Vegetation Phenology Research Articles

Local environment contributes to shape phenological patterns in Mauritia flexuosa L.f

AimsHere, we characterized the vegetative and reproductive phenology of Mauritia flexuosa in an extra-Amazonian environment, in four veredas (humid environment) in the Brazilian semiarid Cerrado region. Our objectives were to identify whether the phenology of palms growing in the veredas follows the same pattern as populations in other localities, such as the Amazon, or if the phenological pattern of M. flexuosa corresponds to local seasonality, to test for correlations between phenophases and local environmental variables, and to summarize the differences in biometric and quantitative parameters of reproductive structures between male and female individuals. LocationThe study was conducted in the Environmental Protection Area (APA) of Rio Pandeiros, municipality of Bonito de Minas, Minas Gerais, southeastern Brazil. MethodsFor phenological monitoring, 100 adult individuals were selected (60 females and 40 males), and monitoring was conducted monthly for 50 months (November 2015 to December 2019). Circular statistics and linear models were used to correlate phenophase data with abiotic variables and compare floral and inflorescence biometry data. ResultsMauritia flexuosa produces spear, green, and old leaves continuously throughout the year. Mauritia flexuosa was classified as a supra-annual presenting flowering/fruiting event at intervals longer than one year. The reproductive and vegetative phenophases were correlated with different abiotic variables (precipitation, photoperiod, and temperature). ConclusionWhen we consider the “bigger picture” and compare different palm populations from Amazonian and non-Amazonian regions, M. flexuosa flowering and fruiting phenology differ depending on locality, sometimes occurring during the dry season and at other times during rainy periods. The phenological differences found here confirm the importance of characterizing species and phenological patterns in different environments of occurrence to better understand the ability of species to adapt to a changing climate.

A robust and unified land surface phenology algorithm for diverse biomes and growth cycles in China by using harmonized Landsat and Sentinel-2 imagery

Land surface phenology (LSP) is beneficial to understand ecosystem response to climate change, vegetation and crop type discrimination, and ecological modeling. However, the existing efforts based on coarse resolution data (≥500 m) cannot perform well in regions with higher spatial heterogeneity and multi-cropping system, such as China. Given that the majority of 10 m/30 m-based phenological research has focused on North America and Europe, developing spatiotemporally explicit LSP data in China is imperative. More importantly, the existing 30 m LSP products are mainly suitable for vegetation types with a single vegetation cycle, but cannot work well for biomes with complex seasonality (e.g., multiple growth cycles). Here we first harmonized three vegetation indices, i.e., the normalized difference vegetation index (NDVI), two-band enhanced vegetation index (EVI2), and land surface water index (LSWI) from Landsat-7/8 and Sentinel-2 imagery on the Google Earth Engine (GEE) platform. We then developed a new 30 m LSP algorithm that unified different phenological cycle-seeking processes per vegetation type and improved the existing algorithm. Furthermore, we used the algorithm to estimate the LSP product (LSP30CHN) for 2016–2019 across China, suitable for all vegetation types. The validation results showed a reasonably high accuracy (R2 > 0.6, RMSE < 15 days, mostly) of the LSP30CHN data against multi-sources in-situ observational (e.g., PhenoCam) and satellite-retrieved vegetation phenology data. Moreover, LSP30CHN data showed a consistent pattern but finer spatial details with the 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) phenology product (MCD12Q2) at the homogenous area. We also found that phenological differences between LSP30CHN and MCD12Q2 increased with surface fragmentation, suggesting the potential of LSP30CHN to delineate phenological information on more fragmented landscapes. In contrast, the 500 m LSP data cannot provide such details in the regions with mixed cropping structures (e.g., corn, rice, and soybean) and multiple cropping index (e.g., single- and double-cropping systems). This study offers high accuracy of the LSP map for China, valuable for finer phenology-based services such as field-level crop management and agricultural phenology monitoring. It opens up new insights about exploring large-scale refined agricultural management and ecological assessment for other regions with complicated, fragmented landscapes and vegetation seasonality.

