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Summary: Leaf phenology variations within plant communities shape community assemblages and influence ecosystem properties and services. However, questions remain regarding quantification, drivers, and productivity impacts of intra‐site leaf phenological diversity.With a 50‐ha subtropical forest plot in China's Heishiding Provincial Nature Reserve (part of the global ForestGEO network) as a testbed, we gathered a unique dataset combining ground‐derived abiotic (topography, soil) and biotic (taxonomic diversity, functional diversity, functional traits) factors. We investigated drivers underlying leaf phenological diversity extracted from high‐resolution PlanetScope data, and its influence on aboveground biomass (AGB) using structural equation modeling (SEM).Our results reveal considerable fine‐scale leaf phenological diversity across the subtropical forest landscape. This diversity is directly and indirectly influenced by abiotic and biotic factors (e.g. slope, soil, traits, taxonomic diversity; r2 = 0.43). While a notable bivariate relationship between AGB and leaf phenological diversity was identified (r = −0.24, P < 0.05), this relationship did not hold in SEM analysis after considering interactions with other biotic and abiotic factors (P > 0.05).These findings unveil the underlying mechanism regulating intra‐site leaf phenological diversity. While leaf phenology is known to be associated with ecosystem properties, our findings confirm that AGB is primarily influenced by functional trait composition and taxonomic diversity rather than leaf phenological diversity. [ABSTRACT FROM AUTHOR]

[Display omitted] Accurate and detailed spatial information on rice cultivation is essential to developing agricultural policy and reducing the negative impacts of agriculture. However, the dependence of most traditional methods on samples severely limits the feasibility of large-scale rice cultivation mapping. This study proposes a robust large-scale sample-free monitoring method for single-season rice in northern China. A new rice phenology index, quantifying dynamic phenological features of rice (i.e., the occurrence of flooding during transplanting and the growth of rice after transplanting), was generated to highlight rice. Subsequently, a constrained cyclic threshold classification strategy was designed to obtain plausible rice maps using statistical data. Innovatively combining rice mapping with statistical data, the most detailed (10 m) single-season rice map in northern China to date was created. Compared with three other high-precision rice map products, the resulting rice map has high accuracy and good local details. The results indicate that the rice phenology index has excellent and robust performance in identifying rice cultivation locations in northern China. Moreover, the proposed mapping method exhibits clear advantages in the tracking of large-scale and historical rice cultivation. As a whole, this study provides a paradigm of using statistical data instead of samples for crop mapping. [ABSTRACT FROM AUTHOR]

The peak of growing season (POG) represents the timing of the maximum capacity of vegetation photosynthesis and acts as a crucial phenological indicator for the carbon cycle in terrestrial ecosystems. However, little is known about how POG responds to extreme climate events such as drought across different biomes. Based on two drought indices, we analyzed the temporal–spatial pattern of drought and POG in China and then investigated how drought influenced the POG in different periods of the early season through correlation analysis. In general, a trend towards increased aridity and earlier POG was found in most areas. The impact of drought on POG differed among periods. On the one hand, an earlier POG enabled plants to reduce evapotranspiration and mitigate the risk of severe summer drought. On the other hand, the drought that occurred in spring impeded plant growth and caused a delay in spring phenology, thereby postponing POG. Summer drought led to an earlier POG in relatively dry biomes but inversely led to a later peak in photosynthetic activity in wetter biomes. We also observed a 1-month/2-month lagged effect of drought on POG in almost half of the areas and a 2-month/ 3-month cumulative effect of drought in the north of 50° N. These findings enhance our understanding of carbon uptake in terrestrial ecosystems by clarifying the mechanisms by which climate change impacts vegetation growth and photosynthetic activity. [ABSTRACT FROM AUTHOR]

Functional traits are pivotal for understanding the functional niche within plant communities. Yet, the relationship between the functional niches of typical forest plant communities across different climatic zones, as defined by functional traits, and their association with community and phylogenetic structures remains elusive. In this study, we examined 215 woody species, incorporating 11 functional traits spanning leaf economy, mechanical support, and reproductive phenology, gathered from forests in four climatic zones from tropical, subtropical, warm-temperate to cold-temperate zones in China and supplemented by the literature. We quantified the functional niche hypervolume (FNH), reflecting the multidimensional functional niche variability. We then probed into the correlation between the FNH and community and phylogenetic structures of forests. Our findings reveal that species richness significantly influences the geographic variance of functional niche space in forest vegetation across different climatic zones. Specifically, a community's species richness correlates positively with the functional niche breadth occupied by the community species. The FNH of woody plants across diverse forest types shows significant associations with both the mean phylogenetic distance (MPD) and the mean nearest phylogenetic taxon distance (MNTD) of the communities. There is a progressive increase in tropical rainforest (TF), subtropical evergreen deciduous broad-leaved mixed forest (SF), and warm-temperate coniferous broad-leaved mixed forest (WF), followed by a decline in the cold-temperate coniferous forest (CF). This pattern suggests potential environmental filtering in CF, which may constrain the spatial extent of plant functional niches. Our research underscores the substantial variability in the FNH across China's typical forest vegetation, highlighting the complex interplay between functional traits, community richness, and phylogenetic distance. [ABSTRACT FROM AUTHOR]

Forest is the largest carbon sink on land, and the timing of forest phenology profoundly influences carbon uptake. However, disparities in the timing and sensitivity of forest phenology, particularly between planted forests (PF) and natural forests (NF), have yet to be determined. In this study, we analyzed the phenological trends and the climate‐sensitivities of China's NF and PF from 2000 to 2022 using field survey data and satellite data. We found that the start of the growing season (start of the season (SOS)) in NF and PF advanced significantly by 0.07 and 0.08 days yr−1, while the end of the growing season (end of the season (EOS)) in both NF and PF delayed by 0.06 days yr−1. Moreover, precipitation and air temperature jointly influenced the timing of phenological events. The pre‐season precipitation delayed the EOS by 0.009 days mm−1 and 0.006 days mm−1 in NF and PF. Moreover, the SOS was more sensitive to precipitation in PF than in NF in arid areas (i.e., pre‐season precipitation <100 mm). Air temperature significantly advanced the SOS by 0.74 and 0.95 days °C−1 in NF and PF and delayed the EOS by 0.89 and 0.71 days °C−1 in NF and PF. Additionally, the sensitivities of SOS and EOS to air temperature significantly differed between NF and PF. Our findings demonstrate a significant phenological discrepancy between PF and NF. The results suggest that NF and PF responded differently to climate variability, implying forest origin should be considered in the projection of carbon sequestration capability under changing climate. Plain Language Summary: Phenology is an important indicator of forest's response to climate change, and its change greatly influences the carbon uptake in forests. In this study, we investigated the phenological changes in China's natural and planted forests, as well as the sensitivity of forest phenology to air temperature and precipitation. We found that the start of the growing season was earlier and the end occurred later in China's forests from 2000 to 2022. Besides, higher air temperature and precipitation could advance the start and delay the end of the growing season in China's forests, respectively. However, the magnitudes of the advancement and delay in phenology to warming and wetting were different between planted and natural forests. We suggest that future studies should consider the impacts of forest origin in biogeochemical simulations. Key Points: The start of the growing season advanced and the end delayed in China's planted and natural forestsThe planted and natural forests differed in their responses to climate variabilityThe start of the growing season was more sensitive to precipitation in planted forests than in natural forests in arid areas [ABSTRACT FROM AUTHOR]

As constituents of one of the vital agricultural ecosystems, paddy fields exert significant influence on the global carbon cycle. Therefore, conducting observations and simulations of CO2 flux in rice paddy is of significant importance for gaining deeper insights into the functionality of agricultural ecosystems. This study utilized an eddy covariance system to observe and analyze the CO2 flux in a rice paddy field in Eastern China and also introduced and parameterized the Jarvis multiplicative model to predict the CO2 flux. Results indicate that throughout the observation period, the range of CO2 flux in the paddy field was −0.1 to −38.4 μmol/(m2·s), with a mean of −12.9 μmol/(m2·s). The highest CO2 flux occurred during the rice flowering period with peak photosynthetic activity and maximum CO2 absorption. Diurnal variation in CO2 flux exhibited a "U"-shaped curve, with flux reaching its peak absorption at 11:30. The CO2 flux was notably higher in the morning than in the afternoon. The nocturnal CO2 flux remained relatively stable, primarily originating from respiratory CO2 emissions. The rice canopy CO2 flux model was revised using boundary line analysis, elucidating that photosynthetically active radiation, temperature, vapor pressure deficit, phenological stage, time, and concentration are pivotal factors influencing CO2 flux. The simulation of CO2 flux using the parameterized model, compared with measured values, reveals the efficacy of the established parameter model in simulating rice CO2 flux. This study holds significant importance in comprehending the carbon cycling process within paddy ecosystems, furnishing scientific grounds for future climate change and environmental management endeavors. [ABSTRACT FROM AUTHOR]

