Response of Spring Phenology to Pre-Seasonal Diurnal Warming in Deciduous Broad-Leaved Forests of Northern China

Abstract

Preseason temperature has always been considered the most critical factor influencing vegetation phenology in the northern hemisphere. While numerous studies have examined the impact of daytime and nighttime warming on vegetation phenology in this region, the specific influence of day and night warming on deciduous broad-leaved forests (DBFs) in Northern China, where significant temperature variations occur between day and night, remains unclear. Furthermore, the sensitivity of daytime and nighttime warming during different preseason periods to phenology has not been quantitatively understood. We analyzed GIMMS3g NDVI data from 1985 to 2015 and employed a double logistic regression model to determine the phenological start of the season (SOS) for DBF in Northern China. To control for monthly precipitation effects, we conducted partial correlation analysis between monthly mean maximum daytime temperature (T_{day_max}), monthly mean minimum nighttime temperature (T_{night_min}), diurnal temperature variation (DTR), and SOS. Our findings over the past 31 years indicate that 75.98% of the area exhibited an advanced trend, with an overall advance of 1.7 days per decade. Interestingly, regardless of T_{day_max}, T_{night_min}, or DTR, most areas had a preseason length of 1 month, accounting for 50.26%, 34.45%, and 44.39%, respectively. Furthermore, approximately 50.68% of the area exhibited a significant negative correlation between preseason temperature and SOS for T_{day_max}, 34.02% for T_{night_min}, and 35.80% for DTR. It can be found that the response of the SOS advance to T_{day_max} in DBFs in Northern China is more obvious than that to T_{night_min} and DTR. Our study revealed that the difference in day and night temperature warming on DBFs in Northern China is not pronounced. Specifically, SOS advanced by 1.8 days, 1.98 days, and 1.95 days for every 1 °C increase in T_{day_max}, T_{night_min}, and DTR, respectively. However, it is important to note that the distribution of advanced days resulting from the warming of these three preseason temperature indicators exhibited spatial heterogeneity. Although many studies have already established the influence of various meteorological indicators on spring phenology, determining which meteorological indicators should be employed to quantify their impact on phenology in different regions and vegetation types remains a subject for further exploration and investigation in the future.

1. Introduction

Vegetation phenology, which reflects the annual growth and development cycles of plants, is a highly sensitive indicator of global climate change and a key metric to monitoring vegetation ecosystem change at the regional and even global scale Yang, et al. [1,2,3,4,5]. The relationship between vegetation phenology and climate represents the dynamic response of terrestrial ecosystems to climate variability, making it a crucial topic in global change research [6,7,8]. Shifts in vegetation phenology can significantly alter terrestrial ecosystem processes, including carbon and water cycles [9,10], as well as the balance of energy exchange between the biosphere and atmosphere [11,12].

Traditional phenological observation methods rely on ground-based approaches such as manual recording, phonecams, and flux monitoring. However, these methods are limited in spatial continuity [13], and the geographical constraints and labor-intensive nature of field phenological observations pose challenges for studying phenology at broader geographic and climatic scales [14]. In contrast, remote sensing-based land surface phenology enables continuous monitoring across larger ecosystem scales [15]. For example, Chen, et al. [16] utilized remotely sensed phenological data (MCD12Q2) to analyze the spatial heterogeneity of vegetation phenology between urban and non-urban areas across 320 cities in China. Their findings revealed significant differences in vegetation phenology between urban and non-urban regions at both national and regional scales. Similarly, Hu, et al. [17] extracted Green-Up Dates from the GIMMS-3g dataset for the period 1982–2022 and analyzed the driving factors using ERA5 meteorological data. Their study showed that over the past 41 years, the spring green-up period in forests has significantly advanced, whereas in grasslands, it has been delayed. These findings underscore the potential of remote sensing in facilitating large-scale vegetation phenology research [18].