Varying effects of tree cover on relationships between satellite-observed vegetation greenup date and spring temperature across Eurasian boreal forests

Earth observation satellites have facilitated the quantification of how vegetation phenology responds to climate warming on large scales. However, satellite image pixels may contain a mixture of multiple vegetation types or species with diverse phenological responses to climate variability. It is unclear how these mixed pixels affect the statistical relationships between satellite-derived vegetation phenology and climate factors. Here, we aim to investigate the impacts of percent tree cover (PTC), a measure of mixed pixel, on the statistical relationships between satellite-derived vegetation greenup date (GUD) and spring air temperature across Eurasian boreal forests at a 0.05° spatial resolution. We estimated GUD using Moderate Resolution Imaging Spectroradiometer (MODIS) time series data. The responses of GUD to interannual variation in spring temperature (April to May) during 2001–2020 were characterized by correlation coefficient (RTAM) and sensitivity (STAM). We then evaluated the local impacts of PTC on spatial variations in RTAM and STAM using partial correlation analysis through spatial moving windows. Our results indicate that, for most areas, forests with higher PTC were associated with stronger RTAM and STAM. Moreover, PTC had stronger local impacts on RTAM and STAM than mean annual temperature and temperature seasonality for 37.3% and 27.4% of the moving windows, respectively. These impacts were spatially varying and different among forest types. Specifically, deciduous broadleaf forests and deciduous needleleaf forests tend to have a higher proportion of these impacts compared to other forest types. Our findings demonstrate the nonnegligible effects of PTC on the statistical responses of GUD to temperature variability at coarse spatial resolution (0.05°) across Eurasian boreal forests.

Temporal Variation in Tower-Based Solar-Induced Chlorophyll Fluorescence and Its Environmental Response in a Chinese Cork Oak Plantation

With the development of spectrum observation technology, solar-induced chlorophyll fluorescence (SIF)—an effective substitute for photosynthesis—has been widely used to monitor crop stress, vegetation phenology and ecosystem productivity. The relationship between fluorescence and photosynthesis is complicated because they are sensitive to environmental changes. Understanding the response of SIF to environmental factors is of great significance for clarifying the variation dynamic and relationship between SIF and photosynthesis under different conditions. In this study, the canopy SIF and the environmental factors of a Quercus variabilis BI. plantation were observed simultaneously, and the response of SIF to environmental factors at a daily scale and at a half-hour scale was analyzed. The results showed that SIF had obvious seasonal and diurnal dynamics and was mainly driven by photosynthetically active radiation (PAR). The influence of PAR, air temperature (Ta), vapor pressure deficit (VPD), soil moisture (SM) and wind speed (Ws) on SIF varied with the lapse of the growing season. After eliminating the covariant effect of PAR on the Ta and VPD during the whole growing season, the relationship between VPD and SIF was found to be negative, and the effect of Ta on SIF disappeared. This study enriched the ground observation dataset and provided support for understanding the variations in the relationship between SIF and photosynthesis under different conditions.

Heterogeneity, marginality, stagementation and driving forces in the Otindag Sandy Land and its ecotones based on GIMMS NDVI3g v1.0

Due to differences in spatial and temporal scales and perspectives, there are academic debates about the understanding of the ecological evolution in Otindag Sandy Land. How vegetation in the sandy hinterland and marginal intersects interacts with the regional climate system and how it responds to human activities remain to be addressed. The motivation for this study is to try to clarify the abovementioned geographical misunderstandings. By setting up a core area (CA), a check area (CK) and four ecotones, various datasets such as the remote sensing vegetation index products GIMMS NDVI3g v1.0 (1982–2015), meteorological data (1982–2015), night-time lighting data (1984–2020), population data (1986–2016) and livestock data (1986–2016) were adopted; and maximum value composite (MVC), average value composites (AVC), linear regression analysis (θslope), phenology analysis (SOS, LOS, and EOS), coefficient of variation (Cov) analysis.and Hurst index (H) prediction were performed; and partial data consistency was validated by MODIS NDVI (2000–2015) dataset. The study found that: (1) vegetation coverage had spatial heterogeneity; (2) vegetation phenology exhibited a spatial differentiation; (3) vegetation growth rate had spatial differences; (4) vegetation response to climate change was regional and significant; (5) the coefficient of variation (Cov) of vegetation was segmental and regional; and(6) the response of vegetation to human activities was not significant. The results demonstrate that the NDVI development in the Otindag Sandy Land and its ecoregions is spatially heterogeneous, regional, marginal and temporally segmental and is more sensitive to climate change than to human activities.

