Length Of The Growing Season Research Articles

Refined Analysis of Vegetation Phenology Changes and Driving Forces in High Latitude Altitude Regions of the Northern Hemisphere: Insights from High Temporal Resolution MODIS Products

The vegetation patterns in high-latitude and high-altitude regions (HLAR) of the Northern Hemisphere are undergoing significant changes due to the combined effects of global warming and human activities, leading to increased uncertainties in vegetation phenological assessment. However, previous studies on vegetation phenological changes often relied on long-term time series of remote sensing products for evaluation and lacked comprehensive analysis of driving factors. In this study, we utilized high temporal resolution seamless MODIS products (MODIS-NDVISDC and MODIS-EVI2SDC) to assess the vegetation phenological changes in High-Latitude-Altitude Regions (HLAR) of the Northern Hemisphere. We quantified the differences in vegetation phenology among different land-use types and determined the main driving factors behind vegetation phenological changes. The results showed that the length of the growing season (LOS) derived from MODIS-NDVISDC was 8.9 days longer than that derived from MODIS-EVI2SDC, with an earlier start of the growing season (SOS) by 1.5 days and a later end of the growing season (EOS) by 7.4 days. Among different vegetation types, deciduous needleleaf forests exhibited the fastest LOS extension (p < 0.01), while croplands showed the fastest LOS reduction (p < 0.05). Regarding land-use transitions, the conversion of built-up land to forest and grassland had the longest LOS. In expanding agricultural areas, the LOS of land converted from built-up land to cropland was significantly higher than that of other land conversions. We analyzed human activities and found that as the human footprint gradient increased, the LOS showed a decreasing trend. Among the climate-related factors, the dominant response of phenology to temperature was the strongest in the vegetation greening period. During the vegetation browning period, the temperature control was weakened, and the control of radiation and precipitation was enhanced, accounting for 20–30% of the area, respectively. Finally, we supplement and prove that the highest contributions to vegetation greening in the Northern Hemisphere occurred during the SOS period (May–June) and the EOS period (October). Our study provides a theoretical basis for vegetation phenological assessment under global change. It also offers new insights for land resource management and planning in high-latitude and high-altitude regions.

Impact of Preseason Climate Factors on Vegetation Photosynthetic Phenology in Mid-High Latitudes of the Northern Hemisphere.

During the period preceding the vegetation growing season (GS), temperature emerges as the pivotal factor determining phenology in northern terrestrial ecosystems. Despite extensive research on the impact of daily mean temperature (Tmean) during the preseason period, the influence of diurnal temperature range (DTR) on vegetation photosynthetic phenology (i.e., the impact of the plant photosynthetic cycle on seasonal time scale) has largely been neglected. Using a long-term vegetation photosynthetic phenology dataset and historical climate data, we examine vegetation photosynthetic phenology dynamics and responses to climate change across the mid-high latitudes of the Northern Hemisphere from 2001 to 2020. Our data reveal an advancing trend in the start of the GS (SOS) by -0.15 days per year (days yr-1), affecting 72.1% of the studied area. This is particularly pronounced in western Canada, Alaska, eastern Asia, and latitudes north of 60°N. Conversely, the end of the GS (EOS) displays a delaying trend of 0.17 days yr-1, impacting 62.4% of the studied area, especially northern North America and northern Eurasia. The collective influence of an earlier SOS and a delayed EOS has resulted in the notably prolonged length of the GS (LOS) by 0.32 days yr-1 in the last two decades, affecting 70.9% of the studied area, with Eurasia and western North America being particularly noteworthy. Partial correlation coefficients of the SOS with preseason Tmean, DTR, and accumulated precipitation exhibited negative values in 98.4%, 93.0%, and 39.2% of the study area, respectively. However, there were distinct regional variations in the influence of climate factors on the EOS. The partial correlation coefficients of the EOS with preseason Tmean, DTR, and precipitation were positive in 58.6%, 50.1%, and 36.3% of the region, respectively. Our findings unveil the intricate mechanisms influencing vegetation photosynthetic phenology, holding crucial significance in understanding the dynamics of carbon sequestration within terrestrial ecosystems amidst climate change.

