Start Of Growing Season Research Articles

Past and future changes in the start, end, and duration of the growing season in Poland

The aim of the study was to determine the direction and rate of the projected changes of the start, end, and duration of the growing season in Poland in two-time horizons: 2021–2050 and 2071–2100. The main part of the paper was preceded by an analysis of changes in the start and end dates and the duration of the growing season in Poland in the period 1966–2020. The growing season in Poland is projected to be the shortest in mountain areas and in the north-eastern regions of Poland, where the date of growing season start is the latest and the date of the growing season end is the earliest. Whereas the longest growing season due to the projected earliest start and latest end dates is expected in the southwestern Poland. In the case of the coast, its late end will be of the greatest importance for its duration as a result of the warming effect of the sea in the autumn–winter period. The most intensive changes are forecasted in the long-term perspective in the case of the scenario regarding a high level of greenhouse gas emissions. The forecasts show that outside mountain areas, the growing season duration will vary from less than 255 days in the northeastern regions to more than 290 days in southwest and western Poland. In the duration of the mountains, the growing season will vary from 180 days on Kasprowy Wierch to 188 days on Śnieżka. This suggests significant changes in agroclimatic conditions in Poland.

A 4‐week advance in the growing season in Napa Valley, California,USA

AbstractThe growing season start and duration, along with other temperature‐related measures of importance to premium wine grapes in Napa Valley, California have changed as climate over the western United States has warmed. The growing season start has varied from year to year with a standard deviation of about 3 weeks, but over the 1958–2016 record a linear fit to the time sequence shows it advanced by more than 4 weeks. Over the study period, advances in the growing season were strongly influenced by temperature increases beginning in the late 1960s with warm anomalies generally persisting through recent years. The date upon which the growing season accumulated 1400 growing degree‐days also shifted earlier by about 4 weeks. Other measures swung to a warmer status, including the mean temperature of the last 45 days of the growing season, which warmed by over 1.5°C. Warming days and especially warming nights contributed to the growing season advance as well as trends towards warmer expressions of other viticultural measures. Years with earlier and warmer growing seasons experienced a substantial reduction in the number of daily cool extremes, and an increase in daily warm extremes, including the number of days whose temperature reaches or exceeds 35°C.

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.

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.

Spring phenology rather than climate dominates the trends in peak of growing season in the Northern Hemisphere.

Shifts in plant phenology regulate ecosystem structure and function, which feeds back to the climate system. However, drivers for the peak of growing season (POS) in seasonal dynamics of terrestrial ecosystems remain unclear. Here, spatial-temporal patterns of POS dynamics were analyzed by solar-induced chlorophyll fluorescence (SIF) and vegetation index in the Northern Hemisphere over the past two decades from 2001 to 2020. Overall, a slow advanced POS was observed in the Northern Hemisphere, while a delayed POS distributed mainly in northeastern North America. Trends of POS were driven by the start of growing season (SOS) rather than pre-POS climate both at hemisphere and biome scale. The effect of SOS on the trends in POS was the strongest in shrublands while the weakest in evergreen broad-leaved forest. These findings highlight the crucial role of biological rhythms rather than climatic factors in exploring seasonal carbon dynamics and global carbon balance.

Mapping fine-spatial-resolution vegetation spring phenology from individual Landsat images using a convolutional neural network