National-scale imperviousness mapping and detection of urban land changes

With high land take for urbanization a serious concern, the European Union and Member States have set quantitative targets for limiting land take in Europe. Consistent and reliable information on land take is crucial for measuring progress in achieving these targets. In the big data era, Earth observation data offers opportunities to regularly and consistently monitor land use change dynamics on a national scale. However, often-used methods such as image classification do not solve the mixed pixels problem common to urban areas with high heterogeneity. Moreover, the urban land use changes detected by these methods are often affected by random noise caused by non-relevant changes in atmospheric conditions, sun angle, vegetation phenology, etc. Therefore, the resulting products are often judged as being of insufficient quality for operational application.In this study, we present an approach to measuring annual land take using imperviousness mapping and change detection. Based on the Google Earth cloud computing platform, we first applied a data mining approach on annual Sentinel-2 image scenes to extract temporal and spectral information at Germany wide scale. Then spectral unmixing algorithm was used to estimate sub-pixel fractions of imperviousness. We end by using imperviousness changes to detect newly built surfaces. The change detection products were validated using high-resolution historical aerial photos and images. The results achieved an accuracy of 87% for detecting new built-up and 98% for detecting changes. The difference between the two numbers is caused by the areas with changes on the surface but not for urban use.We found that the integration of temporal information improves the performance of spectral unmixing than using spectral information alone, because it can better distinguish imperviousness from other land covers with similar spectral signatures but different temporal characteristics. We also found that, due to the sub-pixel analysis approach, spectral-unmixing-derived imperviousness is sensitive to urban features even with a size of just a few pixels. Moreover, the imperviousness layer represents urban land with a continuous index, meaning that random noise, represented as a low-range change of fractions, can be filtered out. Our study demonstrated that remote sensing products can be tailored to fulfil the needs of specific applications supporting sustainable land-use policy. This opens up more possibilities for the diverse and growing user communities of urban land change products.

Demographic consequences of phenological asynchrony for North American songbirds

Changes in phenology in response to ongoing climate change have been observed in numerous taxa around the world. Differing rates of phenological shifts across trophic levels have led to concerns that ecological interactions may become increasingly decoupled in time, with potential negative consequences for populations. Despite widespread evidence of phenological change and a broad body of supporting theory, large-scale multitaxa evidence for demographic consequences of phenological asynchrony remains elusive. Using data from a continental-scale bird-banding program, we assess the impact of phenological dynamics on avian breeding productivity in 41 species of migratory and resident North American birds breeding in and around forested areas. We find strong evidence for a phenological optimum where breeding productivity decreases in years with both particularly early or late phenology and when breeding occurs early or late relative to local vegetation phenology. Moreover, we demonstrate that landbird breeding phenology did not keep pace with shifts in the timing of vegetation green-up over a recent 18-y period, even though avian breeding phenology has tracked green-up with greater sensitivity than arrival for migratory species. Species whose breeding phenology more closely tracked green-up tend to migrate shorter distances (or are resident over the entire year) and breed earlier in the season. These results showcase the broadest-scale evidence yet of the demographic impacts of phenological change. Future climate change-associated phenological shifts will likely result in a decrease in breeding productivity for most species, given that bird breeding phenology is failing to keep pace with climate change.

