End Of Growing Season Research Articles

A Study on the Differences in Vegetation Phenological Characteristics and Their Effects on Water–Carbon Coupling in the Huang-Huai-Hai and Yangtze River Basins, China

Vegetation phenology is a biological factor that directly or indirectly affects the dynamic equilibrium between water and carbon fluxes in ecosystems. Quantitative evaluations of the regulatory mechanisms of vegetation phenology on water–carbon coupling are of great significance for carbon neutrality and sustainable development. In this study, the interannual variation and partial correlation between vegetation phenology (the start of growing season (SOS), the end of growing season (EOS), and the length of growing season (LOS)) and ET (evapotranspiration), GPP (gross primary productivity), WUE (water use efficiency; water–carbon coupling index) in the Huang-Huai-Hai and Yangtze River Basins in China from 2001 to 2019 were systematically quantified. The response patterns of spring (autumn) and growing season WUE to SOS, EOS, and LOS, as well as the interpretation rate of interannual changes, were evaluated. Further analysis was conducted on the differences in vegetation phenology in response to WUE across different river basins. The results showed that during the vegetation growth season, ET and GPP were greatly influenced by phenology. Due to the different increases in ET and GPP caused by extending LOS, WUE showed differences in different basins. For example, an extended LOS in the Huang-Huai-Hai basins reduced WUE, while in the Yangtze River Basin, it increased WUE. After extending the growing season for 1 day, ET and GPP increased by 3.01–4.79 mm and 4.22–6.07 gC/m2, respectively, while WUE decreased by 0.002–0.008 gC/kgH2O. Further analysis of WUE response patterns indicates that compared to ET, early SOS (longer LOS) in the Yellow River and Hai River basins led to a greater increase in vegetation GPP, therefore weakening WUE. This suggests that phenological changes may increase ineffective water use in arid, semi-arid, and semi-humid areas and may further exacerbate drought. For the humid areas dominated by the Yangtze River Basin, changes in phenology improved local water use efficiency.

Multi-factor Impact Analysis of Grassland Phenology Changes on the Qinghai-Xizang Plateau Based on Interpretable Machine Learning

The vegetation phenology of the Qinghai-Xizang Plateau is changing significantly in the context of climate change. However, there are many hydrothermal factors affecting the phenology, and few studies have focused on the effects of multiple factors on the phenology of the Qinghai-Xizang Plateau, resulting in a lack of understanding of the mechanisms underlying phenological changes on the Qinghai-Xizang Plateau. In this study, we used remote sensing data interpretation to analyze the spatial and temporal variability of grassland phenology on the Qinghai-Xizang Plateau from 2002 to 2021, focusing on precipitation, temperature, altitude, soil, and other aspects to reveal the dominant factors of phenological variability using an interpretable machine learning method (SHAP) and to quantify the interactive effects of multiple factors on phenology. The results showed that:① The growing season start (SOS) of grasslands on the Qinghai-Xizang Plateau mostly ranged from 110 to 150 d, with 56.32 % of grasslands showing an early SOS trend; the growing season end (EOS) mostly ranged from 290-320 d, with 67.65 % of grasslands showing a delayed EOS trend; and the growing season length (LOS) mostly ranged from 120 to 210 d, with 65.50 % of the grasslands showing a trend towards longer growing season lengths. ② SOS in grasslands on the Qinghai-Xizang Plateau was mainly influenced by moisture conditions, in which soil moisture between 10 and 25 kg·m-2 in the 0-10 cm soil layer in March promoted the advancement of SOS and peaked at approximately 20 kg·m-2. EOS was mainly influenced by temperature, with higher temperatures in September and October having a stronger effect on EOS latency promotion and peaking at over 8 ℃ and -0.5 ℃, respectively. The main influencing factors of LOS were more consistent with SOS, in which soil moisture between 15 and 25 kg·m-2 in the 0-10 cm soil layer in March promoted the prolongation of LOS and peaked at approximately 18 kg·m-2. ③ There was an obvious interactive effect of water and heat and other factors on phenology; after soil moisture reached 20 kg·m-2 in the 0-10 cm soil layer in March, SOS was more advanced in low-precipitation and low-altitude areas. Better moisture conditions were more conducive to EOS delay at temperatures above 0 ℃ in October, and soil moisture in high precipitation areas promoted LOS prolongation more when soil moisture was between 12 and 22 kg·m-2 in 0-10 cm in March. The results also demonstrated that interpretable machine learning methods could provide a new approach to the analysis of the multifactorial effects of phenological change.