Linking Climate Change Information with Crop Growing Seasons in the Northwest Ethiopian Highlands

In Ethiopia, the impacts of climate change are expected to have significant consequences for agriculture and food security. This study investigates both past (1981–2010) and future (2041–2070) climate trends and their influence on the length of the growing season (LGS) in the North-Western Ethiopian highlands. Climate observations were obtained from the National Meteorological Agency of Ethiopia, while the best performing and highest resolution models from the CMIP5 experiment and RCP6 (Coupled Models Intercomparison Project and representative concentration pathway 6) were used for the analysis. Standard statistical methods were applied to compute soil water content, evaluate climate variability and trends, and assess their impact on the length of the growing season. Maximum temperature (tasmax) and minimum temperature (tasmin) inter-annual variability anomalies show that the region has experienced cooler years than hotter years in the past. However, in the future, the coolest years are expected to decrease by −1.2 °C, while the hottest years will increase by +1.3 °C. During the major rainfall season (JJAS), the area has received an adequate amount of rainfall in the past and is very likely to receive similar rainfall in the future. On the other hand, the rainfall amount in the season February to May (FMAM) is expected to assist only with early planting. For the season October to January (ONDJ), the rainfall amount may help lengthen the growing season of JJAS if properly utilized; otherwise, the season has the potential to destroy crops before and during the harvesting time. The soil water content changes in the future remain close to those of the past period. The length of growing seasons has less variable onset and cessation dates, while in the future, the length of the growing period (LGP) of 174 to 177 days will be suitable for short- and long-cycle crops, as well as double cropping, benefiting crop production yield in the North-Western Ethiopian highlands in the future.

Dry season temperature and rainy season precipitation significantly affect the spatio-temporal pattern of rubber plantation phenology in Yunnan province.

The ongoing global warming trajectory poses extensive challenges to plant ecosystems, with rubber plantations particularly vulnerable due to their influence on not only the longevity of the growth cycle and rubber yield, but also the complex interplay of carbon, water, and energy exchanges between the forest canopy and atmosphere. However, the response mechanism of phenology in rubber plantations to climate change remains unclear. This study concentrates on sub-optimal environment rubber plantations in Yunnan province, Southwest China. Utilizing the Google Earth Engine (GEE) cloud platform, multi-source remote sensing images were synthesized at 8-day intervals with a spatial resolution of 30-meters. The Normalized Difference Vegetation Index (NDVI) time series was reconstructed using the Savitzky-Golay (S-G) filter, coupled with the application of the seasonal amplitude method to extract three crucial phenological indicators, namely the start of the growing season (SOS), the end of the growing season (EOS), and the length of the growing season (LOS). Linear regression method, Pearson correlation coefficient, multiple stepwise regression analysis were used to extract of the phenology trend and find the relationship between SOS, EOS and climate factors. The findings demonstrated that 1) the phenology of rubber plantations has undergone dynamic changes over the past two decades. Specifically, the SOS advanced by 9.4 days per decade (R2 = 0.42, p< 0.01), whereas the EOS was delayed by 3.8 days per decade (R2 = 0.35, p< 0.01). Additionally, the LOS was extended by 13.2 days per decade (R2 = 0.55, p< 0.01); 2) rubber phenology demonstrated a notable sensitivity to temperature fluctuations during the dry season and precipitation patterns during the rainy season. The SOS advanced 2.0 days (r =-0.19, p< 0.01) and the EOS advanced 2.8 days (r =-0.35, p< 0.01) for every 1°C increase in the cool-dry season. Whereas a 100 mm increase in rainy season precipitation caused the SOS to be delayed by 2.0 days (r = 0.24, p< 0.01), a 100 mm increase in hot-dry season precipitation caused the EOS to be advanced by 7.0 days (r =-0.28, p< 0.01); 3) rubber phenology displayed a legacy effect of preseason climate variations. Changes in temperature during the fourth preseason month and precipitation during the fourth and eleventh preseason months are predominantly responsible for the variation in SOS. Meanwhile, temperature changes during the second, fourth, and ninth preseason months are primarily responsible for the variation in EOS. The study aims to enhance our understanding of how rubber plantations respond to climate change in sub-optimal environments and provide valuable insights for sustainable rubber production management in the face of changing environmental conditions.

Effects of Ecological Restoration and Climate Change on Herbaceous and Arboreal Phenology.

