Codominant water control on global interannual variability and trends in land surface phenology and greenness

A global dataset of the fraction of absorbed photosynthetically active radiation for 1982-2022

The fraction of absorbed photosynthetically active radiation (FPAR) is an essential biophysical parameter that characterizes the structure and function of terrestrial ecosystems. Despite the extensive utilization of several satellite-derived FPAR products, notable temporal inconsistencies within each product have been underscored. Here, the new generation of the GIMMS FPAR product, GIMMS FPAR4g, was developed using a combination of a machine learning algorithm and a pixel-wise multi-sensor records integration approach. PKU GIMMS NDVI, which eliminates the orbital drift and sensor degradation issues, was used as the data source. Comparisons with ground-based measurements indicate root mean square errors ranging from 0.10 to 0.14 with R-squared ranging from 0.73 to 0.87. More importantly, our product demonstrates remarkable spatiotemporal coherence and continuity, revealing a persistent terrestrial darkening over the past four decades (0.0004 yr-1, p < 0.001). The GIMMS FPAR4g, available for half-month intervals at a spatial resolution of 1/12° from 1982 to 2022, promises to be a valuable asset for in-depth analyses of vegetation structures and functions spanning the last 40 years.

Trends and drivers of Arctic-boreal fire intensity between 2003 and 2022

Climate change has disproportional effects on Arctic-boreal ecosystems, as the increase of air temperatures in these northern regions is several times higher than the global average. Ongoing warming and drying have resulted in recent record-breaking fire years in Arctic-boreal ecosystems, resulting in substantial carbon emissions that might accelerate climate change. While recent trends in Arctic-boreal burned area have been well documented, it is still unclear how fire intensity has changed. Fire intensity relates to the energy release from combustion and to a large extent drives the impact of a fire on the vegetation and soils, the emission of various gasses and the combustion completeness of different fuels. Here, we used the active fire product from the Moderate Resolution Imaging Spectroradiometer (MODIS) to examine trends in fire radiative power (FRP) over the entire Arctic-boreal region. We found a significant increase in annual median fire intensity between 2003 and 2022 in Eurasian boreal forests, for both daytime (increase of 0.392 MW/km2 per year, R2 = 0.56, p < 0.001) and nighttime fires (increase of 0.175 MW/km2 per year, R2 = 0.47, p < 0.001), while no general trend in FRP was observed in boreal North America. This increase in FRP in Eurasian boreal forests was strongly associated with simultaneous increases in air temperature, vapour pressure deficit, fire weather and fuel availability. We estimated that for Eurasia with each degree increase in air temperature, annual median daytime FRP increases with 1.58 MW/km2 in the tundra and 0.94 MW/km2 in the taiga. Climate change has thus resulted in a widespread and clear increase in fire intensity in central and eastern Eurasia while we could not discern clear trends in Arctic-boreal North America. Arctic-boreal fire intensity may further increase with climate change, with potentially major consequences for fire regimes, carbon emissions and society.

Revealing climatic and groundwater impacts on the spatiotemporal variations in vegetation coverage in marine sedimentary basins of the North China Plain, China

The North China Plain (NCP) is one of the three great plains in China and also serves as a vital region for grain, cotton, and oil production. Under the influence of regional hydrothermal changes, groundwater overexploitation, and seawater intrusion, the vegetation coverage is undergoing continuous alterations. However, a comprehensive assessment of impacts of precipitation, temperature, and groundwater on vegetation in marine sedimentary regions of the NCP is lacking. Heilonggang Basin (HB) is located in the low-lying plain area in the east of NCP, which is part of the NCP. In this study, the HB was chosen as a typical area of interest. We collected a series of data, including the Normalized Difference Vegetation Index (NDVI), precipitation, temperature, groundwater depth, and Total Dissolved Solids (TDS) from 2001 to 2020. Then the spatiotemporal variation in vegetation was analyzed, and the underlying driving mechanisms of vegetation variation were explored in this paper. The results show that NDVI experiences a rapid increase from 2001 to 2004, followed by stable fluctuations from 2004 to 2020. The vegetation in the HB has achieved an overall improvement in the past two decades, with 76% showing improvement, mainly in the central and eastern areas, and 24% exhibiting deterioration in other areas. From 2001 to 2020, NDVI correlates positively with precipitation, whereas its relationship with temperature fluctuates between positive and negative, and is not statistically significant. There is a threshold for the synergistic change of NDVI and groundwater depth. When the groundwater depth is lower than 3.8 m, NDVI increases sharply with groundwater depth. However, beyond this threshold, NDVI tends to stabilize and fluctuate. In the eastern coastal areas, NDVI exhibits a strong positive correlation with groundwater depth, influenced by the surface soil TDS controlled by groundwater depth. In the central regions, a strong negative correlation is observed, where NDVI is primarily impacted by soil moisture under the control of groundwater. In the west and south, a strong positive correlation exists, with NDVI primarily influenced by the intensity of groundwater exploitation. Thus, precipitation and groundwater are the primary driving forces behind the spatiotemporal variability of vegetation in the HB, while in contrast, the influence of temperature is uncertain. This study has elucidated the mechanism of vegetation response, providing a theoretical basis for mitigating adverse factors affecting vegetation growth and formulating rational water usage regulations in the NCP.

Drought shortens subtropical understory growing season by advancing leaf senescence

Subtropical forests, recognized for their intricate vertical canopy stratification, exhibit high resistance to extreme drought. However, the response of leaf phenology to drought in the species-rich understory remains poorly understood. In this study, we constructed a digital camera system, amassing over 360,000 images through a 70% throughfall exclusion experiment, to explore the drought response of understory leaf phenology. The results revealed a significant advancement in understory leaf senescence phenology under drought, with 11.75 and 15.76 days for the start and end of the leaf-falling event, respectively. Pre-season temperature primarily regulated leaf development phenology, whereas soil water dominated the variability in leaf senescence phenology. Under drought conditions, temperature sensitivities for the end of leaf emergence decreased from -13.72 to -11.06 days °C-1, with insignificance observed for the start of leaf emergence. Consequently, drought treatment shortened both the length of the growing season (15.69 days) and the peak growth season (9.80 days) for understory plants. Moreover, this study identified diverse responses among intraspecies and interspecies to drought, particularly during the leaf development phase. These findings underscore the pivotal role of water availability in shaping understory phenology patterns, especially in subtropical forests.

Shifts in sage-grouse arthropod food sources across grazing and environmental gradients in upland meadow communities

Groundwater dependent systems are extremely important habitats for a wide variety of taxa in the Great Basin of North America. The impacts of grazing on these habitats cause shifts in resources and subsequent change in species composition. The Greater sage-grouse, a keystone species of Great Basin ecosystems, rear offspring in these areas during spring and summer months using forbs and arthropods. To examine the impact of grazing on arthropod abundance in these ecosystems, seven meadows, each made up of three unique vegetative communities, were grazed at three intensities across two years (2019-2020) and monitored for environmental variables and abundance of arthropods during peak sage-grouse utilization periods. Additionally, the relationship of field measurements and near-surface digital cameras (phenocams) was examined to better understand how remote sensing technologies can be used to monitor these insect abundance shifts on larger scales. Arthropod taxa abundance responded differently to grazing management and environmental variables. Coleoptera abundance during peak sage-grouse usage periods increased roughly 50% in some meadows with increased grazing intensity. For year-to-year environmental variability in precipitation, Lepidoptera abundance was 114% higher in the drier year, while Coleoptera was 39% lower. Near-surface cameras had varied success with predicting peak insect abundance levels. Lepidoptera and Coleoptera capture rates had strong correlations with phenological indices derived from phenocams, while Formicidae had much weaker relationships.

