Cross Ratio, the ratio of the backscattering coefficient VH/VV from Sentinel-1) and NDVI (Normalized Difference Vegetation Index from Sentinel-2). Our analysis revealed that LSP retrievals from Sentinel-1 are comparable to those of Sentinel-2, particularly for winter crops. The start of season (SOS) timings, as derived from Sentinel-1 and -2, are significantly correlated (average r of 0.78 for winter and 0.46 for summer crops). The correlation is lower for end of season retrievals (EOS, r of 0.62 and 0.34). Agreement between LSP derived from Sentinel-1 and -2 varies among crop types, ranging from r = 0.89 and mean absolute error MAE = 10 days (SOS of dry pulses) to r = 0.15 and MAE = 53 days (EOS of sugar beet). Observed deviations revealed that Sentinel-1 and -2 LSP retrievals can be complementary; for example for winter crops we found that SAR detected the start of the spring growth while multispectral data is sensitive to the vegetative growth before and during winter. To test if our results correspond reasonably to in-situ data, we compared average crop-specific LSP for Germany to average phenology from ground phenological observations of 2018 gathered from the German Meteorological Service (DWD). Our study demonstrated that both Sentinel-1 and -2 can provide relevant and at times complementary LSP information at field- and crop-level.
•Crop-specific phenology retrieved from Senetinel-1 and Sentinel-2 time series.•European Union wide survey LUCAS exploited to focus on major European crops.•Comparable phenology from cross polarization ratio VH/VV and from Sentinel-2 NDVI.•Retrieved phenology consistent with ground phenological observation from DWD.•Sentinel-1 and -2 provide relevant and at times complementary LSP information.
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