Mapping bamboo forest and expansion intensity in China by coupling vegetation phenology and C-band SAR with Sentinel-1 and Sentinel-2 images

Bamboo forest has undergone dramatic expansion due to climate changes and human activities, and its direct effects on both carbon storage and biodiversity of the forest ecosystem have occurred in tropical and subtropical regions, especially in China. However, the uncertainty in tracking bamboo forest extent and expansion intensity substantially influenced our assessment of them due to the poor resolution and persistent cloud covers in optical images (e.g., Landsat and Sentinel-2). We developed a straightforward and superior algorithm by coupling vegetation phenology and cloud-free SAR using Sentinel-1 and -2 images to identify bamboo forest at large scales and higher spatial resolution. Specifically, (1) this study analyzed the spectral and phenological characteristics during the bamboo forest growth season; (2) the optimum parameter sets for the SAR backscatter were calibrated against field data by using the genetic algorithm; (3) and then we generated bamboo forest and expansion intensity maps at 10 m spatial resolution for seven study regions in China in 2020. The results showed that the Kappa of the maps was 0.89, and the OA was 94.7% in all study areas, compared with the field data. The accuracy among each study area also performed well in mapping bamboo forest, indicated by Kappa varying from 0.82 to 0.94, and OA ranging from 91.1% to 97.33%. Compared to the national forestry inventory map, our results showed a significant positive linear relationship, with a higher R2 (0.96, p < 0.001). With the high-resolution results, we found that the tree was most severely affected by bamboo forest, followed by grassland, sparse vegetation, and shrubland. This product can be a key input for many carbon cycles, climate, and vegetation models.

Using long-term data from a whole ecosystem warming experiment to identify best spring and autumn phenology models.

Predicting vegetation phenology in response to changing environmental factors is key in understanding feedbacks between the biosphere and the climate system. Experimental approaches extending the temperature range beyond historic climate variability provide a unique opportunity to identify model structures that are best suited to predicting phenological changes under future climate scenarios. Here, we model spring and autumn phenological transition dates obtained from digital repeat photography in a boreal Picea-Sphagnum bog in response to a gradient of whole ecosystem warming manipulations of up to +9°C, using five years of observational data. In spring, seven equally best-performing models for Larix utilized the accumulation of growing degree days as a common driver for temperature forcing. For Picea, the best two models were sequential models requiring winter chilling before spring forcing temperature is accumulated. In shrub, parallel models with chilling and forcing requirements occurring simultaneously were identified as the best models. Autumn models were substantially improved when a CO2 parameter was included. Overall, the combination of experimental manipulations and multiple years of observations combined with variation in weather provided the framework to rule out a large number of candidate models and to identify best spring and autumn models for each plant functional type.

Monitoring vegetation dynamics (2010–2020) in Shengnongjia Forestry District with cloud-removed MODIS NDVI series by a spatio-temporal reconstruction method

Shengnongjia Forestry District is the national natural reserve with abundant biological resources in China. Monitoring its variations of vegetation for the intimate connection with eco-environmental changes is of great significance. In this paper, the 16-day composite MODIS normalized difference vegetation index (NDVI) products (MOD13A1) with 500 m resolution from 2011 to 2020 were selected to investigate the vegetation dynamics in Shengnongjia Forestry District. To alleviate the cloud contamination in NDVI products, a spatio-temporal prefill method with harmonic analysis of time series (ST-HANTS) is proposed. ST-HANTS first prefills the raw NDVI time series using spatio-temporal information to reduce data gaps, which effectively improves the reconstruction performance of the subsequent HANTS algorithm. In the simulation experiments, ST-HANTS shows the highest average correlation coefficient and the lowest average root mean square error compared with other mainstream methods, including HANTS, Savitzky–Golay filter, wavelet transform, and data assimilation. The reconstruction curves are close to the upper envelope of the NDVI time series, which is more consistent with the vegetation phenology and can effectively capture the key points in the growth cycle. By analyzing the cloud-free NDVI time series reconstructed with ST-HANTS, results reveal the overall trend of Shennongjia vegetation coverage is high in the middle while low at the edge. Except for the population centers and Hongping Airport, the NDVI of most areas is above 0.7 and shows a remarkable increasing tendency. Moreover, the fluctuation degree of NDVI in the whole study area is low, indicating that the ecological environment of Shennongjia is relatively stable. The vegetation variations are influenced by land surface temperature and precipitation, and the vegetation growth response to precipitation exhibits an apparent hysteresis.