With global climate change, changes in vegetation phenology have become increasingly evident. Horqin Sandy Land is located near the eastern part of the West Liaohe River. It is the largest sandy land in China and its ecological environment is fragile. Investigating the changes in vegetation phenology in these sandy areas and determining the relationship between vegetation phenology and meteorological factors are of great importance for predicting the impacts of future climate change and understanding the response mechanisms of ecosystems. In this study, we used the time series of the Normalized Difference Vegetation Index (NDVI) from 2000 to 2021 and extracted the vegetation phenology in the Horqin Sandy Land using high-order curve fitting methods, including the start date of the growing season (SOS), the end date of the growing season (EOS), and the length of the growing season (LOS). We analyzed their temporal variation and used partial correlation analysis to determine their relationship with meteorological factors (temperature and precipitation). In addition, we compared the phenology and microclimate of forest and grassland within the study area. In the Horqin Sandy Land, the vegetation SOS was concentrated between the 115th and 150th day, the EOS was concentrated between the 260th and 305th day, and the LOS ranged from 125 to 190 days. Over the past 22 years, the SOS, EOS, and LOS of vegetation in the Horqin Sandy Land showed trends of delay, shift, and extension, with rates of change of 0.82 d/10a, 5.82 d/10a, and 5.00 d/10a, respectively. The start date of the growing season in the Horqin Sandy Land was mainly influenced by precipitation in April of the current year, while the end date was mainly influenced by precipitation in August of the current year. Overall, the SOS in the forested areas of the Horqin Sandy Land was slightly later than in the grasslands, but the EOS in the forested areas was significantly later than in the grasslands, resulting in a longer LOS in the forests. In addition, annual precipitation and the rate of precipitation increase were higher in the forested areas than in the grasslands, but soil temperature was higher in the grasslands than in the forests. Vegetation phenology in the Horqin Sandy Land has undergone significant changes, mainly manifested in the delayed end date of the growing season, the extended length of the growing season, and the differences between forest and grassland. This indicates that climate change has indeed affected phenological changes and provides a theoretical basis for subsequent ecological restoration and desertification prevention efforts in the region.

Can land surface phenology from Sentinel-2 time-series be used as an indicator of Macaronesian ecosystem dynamics?

Land surface phenology (LSP), the study of phenological patterns of vegetation using vegetation index (VI) time-series derived from multi-spectral satellite imagery, has helped to improve the understanding of the seasonal dynamics of terrestrial ecosystems from local to global scale. High spatial resolution satellite observations have emerged as a new possibility for monitoring the seasonal dynamics of heterogeneous ecosystems. Under-studied Macaronesian ecosystems have specific characteristics that make LSP estimation a complex task (e. g., frequent cloud cover, frequent presence of atmospheric aerosols, different degrees of vegetation density, diversity of plant species, landscape heterogeneity). Thus, this study aims to analyse the potential of LSP based on Sentinel-2 data to monitor the phenological dynamics of vegetation in the Macaronesian ecosystems of Canary Islands (Spain). NDVI time-series were generated using Sentinel-2 data from January 2018 to December 2021. NDVI time-series were smoothed using double logistic function. Three phenometrics, such as the start of the growing season (SOS), the end of the growing season (EOS) and the length of the growing season (LOS), were extracted using a threshold-based method (20%) only for pixels where the mean NDVI value of the smoothed four-year time-series was higher than 0.2. Growing season of the major of Macaronesian vegetation started between late summer and late autumn (start of the wet season) and ended between early spring and early summer (end of the wet season). The most representative Macaronesian tree species had a clear and marked seasonality, except for the laurel forest species. SOS of Juniperus phoenicea subsp. turbinata was slightly later than for Olea europaea var. sylvestris and Pinus canariensis, while the mean EOS was slightly later for Olea europaea var. sylvestris. The intra-specific variability of SOS and EOS in the laurel forests was very high. LSP derived from Sentinel-2 data contributed to understand the seasonal dynamics of the main Macaronesian ecosystems. However, the heterogeneity of laurel forests made it difficult the LSP estimation in laurel forests. Thus, complementary phenological approaches are suggested to improve the knowledge of the seasonal dynamics of this Macaronesian ecosystem.