Monitoring of spatio-temporal changes in crop phenology is an important part of the remote sensing of agricultural ecosystems. In this study, the segment turning point method was utilised to determine several phenological dates of winter wheat in northern Henan from 2003 to 2018. The spatio-temporal variation characteristics of these main phenological dates were analyzed, and the effects of temperature and precipitation on phenological changes were investigated. The results showed that: (1) The segment turning point method had strong space-time adaptability, and the RMSE of extracted phenoloical dates of multi-stations in a single year or single station in multi-years was less than 10d. (2) Roughly bounded by 114°E, the trefoil stage, tillering stage, overwintering stage and rising stage of winter wheat in the west were earlier than those in the east of northern Henan in 2018. (3) From 2003 to 2018, the interannual change rates of the trefoil date, tillering date, overwintering date, rising date, booting date, and milky date of winter wheat were 6.92 d/10a, 4.36 d/10a, 0.74 d/10a, -0.1 d/10a, -3.97 d/10a and -2.91 d/10a, indicating the trend of delaying pre-winter phenology and advancing post-winter phenology. (4) The delay of pre-winter phenology and the advance of post-winter phenology of winter wheat were significantly related to the increase in growing season temperature. The results of the study should provide a basis for further understanding of the effects of climate change on winter wheat phenology and to provide a reference for remote sensing monitoring of winter wheat phenology. [ABSTRACT FROM AUTHOR]

A substantial body of empirical evidence exists to suggest that elevated O3 levels are causing significant impacts on wheat yields at sites representative of highly productive arable regions of China. Here, we extend the DO3SE model (designed to estimate total- and stomatal- O3 deposition for risk assessment) to incorporate a coupled Anet - gsto model to estimate O3 uptake, an O3 damage module (that impacts instantaneous Anet and the timing and rate of senescence), and a crop phenology, carbon allocation and growth model based on the JULES-Crop model. The model structure allows scaling from the leaf to the canopy to allow for multiple leaf populations and canopy layers. The DO3SE-crop model is calibrated and parametrised using O3 fumigation data from Xiaoji, China for the year 2008 and for an O3 tolerant and sensitive cultivar. The calibrated model can simulate key physiological variables, crop development, and yield with a good level of accuracy compared to experimental observations. DO3SE-crop accurately depicted the difference in yield reductions under ambient and elevated O3 treatments for wheat cultivars Y16 (tolerant) and Y2 (sensitive) with regressions of modelled and observed absolute yields resulting in an R² of 0.99 and an RMSE of 9.27 g/m². Further, when evaluated for 2007 and 2009 for all cultivars, the DO3SE-crop model simulated O3-induced yield losses of 4–25 % compared to observed yield losses of 12–34 %, with an R² of 0.73 and an RMSE of 58.41 g/m². Additionally, our results indicate that the variance in yield reduction is primarily attributed to the premature decrease in carbon assimilation to the grains under elevated O3 exposure. This is linked to accelerated leaf senescence, which brings leaf senescence forward by 7–9 days under elevated O3 treatments. [ABSTRACT FROM AUTHOR]

To accurately forecast the future development trend of vegetation in dry areas, it is crucial to continuously monitor phenology, vegetation health indices, and vegetation drought indices over an extended period. This is because drought caused by high temperatures significantly affects vegetation. This study thoroughly investigated the spatial and temporal variations in phenological characteristics and vegetation health indices in the abdominal part of Maowusu Sandland in China over the past 20 years. Additionally, it established a linear correlation between vegetation health and temperature indices in the arid zone. To address the issue of predicting long-term trends in vegetation drought changes, we have developed a method that combines the Informer deep learning model with seasonal and Seasonal Trend decomposition using Loess (STL) and empirical mode decomposition (EMD). Additionally, we have utilized the linearly correlated indices of vegetation health and meteorological data spanning 20 years to predict the Normalized Difference Vegetation Index (NDVI) and Temperature Vegetation Dryness Index (TVDI). The study's findings indicate that over the 20-year observation period, there was an upward trend in NDVI, accompanied by a decrease in both the frequency and severity of droughts. Additionally, the STL-EMD-Informer model successfully predicted the mean absolute percentage error (MAPE = 1.16%) of the future trend in vegetation drought changes for the next decade. This suggests that the overall health of vegetation is expected to continue improving during that time. This work examined the plant growth circumstances in dry locations from several angles and developed a complete analytical method for predicting long-term droughts. The findings provide a strong scientific basis for ecological conservation and vegetation management in arid regions. [ABSTRACT FROM AUTHOR]

Rice production is sensitive to climate change and significantly affected by warming in recent years. To what extent climate warming shifted rice phenology and varied thermal resource condition were explored across five agro-ecological zones in China, based on up-to-date observations of meteorology and rice cultivation in 1981–2020. It was clearly signaled that there was a general advance of 0.3–3.8 days in observed sowing date and a delay of 0.4–3.5 days in observed maturity date in 2001–2020 relative to 1981–2000 in major zones. A vacant time slice of 2.6–28.8 days between observed sowing date and potential sowing date, and a lag of 15.4–56.7 days in potential maturity date compared to observed maturity date were identified in 2001–2020. Within longer growing season, useful accumulated temperature increased by 76.7–117.6 °C·d in 2001–2020 relative to 1981–2000, while disactive accumulated temperature also increased. In Northeast China, actual rice cultivation was undergoing earlier sowing date than potential sowing date and longer growing duration than potential duration, yet leading to upward disactive accumulated temperature. The decrease in the thermal resource utilization in 2001–2020 relative to 1981–2000 was highlighted at 55.3–78.3% stations in major zones, with a decrement of 0.006–0.018 in average magnitude. The changes in thermal resource utilization unveiled that the shifts in actual rice cultivation still could not compensate for the suitability in thermal resource utilization benefited from climate warming. [ABSTRACT FROM AUTHOR]

The response of vegetation phenology to urbanization has become a growing concern. As impervious surfaces change as urbanization advances, the variation in vegetation phenology at the dynamic urbanization level was analyzed to significantly quantify the impact of urbanization processes on vegetation phenology. Based on the MOD13Q1 vegetation index product from 2001 to 2020, vegetation phenology parameters, including the start of the growing season (SOS), the end of the growing season (EOS), and the length of the growing season (GSL), were extracted, and the spatial–temporal variation in vegetation phenology, as well as its response to urbanization, was comprehensively analyzed. The results reveal that (1) from 2001 to 2020, the average rates of change for the SOS, EOS, and GSL were 0.41, 0.16, and 0.57 days, respectively. (2) The vegetation phenology changes showed significant spatial–temporal differences at the urbanization level. With each 10% increase in the urbanization level, the SOS and EOS were advanced and delayed by 0.38 and 0.34 days, respectively. (3) The urban thermal environment was a major factor in the impact of urbanization on the SOS and EOS. Overall, this study elucidated the dynamic reflection of urbanization in phenology and revealed the complex effects of urbanization on vegetation phenology, thus helping policymakers to develop effective strategies to improve urban ecological management. [ABSTRACT FROM AUTHOR]

Climate change has affected crop phenology, leading to changes in agriculture productivity. Previous studies usually take phenology and agriculture as two parallel aspects that climate change would exert influence on, rarely taking the impacts of phenology on agriculture productivity into account. Heat accumulation refers to the sum of the daily average temperature in a certain period, which is an indicator to study the relationship between temperature and the development rate of biological organisms. It indicates the influence of temperature on the growth and development of biological organisms from the aspects of intensity and action time, and is generally calculated by growing degree days (GDD). In this study, trial data on early rice phenology and in situ meteorological data at 48 national agrometeorological experiment stations during 1981 to 2010 were used to investigate the variability of heat accumulation and its driving factors. Using linear function, piecewise function, and sigmoidal function three methods, shifts in heat accumulation during vegetative growth period (VGP), reproductive growth period (RGP), and the whole growth period (WGP) were calculated. Then, the correlations between changes in GDD and (i) three growth durations and (ii) climatic variables were assessed. Our findings indicated that the heat accumulation for early rice was significantly increased by 20% during the WGP, which was mainly attributed to the increase of GDD during the RGP, while GDD during the VGP showed no significant changes. Changes of early rice GDD were closely related to the variations of the RGP duration, which was prolonged by 6.08 day/decade, while VGP duration did not change significantly. Both simple correlation and partial correlation analyses indicated that the positive impacts of growth duration played a dominant role in determining GDD variability, with the average coefficients of 0.91 during VGP, 0.97 during RGP, and 0.93 during WGP, respectively, while temperature played a secondary role. Our studies revealed the dynamics of individual-specific heat accumulation over the past three decades, and emphasized the importance of phenology to agriculture productivity. Therefore, changes in crop phenology should be sufficiently taken into consideration for the better understanding of agriculture dynamics under climate change, and for improving the accuracy of crop model simulation. [ABSTRACT FROM AUTHOR]