Evaluation of Land Surface Phenology in Northern Hemisphere Permafrost Regions

AbstractVegetation phenology interacts strongly with climate through the exchange of carbon, water, momentum, and energy between terrestrial ecosystem and atmosphere. These vegetation dynamics in Northern Hemisphere permafrost regions have substantial uncertainties in previous studies, partly due to differences in datasets. Thus, reliable land surface phenology (LSP) retrievals are crucial for understanding the effects of climate change on ecosystems and biosphere‐atmosphere‐hydrosphere interactions. We assessed various LSP datasets at different spatial resolutions for 2001–2014, generated by different methods based on different satellite observations. We also assessed the accuracy of LSP by comparing with CO2 flux phenology. For start of growing season (SOS), the comprehensive evaluation indicated MODIS phenology showed better consistency and higher accuracy with flux‐derived phenology observations (R = 0.54, RMSE = 36.2 days, bias<5 days). For end of growing season (EOS), we cannot conclusively determine which LSP performs best. In the Northern Hemisphere permafrost regions, SOS occurred at 100–150 days (April–May), and EOS occurred at 260–320 days (September–November). During 2001–2014, SOS occurred earlier by 0.33 ± 0.30 days/yr. Significant trends were observed for 6.4%–27.6% of pixels, averaging −1.30 ± 1.16 days/yr. EOS occurred earlier by 0.25 ± 0.43 days/yr, with significant trends averaging −0.52 ± 0.94 days/yr. Among LSPs, variability in EOS trends was significant, with even the direction of trends differing. This study may provide insights into LSP data selection and further understanding of vegetation dynamics and its mechanisms in permafrost regions.

Influence of climate change and human activities on grassland phenology in Anhui Province

To explore the influence of climate change and human activities on grassland phenology in Anhui Pro-vince, and quantify the contribution rate of climate change and human activities to phenology, we extracted the phenology of grassland, including the start of growing season (SOS) and the end of growing season (EOS), based on the normalized difference vegetation index (NDVI) dataset of Anhui Province from 2003 to 2020. The temporal and spatial characteristics and future evolution trends of phenological changes were analyzed using slope trend ana-lysis, Mann-Kendall non-parametric test, and Hurst index. We further conducted correlation analysis and residual analysis based on the datasets of mean annual temperature and mean annual precipitation to explore the responses of phenology to climate change and human activities, and quantify their contribution rate. The results showed that SOS and EOS showed an advancing trend with a rate of 0.8 and 0.7 days per year from 2003 to 2020. SOS in the sou-thern part of the study area was significantly earlier than in the central and northern regions, while EOS gradually advanced from south to north. Both SOS and EOS in the future showed an advancing trend. SOS was negatively correlated with annual average temperature, while positively correlated with annual precipitation. EOS was negatively correlated with both annual average temperature and annual precipitation. The proportion of the area where SOS was advanced driven by both climate change and human activities was 56.9%, and the value was 48.3% for EOS. Human activities were the main driving factor for phenology, and climate change was the secondary driving factor. The relative contributions of human activities and climate change to SOS were 66.4% and 33.6%, and to EOS were 61.2% and 38.8%, respectively. Human activities had stronger impact on SOS and EOS than climate change, resulting in earlier phenology.

Investigating the effect of temperature, precipitation, and soil salinity changes on Riparian Forests’ phenology using a remote sensing approach