Forest Phenology under Differing Topographic Conditions: A Case Study of Changbai Mountain in Northeast China

Forest phenology is sensitive to climate change, and its responses affect many land surface processes, resulting in a feedback effect on climate change. Human activities have been the main driver of climate change’s long-term shifts in temperature and weather patterns. Forest phenology, understood as the timing of the annual cycles of plants, is extremely sensitive to changes in climate. Quantifying the responses of temperate forest phenology under an elevational range of topographic conditions that mimic climate change is essential for making effective adaptive forest ecosystem management decisions. Our study utilized the Google Earth Engine (GEE), gap filling, and the Savitzky–Golay (GF-SG) algorithm to develop a long-time series spatio-temporal remote sensing data fusion. The forest phenology characteristics on the north slope of Changbai Mountain were extracted and analyzed annually from 2013 to 2022. Our study found that the average start of the growing season (SOS) on the north slope of Changbai Mountain occurred between the 120th–150th day during the study period. The end of the growing season (EOS) occurred between the 270th–300th day, and the length of the growing season (LOS) ranged from the 110th–190th day. A transect from the northeast to southwest of the study area for a 10-year study period found that SOS was delayed by 39 d, the EOS advanced by 32 d, and the LOS was gradually shortened by 63 d. The forest phenology on the north slope of Changbai Mountain showed significant topographic differentiations. With an increase of 100 m in altitude, the mean SOS was delayed by 1.71 d (R2 = 0.93, p &lt; 0.01). There were no obvious trends in EOS variation within the study area altitude gradient. LOS decreased by 1.23 d for each 100 m increase in elevation (R2 = 0.90, p &lt; 0.01). Forests on steep slopes had an earlier SOS, a later EOS, and a longer LOS than forests on gentle slopes. For each degree increase in slope, SOS advanced by 0.12 d (R2 = 0.53, p = 0.04), EOS was delayed by 0.18 d (R2 = 0.82, p = 0.002), and the LOS increased by 0.28 d (R2 = 0.78, p = 0.004). The slope aspect had effects on the EOS and the LOS but had no effect on the SOS. The forest EOS of the south aspect was 3.15 d later than that of the north aspect, and the LOS was 6.47 d longer. Over the 10-year study period, the phenology differences between the north and south aspects showed that the LOS difference decreased by 0.85 d, the SOS difference decreased by 0.34 d, and the EOS difference decreased by 0.53 d per year. Our study illustrates the significance of the coupling mechanism between mountain topography and forest phenology, which will assist our future understanding of the response of mountain forest phenology to climate change, and provide a scientific basis for further research on temperate forest phenology.

Long-term monitoring and phenological analysis of submerged aquatic vegetation in a shallow lake using time-series imagery

Submerged aquatic vegetation (SAV) is a key functional group for the restoration of wetlands and aquatic ecosystems. It plays a vital role in the stability of ecosystem structure and function in shallow lakes and reservoirs. With Baiyangdian Lake as the research area, Landsat TM/ETM+/OLI and Sentinel-2 imagery datasets were used to explore the phenological characteristics of SAV over different time scales and construct a phenological map of the SAV groups. The long-term spatial distribution characteristics and trends of SAV from 1986 to 2021 were analyzed. The results show the following: (1) the harmonic analysis of time series (HANTS) eliminated abnormal observations and noise. Normalized difference vegetation index (NDVI) time series curves generated after HANTS accurately reflected the phenological characteristics of SAV. The early spring submerged aquatic vegetation (SAV1), such as Potamogeton crispus, entered the germination period in late February at the earliest (DOY = 53). The mature period ran from April to May, after which SAV1 began to decline gradually. The length of the growing season (LOS) was about 110 days. SAV2, which was represented by Ceratophyllum demersum, Myriophyllum verticillatum, and Potamogeton pectinatus, germinated in early April (DOY = 99). The mature period ran from July to September. The length of the growing season was 210 days. (2) The decision tree constructed based on the NDVI and modified normalized difference water index (MNDWI) identified SAV with an accuracy of 89.7%. (3) The distribution range of the SAV changed dramatically during 1986–2021. According to the area change, it can be divided into a shrinkage period, expansion period, degeneration period, and initial period of recovery. The succession trend of SAV provide a theoretical scientific basis and technical support for the ecological restoration and management of shallow lake ecosystems.