ABSTRACT Monitoring and mapping vegetation dynamics using remote sensing data are essential for our understanding of land surface processes. Most current satellite-based methods process vegetation index time-series data from a series of images to retrieve key points that correspond to vegetation phenophases. As deep learning approaches have been found to be powerful in processing individual images, we tested the applicability of convolutional neural network (CNN) in mapping vegetation growth days (VGD) and the start of growing season (SOS) from each Landsat image at fine-spatial-resolution. To provide references for both model training and testing, we applied the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) to fuse image pairs of Landsat 8 Operational Land Imager (OLI) and Moderate-resolution Imaging Spectroradiometer (MODIS) for four Landsat tiles in China. We then applied a first derivative method to retrieve VGD for the fused satellite data at fine-spatial-resolution. The CNN model was trained using each fused individual image as the model inputs and derived VGD as the targets. The trained model was further used to map VGD from individual Landsat image. The result could match the reference map well as indicated by the evaluation metrics. In terms of VGD, the method achieved a coefficient of determination of 0.85 and a root mean squared error of 8.17 days. In terms of SOS, the method achieved a coefficient of determination of 0.75 and a root mean squared error of 4.09 days. Compared with existing methods that require time series of satellite data spanning the entire growth cycles to retrieve phenological metrics, this study provides an alternative method to map VGD as well as SOS using individual Landsat image. Our study highlights the power of deep learning models in extracting phenological features from individual remote sensing images. Researchers can use our methods to predict near real-time VGD and SOS in the future.

A dataset of vegetation phenology of Inner Mongolia (2001-2020)

Vegetation phenology is one of the sensitive indicators reflecting global climate change and vegetation growth. Inner Mongolia is an important part of the ecological security barrier of the Mongolian Plateau, and a key area for resource development, environmental protection and ecological security in China. The study on the vegetation phenological changes of Inner Mongolia is of great significance for understanding the characteristics of climate change and extreme climate events in Inner Mongolia. Based on the normalized differential vegetation index (NDVI) in the high-spatial resolution MOD13Q1 data product, we used Google Earth Engine platform to process MODIS-NDVI raw data for format conversion, projection conversion and clipping, and exported NDVI long time series data from 2000 to 2021. Then, we adopted the dynamic threshold method to obtain a dataset of vegetation phenology of Inner Mongolia from 2001 to 2020. With a resolution of 250 m, this dataset contains remote sensing monitoring data of the start of growing season (SOS), the end of growing season (EOS), and the length of growing season (LOS) in Inner Mongolia from 2001 to 2019. It can provide data support for understanding the temporal and spatial variation of vegetation phenology in Inner Mongolia and its response to climate change.

Assessment of Post-Fire Phenological Changes Using MODIS-Derived Vegetative Indices in the Semiarid Oak Forests

Wildfire has significant impact on plant phenology. The plants’ phenological variables, derived from time series satellite data, can be monitored and the changes in satellite imagery may be used to identify the beginning, peak, and end of the growing season. This study investigated the use of remote sensing data and land surface phenology (LSP) parameters to evaluate the impacts of fire. The LSP parameters included the start of growing season (SOS), the length of the growing season (LOS), the end of the growing season (EOS), maximum greenness of the season (Gmax), and minimum greenery in the season (Gmin) in the fire-impacted, semiarid oak forests of Iran. These LSP parameters were extracted from multitemporal normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI2) data, acquired from MODIS sensor images in Zagros of the Ilam province in western Iran. By extracting LSP indices from the NDVI and EVI2 data, the indices were compared between burned forest areas, areas surrounding the burned forests, and unburned areas and for timesteps representing pre-fire, fire (i.e., year of fire), and post-fire (i.e., 2 years) conditions. It was found that for the burned area, there were significant differences in Gmax and the day that Gmax occurred. Furthermore, there was also a significant difference in Gmin between the pre- and post-fire conditions when NDVI was used and a significant difference between Gmax when EVI2 was used. The results also showed that in both time series there was a significant difference between the burned and control area in terms of Gmax. In general, the results showed that the fire had a negative effect on LSP, but in the two years after the fire, there were signs of forest restoration. This study provides necessary information to inform forest and resource conservation and restoration programs.

Spatial-temporal variations of forest and grassland phenology in the Yellow River Basin during 2000-2018.