Satellite monitoring reveals short-term cumulative and time-lag effect of drought and heat on autumn photosynthetic phenology in subtropical vegetation

Comparing with the effect of the average climate change on vegetation phenology, the impacts of extreme climate events remain unclear, especially considering their characteristic cumulative and time-lag effects. Using solar-induced chlorophyll fluorescence (SIF) satellite records, we investigated the cumulative and time-lag effects of drought and heat events on photosynthesis, particularly for the end date of autumn photosynthesis (EOP), in subtropical vegetation in China. Our results showed a negative effect of drought on the delay of EOP, with the cumulative effect on 30.12% (maximum continuous dry days, CDD), 34.82% (dry days, DRD), and 26.14% (dry period, DSDI) of the study area and the general time-lag effect on 50.73% (maximum continuous dry days), 56.61% (dry days), and 47.55% (dry period) of the study area. The cumulative and lagged time were 1-3 months and 2-3 months, respectively. In contrast, the cumulative effect of heat on EOP was observed in 16.27% (warm nights, TN90P), 23.66% (moderate heat days, TX50P), and 19.19% (heavy heat days, TX90P) of the study area, with cumulative time of 1-3 months. The lagged time was 3-4 months, detected in 31.02% (warm nights), 45.86% (moderate heat days), and 36.52% (heavy heat days) of the study area. At the vegetation community level, drought and heat had relatively rapid impacts on EOP in the deciduous broadleaved forest, whereas evergreen forests and bushes responded to heat slowly and took a longer time. Our results revealed that drought and heat have short-term cumulative and time-lag effects on the EOP of subtropical vegetation in China, with varying effects among different vegetation types. These findings provide new insights into the effect of drought and heat on subtropical vegetation and confirm the need to consider these effects in the development of prediction models of autumn phenology for subtropical vegetation.

Plant phenology shifts under climate warming: a systematic review of recent scientific literature

Climate warming-driven temporal shifts in phenology are widely recognised as the foremost footprint of global environmental change. In this regard, concerted research efforts are being made worldwide to monitor and assess the plant phenological responses to climate warming across species, ecosystems and seasons. Here, we present a global synthesis of the recent scientific literature to assess the progress made in this area of research. To achieve this, we conducted a systematic review by following PRISMA protocol, which involved rigorous screening of 9476 studies on the topic and finally selected 215 studies for data extraction. The results revealed that woody species, natural ecosystems and plant phenological responses in spring season have been predominantly studied, with the herbaceous species, agricultural ecosystems and other seasons grossly understudied. Majority of the studies reported phenological advancement (i.e., preponement) in spring, followed by also advancement in summer but delay in autumn. Methodology-wise, nearly two -third of the studies have employed direct observational approach, followed by herbarium-based and experimental approaches, with the latter covering least temporal depth. We found a steady increase in research on the topic over the last decade with a sharp increase since 2014. The global country-wide scientific output map highlights the huge geographical gaps in this area of research, particularly in the biodiversity-rich tropical regions of the developing world. Based on the findings of this global synthesis, we identify the current knowledge gaps and suggest future directions for this emerging area of research in an increasingly warming world.

Effects of intra-annual precipitation patterns on grassland productivity moderated by the dominant species phenology

Phenology and productivity are important functional indicators of grassland ecosystems. However, our understanding of how intra-annual precipitation patterns affect plant phenology and productivity in grasslands is still limited. Here, we conducted a two-year precipitation manipulation experiment to explore the responses of plant phenology and productivity to intra-annual precipitation patterns at the community and dominant species levels in a temperate grassland. We found that increased early growing season precipitation enhanced the above-ground biomass of the dominant rhizome grass, Leymus chinensis, by advancing its flowering date, while increased late growing season precipitation increased the above-ground biomass of the dominant bunchgrass, Stipa grandis, by delaying senescence. The complementary effects in phenology and biomass of the dominant species, L. chinensis and S. grandis, maintained stable dynamics of the community above-ground biomass under intra-annual precipitation pattern variations. Our results highlight the critical role that intra-annual precipitation and soil moisture patterns play in the phenology of temperate grasslands. By understanding the response of phenology to intra-annual precipitation patterns, we can more accurately predict the productivity of temperate grasslands under future climate change.

Optimality principles explaining divergent responses of alpine vegetation to environmental change

Recent increases in vegetation greenness over much of the world reflect increasing CO₂ globally and warming in cold areas. However, the strength of the response to both CO₂ and warming in those areas appears to be declining for unclear reasons, contributing to large uncertainties in predicting how vegetation will respond to future global changes. Here, we investigated the changes of satellite-observed peak season absorbed photosynthetically active radiation (F_max ) on the Tibetan Plateau between 1982 and 2016. Although climate trends are similar across the Plateau, we identified robust divergent responses (a greening of 0.31 ± 0.14% year^-1 in drier regions and a browning of 0.12 ± 0.08% year^-1 in wetter regions). Using an eco-evolutionary optimality (EEO) concept of plant acclimation/adaptation, we propose a parsimonious modelling framework that quantitatively explains these changes in terms of water and energy limitations. Our model captured the variations in F_max with a correlation coefficient (r) of .76 and a root mean squared error of .12 and predicted the divergent trends of greening (0.32 ± 0.19% year^-1 ) and browning (0.07 ± 0.06% year^-1 ). We also predicted the observed reduced sensitivities of F_max to precipitation and temperature. The model allows us to explain these changes: Enhanced growing season cumulative radiation has opposite effects on water use and energy uptake. Increased precipitation has an overwhelmingly positive effect in drier regions, whereas warming reduces F_max in wetter regions by increasing the cost of building and maintaining leaf area. Rising CO₂ stimulates vegetation growth by enhancing water-use efficiency, but its effect on photosynthesis saturates. The large decrease in the sensitivity of vegetation to climate reflects a shift from water to energy limitation. Our study demonstrates the potential of EEO approaches to reveal the mechanisms underlying recent trends in vegetation greenness and provides further insight into the response of alpine ecosystems to ongoing climate change.

Increasing Impact of Precipitation on Alpine-Grassland Productivity over Last Two Decades on the Tibetan Plateau

Understanding the importance of temperature and precipitation on plant productivity is beneficial, to reveal the potential impact of climate change on vegetation growth. Although some studies have quantified the response of vegetation productivity to climate change at local, regional, and global scales, changes in climatic constraints on vegetation productivity over time are not well understood. This study combines the normalized difference vegetation index (NDVI) and the net primary production (NPP) modeled by CASA during the plant-growing season, to quantify the interplay of climatic (growing-season temperature and precipitation, GST and GSP) constraints on alpine-grassland productivity on the Tibetan Plateau, as well as the temporal dynamics of these constraints. The results showed that (1) 42.2% and 36.3% of grassland NDVI and NPP on the Tibetan Plateau increased significantly from 2000 to 2019. GSP controlled grassland growth in dryland regions, while humid grasslands were controlled by the GST. (2) The response strength of the NDVI and NPP to precipitation (partial correlation coefficient RNDVI-GSP and RNPP-GSP) increased substantially between 2000 and 2019. Especially, the RNDVI-GSP and RNPP-GSP increased from 0.14 and 0.01 in the first 10year period (2000–2009) to 0.83 and 0.78 in the second 10-year period (2010–2019), respectively. As a result, the controlling factor for alpine-grassland productivity variations shifted from temperature during 2000–2009 to precipitation during 2010–2019. (3) The increase in precipitation constraints was mainly distributed in dryland regions of the plateau. This study highlights that the climatic constraints on alpine-grassland productivity might change under ongoing climate change, which helps the understanding of the ecological responses and helps predict how vegetation productivity changes in the future.

Distribution and Attribution of Earlier Start of the Growing Season over the Northern Hemisphere from 2001–2018

The start of the growing season (SOS) is a vital ecological indicator for climate change and the terrestrial ecosystem. Previous studies have reported that the SOS over the Northern Hemisphere (NH) has experienced remarkable changes in the past few decades. However, because of the different spatial and temporal coverages of existing SOS studies, a coherent and robust account for SOS changes in the NH has been lacking. Using satellite-retrieved vegetation-phenology datasets, ground observations, and several auxiliary datasets, this study evaluated the performance of the latest MODIS vegetation-dynamics dataset (MCD12Q2-C6) and explored the distribution and attribution of the SOS to climate change over the NH for the period 2001–2018. The validation results using the Chinese Ecosystem Research Network (CERN) and Lilac-leafing observations (Lilac) displayed that the MCD12Q2-C6 has a good performance in SOS monitoring over the NH mid-latitudes. Meanwhile, evidence from MCD12Q2-C6 pointed out that the SOS was advanced by 2.08 days on average over the NH during 2001–2018, especially for Europe, China, and Alaska, United States. In addition, detailed-sensitivity analysis showed that the increased surface air temperature (Ts) (−1.21 ± 0.34 days °C−1) and reduced snow-cover fraction (Sc) (0.62 ± 0.29 days%−1) were the key driving factors of the observed SOS changes over the NH during 2001–2018. Compared with Ts and Sc, the role of total precipitation (Pt) was minor in dominating the spring vegetation-phenology changes at the same period. The findings of this study contribute to our understanding of the responses of SOS to the competing changes of Ts, Pt, and Sc over the NH.