Many previous studies have shown the sensitivity of the plant's phenological cycle to the changes of temperature and precipitation. Other parameters such as pests, salinity and water stresses during the growing season also have a significant effect on changes in the phenological cycle of vegetation. Due to the importance of plants' phenological cycles, this study focuses on the effect of long-term stresses caused by temperature, precipitation and salinity of water and soil on the change of phenological cycle of riparian forest ecosystem around Karun River, Iran. Riparian forests are one of the most important forest ecosystems in arid and semi-arid regions. The dataset includes MODIS products and ETM+, OLI Landsat images of the study area from 2000 to 2020. NDVI, EVI and salinity indices SI1, SI2, SI3 were computed from spectral bands. The results showed that the start of growing season (SOS) with a decreasing slope of 0.02–2.4, the peak of the growing season with a slope of 1.5–4.5 and the end of growing season (EOS) with a slope of 12–22 days per year are subjected to change over the observed years. These changes have shortened the length of growing season (LOS) of plants in 2011 and 2012. The results also show that precipitation and temperature changes cannot fully explain the shift at the SOS, peak and EOS of the growing season. However, the coincidence of soil and water salinity increase in the region -resulted by the dewatering of Gotvand Dam (in 2011) with shorter LOS of riparian forest in the period 2020–2010 has been observed. In addition, the phenology cycle based on NDVI and EVI of both sensors indicates a shorter growth season in 2011–2020 in comparison to its earlier decade. Landsat-based vegetation indices are more accurate in extracting the Riparian forests' phenological information compared to MODIS-based indices. Although the phenology cycles based on Landsat series were more detailed and closer to the field observation compared to the MODIS medium resolution images, MODIS products provide acceptable results especially in monitoring the changes of timing the phenological events in different years.

Exploring Sensitivity of Phenology to Seasonal Climate Differences in Temperate Grasslands of China Based on Normalized Difference Vegetation Index

The affiliation between vegetation phenology and seasonal climate (start and end times of the growing season, or SOS and EOS) provides a basis for acquiring insight into the dynamic response of terrestrial ecosystems to the effects of climate change. Although climate warming is an important factor affecting the advancement or delay of plant phenology, understanding the sensitivity of phenology to seasonal variation in climate factors (e.g., local air temperature, precipitation) is generally lacking under different climate backgrounds. In this study, we investigated the interannual variability of grassland phenology and its spatial variation in temperate regions of China based on satellite-derived products for the normalized difference vegetation index (NDVI) and weather data acquired from 2001 to 2020. We found that due to differences in local climate conditions, the effects of seasonal warming and precipitation on phenology were divergent or even opposite during the 20 years. The sensitivities of the start of growing season (SOS) to both spring temperature and last-winter precipitation was controlled by mean annual precipitation in terms of spatial variation. The SOS in the semi-humid (200–400 mm) region was most sensitive to spring temperature, advancing 5.24 days for each 1 °C rise in the average spring temperature (p < 0.05), while it was most sensitive to last-winter precipitation in arid regions (<200 mm), with SOS advancing up to 2.23 days for every 1 mm increase in the last-winter precipitation (p < 0.05). The end of growing season (EOS) was sensitive to autumn temperature, being delayed 10.13 days for each 1 °C rise in the average autumn temperature in regions with temperatures between −10 °C and −5 °C (p < 0.05). The uncertainty in the determination of the EOS could conceivably be greater than the determination of the SOS due to the dual effects of pre-autumn climate and growth constraints induced by declining fall temperatures. The effect of atmospheric warming on grassland phenology was lessened with increased atmospheric and soil aridity, suggesting that the interaction of regional drought and climate warming is an important source for local-to-regional differences and uncertainties in grass phenological response.

Land Surface Temperature Sensitivity to Changes in Vegetation Phenology Over Northern Deciduous Forests

AbstractChanges in vegetation phenology are important contributors to seasonal climate change over the northern land surface. However, the biogeophysical effects of phenological shifts on land surface temperature (LST) remain uncertain. Here, we demonstrated the sensitivity of LST to advanced start of growing season (SOS) and delayed end of growing season (EOS) during corresponding green‐up and senescence periods over northern deciduous forests. Moreover, the mechanism responsible for LST sensitivity was assessed by quantifying the contributions of biogeophysical properties to LST sensitivity by applying a two resistance mechanism method to an experimental set using Community Land Model version 5. LST sensitivities to advanced SOS and delayed EOS were −2.1 × 10−2 K day−1 and −2.8 × 10−2 K day−1, respectively, indicating cooling effects by phenological shifts. The different responses of the aerodynamic resistance to SOS and EOS could explain the higher LST sensitivity to EOS than that to SOS. The decrease in the aerodynamic resistance in response to delayed EOS was three times larger than that to advanced SOS, thereby inducing a larger surface cooling effect during senescence period through enhanced turbulent heat transfer. Thus, the reduced aerodynamic resistance could explain the cooling effect of the phenological shift, which is an important consideration for projecting seasonal climate change.