The study of vegetation phenology is of great significance for understanding global climate change. The Yellow River basin has a wide spatial range and a complex ecological environment. The phenological characteristics of forest and grassland need further clarification. Based on the MODIS-EVI data from 2000 to 2018, we extracted the phenology of forest and grassland in the Yellow River basin using piecewise logistic and double logistic phenological models with the corresponding curvature change extremum method and derivative method, respectively. The temporal and spatial variations of phenological parameters were analyzed. The start of growing season (SOS) was at 90-165 day of year (DOY), and gradually delayed from southeast to northwest. The increase of 100 m elevation delayed SOS 0.94 d, and the SOS of forest was earlier than that of grassland. The end of growing season (EOS) was at 270-315 DOY, which delayed from west to southeast. For every 100 m increase in altitude, the EOS advanced 0.63 d, with EOS of forest being later than that of grassland. The length of growing season (LOS) was 110-230 d, which shortened gradually from southeast to northwest. The LOS of forest was larger than that of grassland. During the study, SOS showed an advance trend from 2000 to 2018 with a rate of 4.1 d·(10 a)-1, and the proportion of spatial advance area was 73.2%. There was an obvious advance in the central part of the basin. EOS generally showed a significant postponement trend with a rate of 2.3 d·(10 a)-1, and the proportion of spatially delayed area was 63.4%, the phenological advance and delay of forest was less stronger than that of grassland. LOS showed a significant prolongation trend with a rate of 6.4 d·(10 a)-1, and the proportion of spatial extension was 71.8%. The piecewise Logistic and double Logistic phenological models and the corresponding curvature extremum method and derivative method were suitable for the extraction of natural vegetation in the Yellow River Basin. The overall LOS of forest and grassland showed a prolonging trend, which was shortened with the increases of altitude. The LOS of forest was longer than that of grassland in the study area.

Extracting Deciduous Forests Spring Phenology From Sentinel-1 Cross Ratio Index

Deciduous forests spring phenology plays a major role in balancing carbon cycle. The cloud cover affects images acquired from optical sensor and reduces its performance in monitoring phenology. Synthetic Aperture Radar (SAR) can regularly acquire images day and night independent of weather conditions, which offers more frequent observations of vegetation phenology compared to optical sensors. However, it remains unclear how SAR data-derived indices vary across different growth stages of forests. Here we explored the relationship between cross ratio (CR) index derived from Sentinel-1 data and deciduous forest growth process. We proposed a deciduous forests spring phenology extraction method using CR and compared the extracted start of growing season (SOS) with those extracted using Normalized Difference Vegetation Index (NDVI) derived from Sentinel-2 optical satellite data and Green Chromatic Coordinate (GCC) derived from ground PhenoCam data. We extracted the SOS of 41 PhenoCam sites over Continental United States in 2018 using dynamic threshold method. Our results showed that the variations of CR time series are closely related to the phenological processes of deciduous forests. The SOS extracted using CR data showed high consistency with those extracted using GCC (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.46), with slightly lower accuracy compared with NDVI derived results (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.62). Our study illustrates the value and mechanism of deciduous forests spring phenology extraction using SAR data, and provides reference for using SAR data to improve forest phenology extraction in addition to using optical remote sensing data, especially in rainy and cloudy regions.

Assessing Canopy Phenological Variations and Gross Primary Productivity in A Savanna Ecosystem in Yuanjiang, Yunnan Province of Southwest China

Vegetation phenology is an important indicator of environmental change and strongly connected to forest ecosystem productivity change. This study aimed to analyse the pattern of phenological variations derived from digital imagery for the interpretation of ecosystem productivity. For 2014, 2015 and 2016, the seasonal phenological development of savanna was analysed by using towerbased imagery from a digital camera. The green excess index (GEI) was the best at representing the phenological transition dates (PTDs) and useful for investigating the gross primary production (GPP) in the savanna ecosystem. There was a significant correlation between the monthly pattern of the strength of green (Sgreen), green excess index (GEI) and vegetation contrast index (VCI) and GPP throughout the year. Additionally, the annual pattern of colour indices had significant relationship (p &lt; 0.05) with GPP but this was not seasonal. The air temperature (air T) and soil temperature (soil T) were strongly significantly correlated (p &lt; 0.001) with the start of growing season (SGS) and caused the advance in green-up and the timing of the start of the growing season in 2014 and 2016. The short growing season length (GSL) had an impact on the productivity. The colour indices from the digital camera images not only provided the phenological pattern of a forest canopy but also revealed the forest ecosystem productivity by showing the response to environmental factors. Our results indicate that daily continuous digital camera images might be useful for ecologists to use as a tool for future prediction of the long-term phenological modelling.