Characterizing Spatial Patterns of the Response Rate of Vegetation Green-Up Dates to Land Surface Temperature in Beijing, China (2001–2019)

The phenology indicator of vegetation green-up dates (GUD) is prone to being affected by changes in temperature. However, the influencing degree of urbanization-induced temperature warming on vegetation GUDs among different vegetation species along the urban-rural gradient remains inadequately described. In this study, based on the long-term (2001–2019) satellite-derived vegetation GUDs and nighttime land surface temperature (LST) of forests, grasslands, and croplands along the urban-rural gradient with Beijing (China) as a case study area, the responses of vegetation GUDs to temperature changes were quantitatively analyzed, taking into account the vegetation types and distances away from the urban domain. The results show that (1) long-term GUDs and LST are significantly negatively correlated, characterized by a weaker significant correlation near the urban area when compared with its surrounding areas, with the greatest absolute linear correlation coefficients (r) happening at rings 32 km (rmax = −0.93, forests), 20 km and 48 km (rmax = −0.83, grasslands), and 34 km (rmax = −0.82, croplands), respectively; (2) the magnitude of change in GUDs over the past 19 year (2001–2019) are significantly positively correlated with these in LST near the urban area, demonstrating a distance-decay trend, with the greatest advance in GUDs occurring at the ring nearest the urban area, by about 20 days (forests), 24.5 days (grasslands), and 15.6 days (croplands), respectively; (3) the spatial pattern of the response rate of GUDs change to LST change (days K−1) also showed a declining trend with distance, with GUD advanced by 6.8 days K−1 (forests), 7.5 days K−1 (grasslands), and 4.9 days K−1 (croplands) at the closest ring to the urban, decreasing to about 2.3 days K−1 (48 km), 4.1 days K−1 (18 km), and 1 day K−1 (18 km), respectively, indicating a notable influence of temperature warming on vegetation GUDs near the urban domains.

Grassland Phenology Response to Climate Conditions in Biobio, Chile from 2001 to 2020

Plant phenology is affected by climate conditions and therefore provides a sensitive indicator to changes in climate. Studying the evolution and change in plant phenology aids in a better understanding of and predicting changes in ecosystems. Vegetation Indices (VIs) have been recognized for their utility in indicating vegetation activity. Understanding climatic variables and their relationship to VI support the knowledge base of how ecosystems are changing under a new climatic scenario. This study evaluates grassland growth phenology in the Biobio, Chile, biweekly with Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) time series. Four growth parameters for the six agro-climatic regions were analyzed from 2001 to 2020: start and end of the season, time and value of maximum NDVI. For this purpose, the NDVI time series were smoothed using Savitzky–Golay filtering. In addition, by using monthly gridded database climate data, we studied correlations between phenology markers and rainfall, maximum temperature and minimum temperature. The results show that both the start and end of the growing season did not significantly change; however, all agro-climatic regions grow faster and more vigorously. Thus, climatic conditions in Biobio have become more conducive to grassland growth over the 2001–2020 period.

Using spectral indices as early warning signals of forest dieback: The case of drought-prone Pinus pinaster forests

Forest dieback processes linked to drought are expected to increase due to climate warming. Remotely sensed data offer several advantages over common field monitoring methods such as the ability to observe large areas on a systematic basis and monitoring their changes, making them increasingly used to assess changes in forest health. Here we aim to use a combined approximation of fieldwork and remote sensing to explore possible links between forest dieback and land surface phenological and trend variables derived from long Landsat time series. Forest dieback was evaluated in the field over 31 plots in a Mediterranean, xeric Pinus pinaster forest. Landsat 31-year time series of three greenness (EVI, NDVI, SAVI) and two wetness spectral indices (NMDI and TCW) were derived covering the period 1990-2020. Spectral indices from time series were decomposed into trend and seasonality using a Bayesian estimator while the relationships of the phenological and trend variables among levels of damage were assessed using linear and additive mixed models. We have not found any statistical pieces of evidence of extension or shortening patterns for the length of the phenological season over the examined 31-year period. Our results indicate that the dieback process was mainly related to the trend component of the spectral indices series whereas the phenological metrics were not related to forest dieback. We also found that plots with more dying or damaged trees displayed lower spectral indices trends after a severe drought event in the middle of the 1990s, which confirms the Landsat-derived spectral indices as indicators of early-warning signals. Drops in trends occurred earlier for wetness indices rather than for greenness indices which suggests that the former could be more appropriate for dieback detection, i.e. they could be used as early warning signals of impending loss of tree vigor.

Changes in Meadow Phenology in Response to Grazing Management at Multiple Scales of Measurement

Riparian and ground-water dependent ecosystems found in the Great Basin of North America are heavily utilized by livestock and wildlife throughout the year. Due to this constant pressure, grazing can be a major influence on many groundwater dependent resources. It is important for land managers to understand how intensity and timing of grazing affect the temporal availability of these commodities (i.e., biodiversity, water filtration, forage, habitat). Shifts in forage or water availability could potentially be harmful for fauna that rely on them at specific times of the year. Seven meadow communities, each consisting of three distinct vegetative communities, were grazed at three intensities to determine the relationship between grazing management and phenological timing of vegetation. The agreement of on-the-ground measurements, near-surface digital cameras (phenocams), and satellite-based indices of greenness was examined for a two-year period (2019–2020) over these grazing and vegetative community gradients. Field determined phenology, phenocam Green Chromatic Coordinate (GCC), and Landsat Normalized Difference Vegetation Index (NDVI) were all highly correlated and the relationship did not change across the treatments. Timing of growth varied in these ecosystems depending on yearly precipitation and vegetative type. Communities dominated by mesic sedges had growing seasons which stopped earlier in the year. Heavier grazing regimes, however, did not equate to significant changes in growing season. Ultimately, shifts in phenology occurred and were successfully monitored at various spatial and temporal scales.

Assessing Urban Landslide Dynamics through Multi-Temporal InSAR Techniques and Slope Numerical Modeling

Landslides threaten more than before the urbanized areas and are a worldwide growing problem for the already affected communities and the local authorities committed to landslide risk management and mitigation. For this reason, it is essential to analyze landslide dynamics and environmental conditioning factors. Various techniques and instruments exist for landslide investigation and monitoring. Out of these, Multi-temporal Synthetic Aperture Radar Interferometry (MT-InSAR) techniques have been widely used in the last decades. Their capabilities are enhanced by the availability of the active Sentinel-1 mission, whose 6-day revisiting time enables near real-time monitoring of landslides. Interferometric results, coupled with ground measurements or other approaches such as numerical modeling, significantly improve the knowledge of the investigated surface processes. In this work, we processed the C-band SAR images of the available European Space Agency (ESA) satellite missions, using MT-InSAR methods to identify the surface deformations related to landslides affecting the Iași Municipality (Eastern Romania). The results (i.e., velocity maps) point out the most active landslides with velocities of up to 20 mm/year measured along the satellite Line of Sight (LOS). Following, we focused on the most problematic landslide that affects the Țicău neighborhood and is well-known for its significant implications that it had. To better understand its behavior and the sensitivity of the displacements to the environmental factors (i.e., rainfall), we carried out 2D numerical modeling using a finite difference code. The simulated displacement field is consistent with the InSAR displacements and reveals the most active sectors of the landslide and insights about its mechanism.