Furthermore, numerous empirical studies have demonstrated that the start of the growing season (SOS) in the northern hemisphere has advanced in response to rising temperatures [7,19,20,21,22]. Therefore, a comprehensive investigation of how vegetation phenology responds to climate change is essential for enhancing our understanding of the terrestrial carbon cycle and improving the accuracy of ecosystem modeling under future climate change scenarios [5,23,24,25].

Temperature, particularly preseason temperature, is widely recognized as the primary meteorological factor driving shifts in vegetation phenology across the northern hemisphere [22,26]. Theoretically, changes in daily maximum temperature (T_{day_max}) and nighttime minimum temperature (T_{night_min}) during winter and spring may alter plants’ accumulated temperature and chilling requirements, thereby exerting opposing effects on early phenological changes [27]. A higher T_{night_min} can mitigate frost damage, whereas an increased T_{day_max} may exacerbate drought stress in cold and arid regions [28]. In recent decades, substantial research has explored the asymmetric effects of diurnal temperature variations on vegetation phenology [4,29,30,31,32]. Piao, et al. [20] reported that vegetation phenology exhibits greater sensitivity to daytime maximum temperatures (T_{day_max}) than to night minimum temperatures (T_{night_min}) in the northern hemisphere. However, some studies suggest that the influence of daytime temperature on phenology is diminishing, while the role of temperature is becoming increasingly important, particularly at middle and high latitudes [32,33]. While the impacts of diurnal temperature changes on vegetation phenology have been investigated within diverse ecosystems, encompassing settings such as forests and grasslands, as well as at regional and even northern hemisphere scales [4,25,29,33], there remains an unresolved aspect. Specifically, the response of spring phenology in deciduous broad-leaved forests to asymmetric day-night warming (T_{day_max}, T_{night_min}, and diurnal temperature range (DTR: T_{day_max}–T_{night_min})) remains inadequately understood. This knowledge gap is particularly relevant given that climate change is leading to more pronounced nighttime warming compared to daytime warming [30,34].

Forests serve as the largest carbon reservoir within terrestrial ecosystems [35]. Research has identified a clear trend of “earlier springs and later autumns” in forest phenology attributed to climate warming [5,36,37,38]. These shifts could potentially enhance the carbon sequestration capacity of forests in the future, thereby mitigating climate change [39]. Moreover, Mack, et al. [40] introduced a compelling perspective highlighting the ecological benefits of deciduous broad-leaved forests (DBFs), which not only contribute to reducing fire risk but also enhance above-ground carbon storage. Furthermore, tree species composition plays a crucial role in determining the rate of change in forest soil carbon storage. Notably, DBFs exert a more pronounced influence compared to coniferous forests, and deciduous forests have a stronger impact than evergreen forests in this context [41]. Given that DBFs dominate the northern regions of China [42], investigating their phenological response to climate change is of critical importance for assessing future carbon cycle dynamics.

In this study, we analyzed the SOS of DBFs in Northern China from 1985 to 2015, using GIMMS3g–NDVI data. Our aims were to (1) examine the spatiotemporal trends of SOS in DBFs; (2) assess the spatial and temporal distribution of three preseason temperature indicators, including T_{day_max}, T_{night_min}, and DTR-over varying preseason lengths; and, (3) quantify the spatiotemporal sensitivity of SOS to these three preseason temperature indicators under different preseason lengths. To ensure that changes in land use did not confound our analysis [25], we focused exclusively on areas within the DBF category that exhibited no significant anthropogenic disturbances throughout the study period [43]. Our findings contribute to the broader understanding of the relationship between SOS and asymmetric diurnal temperature changes in forest ecosystems, complementing previous studies conducted in other forest types.