Responses of Vegetation Phenology to Urbanization in Plateau Mountains in Yunnan, China

The process of urbanization alters the distribution of land use and gives rise to certain climatic modifications that have a direct influence on vegetation phenology. Mountainous areas have a fragile biological environment, and vegetation phenology is relatively sensitive to urbanization. However, there is a paucity of research on the impact of urbanization in mountainous areas on vegetation phenology. The Urban Agglomeration of Central Yunnan (UACY) is located on the Yunnan–Guizhou Plateau in China. This study explored the vegetation phenological changes in different terrains from 2001 to 2020 in the UACY based on remote sensing data. Using the dynamic gradient method, we examined the response of vegetation phenology to urbanization from three aspects: urban–rural gradient, urbanization intensity (UI), and population density. The results showed that landform has a significant impact on SOS (start of growing season), with a topographic relief difference of 200 m acting as the dividing line. The findings derived from the dynamic gradient analysis indicate that UI has the most significant effect. SOS advances by 5.77 days (R2 = 0.96), EOS (end of growing season) advances by 2.30 days (R2 = 0.83), and LOS (length of growing season) lengthens by 2.59 days (R2 = 0.87) for every 10% increase in UI. This study has the potential to serve as a valuable resource for future urban planning and administration in the UACY. Additionally, it could provide decision-making support for the development of mountainous urban agglomerations in ecological environments.

Dynamic Monitoring of Winter Wheat Health in Mining Subsidence Areas by Combined Active and Passive Remote Sensing Technology

The contradiction between efficient coal mining and arable land capacity enhancement in the coal–grain production overlapping area has constrained grain output and threatened food security. In this study, DInSAR was used to extract the mining subsidence areas (SAs). Multiple red-edged vegetation indices were introduced to explore the growth differences between winter wheat in the SAs and Check Areas (CKs). A crop health index (SACHI) was proposed to comprehensively evaluate the health of winter wheat in SAs. The results showed that: (1) Compared with the CKs, the onset of over wintering season (OWS), start of growing season (SOS), and end of growing season (EOS) of winter wheat lagged behind in the SAs. (2) The winter wheat growths were slower in the SAs and their decline rates were faster than that in the CKs. (3) The SACHI could effectively synthesize the information contained in each component and was highly correlated with them. (4) Mining subsidence had a significant impact on the winter wheat health in the length of growing season (LOS), while the impact was weakened during the OWS. Based on the multi-vegetation indices and the SACHI, the impact of mining subsidence on the winter wheat health can be effectively diagnosed and estimated.

Soil bacterial communities are more sensitive to short-term nitrogen deposition than fungal communities in subtropical Chinese fir forests

The increasing rate of atmospheric nitrogen (N) deposition seriously affects the structure and function of ecosystems. Although soil microorganisms play a crucial role in ecosystem carbon (C) and nutrient cycling, the impact of N deposition on soil microbial communities in subtropical forest ecosystems is unclear. Here, we selected the subtropical Chinese fir (Cunninghamia lanceolata) forest as the research object and conducted simulated N deposition experiments with different levels of N deposition (0 kg N ha−1 yr−1,50 kg N ha−1 yr−1, 100 kg N ha−1 yr−1, and 200 kg N ha−1 yr−1, respectively) to explore the response of soil nutrients, soil microbial community structure, and potential ecological functions to short-term N deposition. We found that the bacterial richness Chao1 index and phylogenetic diversity PD_whole_tree decreased with the N deposition level approximately 13 % −16 % under MN and HN treatment. N deposition significantly altered the composition of soil bacterial communities (P < 0.05), but the responses of different taxa to N deposition were inconsistent at different stages of growing season. N deposition treatment significantly increased the relative abundance of Actinobacteria at both beginning of growing season (BGS) and end of growing season (EGS), decreased the relative abundances of Planctomycetes at BGS, and decreased the relative abundances of Elusimicrobia and Cyanobacteria at both BGS and EGS. Non-metric multidimensional scaling (NMDS) and Anosim (Analysis of similarities) analyses showed that the bacterial communities were clearly distinguished among treatments at both BGS and EGS. However, N deposition had a relatively small impact on fungal communities, only altering the fungal community structure at BGS. The impact of N deposition on soil microbial communities was mainly induced by the alteration in soil water content (SWC), pH, NO3–-N, and available nitrogen (AN). Both bacterial and fungal communities exhibit seasonal community dynamics, while the interaction of N deposition and season was mainly reflected in the impact on bacterial communities. Our research not only provides research ideas for the potential impact of nitrogen deposition on subtropical forest ecosystems in the short term, but also provides a theoretical basis for future scientific management and regulation of forestry, microorganisms, and sustainable management of Chinese fir plantations.

