Start 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.

Snow cover duration delays spring green-up in the northern hemisphere the most for grasslands

Snow is an important factor controlling vegetation functions in high latitudes/altitudes. However, due to the lack of reliable in-situ measurements, the effects of snow on vegetation phenology remains poorly understood. Here, we examine the effects of snow cover duration (SCD) on the start of growing season (SOS) for different vegetation types. SOS and SCD were extracted from in-situ carbon flux and albedo data, respectively, at 51 eddy covariance flux sites in the northern mid-high latitudes. The effects of SCD on SOS vary substantially among different vegetation types. For grassland, preseason SCD outperforms other factors controlling grassland SOS. However, for forests and cropland, the preseason air temperature is the dominant factor in controlling SOS. Preseason SCD mainly influences the SOS by regulating preseason air and soil temperature rather than soil moisture. The CMIP6 Earth system models (ESMs) fail to capture the effect of SCD on SOS. Thus, Random Forest (RF) models were established to predict future SOS changing trends considering the effect of SCD. For grassland and evergreen needleleaf forest, the projected SOS advance rate is slower when SCD is considered. These findings can help us better understand impacts of snow on vegetation phenology and carbon-climate feedbacks in the warming world.

Matching Spring Phenology Indicators in Ground Observations and Remote-Sensing Metrics

Accurate monitoring of leaf phenology, from individual trees to entire ecosystems, is vital for understanding and modeling forest carbon and water cycles, as well as assessing climate change impact. However, the accuracy of many remote-sensing phenological products remains difficult to directly corroborate using ground-based monitoring, owing to variations in the observed indicators and the scales used. This limitation hampers the practical implementation of remote-sensing phenological metrics. In our study, the start of growing season (SOS) from 2016 to 2021 was estimated for the continental USA using Sentinel-2 images. The results were then matched with several ground-based spring vegetation phenology metrics obtained by the USA National Phenology Network (USA-NPN). In this study, we focused on the relationships between the leaf-unfolding degree (LUD), the SOS, and the factors that drive these measures. Our results revealed that: (1) the ground-based leaves and increasing leaf size stages were significantly correlated with the SOS; (2) with the closest match being observed for a leaf spread of 13%; (2) the relationship between the SOS and LUD varied according to the species and ecoregion, and the pre-season cumulative radiation was found to be the main factor affecting the degree of matching between the ground observations and the metrics derived from the Sentinel-2 data. Our investigations provide a ground-based spring phenology metric that can be used to verify or evaluate remote-sensing spring phenology products and will help to improve the accuracy of remote-sensing phenology metrics.

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.

Decoding autumn phenology: Unraveling the link between observation methods and detected environmental cues.

Leaf coloring and fall mark the end of the growing season (EOS), playing essential roles in nutrient cycling, resource allocation, ecological interactions, and as climate change indicators. However, understanding future changes in autumn phenology is challenging due to the multitude of likely environmental cues and substantial variations in timing caused by different derivation methods. Yet, it remains unclear whether these two factors are independent or if methodological uncertainties influence the environmental cues determined. We derived start of growing season (SOS) and EOS at a mixed beech forest in Central Germany for the period 2000-2020 based on four different derivation methods using a unique long-term data set of in-situ data, canopy imagery, eddy covariance measurements, and satellite remote sensing data and determined their influence on a predictor analysis of leaf senescence. Both SOS and EOS exhibited substantial ranges in mean onset dates (39.5 and 28.6 days, respectively) across the different methods, although inter-annual variations and advancing SOS trends were similar across methods. Depending on the data, EOS trends were advanced or delayed, but inter-annual patterns correlated well (mean r = .46). Overall, warm, dry, and less photosynthetically productive growing seasons were more likely to be associated with a delayed EOS, while colder, wetter, and more photosynthetically productive vegetation periods resulted in an earlier EOS. In addition, contrary to recent results, no clear influence of pre-solstice vegetation activity on the timing of senescence was detected. However, most notable were the large differences in sign and strength of potential drivers both in the univariate and multivariate analyses when comparing derivation methodologies. The results suggest that an ensemble analysis of all available phenological data sources and derivation methods is needed for general statements on autumn phenology and its influencing variables and correct implementation of the senescence process in ecosystem models.

Watermelon Production under Protected Culture in Missouri, USA, to Reach the Local Fourth of July Market

Eating watermelon (Citrullus lanatus) is a traditional part of the Fourth of July holidays in the United States; however, growing watermelon in Missouri, USA for the local Fourth of July market requires an early growing season start (beginning of April) under protected culture because of low temperatures and the risk of freezing. Therefore, ‘Yellow Doll’ watermelon production was investigated under low tunnel (LT) and caterpillar high tunnel [HT (walk-in movable two-row tunnel)], and the economic feasibility was assessed by marginal analysis for both protected cultures. Planting in early April allowed harvest to start 1 to 2 weeks before the target market date. In addition, yield increased under HT in comparison with LT and open field (Op). Marginal analysis under the conditions of this study and prices obtained from local farmers’ markets showed a positive marginal rate of return for HT in comparison with the control Op. The marginal rate of return sensitivity study suggests that differences in marketable yield of 300–400 and 200–250 lb/1200 ft2 are necessary under HT and LT, respectively, for the protected culture to be economically feasible with watermelon prices above $0.75/lb and/or $1.00/lb as obtained in local farmers’ markets. Therefore, it is possible and there is potential to produce watermelon under protected culture for the local Fourth of July market. A gain in market share with potential premium prices for watermelon may increase the sustainability of small and medium-size specialty crop farmers in Missouri. To accomplish this, it is necessary to use early cultivars (70 to 80 days to maturity), plant in early April with transplants grown in greenhouses, and make sure to manage tunnels properly to maintain favorable growing conditions, protect against freezing temperatures and ensuring good pollination.