It is essential to monitor the dynamics of vegetation at different scales in space and time to promote the sustainable development of terrestrial ecosystems. We used the Google Earth Engine (GEE) cloud platform to perform a comprehensive analysis of the changes in normalized difference vegetation index (NDVI) Mann-Kendall (MK) + Sen trend in the hinterland region of the Maowusu sandland in China over the last two decades. We performed bias-correlation studies using soil and climate data. Furthermore, we performed a partial Mantel test to analyze the spatial and temporal fluctuations of vegetation health-related indices. Additionally, we developed a logistic dual model of the phenology index using the Lenvenberg–Marquardt technique. The objective was to uncover the factors contributing to the regional shifts in vegetation dynamics. We provide a comprehensive analytic method designed to monitor vegetation over some time and forecast its future recovery. The findings indicate that over the past 20 years, more than 90% of the regional NDVI in the study area has exhibited a consistent and significant upward trend. This trend is primarily influenced by the adverse impact of temperature and the beneficial impact of precipitation. Additionally, long-term phenological indicators in the study area reveal that the vegetation's growth cycle commences on the 125th day of the year and concludes on the 267th day of the year. This suggests that the shorter duration of the vegetation's growth season may be attributed to the local climate and unfavorable groundwater depth conditions. levated temperatures throughout the next spring and autumn seasons would significantly affect the wellbeing of plants, with soil moisture being a crucial determinant of plant development in the examined region. This study presents a wide range of analytical tools for monitoring vegetation over a long period and predicting its future recovery. It considers factors such as vegetation health, phenology, and climatic influences. The study establishes a solid scientific foundation for understanding the reasons behind regional vegetation changes in the future. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Key phenological periods of vegetation (such as the start (SOS) and end (EOS) of the growing season) are affected by climate change and human activities. However, the impact of aridification on these phenological responses is still unclear. In the farming-pastoral ecotone of northern China (FPENC), which is plagued by severe aridification, the climate change-dominant aridification zone (CDA), human activity-dominant aridification zone (HDA), and compound-dominant aridification zone (CHDA) were differentiated, and their phenological responses to different aridification types were studied via multiyear remote sensing inversion and meteorological data. Our results suggest that aridification significantly shortened the vegetation growing season, i.e., the SOS tended to be delayed (0.4–5.12 d/10a), and the EOS tended to be advanced (0–2.89 d/10a). In addition, the negative impact of CHDA, which had the highest aridification intensity, on phenological change was 3 to 4 times than that of CDA and twice than that of HDA. Thresholds were found in the phenological responses to climate change: in aridification areas, where the interannual warming rate exceeded 0.02–0.04 °C/a or the increased rate of interannual precipitation was less than 8 mm/a, prolonged growing seasons transitioned to shortened seasons. Furthermore, there was a nonlinear relationship between human activity contributions and phenological changes. When human activity contributions were between 40 and 70%, the growing season was significantly shortened. The extent of the SOS delay for cultivated land was greater than that for grassland, while the extent of the EOS advance was less for cultivated land than for grassland. Within the same climate zone, cultivated land phenological changes were stabilized by human activities, while grassland phenological changes under natural conditions were more severe and accompanied by signs of vegetation degradation. This study provides incremental knowledge for understanding the impacts of aridification on phenology and for adapting to aridification. [ABSTRACT FROM AUTHOR]

The ongoing climate change‐induced shifts in flowering phenology have emerged as a consequential force impacting biodiversity and ecosystems. Despite the globally recognized significance of flowering phenology as a key reproductive attribute, studies in subtropical regions have been relatively fewer, particularly in comparison to temperate and cold regions. Additionally, the nuanced response of deciduous and evergreen plants to climate change remains insufficiently explored. In addressing this gap, we built a phenological model and a generalized linear mixed effect model to assess the differential responses of key flowering phenological traits, that is, first flowering date (FFD), peak flowering date (PFD), end of flowering date (EFD), and flowering duration (FD), to climate factors (temperature and precipitation) between deciduous and evergreen plants. We observed distinct responses in flowering phenological traits to climate change between deciduous and evergreen plants. Specifically, the advancement of FFD, PFD, and EFD in deciduous in response to temperature rise exceeded that in evergreen plants. FD in evergreen plants exhibited a stronger extension to temperature increase compared to deciduous. Conversely, the phenological change of evergreen plants in response to decreasing precipitation was greater than that of deciduous ones. Since temperature is a decisive climatic factor in affecting phenological changes, climate change‐induced advances in flowering phenology of deciduous plants are still larger than evergreen plants. Projections from our phenological model under future climate scenarios (SSP 1‐2.6 and SSP 5‐8.5) indicate a continuous enlargement of difference in flowering phenology between deciduous and evergreen plants, with this trend escalating into the future (2100>2070>2050>2030). The larger extension in FD of evergreens to climate change suggests a potential increase in their proportion within subtropical forest communities relative to deciduous plants. These insights contribute significantly to our understanding of the intricate dynamics of climate‐induced changes in subtropical plant ecosystems. [ABSTRACT FROM AUTHOR]

The Qilian Mountains (QM) possess a delicate vegetation ecosystem, amplifying the evident response of vegetation phenology to climate change. The relationship between changes in vegetation growth and climate remains complex. To this end, we used MODIS NDVI data to extract the phenological parameters of the vegetation including meadow (MDW), grassland (GSD), and alpine vegetation (ALV)) in the QM from 2002 to 2021. Then, we employed path analysis to reveal the direct and indirect impacts of seasonal climate change on vegetation phenology. Additionally, we decomposed the vegetation phenology in a time series using the trigonometric seasonality, Box-Cox transformation, ARMA errors, and Trend Seasonal components model (TBATS). The findings showed a distinct pattern in the vegetation phenology of the QM, characterized by a progressive shift towards an earlier start of the growing season (SOS), a delayed end of the growing season (EOS), and an extended length of the growing season (LOS). The growth cycle of MDW, GSD, and ALV in the QM species is clearly defined. The SOS for MDW and GSD occurred earlier, mainly between late April and August, while the SOS for ALVs occurred between mid-May and mid-August, a one-month delay compared to the other vegetation. The EOS in MDW and GSD were concentrated between late August and April and early September and early January, respectively. Vegetation phenology exhibits distinct responses to seasonal temperature and precipitation patterns. The advancement and delay of SOS were mainly influenced by the direct effect of spring temperatures and precipitation, which affected 19.59% and 22.17% of the study area, respectively. The advancement and delay of EOS were mainly influenced by the direct effect of fall temperatures and precipitation, which affected 30.18% and 21.17% of the area, respectively. On the contrary, the direct effects of temperature and precipitation in summer and winter on vegetation phenology seem less noticeable and were mainly influenced by indirect effects. The indirect effect of winter precipitation is the main factor affecting the advance or delay of SOS, and the area proportions were 16.29% and 23.42%, respectively. The indirect effects of fall temperatures and precipitation were the main factors affecting the delay and advancement of EOS, respectively, with an area share of 15.80% and 21.60%. This study provides valuable insight into the relationship between vegetation phenology and climate change, which can be of great practical value for the ecological protection of the Qinghai-Tibetan Plateau as well as for the development of GSD ecological animal husbandry in the QM alpine pastoral area. [ABSTRACT FROM AUTHOR]

Land surface phenology plays a crucial role in accurately parameterizing interactions between land ecosystems and the atmosphere. Changes in autumn phenology directly impact the length of vegetation growing season and year-to-year changes in carbon uptake. Previous studies have often focused solely on the end of growing season when characterizing autumn phenology, neglecting the onset and duration of autumn. Here, we extracted the annual autumn phenological metrics, i.e., the start of brown-down phase (SOB), the end of brown-down phase (EOB), and the length of brown-down phase (LOB), for temperate China from 1982 to 2018 based on our self-developed global LSP dataset. We conducted a comparative analysis of SOB, EOB, and LOB in the field of their spatial distribution, temporal trends, and performance on various categories of vegetated regions (i.e., forests, grasslands, croplands, and vegetated areas in urban lands (V_Urban)). The results showed a significant negative correlation between the timing of autumn phenological metrics and latitude in temperate China. Between 1982 and 2018, there were significant positive increasing trends in EOB and LOB in V_Urban, as well as in LOB in forests in temperate China. However, the annual mean SOB, EOB, and LOB did not show significant trends across the entire study area. At the local pixel scale, SOB, EOB, and LOB exhibited a combination of advanced and delayed trends within various vegetated categories. The trends of the same phenological metric were not uniform across these diverse vegetated regions. For instance, the majority of pixels with significant trends in SOB exhibited delayed trends in forests and croplands, while displaying advanced trends in grasslands and V_Urban. In this study, employing three metrics (SOB, EOB, and LOB) to describe autumn phenology enhances our understanding of the impacts of global climate change on ecosystems, offering a more comprehensive and detailed perspective. In the future, satellite-based monitoring and phenological modeling should contemplate incorporating additional potential phenological metrics. [ABSTRACT FROM AUTHOR]