Combination of Vegetation Indices and SIF Can Better Track Phenological Metrics and Gross Primary Production

AbstractAccurate phenological extraction is important for estimating carbon uptake in terrestrial ecosystems under climate change. The emergence of remotely sensed vegetation indices (VIs) and solar‐induced chlorophyll fluorescence (SIF) provides multiple approaches for extracting land surface phenology. However, there is lacking studies to track phenological metrics via multiple VIs and SIF. Therefore, the advantage of combining VIs and SIF to estimate more accurate phenology requires exploration. In this study, we combined the advantages of the normalized difference, enhanced, green‐red, near‐infrared reflectance vegetation indices from MCD43A4 data set, and SIF from CSIF data set to estimate hybrid phenology at 20 eddy flux sites in North America. Results showed that the hybrid phenology derived from the best‐performing start (SOS) and end (EOS) of the growing season among multiple VIs and SIF for each plant functional type and site were both more consistent with those derived from gross primary production (GPP). Specifically, the R2 of hybrid phenology increased by 0.11–0.4 (0.04–0.4) for SOS, 0.01–0.24 (0.09–0.22) for EOS, 0.01–0.7 (0.05–0.34) for the length of the growing season (LOS) based on Gaussian (logistic) method. Moreover, hybrid phenology can improve the explanation of the seasonal and annual variations in GPP. The explanatory power of hybrid phenology for GPP variations increased by 0.05–0.15 (0.02–0.23) for SOS, 0–0.36 (0.11–0.27) for EOS, 0.01–0.51 (0.03–0.4) for LOS, 0.04–0.18 (0.04–0.16) for LOS seasonal GPP maximum based on Gaussian (logistic) method. These findings highlight the potential of combining high‐spatiotemporal structural and coarse‐spatiotemporal physiological vegetation indicators in tracking phenology and GPP.

Relationships between greenhouse gas production and landscape position during short-term permafrost thaw under anaerobic conditions in the Lena Delta

Abstract. Soils in the permafrost region have acted as carbon sinks for thousands of years. As a result of global warming, permafrost soils are thawing and will potentially release greenhouse gases (GHGs) such as methane (CH4) and carbon dioxide (CO2). However, small-scale spatial heterogeneities of GHG production have been neglected in previous incubation studies. Here, we used an anaerobic incubation experiment to simulate permafrost thaw along a transect from upland Yedoma to the floodplain on Kurungnakh Island. Potential CO2 and CH4 production was measured during incubation of the active layer and permafrost soils at 4 and 20 ∘C, first for 60 d (approximate length of the growing season) and then continuing for 1 year. An assessment of methanogen abundance was performed in parallel for the first 60 d. Yedoma samples from upland and slope cores remained in a lag phase during the growing season simulation, while those located in the floodplain showed high production of CH4 (6.5×103 µg CH4-C g−1 C) and CO2 (6.9×103 µg CO2-C g−1 C) at 20 ∘C. The Yedoma samples from the permafrost layer started producing CH4 after 6 months of incubation. We conclude that landscape position is a key factor triggering CH4 production during the growing season time on Kurungnakh Island.

Vegetation phenology patterns in semi-arid savannah woodlands of Gonarezhou National Park, Southeastern Zimbabwe

Vegetation phenology (VP) patterns of semi-arid savannah woodlands ecosystems are essential for sustainable management and conservation since they are indicators of the health status of protected national parks. However, monitoring VP changes are intermittently carried out in semi-arid savannah woodlands ecosystems despite the links to ecosystem function, status, health and service. The paper analyses VP metric patterns, during the start of the season (SOS), maximum vegetation (PEAK), end of the season (EOS) and length of the growing season (LGS) trends from 1981 to 2015. This study was carried out in Gonarezhou National Park (GNP) in Southeastern Zimbabwe. Shrublands, broadleaved deciduous forested woodlands and mixed cover samples were used to depict existing VP patterns. Derivation of VP using remotely sensed Normalised Difference Vegetation Index (NDVI) data was done using the TIMESAT 3.3 programme. Analysis of variance (ANOVA) was used to test the land cover differences while Mann-Kendall (M-K) and Sen's slope tests were used to analyse time-series trends. Due to differences among the different land cover types in GNP, there are spatial variations in phenology, with the SOS, PEAK and EOS indicating later dates significantly. VP spatial and temporal patterns vary markedly as a result of differences in land cover. Further research in patterns of VP using fine and coarse satellite imagery in GNP is required. VP studies need to be linked to distribution and abundance of large mammalian herbivore species.