Effects of aspect on phenology of Larix gmelinii forest in Northeast China

The response of vegetation phenology to global climate change is one of the main forms in terrestrial ecosystem change, the study of vegetation phenology is an important complement to the understanding of how global climate change affects ecosystems in multiple dimensions. We selected the distribution area of Larix gmelinii in The Greater Khingan Mountains as a case area by eliminating the heterogeneity of vegetation types, with the support of Google Earth Engine platform, we studied the effects of different aspects and land surface temperature (LST) on remote sensing phenology (RSP) that is defined as start of growing season (SOS), end of growing season (EOS) and length of growing season (LOS) respectively in the study area through Normalized Difference Vegetation Index (NDVI) changes. The results showed that SOS advanced in different aspects during the study period, and the advance amplitude of SOS on the east and west aspect was greater than that on the south and north. Except for the east aspect, EOS showed a slight postponed, and LOS was prolonged on all aspects. The latitude difference between 51° and 53° N had no significant effect on L. gmelinii in different aspects. LST had an obviously direct effect on the RSP of L. gmelinii in different aspects, and the effect of LST on SOS and LOS was significantly greater than that on EOS. The effect of LST on SOS and LOS was significant in April and spring. The main contributor to the increase of LOS was the advance of SOS, while the postponed of EOS has a relatively small contribution to LOS. Due to the redistribution of meteorological factor by aspect, the spatial and temporal heterogeneity of RSP tends to be complex, so determining the same aspect is one of the main ways to reduce the phenological heterogeneity in the study of vegetation RSP.

Climate conditions in the near-term, mid-term and distant future for growing soybeans in Canada

The soybean industry in Canada aimed to extensively expand soybean production to benefit from new early-maturing varieties and the warming climate. However, setbacks in the soybean industry since 2017 demonstrated the impacts of climate risk and global market uncertainty. Therefore, a better understanding of future climate conditions that will impact soybean growth in Canada is needed for decision-making in the sector, such as prioritizing regions for expansion and developing climate change adaptation strategies through either agronomic management practices or breeding new cultivars. Based on climate projections from a set of global climate models, we analyzed climate conditions for growing soybeans, including growing season start, crop heat units, precipitation, precipitation deficits and climate extremes, in the near-term (2030s), the mid-term (2050s) and the distant future (2070s). We found that a future warmer climate with an increase of 1.6, 2.8 and 4.1 °C in the growing season (May–September) mean temperature averaged over Canada’s land area in the near-term, mid-term and distant future under SSP3-7.0 would favour the expansion of soybean production further north and west. However, an increase of approximately 200 mm in precipitation deficits on the semiarid Canadian Prairies in the mid-term would constrain soybean production unless irrigation could be introduced. Heat- and drought-tolerant cultivars should be developed to adapt soybean production to a changing climate, in addition to the adoption of late-maturing cultivars that would benefit from the lengthened growing season and increased crop heat units.