Plant phenology refers to cyclic plant growth events, and is one of the most important indicators of climate change. Integration of plant phenology information is crucial for understanding the ecosystem response to global change and modeling the material and energy balance of terrestrial ecosystems. Utilizing 24 552 in situ phenological observations of 24 representative woody plant species from the Chinese Phenology Observation Network (CPON), we have developed maps delineating species phenology (SP) and ground phenology (GP) of forests over China from 1951 to 2020. These maps offer a detailed spatial resolution of 0.1 ∘ and a temporal resolution of 1 d. Our method involves a model-based approach to upscale in situ phenological observations to SP maps, followed by the application of weighted average and quantile methods to derive GP maps from the SP data. The resulting SP maps for the 24 woody plants exhibit a high degree of concordance with in situ observations, manifesting an average deviation of 6.9 d for spring and 10.8 d for autumn phenological events. Moreover, the GP maps demonstrate robust alignment with extant land surface phenology (LSP) products sourced from remote sensing data, particularly within deciduous forests, where the average discrepancy is 8.8 d in spring and 15.1 d in autumn. This dataset provides an independent and reliable phenology data source for China on a long-time scale of 70 years, and contributes to more comprehensive research on plant phenology and climate change at both regional and national scales. The dataset can be accessed at 10.57760/sciencedb.07995 (Zhu and Dai, 2023). [ABSTRACT FROM AUTHOR]

Early and accurate prediction and simulation of grain crop yield can help maximize the revision and development of regional food policy, which is crucial for ensuring national food security. The development of unmanned aerial vehicle (UAV) technology is gradually gaining an advantage over satellite remote sensing at the field scale. In this study, we predicted maize yield using canopy vegetation indices (VIs) and crop phenology metrics obtained through UAV with ordinary least squares (OLS), stepwise multiple linear regression (SMLR) and gradient‐boosted regression tree (GBRT). The results reveal that the VIs extracted from UAV imagery had a high correlation with yield (R = 0.92), facilitating crop yield estimation. Additionally, coupling crop phenology significantly improved the prediction accuracy of SMLR, with the highest R2 and lowest RMSE of 0.894, 1.238 × 103 kg ha−1, respectively. But, the enhancement of GBRT by this method was slender. Its simulation outperformed OLS and SMLR with dramatic R2, RMSE, and MAE of 0.892, 1.189 × 103 kg ha−1, and 9.150 × 102 kg ha−1, respectively. Moreover, the blister stage was deemed the optimal stage for maize yield prediction with an accuracy rate exceeding 81%. These demonstrated the feasibility of using UAV images to predict crop yields, providing an important reference at the field scale. [ABSTRACT FROM AUTHOR]

The effect of floral traits, floral rewards and plant water availability on plant–pollinator interactions are well-documented; however, empirical evidence of their impact on flowering phenology in high-elevation meadows remains scarce. In this study, we assessed three levels of flowering phenology, i.e. population-, individual- and flower-level (floral longevity), in two nearby but contrasting (wet versus dry) sub-alpine meadows on Yulong Snow Mountain, southwestern China. We also measured a series of floral traits (pollen number, ovule number, and the ratio of pollen to ovule number per flower, i.e. pollen:ovule ratio [P/O]) and floral rewards (nectar availability and pollen presentation) as plausible additional sources of variation for each phenological level. Floral longevity in the wet meadow was significantly longer than that for the dry meadow, whereas population- and individual-flowering duration were significantly shorter. Our results showed a significant positive relationship between flowering phenology with pollen number and P/O per flower; there was no relationship with ovule number per flower. Further, we found a significant effect of flowering phenology on nectar availability and pollen presentation. Our findings suggest that shorter floral longevity in dry habitats compared to wet might be due to water-dependent maintenance costs of flowers, where the population- and individual-level flowering phenology may be less affected by habitats. Our study shows how different levels of flowering phenology underscore the plausible effects of contrasting habitats on reproductive success. [ABSTRACT FROM AUTHOR]

The accurate monitoring of vegetation phenology is critical for carbon sequestration and sink enhancement. Vegetation phenology in arid zones is more sensitive to climate responses; therefore, it is important to conduct research on phenology in arid zones in response to global climate change. This study compared the applicability of the enhanced vegetation index (EVI), which is superior in arid zones, and global solar-induced chlorophyll fluorescence (GOSIF), which has a high spatial resolution, in extracting vegetation phenology in arid zones, and explored the mechanism of the differences in the effects of environmental factors on the phenology of different vegetation types. Therefore, this study employed a global solar-induced chlorophyll fluorescence (GOSIF) dataset to determine the start and end of the vegetation growth season (SOSSIF and EOSSIF, respectively) in the arid zone of Northwest China from 2001 to 2019. The results were compared with those from the EVI-based MODIS climate product MCD12Q2 (SOSEVI and EOSEVI). Variations in the sensitivity of these climatic datasets concerning temperature, precipitation, and standardised precipitation evapotranspiration index (SPEI) were assessed through partial correlation analysis. Results: Compared to the MCD12Q2 climatic products, SOSSIF and EOSSIF closely matched the observed climate data in the study area. Spring onset was delayed at higher altitudes and latitudes, and the end of the growing season occurred earlier in these areas. Both SOSSIF and EOSSIF significantly advanced from 2001 to 2019 (trend degrees −0.22 and −0.48, respectively). Spring vegetation phenology was chiefly influenced by precipitation while autumn vegetation phenology was driven by both precipitation and SPEI. GOSIF-based climate data provides a more accurate representation of vegetation phenology compared to traditional vegetation indices. The findings of this study contribute to a deeper understanding of the potential ability of EVI and SIF to reveal the influence of vegetation phenology on the carbon cycle. [ABSTRACT FROM AUTHOR]

Summary: Global warming is advancing the timing of spring leaf‐out in temperate and boreal plants, affecting biological interactions and global biogeochemical cycles. However, spatial variation in spring phenological responsiveness to climate change within species remains poorly understood.Here, we investigated variation in the responsiveness of spring phenology to temperature (RSP; days to leaf‐out at a given temperature) in 2754 Ginkgo biloba twigs of trees distributed across subtropical and temperate regions in China from 24°N to 44°N.We found a nonlinear effect of mean annual temperature on spatial variation in RSP, with the highest response rate at c. 12°C and lower response rates at warmer or colder temperatures due to declines in winter chilling accumulation. We then predicted the spatial maxima in RSP under current and future climate scenarios, and found that trees are currently most responsive in central China, which corresponds to the species' main distribution area. Under a high‐emission scenario, we predict a 4‐degree latitude shift in the responsiveness maximum toward higher latitudes over the rest of the century.The identification of the nonlinear responsiveness of spring phenology to climate gradients and the spatial shifts in phenological responsiveness expected under climate change represent new mechanistic insights that can inform models of spring phenology and ecosystem functioning. [ABSTRACT FROM AUTHOR]