Comprehensive Insights into Spatial-Temporal Evolution Patterns, Dominant Factors of NDVI from Pixel Scale, as a Case of Shaanxi Province, China

Based on long term NDVI (1982–2015), climate, topographic factors, and land use type data information in Shaanxi Province, multiple methods (linear regression, partial and multiple correlation analysis, redundancy analysis and boosted regression trees method) were conducted to evaluate the spatial-temporal change footprints and driving mechanisms in the pixel scale. The results demonstrated that (1) the overall annual average and seasonal NDVI in this region showed a fluctuating upward trend, especially in spring. The difference between the end of season (eos) and start of season (sos) gradually increased, indicating the occurrence of temporal “greening” across most Shaanxi Province. (2) The overall spatial distribution of annual mean NDVI in Shaanxi Province was prominent in the south and low in the north, and 98.83% of the areas had a stable and increasing trend. Pixel scale analysis reflected the spatial continuity and heterogeneity of NDVI evolution. (3) Trend and breakpoint evaluation results showed that evolutionary trends were not homogeneous. There were obvious breakpoints in the latitude direction of NDVI evolution in Shaanxi Province, especially between 32–33 °N and in the north of 37 °N. (4) Compared with precipitation, the annual average temperature was significantly correlated with the vegetation indices (annual NDVI, max NDVI, time integrated NDVI) and phenology metrics (sos, eos). (5) Considering the interaction between environmental variables, the NDVI evolution was dominated by the combined influence of climate and geographic location factors in most areas.

Monitoring Vegetation Change and Its Potential Drivers in Inner Mongolia from 2000 to 2019

Inner Mongolia in China is a typically arid and semi-arid region with vegetation prominently affected by global warming and human activities. Therefore, investigating the past and future vegetation change and its impact mechanism is important for assessing the stability of the ecosystem and the ecological policy formulation. Vegetation changes, sustainability characteristics, and the mechanism of natural and anthropogenic effects in Inner Mongolia during 2000–2019 were examined using moderate resolution imaging spectroradiometer normalized difference vegetation index (NDVI) data. Theil–Sen trend analysis, Mann–Kendall method, and the coefficient of variation method were used to analyze the spatiotemporal variability characteristics and sustained stability of the NDVI. Furthermore, a trend estimation method based on a Seasonal Trend Model (STM), and the Hurst index was used to analyze breakpoints and change trends, and predict the likely future direction of vegetation, respectively. Additionally, the mechanisms of the compound influence of natural and anthropogenic activities on the vegetation dynamics in Inner Mongolia were explored using a Geodetector Model. The results show that the NDVI of Inner Mongolia shows an upward trend with a rate of 0.0028/year (p < 0.05) from 2000 to 2019. Spatially, the NDVI values showed a decreasing trend from the northeast to the southwest, and the interannual variation fluctuated widely, with coefficients of variation greater than 0.15, for which the high-value areas were in the territory of the Alxa League. The areas with increased, decreased, and stable vegetation patterns were approximately equal in size, in which the improved areas were mainly distributed in the northeastern part of Inner Mongolia, the stable and unchanged areas were mostly in the desert, and the degraded areas were mainly in the central-eastern part of Inner Mongolia, it shows a trend of progressive degradation from east to west. Breakpoints in the vegetation dynamics occurred mainly in the northwestern part of Inner Mongolia and the northeastern part of Hulunbuir, most of which occurred during 2011–2014. The future NDVI trend in Inner Mongolia shows an increasing trend in most areas, with only approximately 10% of the areas showing a decreasing trend. Considering the drivers of the NDVI, we observed annual precipitation, soil type, mean annual temperature, and land use type to be the main driving factors in Inner Mongolia. Annual precipitation was the first dominant factor, and when these four dominant factors interacted to influence vegetation change, they all showed interactive enhancement relationships. The results of this study will assist in understanding the influence of natural elements and human activities on vegetation changes and their driving mechanisms, while providing a scientific basis for the rational and effective protection of the ecological environment in Inner Mongolia.

Sensitivity of Spring Phenology Simulations to the Selection of Model Structure and Driving Meteorological Data

Accurate estimation of the timing of intensive spring leaf growth initiation at mid and high latitudes is crucial for improving the predictive capacity of biogeochemical and Earth system models. In this study, we focus on the modeling of climatological onset of spring leaf growth in Central Europe and use three spring phenology models driven by three meteorological datasets. The MODIS-adjusted NDVI3g dataset was used as a reference for the period between 1982 and 2010, enabling us to study the long-term mean leaf onset timing and its interannual variability (IAV). The performance of all phenology model–meteorology database combinations was evaluated with one another, and against the reference dataset. We found that none of the constructed model–database combinations could reproduce the observed start of season (SOS) climatology within the study region. The models typically overestimated IAV of the leaf onset, where spatial median SOS dates were best simulated by the models based on heat accumulation. When aggregated for the whole study area, the complex, bioclimatic index-based model driven by the CarpatClim database could capture the observed overall SOS trend. Our results indicate that the simulated timing of leaf onset primarily depends on the choice of model structure, with a secondary contribution from the choice of the driving meteorological dataset.

Strong controls of daily minimum temperature on the autumn photosynthetic phenology of subtropical vegetation in China

BACKGROUND

Vegetation phenology research has largely focused on temperate deciduous forests, thus limiting our understanding of the response of evergreen vegetation to climate change in tropical and subtropical regions.

RESULTS

Using satellite solar-induced chlorophyll fluorescence (SIF) and MODIS enhanced vegetation index (EVI) data, we applied two methods to evaluate temporal and spatial patterns of the end of the growing season (EGS) in subtropical vegetation in China, and analyze the dependence of EGS on preseason maximum and minimum temperatures as well as cumulative precipitation. Our results indicated that the averaged EGS derived from the SIF and EVI based on the two methods (dynamic threshold method and derivative method) was later than that derived from gross primary productivity (GPP) based on the eddy covariance technique, and the time-lag for EGSsif and EGSevi was approximately 2 weeks and 4 weeks, respectively. We found that EGS was positively correlated with preseason minimum temperature and cumulative precipitation (accounting for more than 73% and 62% of the study areas, respectively), but negatively correlated with preseason maximum temperature (accounting for more than 59% of the study areas). In addition, EGS was more sensitive to the changes in the preseason minimum temperature than to other climatic factors, and an increase in the preseason minimum temperature significantly delayed the EGS in evergreen forests, shrub and grassland.

CONCLUSIONS

Our results indicated that the SIF outperformed traditional vegetation indices in capturing the autumn photosynthetic phenology of evergreen forest in the subtropical region of China. We found that minimum temperature plays a significant role in determining autumn photosynthetic phenology in the study region. These findings contribute to improving our understanding of the response of the EGS to climate change in subtropical vegetation of China, and provide a new perspective for accurately evaluating the role played by evergreen vegetation in the regional carbon budget.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1186/s40663-021-00309-9.

Contrasting Geographic Patterns of Ignition Probability and Burn Severity in the Mojave Desert

The extent and frequency of fire has increased in many arid systems over the last century, with a large proportion of area in some regions undergoing transitions to novel conditions. Portions of the Mojave Desert in southwestern North America have undergone such transitions, most often from woody to herbaceous-dominated systems. These transitions have often been attributed to the proliferation of invasive annual grasses that promote more frequent fire, but recent evidence indicates that transitions can also occur independent of fire frequency if burn severity is high. In addition, high probability of ignition (i.e., potentially high fire frequency) and high burn severity may not always be geographically related. Therefore, our goals were to: (1) map potential burn severity, fire frequency, and probability of ignition across the Mojave; and, (2) evaluate spatial association among predicted burn severity, fire frequency and probability of ignition. We first mapped perimeters of 250 wildfires &gt; 405 ha that occurred from 1972 to 2010, then extracted data on fire frequency (number of times burned from 1972 to 2010), burn severity (the difference Normalized Burn Ratio), and 15 predictor variables representing physiography, climate, ignition, and vegetation. Maximum entropy was used to predict probability of ignition and Random Forest models were used to predict dNBR and fire frequency. Areas with high burn severity and high ignition probability had opposite spatial trends; areas with high burn severity were predicted to predominantly be in the northwest part of the region whereas areas with high ignition probability were predicted to be in the northeast. The models indicate the existence of a number of spatially structured but temporally dynamic fire regimes throughout the Mojave Desert. Two prevalent and ecologically significant regimes include one with frequent fires of low to moderate severity and another with infrequent fire of high severity. Areas with high fire frequency are currently limited in extent (&lt;1% total area). However, cover of invasive grasses can remain high decades after a burn of high or moderate severity, so grass-fire cycles could develop in areas where there may be expectations of infrequent fire as well as those with relatively high fire frequency.