Characterizing the climate-phenology-hydrology associations in a subtropical forested watershed, central Taiwan

Upstream forested watersheds supply critical ecosystem services through providing clean freshwater and maintaining stable hydrological conditions. Responses of vegetation phenology to climatic variations have vital implications for hydrological regimes that are region-specific, but the associations of climate-phenology-hydrology have rarely been examined especially in tropical/subtropical regions. In this study, we utilized 46-year (1975–2020) hydroclimate records in forested watershed at central Taiwan, and showed that precipitation and streamflow anomalies and incidences of meteorological and hydrological droughts are becoming prominent after 2001. We further investigated the effects of monthly temperature and precipitation on vegetation growth using monthly EVI (enhanced vegetation index) of a watershed derived from MODIS (Moderate Resolution Imaging Spectroradiometer), and explored the effects of seasonal precipitation on the variations of vegetation phenological and subsequent watershed streamflow between 2001 and 2020. The EVI and temperature showed a linear relationship without time-lag effect (R2 = 0.50, p < 0.001), whereas EVI and precipitation exhibited a log-linear relationship with 2-month lag (R2 = 0.40, p < 0.001), indicating the accumulation of rainfall during relatively dry period was crucial for vegetation growth. Structural equation modeling revealed that earlier start of growing season (SOS) caused by relatively high spring rainfall (February-March) led to longer growing season (LOS) and higher P-Q deficit (precipitation minus runoff) during the growing season. Nevertheless, the large amount of precipitation during growing season has no effect on the end of growing season (EOS), LOS and P-Q deficit. Neither EOS has influence on LOS and P-Q deficit. Understanding the vegetation responses to climatic variations is required for future hydrologic regime projections, especially under changing climate.

Effect of seasonal snow on the start of growing season of typical vegetation in Northern Hemisphere

Under global warming, seasonal snow takes faster melting rate than before, which greatly changes the hydrological cycle. In this study, by targeting three typical seasonal snow-covered land types (i.e., open shrubland, evergreen needleleaf forest and mixed forest) in the Northern Hemisphere, the start of growing season (SGS) has been found obviously advanced in the past years, greatly contributed by the faster melting rate of seasonal snow. It is manifested that significantly positive correlation has been found between SGS and May snow depth for open shrubs, March and April snow depth for evergreen needleleaf forests and March snow depth for mixed forests. However, such close association is not appeared in all the climate conditions of same vegetation. In the future, as the rate of melting snow becomes faster in the high emission of greenhouse gasses than the current situation, continuously advanced SGS will accelerate the change of vegetation distribution in the Northern Hemisphere. These findings offer insights into understanding the effect from seasonal snow on vegetation and promote the sustainable utilization of regional vegetation in the Northern Hemisphere.

LAI-Based Phenological Changes and Climate Sensitivity Analysis in the Three-River Headwaters Region

Global climate changes have a great impact on terrestrial ecosystems. Vegetation is an important component of ecosystems, and the impact of climate changes on ecosystems can be determined by studying vegetation phenology. Vegetation phenology refers to the phenomenon of periodic changes in plants, such as germination, flowering and defoliation, with the seasonal change of climate during the annual growth cycle, and it is considered to be one of the most efficient indicators to monitor climate changes. This study collected the global land surface satellite leaf area index (GLASS LAI) products, meteorological data sets and other auxiliary data in the Three-River headwaters region from 2001 to 2018; rebuilt the vegetation LAI annual growth curve by using the asymmetric Gaussian (A-G) fitting method and extracted the three vegetation phenological data (including Start of Growing Season (SOS), End of Growing Season (EOS) and Length of Growing Season (LOS)) by the maximum slope method. In addition, it also integrated Sen’s trend analysis method and the Mann-Kendall test method to explore the temporal and spatial variation trends of vegetation phenology and explored the relationship between vegetation phenology and meteorological factors through a partial correlation analysis and multiple linear regression models. The results of this study showed that: (1) the SOS of vegetation in the Three-River headwaters region is concentrated between the beginning and the end of May, with an interannual change rate of −0.14 d/a. The EOS of vegetation is concentrated between the beginning and the middle of October, with an interannual change rate of 0.02 d/a. The LOS of vegetation is concentrated between 4 and 5 months, with an interannual change rate of 0.21 d/a. (2) Through the comparison and verification with the vegetation phenological data observed at the stations, it was found that the precision of the vegetation phonology extracted by the A-G method and the maximum slope method based on GLASS LAI data is higher (MAE is 7.6 d, RMSE is 8.4 d) and slightly better than the vegetation phenological data (MAE is 9.9 d, RMSE is 10.9 d) extracted based on the moderate resolution imaging spectroradiometer normalized difference vegetation index (MODIS NDVI) product. (3) The correlation between the SOS of vegetation and the average temperature in March–May is the strongest. The SOS of vegetation is advanced by 1.97 days for every 1 °C increase in the average temperature in March–May; the correlation between the EOS of vegetation and the cumulative sunshine duration in August–October is the strongest. The EOS of vegetation is advanced by 0.07 days for every 10-h increase in the cumulative sunshine duration in August–October.