Spatiotemporal Patterns of Land Surface Phenology from 2001 to 2021 in the Agricultural Pastoral Ecotone of Northern China

Vegetation phenology is one of the most sensitive indicators to understanding terrestrial ecosystem status and change. However, few studies have been conducted to reveal vegetation phenology variation characteristics over the past two decades, especially under the background of the global warming hiatus since 1998. The agricultural pastoral ecotone of northern China (APENC) is an ideal place to analyze land surface phenology (LSP) variation. Therefore, the spatiotemporal patterns of LSP were quantitatively analyzed at regional, basin and pixel scales based on time-series MODIS NDVI data (2001–2021). Results showed that: (i) The start of the growing season (SOS) occurred in 105–141 Julian days, the end of the growing season (EOS) was between 257 and 281 Julian days and the length of the growing season (LOS) varied from 130 to 172 days. The later SOS was mainly distributed in croplands and typical grassland areas, while the early SOS was observed in forests and sandy vegetation coverage areas. The early EOS occurred in typical grasslands, and the later EOS was concentrated in the southeast boundary. The magnitude of the SOS and LOS fluctuation was less than EOS. (ii) The SOS and EOS exhibited overall insignificant advanced and delayed trends at a rate of −0.09 days·yr−1 and 0.12 days·yr−1, respectively, and the LOS displayed an insignificant extended trend at a rate of 0.26 days·yr−1 at a regional scale. The trends of phenological metrics were consistent with the APENC in the Yellow River and Haihe River Basins. The shortened trend of LOS occurred due to the delayed SOS and advanced EOS in the Songliao River and Continental Basins. (iii) The SOS variation gradually changed from an advanced trend to a delayed trend from a southwest to northeast direction in cropland and grassland ecosystems, whereas an opposite trend was found for EOS. The LOS exhibited a significant extended trend due to the significant advanced and delayed trend of SOS and EOS at p &lt; 0.01 in forest ecosystems. This work provides a critical reference for the vegetation phenology dynamic research of semi-arid and semi-humid regions.

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.

Spatiotemporal patterns of remotely sensed phenology and their response to climate change and topography in subtropical bamboo forests during 2001-2017: a case study in Zhejiang Province, China

ABSTRACT Vegetation phenology has long been adapted to environmental change and is highly sensitive to climate change. Shifts in phenology also affect feedbacks of vegetation to environmental factors such as topography and climate by influencing spatiotemporal fluctuations in productivity, carbon fixation, and the carbon water cycle. However, there are limited studies which explores the combined effects of the climate and terrain on phenology. Bamboo forests exhibit the outstanding phenological phenomena and play an important role in maintaining global carbon balance in climate change. Therefore, the interaction mechanisms of climate and topography on bamboo forest phenology were analyzed in Zhejiang Province, China during 2001–2017. The partial least squares path model was applied to clarify the interplay between the climate and terrain impacts on phenology under land cover/use change. The results revealed that the average start date of the growing season (SOS) significantly advanced by 0.81 days annually, the end date of the growing season (EOS) was delayed by 0.27 days annually, and the length of the growing season (LOS) increased by 1.08 days annually. There were obvious spatial differences in the partial correlation coefficients between the climate factors and phenological metrics. Although the SOS, EOS and LOS were affected by different climatic factors, precipitation was the dominant factor. Due to the sensitivity of the SOS and EOS to precipitation, a 100 mm increase in regional annual precipitation would cause the average SOS to advance by 0.18 days and the EOS to be delayed by 0.12 days. Regarding the terrain factors affecting climate conditions, there were clear differences in the influences of different altitudes, slopes and aspect gradients on bamboo forest phenology. This study further showed that topographic factors mainly affected the interannual variations in phenological metrics under land cover/use change by affecting precipitation. This study clarified the spatial pattern of bamboo forest phenology and the interactive mechanisms between vegetative phenology and environmental conditions, as this information is crucial in assessing the impact of phenological change on the carbon sequestration potential of bamboo forests.

Impact of extreme climates on land surface phenology in Central Asia

Global climate change has led to an increasing frequency and intensity of extreme climates (e.g., extreme temperature and precipitation), and its impact on land surface phenology (LSP) has attracted increasing attention. This study evaluated the effects of extreme climates on land surface phenology in Central Asia (CA) based on 13 extreme climate indices (ECIs). The results showed that the start of the season (SOS) of the LSP was gradually delayed, the end of the season (EOS) advanced, and the length of the growing season (LOS) shortened. Most ECIs showed varying degrees of an increasing trend, except the cold indices of the cool days (TX10p) and the cool nights (TN10p) and the precipitation indices of the max one-day precipitation amount (RX1day) and the max five-day precipitation amount (RX5day). The LSP was influenced by extreme climates, and 13 ECIs were mainly negatively correlated with the SOS and positively correlated with the EOS and LOS. Increased extreme temperature and decreased extreme precipitation contributed to the change in the LSP. The most important impact on the SOS was from the max Tmin (TNx), the mean Tmax (TXmean), while the mean Tmin (TNmean) and warm days (TX90p) also had noticeable effects. The most important impact on the EOS and LOS was from TX90p, followed by TX10p and TXmean. Based on climate model projections from the Coupled Model Intercomparison Project Phase 6 (CMIP6), future extreme climates are projected to intensify under Shared Socioeconomic Pathways (SSPs) and will likely continue to affect the LSP. This study helps provide a basis for formulating scientific and reasonable vegetation restoration strategies and disaster warnings in Central Asia.