Trends in Satellite Earth Observation for Permafrost Related Analyses—A Review

Climate change and associated Arctic amplification cause a degradation of permafrost which in turn has major implications for the environment. The potential turnover of frozen ground from a carbon sink to a carbon source, eroding coastlines, landslides, amplified surface deformation and endangerment of human infrastructure are some of the consequences connected with thawing permafrost. Satellite remote sensing is hereby a powerful tool to identify and monitor these features and processes on a spatially explicit, cheap, operational, long-term basis and up to circum-Arctic scale. By filtering after a selection of relevant keywords, a total of 325 articles from 30 international journals published during the last two decades were analyzed based on study location, spatio-temporal resolution of applied remote sensing data, platform, sensor combination and studied environmental focus for a comprehensive overview of past achievements, current efforts, together with future challenges and opportunities. The temporal development of publication frequency, utilized platforms/sensors and the addressed environmental topic is thereby highlighted. The total number of publications more than doubled since 2015. Distinct geographical study hot spots were revealed, while at the same time large portions of the continuous permafrost zone are still only sparsely covered by satellite remote sensing investigations. Moreover, studies related to Arctic greenhouse gas emissions in the context of permafrost degradation appear heavily underrepresented. New tools (e.g., Google Earth Engine (GEE)), methodologies (e.g., deep learning or data fusion etc.) and satellite data (e.g., the Methane Remote Sensing LiDAR Mission (Merlin) and the Sentinel-fleet) will thereby enable future studies to further investigate the distribution of permafrost, its thermal state and its implications on the environment such as thermokarst features and greenhouse gas emission rates on increasingly larger spatial and temporal scales.

Status of Phenological Research Using Sentinel-2 Data: A Review

Remote sensing of plant phenology as an indicator of climate change and for mapping land cover has received significant scientific interest in the past two decades. The advancing of spring events, the lengthening of the growing season, the shifting of tree lines, the decreasing sensitivity to warming and the uniformity of spring across elevations are a few of the important indicators of trends in phenology. The Sentinel-2 satellite sensors launched in June 2015 (A) and March 2017 (B), with their high temporal frequency and spatial resolution for improved land mapping missions, have contributed significantly to knowledge on vegetation over the last three years. However, despite the additional red-edge and short wave infra-red (SWIR) bands available on the Sentinel-2 multispectral instruments, with improved vegetation species detection capabilities, there has been very little research on their efficacy to track vegetation cover and its phenology. For example, out of approximately every four papers that analyse normalised difference vegetation index (NDVI) or enhanced vegetation index (EVI) derived from Sentinel-2 imagery, only one mentions either SWIR or the red-edge bands. Despite the short duration that the Sentinel-2 platforms have been operational, they have proved their potential in a wide range of phenological studies of crops, forests, natural grasslands, and other vegetated areas, and in particular through fusion of the data with those from other sensors, e.g., Sentinel-1, Landsat and MODIS. This review paper discusses the current state of vegetation phenology studies based on the first five years of Sentinel-2, their advantages, limitations, and the scope for future developments.

Nutrients and water availability constrain the seasonality of vegetation activity in a Mediterranean ecosystem

Anthropogenic nitrogen (N) deposition and resulting differences in ecosystem N and phosphorus (P) ratios are expected to impact photosynthetic capacity, that is, maximum gross primary productivity (GPP_max ). However, the interplay between N and P availability with other critical resources on seasonal dynamics of ecosystem productivity remains largely unknown. In a Mediterranean tree-grass ecosystem, we established three landscape-level (24 ha) nutrient addition treatments: N addition (NT), N and P addition (NPT), and a control site (CT). We analyzed the response of ecosystem to altered nutrient stoichiometry using eddy covariance fluxes measurements, satellite observations, and digital repeat photography. A set of metrics, including phenological transition dates (PTDs; timing of green-up and dry-down), slopes during green-up and dry-down period, and seasonal amplitude, were extracted from time series of GPP_max and used to represent the seasonality of vegetation activity. The seasonal amplitude of GPP_max was higher for NT and NPT than CT, which was attributed to changes in structure and physiology induced by fertilization. PTDs were mainly driven by rainfall and exhibited no significant differences among treatments during the green-up period. Yet, both fertilized sites senesced earlier during the dry-down period (17-19 days), which was more pronounced in the NT due to larger evapotranspiration and water usage. Fertilization also resulted in a faster increase in GPP_max during the green-up period and a sharper decline in GPP_max during the dry-down period, with less prominent decline response in NPT. Overall, we demonstrated seasonality of vegetation activity was altered after fertilization and the importance of nutrient-water interaction in such water-limited ecosystems. With the projected warming-drying trend, the positive effects of N fertilization induced by N deposition on GPP_max may be counteracted by an earlier and faster dry-down in particular in areas where the N:P ratio increases, with potential impact on the carbon cycle of water-limited ecosystems.

Analysis of Anomalies and Trends of Climate Change Indices in Zacatecas, Mexico

Sufficient evidence is currently available to demonstrate the reality of the warming of our planet’s climate system. Global warming has different effects on climate at the regional and local levels. The detection of changes in extreme events using instrumental data provides further evidence of such warming and allows for the characterization of its local manifestations. The present study analyzes changes in temperature and precipitation extremes in the Mexican state of Zacatecas using climate change indices developed by the Expert Team on Climate Change Detection and Indices (ETCCDI). We studied a 40-year period (1976–2015) using annual and seasonal time series. Maximum and minimum temperature data were used, as well as precipitation statistics from the Mexican climatology database (CLICOM) provided by the Mexican Meteorological Service. Weather stations with at least 80% of data availability for the selected study period were selected; these databases were subjected to quality control, homogenization, and data filling using Climatol, which runs in the R programming language. These homogenized series were used to obtain daily grids of the three variables at a resolution of 1.3 km. Results reveal important changes in temperature-related indices, such as the increase in maximum temperature and the decrease in minimum temperature. Irregular variability was observed in the case of precipitation, which could be associated with low-frequency oscillations such as the Pacific Decadal Oscillation and the El Niño–Southern Oscillation. The possible impact of these changes in temperature and the increased irregularity of precipitation could have a negative impact on the agricultural sector, especially given that the state of Zacatecas is the largest national bean producer. The most important problems in the short term will be related to the difficulty of adapting to these rapid changes and the new climate scenario, which will pose new challenges in the future.

Change in Autumn Vegetation Phenology and the Climate Controls From 1982 to 2012 on the Qinghai-Tibet Plateau

Autumn vegetation phenology plays a critical role in the survival and reproduction of vegetation in changing environments. Using GIMMS3g (Global Inventory Modeling and Mapping Studies), MODIS (Moderate Resolution Imaging and Spectroradiometer), and SPOT (Systeme Probatoire d'Observation de la Terre) remote sensing data, we investigated the spatial and temporal dynamics of the vegetation dormancy onset date (DOD) and its response to temperature, precipitation, and cold degree days (CDD) in different biomes on the Qinghai-Tibet Plateau (QTP) from 1982 to 2012. Our results indicated that there was no significant temporal trend in the DOD for the vegetation on the QTP but found clear regional characteristics in the DOD trends with a notably advancing trend in the central region and a widespread delay in the southwestern region (>1 day year^-1, P < 0.05). Our results also indicated that temperature plays an important role in the trend of delays in vegetation autumn phenology; in particular, the preseason temperature can delay the DOD significantly; the positive correlations were observed in more than 71% of the study areas. Consistent with previous studies, we observed significant negative correlations between preseason CDD and DOD; the negative correlations were observed in more than 72% of the study areas for all the data sets. In contrast, the effects of precipitation on DOD were biome dependent. We found that precipitation could promote the extension of the growing season in meadow and grass biomes but produce weak effects on vegetation dormancy in forest biomes. Therefore, not only the magnitude but also the timing of changes in temperature and precipitation determines the effects of climate factors on DOD and further suggests that biome-specific phenological responses also need to be integrated into vegetation phenology models for future climate change investigations on the QTP.