Effect of Altitude and Topography on Vegetation Phenological Changes in the Niubeiliang Nature Reserve of Qinling Mountains, China

Due to the fragility of the habitats in mountain nature reserves, the vegetation is extremely sensitive to climate change, and its phenological changes are more specific. Therefore, it is of great significance to study the effects of topography and climate on the vegetation phenology in mountain nature reserves. Based on the vegetation phenology data retrieved from MODIS EVI2 during 2000 to 2017, combined with temperature data, spatial trend analysis and correlation analysis methods were used to study the effects of topographic and climatic factors on vegetation phenology in the Niubeiliang Nature Reserve of the Qinling Mountains. The results showed that the GSS (growing season start) was advanced with a rate of 4.24 days/10a, and the rates in the northern and southern slopes were almost the same; the GSE (growing season end) was delayed with a rate of 3.29 days/10a, and the rate in the northern slope was faster; and the GSL (growing season length) was prolonged. There were seasonal differences and north–south differences in the effects of topography on the phenophase. The phenophase changed regularly with the increase in altitude. The higher the altitude, the more significant the effect. The steeper the slope, the later the GSS, the earlier the GSE, and the more significant its effect on the GSE. The aspect had little effect on GSS but a more significant effect on GSE, which was the latest on the sunny slope and the earliest on the zero slope. Temperature affected both the GSS advance and the GSE delay, and both had a time-lag effect of approximately 2–3 months. Its effect was more significant in the GSE, in the southern slopes, and in the high-altitude areas.

Impact of Snowpack on the Land Surface Phenology in the Tianshan Mountains, Central Asia

The accumulation and ablation processes of seasonal snow significantly affect the land surface phenology in a mountainous ecosystem. However, the ability of snow to regulate the alpine land surface phenology in the arid regions is not well described in the context of climate change. The impact of snowpack changes on land surface phenology and its driving factors were investigated in the Tianshan Mountains using the land surface phenology metrics derived from satellited products and a snow dataset from downscaled regional climate model simulations covering the period from 1983 to 2015. The results demonstrated that the annual mean start of growing season (SOS) and length of growing season (LOS) experienced a significant (p &lt; 0.05) decrease and increase with a rate of −2.45 days/decade and 2.98 days/decade, respectively. The significantly advanced SOS and increased LOS were mainly seen in the Western Tianshan Mountains and Ili Valley regions with elevations from 2500 to 3500 m a.s.l and below 3000 m a.s.l, respectively. During the early spring, the significant decline in snow cover fraction (SCF) could advance the SOS. In contrast, snowmelt amount and annual maximum snow water equivalent (SWE) have an almost equally substantial positive correlation with annual maximum vegetation greenness. In particular, the SOS of grassland was the most sensitive to variations of snow cover fraction during early spring than that of other vegetation types, and their strong relationship was mainly located at elevations from 1500 to 2500 m a.s.l. Its greenness was significantly controlled by the annual maximum snow water equivalent in all elevation bands. Both decreased SCF and increased temperature in the early spring caused a significant advance of the SOS, consequently prolonging the LOS. Meanwhile, more SWE and snowmelt amount could significantly promote vegetation greenness by regulating the soil moisture. The results can improve the understanding of the snow ecosystem services in the alpine regions under climate change.