Effects of Phenological Changes on Plant Production—From the View of Stipa krylovii

Global warming has changed plant phenology and induced variations in the productivity of terrestrial ecosystems. Recent studies have shown inconsistent results regarding the influence of phenological changes on plant production. We carried out a three-year in situ experiment in Inner Mongolia and used Stipa krylovii as an example to examine the phenological changes and their importance to plant production under changes in temperature and precipitation. We found that precipitation, temperature, and their interactions had no significant impact on the start of the growing season (SOS) or vegetative growth length (VGL). Precipitation had significant impacts on the end of the growing season (EOS), the length of the growing season (LOS), and reproductive growth length (RGL). The precipitation addition treatments of T2.0W + 50% (2 °C warming and 50% precipitation addition) and T1.5W + 50% (1.5 °C warming and 50% precipitation addition) significantly delayed the EOS by 6.7 d and 5.4 d, and significantly prolonged the LOS by 9.3 d and 9.3 d, respectively. Precipitation significantly changed the net CO2 assimilation rate (Pn) of the heading stage. There was no significant difference in the dry mass among all the treatments. The SOS and VGL had significant negative impacts on the dry mass of Stipa krylovii, while temperature, precipitation, and the EOS had no significant direct effect on it. Our results imply that the SOS was more important than the EOS in regulating the plant production of Stipa krylovii. This study can facilitate the understanding of the response of productivity to phenological dynamics and improve the accuracy of simulating the terrestrial ecosystem carbon budget.

Global Assessment of Cumulative and Time-Lag Effects of Drought on Land Surface Phenology

ABSTRACT Increased frequency and intensity of droughts under climate change will have a significant impact on land surface phenology, however, the drought-phenology interactions that are associated with complex temporal effects are not well understood. This study examined the response of land surface phenology to drought cumulative and time-lag effects by using the standardized precipitation and evapotranspiration index (SPEI) and explored the influence of hydrothermal and plant physiological factors on the phenology-drought relationship. We used the maximum correlation (rmax-cml ) between phenology and cumulative SPEI (1- to 12-month) to determine the cumulative effect. The maximum correlation (rmax-lag ) between phenology and lagged SPEI (1-month) was utilized to analyze the time-lag effect. Overall, the cumulative effect affected 25.36% of the vegetated area at the start of the growing season (SOS), 26.43% of the end of the growing season (EOS), and 26.57% of the length of the growing season (LOS). SOS was negatively affected by long-term SPEI, whereas EOS and LOS had positive correlations with short-term SPEI. The rmax-cml for shrubland was the largest, and the SOS and LOS response time scales of the forest were the shortest. The rmax-cml was larger in arid and semi-arid (AR and SAR) than in humid and semi-humid (HU and SHU). Meanwhile, the response time scales were longer in HU and SHU than in AR and SAR. The time-lag effect had a larger area of impact on land surface phenology than the cumulative effect, the areas were 46.12%, 47.93%, and 50.45% for SOS, EOS, and LOS, respectively, and the lagged time scales were longer. The phenology-SPEI correlation was dominantly driven by hydrological conditions, and the time scales were mainly affected by thermal factors. Moreover, the onset of phenology and the growth/senescence rate of plants influenced the relationship, suggesting that hydrothermal conditions may shift the time of phenology by regulating the growth/senescence rate and may thus modulate the phenology–drought interaction.