Significant influences of extreme climate on autumn phenology in Central Asia grassland

Over the past half-century, there has been a significant rise in the frequency and intensity of extreme climate events in dryland ecosystems. While vegetation phenology acts as an important indicator of how ecosystems respond to climate change, the impact of extreme climate events on autumn phenology in dryland ecosystems remains uncertain. In this study, we firstly assessed the temporal variations in the end of growing season (EOS) in Central Asia using the Normalized Difference Vegetation Index and enhanced vegetation index over 2001–2018. Then, we assessed the impacts of eight climate indices, which capture the average and extreme conditions of the local climate, on EOS across diverse vegetation types using linear mixed models. Finally, we conducted partial correlation analysis to explore how the primary influencing factors affect EOS along an elevational gradient. Our results suggested increased occurrences of drought and heat events in the pre-growing season (April–September) in Central Asia. Under the background, a general trend towards earlier EOS (ca. 1.01 days/decade) was observed for grassland. This trend was mostly attributed to the changes in extreme climate indices (CIextreme), especially heavy rainfall and heat stress. The total explanatory power of CIextreme was almost twice that of mean climate indices (CImean). However, the impact of CIextreme was less pronounced in regions with elevations exceeding 1500 m. For forests and shrublands, the EOS exhibited delaying trend of 1.29 days/decade and advancing trend of 1.21 days/decade, respectively. Contrary to grasslands, the variations in EOS of forests and shrublands were largely influenced by the shift of CImean, especially mean minimum temperature and accumulated precipitation. The contribution of CIextreme accounted for only 1/6 of the contribution of CImean, likely attributed to the adaptation of forests to cold conditions and shrublands to arid conditions. The results provide valuable insights into the phenological response to extreme climate events and serve as a reference for ecosystem management in arid regions.

Droughts force temporal change and spatial migration of vegetation phenology in the northern Hemisphere

Ecosystems are exposed to continuous shifts in droughts. This shows up as a point on a pixel that moves continuously in a specific direction and angle (spatial migration) to preserve its original climate niche. We currently do not have a good understanding whether ecosystems exposed to droughts are more sensitive to droughts temporal trends or spatial migrations. Using dataset of vapor pressure deficit (VPD), total water storage anomaly (TWSA), and standardized soil moisture index (SSMI), we tested the spatiotemporal sensitivity of vegetation phenology to extreme droughts and compound extreme droughts, respectively. On the temporal scale, we found that the differences in the sensitivity of vegetation phenology to VPD, TWSA, and SSMI were mainly reflected in the lag time. Compared to normal years, however, the start of growing season (SOS) was advanced and the end of growing season (EOS) was delayed in most pixels even under extreme preseason droughts. Such results can be attributed to the extreme atmospheric driving forces. On the spatial scale, the spatial migration velocity of SOS and EOS was best correlated with the extreme atmospheric drought (r = 0.5, p < 0.01) and the extreme soil drought in the third layer (r = 0.43, p < 0.01), respectively. Compared to extreme droughts, however, the effects of compound extreme droughts on the spatial migration of vegetation phenology only produced weak negative feedback (|r| < 0.1, p < 0.01). This indicate that the velocity of vegetation phenology was consistent with the velocity of single extreme droughts. Our results emphasize the temporal sensitivity of droughts on vegetation phenology and the accompanying effects of extreme droughts on the spatial shifts in vegetation phenology.

Study on the Spatial and Temporal Distribution of Urban Vegetation Phenology by Local Climate Zone and Urban–Rural Gradient Approach

Understanding variations in the temporal and spatial distribution of vegetation phenology is essential for adapting to and mitigating future climate change and urbanization. However, there have been limited vegetation phenology studies within small-scale areas such as urban environments over the past decades. Therefore, the present study focuses on Jinan city, Shandong Province, China as the study area and employs a more refined local climate zone (LCZ) approach to investigate spatial and temporal variations in vegetation phenology. The three phenological indicators used in this study from 2007 to 2018, namely, the start of growing season (SOS), the end of growing season (EOS), and the length of growing season (LOS), were provided by MODIS satellite data. The SOS, EOS, and LOS were superimposed on the LCZ and urban–rural gradient to analyze the changes in vegetation phenology, and the applicability of these two analysis methods in the study of urban vegetation phenology was compared by the honest significant difference test. We found that the SOS, EOS, and LOS of vegetation in the study area generally showed an advance, delay, and extension trend, respectively. The means of the SOS and EOS along different LCZ types varied noticeably more than those along urban–rural gradients. In 2016, 77.5%, 80.0%, and 75.8% of LCZ pairs indicated statistically significant differences for SOS, EOS, and LOS, respectively. This study provides a new perspective for the study of urban vegetation phenology which can help in management of urban-scale environments, identification of areas rich in biodiversity, and conservation and restoration of biodiversity in urban areas.