Impacts of climate, phenology, elevation and their interactions on the net primary productivity of vegetation in Yunnan, China under global warming

Both net primary productivity (NPP) and vegetation phenology play essential roles in influencing the carbon sequestration of terrestrial ecosystems. However, the relationship between vegetation phenology and NPP remains unclear under the effects of global warming. This study used Geodetector to analyze the interaction mechanisms between climate, phenology, elevation, and NPP in Yunnan, China. The results are as follows. (1) NPP is positively correlated with NDVI, LOS, EOS, TEMP, PREC and SRAD, and negatively correlated with ELEV, NDBI and SOS. However, the main influencing factors leading to the variation of NPP differ with the region. (2) The spatial distribution of NPP and LOS in Yunnan Province is mainly influenced by the monsoon, showing a pattern of high in the southwest and low in the northeast, and the interaction between PREC and LOS is the main reason for the variation of NPP. (3) In the high-altitude region of northwest Yunnan, the interaction between altitude and PREC is the primary factor affecting NPP variation. (4) In the tropical monsoon forest region of southern Yunnan, the interaction between SRAD and PREC is the main cause of NPP changes. (5) Despite having a small effect on NPP, SOS was the only phenological factor that showed a significant linear relationship with NPP. This study reveals the complex and diverse interactions between climate, phenology, elevation, and NPP in different regions of Yunnan, and provides a new perspective for understanding the carbon cycle and ecological processes of terrestrial ecosystems under global warming.

Landscape heterogeneity increases the stability of wild ungulate populations facing climatic variability in Mediterranean ecosystems

Mediterranean environments are characterized by strong intra- and inter-annual fluctuations in plant primary production, which are likely to regulate the carrying capacity and density-dependent responses of ungulate populations. These effects may, however, vary across spatial and temporal scales. Habitat heterogeneity, particularly when associated to differentiated phenological responses, may allow wild ungulates to mitigate temporal fluctuations in plant production by using different resources along the year. In this work, we use a 15-years dataset (including remote-sensing data on vegetation distribution, phenology and production, as well as ungulate population counts) to assess how temporal variability in plant primary production and livestock abundance influence the population dynamics of two wild ungulates: native red deer, Cervus elaphus, and introduced fallow deer, Dama dama. Results show that temporal alternation in the phenological cycles of the four different vegetation types increased plant production, thus food availability for ungulates, within each year. Furthermore, complementarity in the responses of different vegetation types to variations in the amount and timing of rainfall increased the predictability of food availability across different years. This complementarity effect was further increased by the contrasting responses of ungulate populations to variation in the production of different vegetation types. Furthermore, domestic ungulates had positive effects on wild ungulate density at low to intermediate abundances, but high livestock densities decreased ungulate density and constrained the stability of the plant-ungulate system in response to the impact of climatic variation, particularly under climate change. Our findings deepen the knowledge on vegetation-ungulate interactions in Mediterranean areas, potentially contributing to develop better management strategies of ungulate populations and adapt them to ongoing climate change.