Constraining modelled global vegetation dynamics and carbon turnover using multiple satellite observations

The response of land ecosystems to future climate change is among the largest unknowns in the global climate-carbon cycle feedback. This uncertainty originates from how dynamic global vegetation models (DGVMs) simulate climate impacts on changes in vegetation distribution, productivity, biomass allocation, and carbon turnover. The present-day availability of a multitude of satellite observations can potentially help to constrain DGVM simulations within model-data integration frameworks. Here, we use satellite-derived datasets of the fraction of absorbed photosynthetic active radiation (FAPAR), sun-induced fluorescence (SIF), above-ground biomass of trees (AGB), land cover, and burned area to constrain parameters for phenology, productivity, and vegetation dynamics in the LPJmL4 DGVM. Both the prior and the optimized model accurately reproduce present-day estimates of the land carbon cycle and of temporal dynamics in FAPAR, SIF and gross primary production. However, the optimized model reproduces better the observed spatial patterns of biomass, tree cover, and regional forest carbon turnover. Using a machine learning approach, we found that remaining errors in simulated forest carbon turnover can be explained with bioclimatic variables. This demonstrates the need to improve model formulations for climate effects on vegetation turnover and mortality despite the apparent successful constraint of simulated vegetation dynamics with multiple satellite observations.

A Climate-Smart Approach to the Implementation of Land Degradation Neutrality within a Water Catchment Area in Kenya

At the sub-national level, the United Nations Convention to Combat Desertification (UNCCD) proposes the analysis and contextualization of land degradation-neutrality (LDN) at a water catchment scale to provide decision support for the formulation of policies and programmes towards transformative LDN interventions. Building on a number of national LDN studies in Kenya, an approach for the implementation of LDN that is based on the spatial and temporal characterization of key land degradation and climate change variables was defined. For a selected water catchment area, the LDN baseline was computed, the drivers that affect land degradation and regeneration trends within the main land cover types were identified and described, the trends of key climate change variables were described, and appropriate sustainable land management interventions for the main land cover types were identified. A climate-smart landscape approach that delineated the catchment area into zones focused on adaptation, and both adaptation and mitigation objectives was then proposed. The operationalization of a climate-smart landscape will require significant investment to not only provide an understanding of the bio-physical processes and interactions occurring at the catchment level but also to develop the institutional and technical capacities of relevant actors. The landscape approach proposed for the catchment area has the potential to improve livelihoods and the productivity of ecosystems while concurrently facilitating synergies between land degradation, climate change, and other development objectives.

Validation of Earth Observation Time-Series: A Review for Large-Area and Temporally Dense Land Surface Products

Large-area remote sensing time-series offer unique features for the extensive investigation of our environment. Since various error sources in the acquisition chain of datasets exist, only properly validated results can be of value for research and downstream decision processes. This review presents an overview of validation approaches concerning temporally dense time-series of land surface geo-information products that cover the continental to global scale. Categorization according to utilized validation data revealed that product intercomparisons and comparison to reference data are the conventional validation methods. The reviewed studies are mainly based on optical sensors and orientated towards global coverage, with vegetation-related variables as the focus. Trends indicate an increase in remote sensing-based studies that feature long-term datasets of land surface variables. The hereby corresponding validation efforts show only minor methodological diversification in the past two decades. To sustain comprehensive and standardized validation efforts, the provision of spatiotemporally dense validation data in order to estimate actual differences between measurement and the true state has to be maintained. The promotion of novel approaches can, on the other hand, prove beneficial for various downstream applications, although typically only theoretical uncertainties are provided.

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

Vegetation seasonality assessment through remote sensing data is crucial to understand ecosystem responses to climatic variations and human activities at large-scales. Whereas the study of the timing of phenological events showed significant advances, their recurrence patterns at different periodicities has not been widely study, especially at global scale. In this work, we describe vegetation oscillations by a novel quantitative approach based on the spectral analysis of Normalized Difference Vegetation Index (NDVI) time series. A new set of global periodicity indicators permitted to identify different seasonal patterns regarding the intra-annual cycles (the number, amplitude, and stability) and to evaluate the existence of pluri-annual cycles, even in those regions with noisy or low NDVI. Most of vegetated land surface (93.18%) showed one intra-annual cycle whereas double and triple cycles were found in 5.58% of the land surface, mainly in tropical and arid regions along with agricultural areas. In only 1.24% of the pixels, the seasonality was not statistically significant. The highest values of amplitude and stability were found at high latitudes in the northern hemisphere whereas lowest values corresponded to tropical and arid regions, with the latter showing more pluri-annual cycles. The indicator maps compiled in this work provide highly relevant and practical information to advance in assessing global vegetation dynamics in the context of global change.

Satellite detection of cumulative and lagged effects of drought on autumn leaf senescence over the Northern Hemisphere

Climate change has substantial influences on autumn leaf senescence, that is, the end of the growing season (EOS). Relative to the impacts of temperature and precipitation on EOS, the influence of drought is not well understood, especially considering that there are apparent cumulative and lagged effects of drought on plant growth. Here, we investigated the cumulative and lagged effects of drought (in terms of the Standardized Precipitation-Evapotranspiration Index, SPEI) on EOS derived from the normalized difference vegetation index (NDVI3g) data over the Northern Hemisphere extra-tropical ecosystems (>30°N) during 1982-2015. The cumulative effect was determined by the number of antecedent months at which SPEI showed the maximum correlation with EOS (i.e., R_max-cml ) while the lag effect was determined by a month during which the maximum correlation between 1-month SPEI and EOS occurred (i.e., R_max-lag ). We found cumulative effect of drought on EOS for 27.2% and lagged effect for 46.2% of the vegetated land area. For the dominant time scales where the R_max-cml and R_max-lag occurred, we observed 1-4 accumulated months for the cumulative effect and 2-6 lagged months for the lagged effect. At the biome level, drought had stronger impacts on EOS in grasslands, savannas, and shrubs than in forests, which may be related to the different root functional traits among vegetation types. Considering hydrological conditions, the mean values of both R_max-cml and R_max-lag decreased along the gradients of annual SPEI and its slope, suggesting stronger cumulative and lagged effects in drier regions as well as in areas with decreasing water availability. Furthermore, the average accumulated and lagged months tended to decline along the annual SPEI gradient but increase with increasing annual SPEI. Our results revealed that drought has strong cumulative and lagged effects on autumn phenology, and considering these effects could provide valuable information on the vegetation response to a changing climate.

Differentiated seasonal vegetation cover dynamics of degraded grasslands in Inner Mongolia recorded by continuous photography technique

Influence of climate change on the grassland phenology has attracted more and more attentions of ecologists. Although dozens of studies have been conducted, there have been few records examining the phenology differences of grasslands with different plant species compositions. Using continuous photography and image processing methods, this study examined seasonal vegetation cover dynamics of grasslands along a degradation gradient to clarify the influence of vegetation composition on the dynamics of vegetation cover during growing season. Our results revealed that phenological patterns of grasslands differentiated with their degradation status. Abandoned farmland (AF) and severely degraded grassland (SD) with most annuals and least climax species had the earliest start of growing season, while AF and extremely degraded grassland (ED) dominated by grasses had the earliest end of growing season. The start and end of growing season were strongly related to the relative cover of climax species and grasses. The results presented in this study support the possibility of using digital photography to capture the role of plant species composition on vegetation phenology in grasslands.