Spatial–Temporal Pattern and Influencing Factors of Vegetation Phenology and Net Primary Productivity in the Qilian Mountains of Northwest China

Understanding how vegetation growth responds to climate change is a critical requirement for predicting future ecosystem dynamics. Global warming causes significant changes in the vegetation characteristics of mountain ecosystems, particularly affecting vegetation phenology and net primary productivity (NPP). The Qilian Mountains are located in an arid and semiarid region, and the mechanisms of vegetation phenology in response to climate change still need to be further explored. We used MODIS data (2001–2020) to extract vegetation phenology and NPP, quantitatively evaluated their spatial–temporal dynamics, and analyzed the response mechanism of vegetation phenology–climate and vegetation phenology–NPP combined with meteorological data. The results showed that from southeast to northwest, the vegetation phenology changes significantly with the change in vegetation type, with SOS (start of the growing season) advancing at a rate of −0.415 d/year, EOS (end of the growing season) and LOS (length of the growing season) delaying at a rate of 0.20 d/year and 0.374 d/year, respectively, and NPP continues to increase. There was also an elevation gradient effect, with SOS delayed by 15.6 d/km, EOS advanced by 12.02 d/km and LOS shortened by 19.24 d/km. We found that the preseason temperature and SPEI (standardized precipitation evapotranspiration index) have a strong influence on the SOS and EOS, with the mean minimum temperature being the most significant and requiring attention, while the influence of precipitation cannot be ignored. We also found that the vegetation phenology is closely related to NPP, and SOS has the most significant effect. This study will provide a scientific basis for the response mechanisms of vegetation phenology in arid and semiarid regions under climate change. It will provide a reference for the implementation of effective ecosystem management.

Spatial-Temporal Relationship Analysis of Vegetation Phenology and Meteorological Parameters in an Agro-Pasture Ecotone in China

Vegetation phenology is a sensitive indicator of climate change, and can help understand the response of vegetation cycles to climate, which is important for understanding the impact of global climate change on terrestrial ecosystems. In this study, based on the enhanced vegetation index (EVI) time-series data, derived from the moderate-resolution imaging spectroradiometer (MODIS) data, the climate parameters were extracted using the Savitzky–Golay (S–G) filtering method to explore the spatial and temporal variation characteristics of the vegetation phenology in an agro-pasture ecotone in China, from 2000 to 2020. In addition, the response characteristics of the vegetation phenology to the climate elements (temperature and precipitation) were also analyzed. The results showed the following: (1) The start of the growing season (SOS) was widely advanced, and that was caused by climate change. The end of the growing season (EOS) was delayed, and the length of the growing season (LOS) was gradually extended with a large interannual fluctuation in the SOS and the LOS in the region; (2) the SOS showed significant negative correlations with the air temperature and precipitation. Precipitation was mainly positively correlated with the EOS, but there was no significant difference in the correlation between temperature and the EOS. In general, pre-season precipitation is the main driver of the vegetation phenology, while the influence of temperature on the phenology is less obvious; (3) in the semi-arid area and arid area, the phenology was mainly influenced by precipitation. The response of the vegetation phenology to the temperatures in different temperature zones was found to be regular, showing high spatial differences. In general, the higher the cumulus temperature, the lower the negative effect of the temperature on both the SOS and EOS. These results may provide new reference to study the non-systematic changes of the vegetation phenology in response to climate change.

Responses of net primary productivity to phenological dynamics based on a data fusion algorithm in the northern Qinghai-Tibet Plateau

Phenology is a key measure of how well an ecosystem functions and is pivotal in revealing vegetation productivity. Net Primary Productivity (NPP) and phenology have profound implications for the exploration of regional ecosystem processes. Several studies have investigated the effects of phenological indicators on NPP. However, these studies were carried out at a coarse resolution, making it difficult to obtain sufficient information on vegetation structure and dynamics; they also, potentially underestimate how productivity is influenced by phenology. This work uses fused NDVI images employing the ESTARFM model to determine how vegetation NPP reacts to phenology in the northern Qinghai-Tibet Plateau. The results showed that NPP was only 59.93 gC·m−2·year -1. The start of the growing season (SGS) was concentrated from March to May, whereas the end of the growing season (EGS) spanned from late September to early November. The length of the growing season (LGS) spanned 4–8 months during 2000–2020, NPP showed an increasing trend, SGS and EGS advanced, and LGS showed a prolonged trend. SGS and EGS were negatively correlated with NPP and seasonal temperature, however NPP was positively correlated with seasonal precipitation. LGS was positively correlated with NPP and interannual temperature, whereas it was negatively correlated with interannual precipitation. This study will aid in providing an accurate understanding of how NPP responds to phenological dynamics. Concurrently, collective understanding of terrestrial ecosystem responses to changes on a global scale will be advanced.