Monitoring vegetation sensitivity to drought events in China

The frequency and severity of drought events have increased over the decades under the influence of global warming. Continued drought increases the risk of vegetation degradation. Many studies have investigated the responses of vegetation to drought but rarely from the perspective of drought events. Moreover, the spatial distributions of vegetation sensitivity to drought events are not well understood in China. Thus, the spatiotemporal patterns of drought events were quantified based on the run theory at different time-scales in this study. The relative importance of drought characteristics for vegetation anomalies during drought events were calculated by using the BRT model. Then, the sensitivity of vegetation anomalies and vegetation phenology was quantified by dividing standardized anomalies of vegetation parameters (NDVI and phenological metrics) and SPEI during drought events for different regions in China. The results show that Southern Xinjiang and Southeast China experienced relatively higher values of drought severity, especially at the 3-month and 6-month scales. Most arid areas experienced more drought events but of low severity, while some humid zones underwent few drought events but of high severity. Notable negative NDVI anomalies appeared in the Northeast China and Southwest China, while positive NDVI anomalies were observed in Southeast China and Northern central region. Drought interval, intensity and severity contributed approximately 80 % of the model's explained vegetation variance in most regions. The sensitivity of vegetation anomalies to drought events (VASD) varied regionally in China. The Qinghai-Tibet Plateau and Northeast China tended to exhibit higher sensitivity to drought events. Vegetation in these regions with high sensitivity faced a high risk of degradation and could function as warning signals of vegetation degradation. Drought events at high timescales had a greater impact on vegetation sensitivity in dry zones, while they had a smaller impact on humid areas. With the increase in drought degree of climate zones and the decrease in vegetation coverage, VASD showed a gradual increase. Furthermore, a strong negative correlation between VASD and the aridity index (AI) was observed in all vegetation types. The change in VASD for sparse vegetation was the largest with the change in AI. For vegetation phenology, drought events in most regions delayed the end of the growing season and extended the length of growing season, especially for sparse vegetation. The start of the growing season was advanced in most humid areas, while being delayed in most dry areas during drought events. Knowledge of vegetation sensitivity to drought events will be beneficial to provide decision-making references for the prevention and control of vegetation degradation, especially in the ecological fragile regions.

Impact of Climate Change on Alpine Phenology over the Qinghai–Tibet Plateau from 1981 to 2020

Climate change is a major driver of cyclical and seasonal changes in alpine phenology. This study investigated how climate change affects vegetation phenology’s spatial and temporal responses on the Qinghai–Tibet plateau (QTP) from 1981 to 2020. We used the daily two-band enhanced vegetation index (EVI2) at a 0.05° spatial resolution for 1981–2014, 16-day moderate resolution imaging spectroradiometer (MODIS) enhanced vegetation index data for 2000–2020 at 250 m spatial resolution, and climate records from 106 meteorological stations from 1981 to 2020 to construct linear regression models and Mann–Kendall point test to understand the changes on QTP vegetation in response to climate change. We found that the temperature in April, July, and September controls vegetation growth, and spring precipitation (p &lt; 0.05) influences the length of the growing period, with a partial correlation coefficient of −0.69. Over the past 40 years, temperature and precipitation changes on the QTP have not shown abrupt shifts despite the increasingly warm and dry spring climate. We observed a meridional distribution trend in the correlation between precipitation and alpine vegetation greening, browning and the length of the growing period. In regions experiencing strong warming, vegetation growth was hindered by a lack of precipitation. We conclude that climatic factors alone cannot fully explain the changing trends in vegetation phenology across the QTP.

Traditional ecological knowledge-based calendar system for sustainable seasonal grazing in the Pamir Mountains

Indigenous mountain communities are on the cutting edge of environmental threats, their responses, and the preservation of traditional knowledge that ensures the harmony between the environment and sustainable resource use. In the context of seasonal grazing, a key livelihood activity in the eastern Pamir Mountains, this study aimed to document a traditional ecological calendar-like management system and scientifically validate its effectiveness. Through an ethno-ecological survey comprising focus group discussions and semi-structured interviews, we examined the variations in seasonal grazing practices based on ecological calendars. The study quantified and evaluated the indicators' efficacy in guiding these seasonal activities by analyzing time series satellite data of Normalized Difference Vegetation Index (NDVI). Additionally, the research assessed the potential alterations required in the future under anticipated climatic scenarios (SSP126 and SSP585) using the random forest algorithm. The findings underscored the alignment between seasonal migration patterns, grazing as the primary seasonal activity, and spatiotemporal variations in vegetation phenology. Our analysis revealed that in the future local herders may need to spend more or shorter time in pastures at different elevation compared to present because of possible change in the phenology. Our findings demonstrate the high validity of this calendar system in local resource management, and with modification it would be equally important in the future under new climatic scenarios.