Climate-phenology-hydrology interactions in northern high latitudes: Assessing the value of remote sensing data in catchment ecohydrological studies

We assessed the hydrological implications of climate effects on vegetation phenology in northern environments by fusion of data from remote-sensing and local catchment monitoring. Studies using satellite data have shown earlier and later dates for the start (SOS) and end of growing seasons (EOS), respectively, in the Northern Hemisphere over the last 3 decades. However, estimates of the change greatly depend on the satellite data utilized. Validation with experimental data on climate-vegetation-hydrology interactions requires long-term observations of multiple variables which are rare and usually restricted to small catchments. In this study, we used two NDVI (normalized difference vegetation index) products (at ~25 & 0.5 km spatial resolutions) to infer SOS and EOS for six northern catchments, and then investigated the likely climate impacts on phenology change and consequent effects on catchment water yield, using both assimilated data (GLDAS: global land data assimilation system) and direct catchment observations. The major findings are: (1) The assimilated air temperature compared well with catchment observations (regression slopes and R² close to 1), whereas underestimations of summer rainstorms resulted in overall underestimations of precipitation (regression slopes of 0.3-0.7, R² ≥ 0.46). (2) The two NDVI products inferred different vegetation phenology characteristics. (3) Increased mean pre-season temperature significantly influenced the advance of SOS and delay of EOS. The precipitation influence was weaker, but delayed SOS corresponding to increased pre-season precipitation at most sites can be related to later snow melting. (4) Decreased catchment streamflow over the last 15 years could be related to the advance in SOS and extension of growing seasons. Greater streamflow reductions in the cold sites than the warm ones imply stronger climate warming impacts on vegetation and hydrology in colder northerly environments. The methods used in this study have potential for better understanding interactions between vegetation, climate and hydrology in observation-scarce regions.

Using Hidden Markov Models for Land Surface Phenology: An Evaluation Across a Range of Land Cover Types in Southeast Spain

Land Surface Phenology (LSP) metrics are increasingly being used as indicators of climate change impacts in ecosystems. For this purpose, it is necessary to use methods that can be applied to large areas with different types of vegetation, including vulnerable semiarid ecosystems that exhibit high spatial variability and low signal-to-noise ratio in seasonality. In this work, we evaluated the use of hidden Markov models (HMM) to extract phenological parameters from Moderate Resolution Imaging Spectroradiometer (MODIS) derived Normalized Difference Vegetation Index (NDVI). We analyzed NDVI time-series data for the period 2000–2018 across a range of land cover types in Southeast Spain, including rice croplands, shrublands, mixed pine forests, and semiarid steppes. Start of Season (SOS) and End of Season (EOS) metrics derived from HMM were compared with those obtained using well-established smoothing methods. When a clear and consistent seasonal variation was present, as was the case in the rice croplands, and when adjusting average curves, the smoothing methods performed as well as expected, with HMM providing consistent results. When spatial variability was high and seasonality was less clearly defined, as in the semiarid shrublands and steppe, the performance of the smoothing methods degraded. In these cases, the results from HMM were also less consistent, yet they were able to provide pixel-wise estimations of the metrics even when comparison methods did not.

Soil and climate differently impact NDVI patterns according to the season and the stand type

Several studies use satellite-based normalized difference vegetation index (NDVI) to monitor the impact of climate change on vegetation covers. Good understanding of the drivers of NDVI patterns is hindered by the difficulties in disentangling the effects of environmental factors from anthropogenic changes, by the limited number of environmental predictors studied, and by the diversity of responses according to periods and land covers. This study aims to improve our understanding of the different environmental drivers of NDVI spatial variations for different stand type characteristics of mountain and Mediterranean biomes. Using NDVI values extracted from MODIS Terra time series, we calculated Spring Greenness (SG) and annual Relative Greenness (RGRE) to depict spring and summer vegetation activity, respectively, in a contrasted area of 10,255 km² located in the south of France. We modeled SG and RGRE at different scales, using 20 environmental predictors characterizing available energy, water supply, and nutrient supply calculated for different periods of the year. In spring, high minimum temperatures, good nitrogen availability, and acidic or neutral pH turned out to be determining for greenness, particularly for stand types located in altitude. In summer, an important soil water reserve and low temperatures promoted vegetation dynamics, particularly for stands located in areas with a Mediterranean climate. Our results show that NDVI dynamics was not only driven by climatic variability, and should not be studied using only mean temperature and rainfall. They highlight that different environmental factors act complementarily, and that soil parameters characterizing water stress and soil nutrition should be taken into account. While the factors limiting NDVI values varied according to the season and the position of the stands along the ecological gradients, we identified a global temperature and water-stress threshold when considering the whole vegetation.

Seasonal diet composition of Pyrenean chamois is mainly shaped by primary production waves

In alpine habitats, the seasonally marked climatic conditions generate seasonal and spatial differences in forage availability for herbivores. Vegetation availability and quality during the growing season are known to drive life history traits of mountain ungulates. However, little effort has been made to understand the association between plant phenology and changes in the foraging strategies of these mountain dwellers. Furthermore, this link can be affected by the seasonal presence of livestock in the same meadows. The objective of this work was to study the seasonal changes in diet composition of Pyrenean chamois (Rupicapra p. pyrenaica) and its relationship to primary production trends in a Mediterranean alpine environment. Moreover, diet composition in two populations with contrasting livestock pressure was compared in order to study the effect of sheep flocks on the feeding behaviour of chamois. From 2009 to 2012, monthly diet composition was estimated by cuticle microhistological analysis of chamois faeces collected in the eastern Pyrenees. The primary production cycle was assessed by remote sensing, using the Normalized Difference Vegetation Index. Additionally, the diet of sheep sharing seasonally the subalpine and alpine meadows with chamois was analysed. Diet selection of chamois and sheep and their overlap was also assessed. Our results show an intra-annual variation in the diet composition of Pyrenean chamois and demonstrate a strong relationship between plant consumption dynamics and phenology in alpine areas. In addition, Calluna vulgaris, Cytisus spp. and Festuca spp., as well as forbs in the summer, are found to be key forage species for Pyrenean chamois. Furthermore, this study couldn’t detect differences between both chamois populations despite the presence of sheep flocks in only one area. However, the detection of a shift in the diet of chamois in both areas after the arrival of high densities of multi-specific livestock suggest a general livestock effect. In conclusion, Pyrenean chamois are well adapted to the variations in the seasonal availability of plants in alpine habitats but could be disturbed by the seasonal presence of livestock. Due to the key plants in their diet, we suggest that population management programmes should focus on the preservation of mixed grasslands composed of patches of shrubs and herbs. The effects of climate change and shrub expansion should be studied as they may potentially affect chamois population dynamics through changes in habitat composition and temporal shifts in forage availability.

Acceleration of global vegetation greenup from combined effects of climate change and human land management

Global warming and human land management have greatly influenced vegetation growth through both changes in spring phenology and photosynthetic primary production. This will presumably impact the velocity of vegetation greenup (Vgreenup, the daily rate of changes in vegetation productivity during greenup period), yet little is currently known about the spatio-temporal patterns of Vgreenup of global vegetation. Here, we define Vgreenup as the ratio of the amplitude of greenup (Agreenup) to the duration of greenup (Dgreenup) and derive global Vgreenup from 34-year satellite leaf area index (LAI) observations to study spatio-temporal dynamics of Vgreenup at the global, hemispheric, and ecosystem scales. We find that 19.9% of the pixels analyzed (n = 1,175,453) experienced significant trends toward higher greenup rates by an average of 0.018 m²  m^-2  day^-1 for 1982-2015 as compared to 8.6% of pixels with significant negative trends (p < 0.05). Global distribution and dynamics of Vgreenup show high spatial heterogeneity and ecosystem-specific patterns, which is primarily determined by the high spatial variation in Agreenup, while the temporal dynamics of Vgreenup are directly controlled by both changes in Dgreenup and Agreenup. Areas with the largest Vgreenup and largest positive trends are both observed in deciduous and mixed forests as compared to nonforest ecosystems showing both lower Vgreenup and trends. For nonforest ecosystems, human-managed ecosystems (e.g., rangelands and rainfed croplands) exhibited higher Vgreenup and positive trends than those of natural counterparts, suggesting strong imprints of human land management on terrestrial ecosystem functioning. Globally, warming has accelerated Vgreenup in temperature-constrained high latitude forest ecosystems and arctic regions, but decelerated Vgreenup in temperate and arid/semiarid areas. These results suggest that the combined effects of climate change and human land management have greatly accelerated global vegetation greenup, with important implications for changes in terrestrial ecosystem functioning and global carbon cycling.