Chestnut trees hold a prominent position in China as an economically significant forest species, offering both high economic value and ecological advantages. Identifying the distribution of chestnut forests is of paramount importance for enhancing efficient management practices. Presently, many studies are employing remote sensing imaging methods to monitor tree species. However, in comparison to the common classification of land cover types, the accuracy of tree species identification is relatively lower. This study focuses on accurately mapping the distribution of planted chestnut forests in China, particularly in the Huairou and Miyun regions, which are the main producing areas for Yanshan chestnuts in northeastern Beijing. We utilized the Google Earth Engine (GEE) cloud platform and Sentinel-2 satellite imagery to develop a method based on vegetation phenological features. This method involved identifying three distinct phenological periods of chestnut trees: flowering, fruiting, and dormancy, and extracting relevant spectral, vegetation, and terrain features. With these features, we further established and compared three machine learning algorithms for chestnut species identification: random forest (RF), decision tree (DT), and support vector machine (SVM). Our results indicated that the recognition accuracy of these algorithms ranked in descending order as RF > DT > SVM. We found that combining multiple phenological characteristics significantly improved the accuracy of chestnut forest distribution identification. Using the random forest algorithm and Sentinel-2 phenological features, we achieved an impressive overall accuracy (OA) of 98.78%, a Kappa coefficient of 0.9851, and a user's accuracy (UA) and producer's accuracy (PA) of 97.25% and 98.75%, respectively, for chestnut identification. When compared to field surveys and official area statistics, our method exhibited an accuracy rate of 89.59%. The implementation of this method not only offers crucial data support for soil erosion prevention and control studies in Beijing but also serves as a valuable reference for future research endeavors in this field. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Astragalus membranaceus var. mongholicus (Astragalus) is a perennial medicinal plant belonging to Leguminosae. Its main agronomic and industrial interest is the accumulation of astragaloside IV and calycosin‐7‐O‐β‐D‐glucoside in roots, two components that play crucial pharmacological roles in the finished pharmaceutical products. Phenological research is a prerequisite for improving the yield quality of cultivated medicinal plants. However, there are no standardized phenological studies on Astragalus. To expand phenotyping knowledge on this medicinal plant, researchers and breeders need a universal scale to describe the development of this plant. This study aims to define the phenological growth stages of Astragalus, based on the existing Biologische Bundesanstalt, Bundessortenamt und Chemische Industrie (BBCH) scale to provide a standard scale for Astragalus, and grown under temperate conditions of production and climate, proposes a disease and pest control reference. This study was conducted in Inner Mongolia, North China. The existing BBCH scale with a three‐digit code was used to define Astragalus's phenological growth codes, supplemented with pictures. Phenological observations were performed twice monthly, based on the developmental stages. The chronology and duration of each stage were also observed and described according to the accumulation of degree days during the growing season. Based on the data obtained, the influence of the average temperature and GDD on the phenophases was evaluated. The phenological description is divided into two principal growth stages: five for vegetative growth (perennating bud, leaf development, formation of branches, main stem elongation and root development), four for reproductive growth (reproductive organ development, flowering, fruit development and fruit maturation), and one for senescence according to the BBCH scale. Seventy secondary growth stages were described within the 10 principal growth stages. Under each secondary growth stage, six mesostages are also taken into account, which contains the distinct patterns of the phenological characteristics in Astragalus varieties and the process of transplanting seedlings. A practical management program for disease and pest control was also proposed by using the BBCH code and the phenological data proposed in this work. Our study presents the first BBCH scale established for Astragalus cultivated in temperate conditions and a chronology of phenological stages based on the accumulation of thermal time, through growing degree day calculation. This work provides a general tool that can be widely used by researchers, breeders, and manufacturers of medicinal materials leading to better agricultural production and scientific communication. [ABSTRACT FROM AUTHOR]

• We studied the urban–rural gradient of urban vegetation phenology changes (△UVP) and its drivers across 1500+ cities in China. • Along the urban–rural gradient, the SOS advance and EOS delay increase first, decrease later, and finally steady. • The △UVP is contributed more by climate change than by urbanization level for urban subzones. A comprehensive investigation on the long-term changes of urban vegetation phenology (△UVP) is critical for a better understanding of changes in urban ecosystems. However, the urban–rural gradient of the △UVP across cities with diverse climate zones and city sizes, as well as the associated drivers, remains largely unclear. Here, using multi-source remote sensing data from 2001 to 2019, we investigated the urban–rural gradient (including six urban–rural subzones, i.e., the old urban core, new urban core, new urban area, suburban area, exurban area, and rural area) of the △UVP across 1500 + cities in China and analyzed the impacts of urbanization level and climate change on the △UVP. First, over the past two decades, the △UVP during spring (i.e., the changes of the start of growing season, termed △SOS) is generally negative (advanced SOS; 73 % ± 4 %) and that during autumn (i.e., changes of the end of growing season, termed △EOS) is mainly positive (delayed EOS; 63 % ± 3 %) in all urban–rural subzones. The spatial distribution of the △UVP based on enhanced vegetation index (EVI) data is relatively consistent with that based on solar-induced chlorophyll fluorescence (SIF) data. Along the urban–rural gradient, both the SOS advance and EOS delay exhibit an increasing first, decreasing later, and finally steady curve, with the maximum occurring in the new urban area at − 12.4 ± 18.5 days (for △SOS) and 3.8 ± 9.9 days (for △EOS). Second, the SOS changes from an advance to a delay from the middle temperate to southern subtropical zones, while the EOS changes from a delay to an advance. The magnitudes of both △SOS and △EOS show a general trend of increasing first and then decreasing with decreasing latitude. Third, the SOS advance and EOS delay generally decrease with city size for the old urban core, increase initially and decrease later with city size for the three suburban subzones, while they show little deviations with city size for the new urban core and new urban area. Finally, in the three urban subzones, the mean △UVP across all the cities is contributed more by climate change (mean: △SOS: 73 %; △EOS: 78 %) than by urbanization level (mean: △SOS: 27 %; △EOS: 22 %). Our findings can improve the understanding of urban vegetation changes in the context of rapid urbanization and climate change. [ABSTRACT FROM AUTHOR]

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Phenological responses of vegetation to the ongoing warming trend impact current and future primary productivity. However, few studies focus on wood phenology because its observed data are much scarcer, which hinders the estimation and prediction of forest carbon budgets over large regions. Here, we use a physiological process-based tree-ring growth model (Vaganov-Shashkin model) to investigate the spatial and temporal variations of spring xylem phenology (start of the growing season of xylem, SOSx) in tree-ring sites of China during 1962–2016 CE. The model is calibrated on measured tree-ring width chronologies (70 tree-ring chronologies) and successfully validated with field observations of xylogenesis. We found that spring xylem phenology significantly advances during 1962–2016 CE period under global warming, with the rate of advance quickly increasing after the 1990s to an average of 0.25 days per year. The preseason daily mean temperature is the main climatic driver for spring xylem phenology as indicated by its significant correlations with SOSx at most sites (71%). Warmer preseason allows heat requirements for tree growth to be reached more quickly, with increase of 1°C in temperature of preseason anticipates SOSx by 6 to 7 days, which will benefit the radial growth of trees in the relatively cold-humid environments. In addition, the significant positive correlation between the simulated spring xylem phenology and remote sensing derived phenology highlights the primary and secondary growth may be governed by the same variable (temperature) and change in the same direction with global warming. This study provides the long-term perspective on the spring xylem phenology variations covering most of China. [ABSTRACT FROM AUTHOR]

Phenology is an important factor indicating environmental changes and regulates the variations of carbon, water, and energy exchange. However, phenology models exhibit large uncertainties due to limited understanding of its mechanisms. In this study, we modified deciduous phenology scheme based on the evaluation of different phenological models using long‐term observations at Chinese Ecosystem Research Network with CLM4.5. The alternating leaf unfolding model and summer‐influenced autumn leaf falling model that we proposed, performed best in simulating leaf‐unfolding and leaf‐falling. Compared with the observed and remote‐sensed phenology, the modified model could better simulate the phenological dates at the site and regional scale. Moreover, the modified model improved the simulation of gross primary productivity (GPP) by decreasing the errors of modeled carbon uptake duration and amplitude. Furthermore, the advance in leaf‐unfolding slowed down from 0.20 days/year during 1981–2015 to 0.11 days/year during 2016–2100 under RCP4.5 because of the slowdown of climate warming, but the delay in leaf‐falling changed little. By the last decade of the twenty‐first century, the leaf‐unfolding would advance (8 days) and leaf‐falling would delay (16 days). The subtropical region had large interannual variation (IAV) in leaf‐unfolding because of the high sensitivity to temperature. The phenological dates IAV in the cold temperate region increased due to enhanced temperature IAV. We suggest that the deciduous phenology models, especially the leaf‐falling process, used in Community Land Model need to be improved to reduce the errors in predicting phenology and carbon flux in the future. Plain Language Summary: As an important factor indicating climate and environmental changes, phenology plays an important role in regulating the variation of carbon, water, and energy exchange. Due to limited understanding of phenology mechanisms, the simulation of phenology remains large uncertainties. In this study, we evaluated and modified the leaf unfolding and leaf falling models using the long‐term phenological observations with the Community Land Model (CLM4.5), validated the modified leaf‐unfolding and leaf‐falling models by observed phenology and remote‐sensing phenology data, examined the ability of CLM4.5 with modified deciduous phenology submodels in simulating gross primary productivity, and used the modified phenology models to predict the changes in phenological dates in the future. Our results suggest that the deciduous phenology models, especially the leaf‐falling process, used in the CLM is urgent to be improved to reduce the errors in predicting growing season length and carbon fluxes in deciduous forests in the context of climate change. Key Points: The involvement of chill accumulation with heat requirement in leaf unfolding models can improve the model performanceWe propose a new leaf falling model by considering the relationship between summer temperature and aging state threshold, which improves the model performanceThe revise of deciduous phenological model in CLM4.5 improves the simulation of gross primary productivity by influencing carbon uptake duration and amplitude [ABSTRACT FROM AUTHOR]