Impacts of climate change on vegetation phenology over the Great Lakes Region of Central Asia from 1982 to 2014

Dryland ecosystems in the Great Lakes Region of Central Asia (GLRCA) are highly sensitive to climate change due to the climate of spring precipitation. Although shifts in vegetation phenology have been widely attributed to rising temperature, the effects of solar radiation and drought on phenology remain largely unknown. Understanding the mechanisms of vegetation phenology response to climatic factors is essential for assessing the impact of climate change on dryland ecosystems. In this study, we investigated the spatial and temporal variations of vegetation phenology across the GLRCA using a long-term series of Normalized Difference Vegetation Index (NDVI), and then examined the response of vegetation phenology to climate change within different climate zones by combining with climate data (surface temperature, soil moisture, short-wave radiation, and standardized precipitation evapotranspiration index (SPEI)). The results suggested that the start of growing season (SGS) and the end of growing season (EGS) were significantly earlier regionally by −0.143 days/year and −0.363 days/year, respectively. Because of changes in SGS and EGS, length of growing season (LGS) across the GLRCA was shortened at a rate of −0.442 days/yr, which was mainly attributed to advanced EGS. Additionally, SGS of vegetation was negatively correlated with surface temperature but positively correlated with soil moisture and SPEI. These results indicated that surface temperature was a major determinant of advanced spring phenology, while increased soil moisture and mitigated drought would delay spring phenology. The response of autumn phenology to surface temperature and short-wave radiation varied across different climate zones. In arid climate zone, autumn phenology was obviously advanced with the increase of surface temperature and short-wave radiation. In cold climate zone, higher surface temperature and short-wave radiation postponed autumn phenology. Meanwhile, the thermal growing season did not accurately characterize the actual vegetation growing season because GLRCA phenology was different from most of Northern Hemisphere.

Spatial and Temporal Variability of Key Bio-Temperature Indicators and Their Effects on Vegetation Dynamics in the Great Lakes Region of Central Asia

Dryland ecosystems are fragile to climate change due to harsh environmental conditions. Climate change affects vegetation growth primarily by altering some key bio-temperature thresholds. Key bio-temperatures are closely related to vegetation growth, and slight changes could produce substantial effects on ecosystem structure and function. Therefore, this study selected the number of days with daily mean temperature above 0 °C (DT0), 5 °C (DT5), 10 °C (DT10), 20 °C (DT20), the start of growing season (SGS), the end of growing season (EGS), and the length of growing season (LGS) as bio-temperature indicators to analyze the response of vegetation dynamics to climate change in the Great Lakes Region of Central Asia (GLRCA) for the period 1982–2014. On the regional scale, DT0, DT5, DT10, and DT20 exhibited an overall increasing trend. Spatially, most of the study area showed that the negative correlation between DT0, DT5, DT10, DT20 with the annual Normalized Difference Vegetation Index (NDVI) increased with increasing bio-temperature thresholds. In particular, more than 88.3% of the study area showed a negative correlation between annual NDVI and DT20, as increased DT20 exacerbated ecosystem drought. Moreover, SGS exhibited a significantly advanced trend at a rate of −0.261 days/year for the regional scale, while EGS experienced a significantly delayed trend at a rate of 0.164 days/year. Because of changes in SGS and EGS, LGS across the GLRCA was extended at a rate of 0.425 days/year, which was mainly attributed to advanced SGS. In addition, our study revealed that about 53.6% of the study area showed a negative correlation between annual NDVI and LGS, especially in the north, indicating a negative effect of climate warming on vegetation growth in the drylands. Overall, the results of this study will help predict the response of vegetation to future climate change in the GLRCA, and support decision-making for implementing effective ecosystem management in arid and semi-arid regions.