Spatial heterogeneity of vegetation phenology caused by urbanization in China based on remote sensing

Vegetation phenology changes caused by urbanization could lead to shifts in ecosystem services in urban areas and impact on human health. The characteristics of urbanization affect vegetation phenology need to be emphasized, especially in China with a complex natural environment and rapid urbanization background. In this study, we used remote sensing-based phenological data (MODIS MCD12Q2) to analyze the spatial heterogeneity of vegetation phenology caused by urbanization between urban and non-urban areas in 320 cities across China. We found a significant difference between vegetation phenology in urban and its corresponding non-urban area at national and the regional scale. For national scale, the start of the growing season (SOS) was significantly advanced by 2.53 days (P < 0.001), and the end of the growing season (EOS) was significantly delayed by 6.72 days (P < 0.001), resulting in the growing season length (GSL) was significantly extended by 9.25 days (P < 0.001). For regional scale, the changes of SOS, EOS, and GSL caused by urbanization varied from seven vegetation zones in China. As expected for the Tropical monsoon rain forest and rain forest zone (TR) and Tibetan plateau alpine vegetation zone (TP), and Warm-temperate broadleaf deciduous forest zone (WTB), vegetation phenology in other four vegetation zones shows significant differences between urban and non-urban areas. Furthermore, the potential factors driving phenological changes through urbanization were discussed, which will be of great help in understanding the urban ecological process in future studies.

Spatial-temporal dynamics of land surface phenology over Africa for the period of 1982–2015

Knowledge of the dynamics of vegetation phenology is essential for the understanding of vegetation-climate interactions. Although the interest in phenology study is growing, vegetation phenology in Africa received far less attention compared to the Northern Hemisphere. Africa straddles the northern and southern hemispheres, and the climate has a clear latitudinal gradient, which facilitates the study of the interaction between phenology and climate. In this study, the latitudinal and longitudinal gradients and temporal trends of start of growing season (SOS), peak of growing season (POS), and end of growing season (EOS) were examined using long-term satellite dataset during 1982–2015. The latitudinal variations in these phenology metrics were larger in the northern than those in the southern Africa, especially from 6°N northwards to 16°N. The latitudinal variations in southern Africa had no clear patterns due to the more complex climate systems. For the longitudinal variation, the temporal trends in POS and EOS exhibited a gradient-decreasing rate in northern Africa. Over the period from 1982 to 2015, the overall trends of the phenology in Africa were ‘later SOS’, ‘later POS’, and ‘later EOS’. The faster rate of delay in EOS than in SOS resulted in a prolonged length of growing season (LOS) with 0.50 days/year on average in northern Africa, while a slower rate of delay in EOS than in SOS resulted in a shorter LOS with −0.12 days/year in southern Africa. The prolonged LOS in northern Africa contributes to the increase in the yearly-averaged Normalized Difference Vegetation Index (NDVI) from 1982 to 2000. Nevertheless, the NDVI appeared to have reached saturation around the 2000s, although the LOS was still extending after 2000s. Overall, the findings of this study provide an overall view of the spatial and temporal patterns of land surface phenology in the African continent, and a necessary component for future studies on the response of phenology to climate.

Urban environments provide new perspectives for forecasting vegetation phenology responses under climate warming.