Ecosystem water use efficiency (WUE) is an integrated physiological metric for the coupling cycle between terrestrial carbon, water, and energy. How WUE responds to vegetation phenology (e.g., SOS, EOS-start, end of growing season, and GSL-growing season length) shifting in temperate semi-arid regions is a hot spot in relative research fields. Based on remote-sensing products and in-situ measured climate data, this study discussed how gross primary productivity (GPP), evapotranspiration (ET), and WUE (quantified by GPP/ET) would change with the altering vegetation phenology and climate in the untouched semi-arid forests and grasslands of the Chinese Loess Plateau during 2001–2020. Our results show that vegetation tended to green-up earlier and brown-down later from 2001 to 2020, causing an extended GSL. The forests had an earlier SOS, later EOS, and longer GSL than the grasslands, but the latter had a bigger variation amplitude. The WUE in the study area decreased significantly during spring and summer, while the grassland WUE increased in autumn; the annual mean reduction rate in grassland WUE was approximately twice that of woodland. Earlier SOS could increase forest WUE but reduce grassland WUE in spring, mainly because leaf unfolding has a more pronounced limitation on soil evaporation beneath the forest canopy. EOS had less impact on WUE, and no apparent difference existed between these two ecosystems. Climate change could affect WUE directly by changing GPP and ET and indirectly by regulating vegetation phenology. Warming can increase GPP and ET, causing an earlier SOS, further promoting GPP and ET (except forest ET). Precipitation significantly affected forest GPP and ET in spring, grassland GPP and ET in summer, and grassland ET in autumn; precipitation affects spring grassland WUE mainly via regulating SOS. Enhanced solar radiation could suppress grassland GPP in spring, promote forest ET in autumn, and regulate grassland WUE by affecting phenology. This study is meaningful for improving the process-based vegetation model and studying arid and semi-arid ecosystems' responses to a changing climate. [ABSTRACT FROM AUTHOR]

Climate change can induce phytoplankton blooms (PBs) in eutrophic lakes worldwide, and these blooms severely threaten lake ecosystems and human health. However, it is unclear how urbanization and its interaction with climate impact PBs, which has implications for the management of lakes. Here, we used multi‐source remote sensing data and integrated the Virtual‐Baseline Floating macroAlgae Height (VB‐FAH) index and OTSU threshold automatic segmentation algorithm to extract the area of PBs in Lake Dianchi, China, which has been subjected to frequent PBs and rapid urbanization in its vicinity. We further explored long‐term (2000–2021) trends in the phenological and severity metrics of PBs and quantified the contributions from urbanization, climate change, and also nutrient levels to these trends. When comparing data from 2011–2021 to 2000–2010, we found significantly advanced initiation of PBs (28.6 days) and noticeably longer duration (51.9 days) but an insignificant trend in time of disappearance. The enhancement of algal nutrient use efficiency, likely induced by increased water temperature and reduced nutrient concentrations, presumably contributed to an earlier initiation and longer duration of PBs, while there was a negative correlation between spring wind speed and the initiation of PBs. Fortunately, we found that both the area of the PBs and the frequency of severe blooms (covering more than 19.8 km2) demonstrated downward trends, which could be attributed to increased wind speed and/or reduced nutrient levels. Moreover, the enhanced land surface temperature caused by urbanization altered the thermodynamic characteristics between the land and the lake, which, in turn, possibly caused an increase in local wind speed and water temperature, suggesting that urbanization can differently regulate the phenology and severity of PBs. Our findings have significant implications for the understanding of the impacts of urbanization on PB dynamics and for improving lake management practices to promote sustainable urban development under global change. [ABSTRACT FROM AUTHOR]

The terrestrial gross primary productivity (GPP) has increased over the past two decades. However, the climatic attribution and the physiological and phenological processes that control the trends in the GPP are still unclear. Here, we used remote-sensing-based vegetation GPP and phenology datasets, analyzed the spatial and temporal variation in the GPP, investigated the influence of the growing season length (GSL) and the maximum value of gross primary productivity (GPPmax) on the annual GPP, and quantified the effect of climate variables on the annual GPP. Our results identified a significant increase in the annual GPP (11.97 gC/m2/yr) during 2001–2020 in China's deciduous forest. The GPPmax trend dominated the trends in the GPP, when compared with the GSL. Moreover, climate warming in summer contributes to the increase in the GPP and the GPPmax, while the extension of the GSL is primarily due to the temperature rise in spring. The annual GPP of the planted forest showed a higher increasing rate than the natural forest, due to the significant enhancement of the GPPmax and the high sensitivity of the GSL to climatic factors in the planted forest. Our findings provide a new perspective on the phenological and physiological causes of the trends in the GPP, and emphasize the importance of capturing the variability in the GPPmax when modeling the GPP. [ABSTRACT FROM AUTHOR]

Leveraging circular regression, this study analyzed phenological data from China spanning the period 2003 to 2015, meticulously examining the effects of temperature, precipitation, and CO2 concentrations on the phenological patterns of woody and herbaceous plants. For woody plants, the results showed that rising temperatures and increased precipitation notably advanced early growth phases, such as budburst, leaf unfolding, and first flowering (p < 0.001). Specifically, CO2 concentrations had a pronounced impact on the leaf unfolding phase (p < 0.001). In contrast, autumnal events, particularly fruit maturity, autumn coloring, and leaf fall, were delayed by warmer temperatures and higher precipitation (p < 0.001), Of these events, only fruit maturity demonstrated sensitivity to CO2 concentration variations. In the realm of herbaceous plants, elevated temperatures and precipitation collectively hastened the budburst phase (p < 0.001), which is an effect further accentuated by high CO2 levels (p < 0.001). Moreover, rising temperatures and augmented precipitation were instrumental in advancing the flowering phase (p < 0.001). Conversely, warmer conditions slowed down the fruiting process (p < 0.001), with this delay somewhat mitigated by the effects of increased precipitation. Interestingly, while CO2 concentrations had negligible influence on the flowering and fruiting stages, they noticeably delayed seed dispersal and the initiation of senescence (p < 0.001). Overall, the prevailing trend suggests that plants, whether woody or herbaceous in nature, tend to prolong their growth season under warmer and more humid conditions. The influence of CO2 concentration, however, is contingent upon the specific phenological phase and plant type. Our findings emphasize the nuanced and stage-specific responses of plant phenology to temperature, precipitation, and CO2, highlighting the value of using circular regression in ecological studies. [ABSTRACT FROM AUTHOR]

Shedding light on the response of oceanic dimethyl sulfide flux (FDMS) on the sea shelf to multi‐pressures (intensified terrigenous nutrient inputs and climate change) is of great importance for regional aerosol budget. Here, we designed 4‐group experiments in scenario (the 1980s, 2010s, 2050s, and 2090s) for the Yellow and East China seas (YECS) based on climate change signals gained from CMIP6 and a 3‐dimension biophysical and geochemical model. Results show that the climatological annual mean of YECS's FDMS will rise from ∼6 μmol m−2 day−1 in the 1980s to >9 μmol m−2 day−1 in the 2090s under Shared Socioeconomic Pathway (SSP) 2−4.5/5−8.5 scenario, and with multi‐pressures, the FDMS peak in the 2050s is one season earlier when compared to that in the 1980s. The elevated terrigenous inputs affect the phytoplankton biomass and/or community on the inner shelf, leading to the rise and phenology change in the YECS's FDMS between the1980s and 2010s, while climate change has little impact. The influence of climate change on the FDMS is projected to increase in the 2050s; climate change will change seasonal pattern of the FDMS on the central part (middle and outer shelves) of East China Sea (CECS) under every SSP scenario. Thereinto, wind change exerts a crucial role, it will enhance summertime offshore transport and, accordingly, stimulate phytoplankton growth in summer changing the phenology of FDMS on the CECS. Key Points: Dimethyl sulfide flux (FDMS) rises during the 1980s–2090s in the Yellow and East China seas accompanying by a change in FDMS phenologyElevated riverine nutrient inputs affect phytoplankton biomass and/or community on the inner shelf, triggering the past phenology changeClimate change enhances summertime offshore transport, promoting the phytoplankton biomass and changing FDMS phenology on offshore waters [ABSTRACT FROM AUTHOR]

Leaf phenology (evergreen vs. deciduous) and morphology (simple vs. compound) are known to be related to water use strategies in tree species and critical adaptation to certain climatic conditions. However, the effect of these two traits and their interactions on the coordination between minor vein density (MVD) and stomatal density (SD) remains unclear. In this study, we examined the leaves of 108 tree species from plots in a primary subtropical forest in southern China, including tree species with different leaf morphologies and phenologies. We assessed nine leaf water-related functional traits for all species, including MVD, SD, leaf area (LA), minor vein thickness (MVT), and stomatal length (SL). The results showed no significant differences in mean LA and SD between either functional group (simple vs. compound and evergreen vs. deciduous). However, deciduous trees displayed a significantly higher mean MVD compared to evergreen trees. Similarly, compound-leaved trees have a higher (marginally significant) MVD than simple-leaved trees. Furthermore, we found that leaf morphology and phenology have significantly interactive effects on SL, and the compound-leafed deciduous trees exhibited the largest average SL among the four groups. There were significant correlations between the MVD and SD in all different tree groups; however, the slopes and interceptions differed within both morphology and phenology. Our results indicate that MVD, rather than SD, may be the more flexible structure for supporting the coordination between leaf water supply and demand in different leaf morphologies and phenologies. The results of the present study provide mechanistic understandings of the functional advantages of different leaf types, which may involve species fitness in community assembly and divergent responses to climate changes. [ABSTRACT FROM AUTHOR]