Land surface phenology and its response to climate change in the Guangdong-Hong Kong-Macao Greater Bay Area during 2001–2020

Land surface phenology (LSP), an important branch of vegetation phenology, which is sensitive to climate change and play important role in ecosystem. As a typical subtropical area, in spite of its complex vegetation structure, the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) LSP was not focused fully. Hence, In this study, a long time series LSP data from 2001 to 2020 was obtained based on MODIS normalized difference vegetation index (NDVI) using classical Savitzky-Golay filter. Subsequently, phenology spatial pattern and spatiotemporal trends and their correlation with climate change were analyzed for different vegetation types in the Guangdong-Hong Kong-Macao Greater Bay Area. The results revealed an obvious spatial heterogeneity and significant annual variation trend in vegetation phenology. The start of growing season (SOS) and the end of growing season (EOS) appeared delayed trend and the length of growing season (LOS) showed a prolonged trend, with average slope values of 0.55, 0.52 and 0.81 days per year, respectively. In general, preseason precipitation and solar radiation are the main climatic factors regulating vegetation phenology in the Greater Bay Area, and the enhancement of preseason precipitation and solar radiation is the main reason for the delay of vegetation withering and yellowing period and the extension of growing season. Ecosystems of the Greater Bay Area were mainly depending on preseason precipitation and solar radiation to regulate the leaf senescence and control plant growth duration. The present study can enhance performance of vegetation phenology models and provide useful information on ecological protection in subtropical areas.

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.

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.

Investigation of the Effect of Thermal Tension in Hot Seasons and Interruption of Irrigation at the End of the Growth Season by Silicon Nutrient Solution Sulfonated With S8 Elemental Sulfur on Fresh and Dry Fodder Yield and Protein Content of Alfalfa (Medicago sativa L.)

This research is an introduction to the combined nutrient of mineral salts by Silicon sulfonated with S8 through aerial organs of the Alfalfa plant. This type of combined nutrition by S8 is called &amp;ldquo;Matrix Nutrition&amp;rdquo;. This experiment was launched in October 2022 to study the effect of S8 on increasing the yield of fresh and dry fodder and the percentage of Alfalfa protein content using combined nutrition or Matrix Nutrition on the aerial organ of Alfalfa plant in the condition of interruption of irrigation at the end of the growing season in Moghan region, Ardabil province in Iran. The experiment was carried out in 2 plots, each one being one-hectare land. (In the first plot, S8 was applied for 5 litres per 1000 litres of water in the growth stages as foliar spraying after each crop harvest. No S8 was applied in the second plot.) The 5th and 6th harvests coincided with a dry season in this region and no irrigation was applied in this experiment for the 5th and 6th harvests. Both plots were only irrigated similarly for 1st to 4th harvest. The results obtained from a total of 5 harvests of the Hamedani Variant Alfalfa plant in the Control Plot and 6 harvests of the Hamedani Variant Alfalfa plant in the Experiment Plot indicated the highest yield in the 1st plot treated with S8 in terms of fresh and dry fodder, percentage of dry materials of fodder and protein content with a significant difference of 1% and 5% compared to Control Plot. The difference between the two plots was: fresh fodder 11,610 Kgs/Hectare, i.e., 91.27% higher yield, dry fodder 4,099 Kgs/Hectare, i.e., 140.95% higher yield, increase in dry material percentage for 5.99% and higher protein content of fodder for 1.59% which indicated a significant positive effect of application of S8 nutrient solution as foliar spraying on the increase of yield in fresh and dry fodder in the growing season and specifically at the time of thermal tension (specifically on 2nd and 4th harvest in control plot on June and August) and interruption of irrigation at the end of growth season (5th and 6th harvest of experiment plot).

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.

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.