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.

Warming increases the differences among spring phenology models under future climate change.

Phenological models are built upon an understanding of the influence of environmental factors on plant phenology, and serve as effective tools for predicting plant phenological changes. However, the differences in phenological model predictive performance under different climate change scenarios have been rarely studied. In this study, we parameterized thirteen spring phenology models, including six one-phase models and seven two-phase models, by combining phenological observations and meteorological data. Using climatic data from two Shared Socioeconomic Pathways (SSP) scenarios, namely SSP126 (high mitigation and low emission) and SSP585 (no mitigation and high emission), we predicted spring phenology in Germany from 2021 to 2100, and compared the impacts of dormancy phases and driving factors on model predictive performance. The results showed that the average correlation coefficient between the predicted start of growing season (SOS) by the 13 models and the observed values exceeded 0.72, with the highest reaching 0.80. All models outperformed the NULL model (Mean of SOS), and the M1 model (driven by photoperiod and forcing temperature) performed the best for all the tree species. In the SSP126 scenario, the average SOS advanced initially and then gradually shifted towards a delay starting around 2070. In the SSP585 scenario, the average SOS advanced gradually at a rate of approximately 0.14 days per year. Moreover, the standard deviation of the simulated SOS by the 13 spring phenology models exhibited a significant increase at a rate of 0.04 days per year. On average, two-phase models exhibited larger standard deviations than one-phase models after approximately 2050. Models driven solely by temperature showed larger standard deviations after 2060 compared to models driven by both temperature and photoperiod. Our findings suggest investigating the release mechanisms of endodormancy phase and incorporating new insights into future phenological models to better simulate the changes in plant phenology.

Spatiotemporal variations of leaf senescence velocity on the Tibetan Plateau grasslands

Leaf senescence plays a pivotal role in the regulation of carbon and nutrient cycles within terrestrial ecosystems. However, our understanding of leaf senescence velocity (LSV) remains comparatively limited when compared to the end of the growing season (EOS). In this study, we extracted the LSV in Tibet Plateau (TP) over the period 2001–2018 based on the satellite-derived normalized difference greenness index (NDGI), then we evaluated the influences of climate drivers on the spatio-temporal variations of LSV. Lastly, we explored the implications of LSV on vegetation growth by analyzing the correlation between LSV and the start of growing season (SOS) and the annual net primary production (NPP). Our findings revealed that the multi-year averaged LSV ranged from 30 to 70% mon-1 and displayed a discernible spatial gradient, declining from west to east, and the spatial pattern of LSV was mainly controlled by radiation. Trend tests and partial correlation analyses unveiled a temporal decrease in LSV within the central TP region, attributed to rising temperatures, while an increase was observed in the southwestern TP due to water deficits. We also found that LSV had a strong impact on current-year net NPP and following-year SOS. This suggests that LSV may play a significant role in regulating carbon exchange during the current year and influencing the onset of spring green-up in the subsequent year. By emphasizing the continuous nature of leaf senescence, our study provides fresh insights into the intricate interactions between vegetation growth and climate change, contributing to the existing body of knowledge in this field.

An earlier start of growing season can affect tree radial growth through regulating cumulative growth rate

Climate change is profoundly affecting phenology, especially in the Northern Hemisphere. Nevertheless, how tree radial growth responds to phenological shifts remains to be explored. It is necessary to analyze the specific response process of tree growth to phenology. In this study, 61 tree-ring chronologies from Central Asia were used to analyze the effects of phenological shifts on tree radial growth. Based on previous studies and hydrothermal characteristics, we divided the study area into two parts, with 49° north latitude as the boundary. The results of the model-based simulation indicated an obvious advance to the start of growing season (SoGS) in the period from 1959 to 2010/2016. Correlation analyses showed that there were significant negative correlations between the SoGS and radial growth in the north of the study area but not in the south. Path analyses indicated that radial growth between north and south did not respond consistently to the earlier SoGS. In the north, radial growth mainly benefited from the alleviation of low temperature limitations, thereby elevating the cumulative growth rate. But in the dry south, aridity stress in the early growing season counteracted the positive effect of temperature on tree radial growth through decreasing the cumulative growth rate. In general, we determined that an earlier SoGS influences tree radial growth by regulating the cumulative growth rate. This study provides a theoretical basis for promoting the accurate assessment of carbon sink potential on a larger scale and for predicting forest dynamics.

Increased productivity of temperate vegetation in the preceding year drives early spring phenology in the subsequent year in northern China

Under global warming, rising temperature have advanced spring phenology in recent decades. However, the internal physiological mechanisms driving changes in spring phenology still remain poorly understood. Here, we investigated the effects of temperate vegetation gross primary productivity (GPP) during the preceding year on spring phenology of the subsequent year based on the start of growing season (SOS) extracted from NDVI datasets between 1982 and 2015. We found that the preceding year's GPP had an effect on the subsequent year's SOS, equivalent to 33 %–50 % of effect of the preseason's mean temperature. Specifically, in the temperate and semi-humid or humid conditions, the preceding year's GPP had a stronger effect on SOS than in boreal or semi-arid conditions. In addition, the SOS of the dwarf vegetation, with less transport pressure and higher carbon concentrations, was more sensitive to the preceding year's GPP than that of tall forests. We found the effects of the preceding year's GPP on SOS varied with space and vegetation types. Therefore, the physiological mechanism should be considered in future spring phenology model separately according to space and vegetation types, to improve the accuracy of future phenology and then global carbon sequestration predictions.

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.