Given that already‐observed temperature increase within cities far exceeds the projected global temperature rise by the end of the century, urban environments often offer a unique opportunity for studying ecosystem response to future warming. However, the validity of thermal gradients in space serving as a substitute for those in time is rarely tested. Here, we investigated vegetation phenology dynamics in China's 343 cities and empirically test whether phenological responses to spatial temperature rise in urban settings can substitute for those to temporal temperature rise in their natural counterparts based on satellite‐derived vegetation phenology and land surface temperature from 2003 to 2018. We found prevalent advancing spring phenology with "high confidence" and delaying autumn phenology with "medium confidence" under the context of widespread urban warming. Furthermore, we showed that space cannot substitute for time in predicting phenological shifts under climate warming at the national scale and for most cities. The thresholds of ~11°C mean annual temperature and ~600 mm annual precipitation differentiated the magnitude of phenological sensitivity to temperature across space and through time. Below those thresholds, there existed stronger advanced spring phenology and delayed autumn phenology across the spatial urbanization gradients than through time, and vice versa. Despite the complex and diverse relationships between phenological sensitivities across space and through time, we found that the directions of the temperature changes across spatial gradients were converged (i.e., mostly increased), but divergent through temporal gradients (i.e., increased or decreased without a predominant direction). Similarly, vegetation phenology changes more uniformly over space than through time. These results suggested that the urban environments provide a real‐world condition to understand vegetation phenology response under future warming. [ABSTRACT FROM AUTHOR]

Long-time series and large-scale crop calendars provide valuable information for rational crop cultivation and management under climate change scenarios as a prerequisite for ensuring regional food security. Although some Chinese rice phenological products exist, there is a lack of studies on the long rice cropping calendar for early, middle, and late rice according to the actual cropping dates. Unlike in situ monitoring and statistical rice phenology information, long-time series medium-resolution remote sensing satellite images provide the possibility and accuracy of real-time crop phenology monitoring. Based on MODIS data products and the improved PhenoRice algorithm, this study obtained phenological information on rice in seven major agricultural zones in China from 2003 to 2020. First, to effectively explore the changes in rice growing seasons over 18 years, we identified early, middle, and late rice according to specific cropping dates. Second, we selected 30 % of the recorded phenology data from Agricultural Meteorological Stations (AMSs) for parameter calibration and added a season division module to the PhenoRice algorithm to obtain a 250 m resolution raster dataset of rice crop calendars named ChinaRiceCalendar. However, it is consistent with the station data, RiceAtlas, and ChinaCropPhen1km. The validation accuracy R² exceeded 0.95, 0.75, and 0.7 with the data recorded from AMSs, RiceAtlas, and ChinaCropPhen1km, respectively. In addition, we observed that the rice planting dates in China were delayed by 2.4 days/10a while the maturity dates were earlier by 5.5 days/10a during 2003-2020. ChinaRiceCalendar provides insights into practical rice farming measures and the response of rice cropping dates to environmental conditions across China. [ABSTRACT FROM AUTHOR]

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 < 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 < 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. [ABSTRACT FROM AUTHOR]

Biodiversity is and always has been an important issue in ecological research. Biodiversity can reflect niche partitioning among species at several spatial and temporal scales and is generally highest in the tropics. One theory to explain it is that low-latitude tropical ecosystems are dominated by species that are generally only distributed over a narrow area. This principle is known as Rapoport's rule. One previously unconsidered extension of Rapoport's rule may be reproductive phenology, where variation in flowering and fruiting length may reflect a temporal range. Herein, we collected reproductive phenology data for more than 20,000 species covering almost all angiosperm species in China. We used a random forest model to quantify the relative role of seven environmental factors on the duration of reproductive phenology. Our results showed that the duration of reproductive phenology decreased with latitude, although there was no obvious change across longitudes. Latitude explained more of the variation in the duration of flowering and fruiting phases in woody plants than in herbaceous plants. Mean annual temperature and the length of the growing season strongly influenced the phenology of herbaceous plants, and average winter temperature and temperature seasonality were important drivers of woody plant phenology. Our result suggests the flowering period of woody plants is sensitive to temperature seasonality, while it does not influence herbaceous plants. By extending Rapoport's rule to consider the distribution of species in time as well as space, we have provided a novel insight into the mechanisms of maintaining high levels of diversity in lowlatitude forests. [ABSTRACT FROM AUTHOR]

It is well accepted that the phenology of insects whose life activities are closely related to temperature is changing in response to global climate warming. To investigate the impacts of climate warming on the phenology of Bactrocera dorsalis (Hendel) across large temporal and spatial scales, this study collected historical data on the occurrence and population dynamic of this pest in China, and systematically explored its phenological responses. The results showed a delayed trend for the dates of first occurrence, end occurrence, population initial growth, and population peak of B. dorsalis in China during 40 years, and the changes of the latter two phenological parameters were significant. The mean temperature in spring and summer were the key climatic factors affecting the occurrence and population growth of B. dorsalis in China, respectively. Moreover, the B. dorsalis data in eastern, southern, central, and southwestern China showed spatial heterogeneity of phenological responses to climate warming at a regional scale. B. dorsalis phenology and their changing patterns with climate warming varied by geographical location. This study provides valuable information for future monitoring, prediction, and prevention of the oriental fruit fly in the context of climate warming. [ABSTRACT FROM AUTHOR]

Artificial irrigation is critical for improving soil moisture conditions and ensuring crop growth. Its irrational deployment can lead to ecological and environmental issues. Mapping and understanding the changes in irrigated areas are vital to effectively managing limited water. However, most researchers map irrigated areas with a single data resource, which makes it hard to detect irrigated signals in complex situations. The case study area for this paper was China's winter wheat region, and an irrigated area map was generated by analyzing the effects of artificial irrigation on crop phenological characteristics and soil moisture time series. The mapping process involved three steps: (1) generating a basic irrigated map by employing the ISODATA classification method on the Kolmogorov–Smirnov test irrigation signals from the microwave remote sensing data and reanalysis data; (2) creating the other map with the maximum likelihood ratio classification and zoning scheme on the phenological parameters extracted from the NDVI time series; and (3) fusing these two maps at the decision level to obtain the final map with a higher spatial resolution of 1 km. The map was evaluated against existing irrigated area data and was highly compatible with GMIA 5.0. The overall accuracy (OA) was 73.49%. [ABSTRACT FROM AUTHOR]

Seed rain phenology (the start and end date of seed rain) is an essential component of plant phenology, critical for understanding population regeneration and community dynamics. However, intra- and inter-annual changes of seed rain phenology along environmental gradients have rarely been studied and the responses of seed rain phenology to climate variations are unclear. We monitored seed rain phenology of four forest communities in four years at different elevations (900 m, 1450 m, 1650 m, 1900 m a.s.l.) of a subtropical mountain in Central China. We analyzed the spatiotemporal patterns of seed rain phenology of 29 common woody plant species (total observed species in the seed rain), and related the phenological variations to seed number and climatic variables using mixed-effect models with the correlation matrix of phylogeny. We found that changes in the period length were mainly driven by the end rather than the start date. The end date and the period length of seed rain were significantly different between the mast and non-mast seeding years, while no significant elevation-related trend was detected in seed rain phenology variation. Seed number, mean temperature in spring (Tspr), and winter (Twin), summer precipitation (Psum) had significant effects on seed rain phenology. When Tspr increased, the start date of seed rain advanced, while the end date was delayed and the seed rain period length was mainly prolonged by a higher seed number, Twin and Psum. Forest canopy might have a buffering effect on understory climatic conditions, especially in precipitation that lead to difference in seed rain phenology between canopy and shrub species. Our novel evidence of seed rain phenology can improve prediction of community regeneration dynamics in responding to climate changes. [ABSTRACT FROM AUTHOR]

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

Tea plants are evergreen broad-leaved perennial shrubs, and their spectral characteristics are very similar to those of other evergreen vegetation, making it difficult to distinguish them. Currently, the most commonly used method of tea plantation extraction is machine learning, which classifies tea plantation through various feature combinations and algorithms. The disadvantage of these methods is that they require a large number of local training samples, making it challenging to produce an accurate model applicable to a large region. Furthermore, complex feature combinations and indicators may result in over-fitting, reducing the accuracy of the results. Therefore, it is necessary to develop a new algorithm suitable for tea plantation extraction in extensive regions. This paper uses Shihe District, Henan Province, China as a case in 2019, combined with Landsat-7/8 and Sentinel-2A/B images, and develops a new phenological-based algorithm to extract the tea plantation area. Firstly, we generated an evergreen vegetation map. Secondly, based on high-quality time series curves, the tea plant growth period was divided into seven parts to extract phenological indicators for classification. Finally, the tea plantation in the study area was extracted on a per-pixel basis. The overall accuracy of the algorithm is 87.59%, and the Kappa coefficient is 0.80. This study demonstrates the potential of the phenology-algorithms in extracting tea plantation areas and provides an advanced scheme and scientific basis for extracting tea plantation in other years, and also offers a reference for identifying tea plantation in other regions. Additionally, this paper generated a map of classified phenological indicators to provide guidance for monitoring tea plants growth. [ABSTRACT FROM AUTHOR]