Variability of Satellite Derived Phenological Parameters across Maize Producing Areas of South Africa

Changes in phenology can be used as a proxy to elucidate the short and long term trends in climate change and variability. Such phenological changes are driven by weather and climate as well as environmental and ecological factors. Climate change affects plant phenology largely during the vegetative and reproductive stages. The focus of this study was to investigate the changes in phenological parameters of maize as well as to assess their causal factors across the selected maize-producing Provinces (viz: North West, Free State, Mpumalanga and KwaZulu-Natal) of South Africa. For this purpose, five phenological parameters i.e., the length of season (LOS), start of season (SOS), end of season (EOS), position of peak value (POP), and position of trough value (POT) derived from the MODIS NDVI data (MOD13Q1) were analysed. In addition, climatic variables (Potential Evapotranspiration (PET), Precipitation (PRE), Maximum (TMX) and Minimum (TMN) Temperatures spanning from 2000 to 2015 were also analysed. Based on the results, the maize-producing Provinces considered exhibit a decreasing trend in NDVI values. The results further show that Mpumalanga and Free State Provinces have SOS and EOS in December and April respectively. In terms of the LOS, KwaZulu-Natal Province had the highest days (194), followed by Mpumalanga with 177 days, while North West and Free State Provinces had 149 and 148 days, respectively. Our results further demonstrate that the influences of climate variables on phenological parameters exhibit a strong space-time and common covariate dependence. For instance, TMN dominated in North West and Free State, PET and TMX are the main dominant factors in KwaZulu-Natal Province whereas PRE highly dominated in Mpumalanga. Furthermore, the result of the Partial Least Square Path Modeling (PLS-PM) analysis indicates that climatic variables predict about 46% of the variability of phenology indicators and about 63% of the variability of yield indicators for the entire study area. The goodness of fit index indicates that the model has a prediction power of 75% over the entire study area. This study contributes towards enhancing the knowledge of the dynamics in the phenological parameters and the results can assist farmers to make the necessary adjustment in order to have an optimal production and thereby enhance food security for both human and livestock.

Using Near-Infrared-Enabled Digital Repeat Photography to Track Structural and Physiological Phenology in Mediterranean Tree–Grass Ecosystems

Tree–grass ecosystems are widely distributed. However, their phenology has not yet been fully characterized. The technique of repeated digital photographs for plant phenology monitoring (hereafter referred as PhenoCam) provide opportunities for long-term monitoring of plant phenology, and extracting phenological transition dates (PTDs, e.g., start of the growing season). Here, we aim to evaluate the utility of near-infrared-enabled PhenoCam for monitoring the phenology of structure (i.e., greenness) and physiology (i.e., gross primary productivity—GPP) at four tree–grass Mediterranean sites. We computed four vegetation indexes (VIs) from PhenoCams: (1) green chromatic coordinates (GCC), (2) normalized difference vegetation index (CamNDVI), (3) near-infrared reflectance of vegetation index (CamNIRv), and (4) ratio vegetation index (CamRVI). GPP is derived from eddy covariance flux tower measurement. Then, we extracted PTDs and their uncertainty from different VIs and GPP. The consistency between structural (VIs) and physiological (GPP) phenology was then evaluated. CamNIRv is best at representing the PTDs of GPP during the Green-up period, while CamNDVI is best during the Dry-down period. Moreover, CamNIRv outperforms the other VIs in tracking growing season length of GPP. In summary, the results show it is promising to track structural and physiology phenology of seasonally dry Mediterranean ecosystem using near-infrared-enabled PhenoCam. We suggest using multiple VIs to better represent the variation of GPP.

Global land change from 1982 to 2016

Land change is a cause and consequence of global environmental change1,2. Changes in land use and land cover considerably alter the Earth's energy balance and biogeochemical cycles, which contributes to climate change and-in turn-affects land surface properties and the provision of ecosystem services1-4. However, quantification of global land change is lacking. Here we analyse 35 years' worth of satellite data and provide a comprehensive record of global land-change dynamics during the period 1982-2016. We show that-contrary to the prevailing view that forest area has declined globally5-tree cover has increased by 2.24 million km2 (+7.1% relative to the 1982 level). This overall net gain is the result of a net loss in the tropics being outweighed by a net gain in the extratropics. Global bare ground cover has decreased by 1.16 million km2 (-3.1%), most notably in agricultural regions in Asia. Of all land changes, 60% are associated with direct human activities and 40% with indirect drivers such as climate change. Land-use change exhibits regional dominance, including tropical deforestation and agricultural expansion, temperate reforestation or afforestation, cropland intensification and urbanization. Consistently across all climate domains, montane systems have gained tree cover and many arid and semi-arid ecosystems have lost vegetation cover. The mapped land changes and the driver attributions reflect a human-dominated Earth system. The dataset we developed may be used to improve the modelling of land-use changes, biogeochemical cycles and vegetation-climate interactions to advance our understanding of global environmental change1-4,6.

Challenges in Complementing Data from Ground-Based Sensors with Satellite-Derived Products to Measure Ecological Changes in Relation to Climate-Lessons from Temperate Wetland-Upland Landscapes

Assessing climate-related ecological changes across spatiotemporal scales meaningful to resource managers is challenging because no one method reliably produces essential data at both fine and broad scales. We recently confronted such challenges while integrating data from ground- and satellite-based sensors for an assessment of four wetland-rich study areas in the U.S. Midwest. We examined relations between temperature and precipitation and a set of variables measured on the ground at individual wetlands and another set measured via satellite sensors within surrounding 4 km² landscape blocks. At the block scale, we used evapotranspiration and vegetation greenness as remotely sensed proxies for water availability and to estimate seasonal photosynthetic activity. We used sensors on the ground to coincidentally measure surface-water availability and amphibian calling activity at individual wetlands within blocks. Responses of landscape blocks generally paralleled changes in conditions measured on the ground, but the latter were more dynamic, and changes in ecological conditions on the ground that were critical for biota were not always apparent in measurements of related parameters in blocks. Here, we evaluate the effectiveness of decisions and assumptions we made in applying the remotely sensed data for the assessment and the value of integrating observations across scales, sensors, and disciplines.

Simulating the onset of spring vegetation growth across the Northern Hemisphere

Changes in the spring onset of vegetation growth in response to climate change can profoundly impact climate-biosphere interactions. Thus, robust simulation of spring onset is essential to accurately predict ecosystem responses and feedback to ongoing climate change. To date, the ability of vegetation phenology models to reproduce spatiotemporal patterns of spring onset at larger scales has not been thoroughly investigated. In this study, we took advantage of phenology observations via remote sensing to calibrate and evaluated six models, including both one-phase (considering only forcing temperatures) and two-phase (involving forcing, chilling, and photoperiod) models across the Northern Hemisphere between 1982 and 2012. Overall, we found that the model that integrated the photoperiod effect performed best at capturing spatiotemporal patterns of spring phenology in boreal and temperate forests. By contrast, all of the models performed poorly in simulating the onset of growth in grasslands. These results suggest that the photoperiod plays a role in controlling the onset of growth in most Northern Hemisphere forests, whereas other environmental factors (e.g., precipitation) should be considered when simulating the onset of growth in grasslands. We also found that the one-phase model performed as well as the two-phase models in boreal forests, which implies that the chilling requirement is probably fulfilled across most of the boreal zone. Conversely, two-phase models performed better in temperate forests than the one-phase model, suggesting that photoperiod and chilling play important roles in these temperate forests. Our results highlight the significance of including chilling and photoperiod effects in models of the spring onset of forest growth at large scales, and indicate that the consideration of additional drivers may be required for grasslands.

Forest dynamics to precipitation and temperature in the Gulf of Mexico coastal region

The forest is one of the most significant components of the Gulf of Mexico (GOM) coast. It provides livelihood to inhabitant and is known to be sensitive to climatic fluctuations. This study focuses on examining the impacts of temperature and precipitation variations on coastal forest. Two different regression methods, ordinary least squares (OLS) and geographically weighted regression (GWR), were employed to reveal the relationship between meteorological variables and forest dynamics. OLS regression analysis shows that changes in precipitation and temperature, over a span of 12 months, are responsible for 56% of NDVI variation. The forest, which is not particularly affected by the average monthly precipitation in most months, is observed to be affected by cumulative seasonal and annual precipitation explicitly. Temperature and precipitation almost equally impact on NDVI changes; about 50% of the NDVI variations is explained in OLS modeling, and about 74% of the NDVI variations is explained in GWR modeling. GWR analysis indicated that both precipitation and temperature characterize the spatial heterogeneity patterns of forest dynamics.