Given that already-observed temperature increase within cities far exceeds the projected global temperature rise by the end of the century, urban environments often offer a unique opportunity for studying ecosystem response to future warming. However, the validity of thermal gradients in space serving as a substitute for those in time is rarely tested. Here, we investigated vegetation phenology dynamics in China's 343 cities and empirically test whether phenological responses to spatial temperature rise in urban settings can substitute for those to temporal temperature rise in their natural counterparts based on satellite-derived vegetation phenology and land surface temperature from 2003 to 2018. We found prevalent advancing spring phenology with "high confidence" and delaying autumn phenology with "medium confidence" under the context of widespread urban warming. Furthermore, we showed that space cannot substitute for time in predicting phenological shifts under climate warming at the national scale and for most cities. The thresholds of ~11°C mean annual temperature and ~600 mm annual precipitation differentiated the magnitude of phenological sensitivity to temperature across space and through time. Below those thresholds, there existed stronger advanced spring phenology and delayed autumn phenology across the spatial urbanization gradients than through time, and vice versa. Despite the complex and diverse relationships between phenological sensitivities across space and through time, we found that the directions of the temperature changes across spatial gradients were converged (i.e., mostly increased), but divergent through temporal gradients (i.e., increased or decreased without a predominant direction). Similarly, vegetation phenology changes more uniformly over space than through time. These results suggested that the urban environments provide a real-world condition to understand vegetation phenology response under future warming.

Variation in Vegetation Phenology and Its Response to Climate Change in Marshes of Inner Mongolian.

Inner Mongolia has a large area of marsh wetland in China, and the marsh in this region is important for maintaining ecological balance. Understanding variations in vegetation phenology of marsh ecosystems and their responses to climatic change is crucial for vegetation conservation of marsh wetlands in Inner Mongolia. Using the climate and NDVI data during 2001-2020, we explored the spatiotemporal changes in the start (SOS), end (EOS), and length (LOS) of vegetation growing season and analyzed the effects of climate change on vegetation phenology in the Inner Mongolia marshes. Results showed that SOS significantly (p < 0.05) advanced by 0.50 days/year, EOS significantly delayed by 0.38 days/year, and thus LOS considerably increased by 0.88 days/year during 2001-2020 in marshes of Inner Mongolia. Warming temperatures in winter and spring could significantly (p < 0.05) advance the SOS, and increased summer and autumn temperatures could delay EOS in Inner Mongolia marshes. We found for the first time that daytime maximum temperature (Tmax) and night minimum temperature (Tmin) had asymmetric effects on marsh vegetation phenology. Increasing Tmax had a stronger advancing effect on SOS than increasing Tmin from December to April. The increase of Tmin in August could obviously delayed EOS, while increasing Tmax in August had no significant effect on EOS. This study highlights that the asymmetric influences of nighttime and daytime temperatures should be taken into account in simulating marsh vegetation phenology in temperate arid and semi-arid regions worldwide, particularly in the context of global asymmetric diurnal warming.

Responses of Vegetation Phenology to Urbanisation and Natural Factors along an Urban-Rural Gradient: A Case Study of an Urban Agglomeration on the Northern Slope of the Tianshan Mountains

Understanding the responses of vegetation phenology to natural and human disturbances is essential for better understanding ecosystems. In this study, Moderate Resolution Imaging Spectroradiometer data and products were used together with other relevant data to analyse vegetation phenological responses to urbanisation and natural factors in the major urban agglomerations of the Urumqi-Changji, Shihezi-Manasi, and Wusu-Kuidun-Dushanzi regions on the Urban Agglomeration on the Northern Slope of the Tianshan Mountains (UANSTM). Vegetation phenology distributed along an urban-rural gradient showed distinct variability, with start of growing season (SOS), end of growing season (EOS), and growing season length (GSL) occurring earlier, later, and longer, respectively, in urban areas than those in suburban and rural areas. In the Urumqi-Changji region, the earliest SOS, the later EOS, and the longest GSL occurred. Surface urban heat island intensity (SUHII) was most pronounced in the Urumqi-Changji region, with a heat island intensity of 1.77–3.34 °C. Vegetation phenology was influenced by both urbanisation and natural factors, whose contributions were 44.2% to EOS and 61.8% to SOS, respectively. The results of this study emphasise the importance of quantifying the vegetation phenological responses to human disturbances, including climate change, along the urban-rural gradient on the UANSTM.