The Fifth IPCC Assessment Report indicates that climate change will affect crop growth and threaten the stability of food systems. Accumulated temperature, which is closely related to vegetation phenology and cropping systems, is an important indicator of heat in a region. Studying the history and future accumulated temperature changes can provide scientific reference for the change of crop phenology and cropping system, which is important for the improvement of grain production in China. Based on the MK trend test, MK abrupt change test and interpretable machine learning model, this study analyzes the spatial and temporal variation of accumulated temperature in China from 1979 to 2018, predicts its future variation based on CMIP6, and investigates the dominant influencing factors among different agricultural regions. The study found that (1) the accumulated temperature belt shows a northward shift and retreat trend toward higher altitudes, and the area of the high accumulated temperature belt increases year by year, leading to the narrowing of the area of the low accumulated temperature belt year by year, and the trend remains unchanged under the future scenario; meanwhile, the northward shift trend of the accumulated temperature belt is greatly mitigated and curbed under the SSP126 scenario. (2) The changes of accumulated temperature belt are mainly influenced by the increase of accumulated temperature duration days, and secondarily by the increase of temperature. The contribution brought by the first day of accumulated temperature from 1979 to 2018 is greater than that brought by the last day of accumulated temperature, while in the future scenario, on the contrary, changes in vegetation phenology delay should be given more attention. [ABSTRACT FROM AUTHOR]

The complex topography of subtropical montane forests favors the coexistence of diverse plant species, making these species-rich forests a high priority for biodiversity monitoring, prediction, and conservation. Mapping tree species distribution accurately in these areas is an essential basis for biodiversity research and is often challenging due to their complex structure. Remote sensing has widely been used for mapping tree species, but relatively little attention has been paid to species-rich montane forests. In this study, the capability of high-resolution UAV remote sensing imagery for mapping six tree species, standing dead trees, and canopy gaps was tested in a subtropical montane forest at an elevation of 816~1165 m in eastern China. Spectral, spatial geometrical, and textural features in a specific phenological period when obvious color differences among the leaves of different species were extracted, and four object-based classification algorithms (K-nearest neighbor (KNN), classification and regression tree (CART), support vector machine (SVM), and random forest (RF)) were used for tree species classification. We found that: (1) mapping tree species distribution using low-cost UAV RGB imagery in a specific leaf phenological period has great application potential in subtropical montane forests with complex terrain. (2) Plant spectral features in the leaf senescence period contributed significantly to species classification, while the contribution of textural features was limited. The highest classification accuracy was 83% using KNN with the combination of spectral and spatial geometrical features. (3) Topographical complexity had a significant impact on mapping species distribution. The classification accuracy was generally higher in steep areas, especially in the low slope area. [ABSTRACT FROM AUTHOR]

Global climate change has posed direct challenges to crop production. Among them, maize can be one of the most widely cultivated crops in China. It was important to systematically explore the impacts of future climate change on the potential yield, in order to ensure a high and stable yield and food security. In this research, a systematic investigation was implemented to explore the effects of future climate change on maize productivity using the daily meteorological data of two climate scenarios (Shared Socioeconomic Pathways, SSP1-2.6 and SSP5-8.5) output from MPI-ESM1.2-HR model under the Inter Statistical Impact Model Intercomparison Project from 1981 to 2100, the maize phenology data, and the soil data of the agrometeorological stations of the China Meteorological Administration. Firstly, the changes in agricultural resources were analyzed during the maize growth period in five subregions of the potential maize planting areas. Then, the Agricultural Production Systems sIMulator (APSIM-Maize) was validated to simulate the length of the whole growth period, vegetative period, reproductive period, potential yield, and rainfed potential yield of maize from 1981 to 2100. And the average value and coefficient of variation were selected to quantify the impact of future climate change on the yield of maize in China. The result showed that: 1) Under both SSP1-2.6 and SSP5-8.5 scenarios, there was a rising trend in the temperature and ≥10o C effective accumulated temperature (EAT) during the whole growth period of maize. The rising amplitude was higher under SSP5-8.5 than that under SSP1-2.6. There was a large interannual fluctuation of precipitation, indicating an insignificant overall change trend. There was a slight increase under SSP1-2.6, but a decrease under SSP5-8.5. The total solar radiation increased first and then decreased. 2) Without adaptation measures, the whole growth period, vegetative period and reproductive period of maize were shortened under climate change. The shortening trend under the SSP5-8.5 scenario was greater than that under the SSP1-2.6 scenario. The shortening trend increased significantly, as time went on. 3) Without adaptation measures, future climate change reduced the potential yield and rain-fed potential yield of maize. The yield reduction rates under the SSP5-8.5 scenario were greater than that under the SSP1-2.6 scenario. The average yield reduction rates of potential yield under SSP5-8.5 and SSP1-2.6 scenarios were 13.8% and 11.9%, respectively, while the average yield reduction rates of rain-fed potential yield were 17.5% and 14.0%, respectively. Future climate change slightly improved the stability of the potential yield and the rain-fed potential yield of maize, but there were differences between subregions. Therefore, future climate change can be expected to decrease the productivity of maize, but slightly improve its stability. [ABSTRACT FROM AUTHOR]

Studying the start (SOS) and end (EOS) of the vegetation growing season can improve vegetation prediction under climate change. Climatic factors have temporal effects on vegetation growth, including the no time effect (no), time-lag effect (lag), time-accumulation effect (acc), and both time-lag and -accumulation effects (lagacc). The linear regression equations between SOS/EOS and climatic factors were constructed for the Loess Plateau (LP). Subsequently, we analyzed the effects of single and multiple climatic factors on vegetation phenology under four temporal effect scenarios and investigated the response of vegetation phenology to the time-lag and time-accumulation effects of climatic factors, under the lagacc scenario. Among the four temporal effects, lagacc explained the effects of climate on vegetation phenology to the greatest degree, and it is the optimal temporal effect for simulating the relationship between vegetation phenology and climate on the LP. Moreover, the explanation degrees of multiple climatic factors were higher than those of single climatic factors across different temporal effects and vegetation types. Simultaneously considering multiple climatic factors improved predictability of their impact on vegetation phenology. Under lagacc, responses of SOS to temperature (TMP) and precipitation (PRE) exhibited 1.44 ± 0.43/3.49 ± 0.85 and 1.38 ± 0.30/3.38 ± 0.71 months lag/accumulation, respectively, and responses of EOS to TMP and PRE exhibited 1.35 ± 0.17/2.37 ± 0.34 and 1.59 ± 0.19/4.16 ± 0.50 months lag/accumulation, respectively, across the entire LP. The results show that both lagacc and multiple climatic factors require consideration when establishing relationships between climatic factors and vegetation phenology. [ABSTRACT FROM AUTHOR]

Sustaining cotton (Gossypium hirsutum L.) production under limited water availability and climate change in an extremely arid oasis is a key challenge for the stakeholders. This study was conducted to quantify the climate change impacts on cotton phenology and seed yield under full (638 mm) and deficit (478 mm) irrigation regimes in an extremely arid oasis in China. The Root Zone Water Quality (RZWQM2) model with the integration of six global circulation models (GCMs) under two representative concentration pathways (RCP 4.5 and 8.5) was used to determine the potential impacts of climate change on cotton for future periods (2022–2047, 2048–2073, and 2073–2099) compared to baseline (1975–2000). The results revealed that number of days to anthesis and maturity was expected to be reduced under RCP 4.5 and RCP 8.5 with full and deficit irrigation for future periods compared to baseline. However, this reduction was maximum under RCP 8.5 for 2074–2099 with full irrigation treatment. Seed cotton yield was also expected to decrease by 13–18% (RCP4.5) and 14–18% (RCP 8.5) with full irrigation, while decline in yield was 10–14% (RCP 4.5) and 11-19.6% (RCP 8.5) under deficit irrigation for future periods. The maximum decline in yield appeared with deficit irrigation under RCP 8.5 for 2074–2099. This reduction in seed cotton yield is primarily attributed to elevated temperature in the future climate. A 25% deficit of irrigation compared to normal irrigation has also ensured a reasonable seed yield in future climate, therefore it could be considered as an irrigation management strategy in future for cotton production in extremely arid regions. Findings of this study will provide a better guidance to cotton growers for applying deficit irrigation to sustain cotton production under changing climate with limited water availability in XUAR and other similar agro-climatic regions. [ABSTRACT FROM AUTHOR]