2. Materials and Methods

2.1. Study Regions

This study focuses on DBFs in Northern China, primarily covering North China, Northeast, and Northwest regions. The analysis includes 12 municipalities and provinces: Gansu (GS), Ningxia (NX), Shaanxi (SN), Henan (HN), Shanxi (SX), Hebei (HB), Tianjin (TJ), Beijing (BJ), Inner Mongolia (NM), Liaoning (LN), Jilin (JL), and Heilongjiang (HL). The region spans 33.23 × 10⁶ km², with elevations ranging from 3.5 to 5798 m. It extends longitudinally from 92°46′ to 135°5′ E and latitudinally from 31°22′ to 53°3′ N (Figure 1). Located north of the Qinling Mountain-Huaihe River, this area experiences a temperate monsoon climate. The hottest month sees average temperatures of 9 to 15 °C, while the coldest month averages around −6 °C. Annual precipitation ranges from 440 to 980 mm [23], with the bulk falling in July and August [44]. The dominant tree species forming the community of deciduous DBF in this region are primarily oak species, including Quercus liaotungensis, Quercus acutissima, Quercus variabilis, Quercus aliena var. acutiserrata, and Quercus mongolica [42,45].

2.2. Datasets

2.2.1. GIMMS3g-NDVI

The Global Inventory Modeling and Mapping Studies (GIMMs3g) NDVI dataset (https://data.casearth.cn/sdo/detail/5c19a5660600cf2a3c557ad2, accessed on 1 January 2025), derived from the Advanced Very High Resolution Radiometer (AVHRR) [46,47], is widely recognized as one of the most reliable NDVI products due to its long-term, continuous daily records [48,49]. The GIMMS3g NDVI data were synthesized by the bimonthly composite method [50], with a time resolution of 15 d and a spatial resolution of 1/12° [50], and NDVI will be resampled to 1 km in this study. The data have been recalibrated, which reduces the influence of snow [33], and have been extensively used for vegetation phenology extraction at both regional and global scales [51,52,53,54,55]. Long-term observation ensures a sufficient amount of effective data to accurately reflect the seasonal dynamics of vegetation, providing a robust and reliable data foundation for applied research, including land use change studies and spatiotemporal vegetation pattern analysis. To further enhance data reliability, the NDVI dataset in this study was reconstructed using the Savitzky–Golay filter, following the methodology of Yang, et al. [56].

2.2.2. Deciduous Broadleaved Forest

The Global 30 m land-cover classification with a fine classification system (GLC_FCS30), derived from all Landsat data (TM, ETM+, and OLI), provides 29 land use types at a spatial resolution of 30 m with a temporal interval of 5 years [57]. Relevant research has shown that the accuracy of GLC_FCS30 is superior to other global land cover products, such as FROM_GLC and GlobeLand [58,59]. In this study, we performed an overlay analysis on seven time periods (1985, 1990, 1995, 2000, 2005, 2010, and 2015) to identify forest pixels that remained unchanged over these intervals. To achieve this, we applied the mode clustering algorithm in Google Earth Engine (GEE) to aggregate 30 m forest pixels to a 1 km resolution and extract DBF pixels.

2.2.3. Climate Data

In this study, we analyzed the response of the start of season (SOS) to climate change using data from the China Meteorological Forcing Dataset (CMFD), which is accessible at (https://data.tpdc.ac.cn/zh-hans/data/8028b944-daaa-4511-8769-965612652c49/, accessed on 1 January 2025). The dataset offers a temporal resolution of 3 h and a spatial resolution of 0.1°. It is a comprehensive compilation of international reanalysis data, GLDAS data, GEWEX-SRB radiation data, and TRMM precipitation data, complemented by routine meteorological observations from the China Meteorological Administration [60]. To be consistent with NDVI and DBF, the precipitation and temperature data were resampled to 1 km [61], and we calculate the monthly mean of T_{day_max}, T_{night_min}, and DTR from 1985 to 2015.

2.3. Methods

2.3.1. Retrieving SOS from NDVI Time Series

Several commonly used models are employed to determine the phenological period of vegetation, including the double logistic model [62,63], amplitude threshold method [6,64], curvature method [65,66,67], asymmetric Gaussian model [68], among others. Compared to other phenological fitting models, the double logistic model utilizes distinct sigmoid curves to characterize both the greening and senescence phases of vegetation [69].Therefore, we employed the double logistic model (Equation (1)) to estimate the SOS.

(1)$f\left(t\right)=v_1+v_2\left({\displaystyle \frac{1}{1+e^{-m_1\left(t-n_1\right)}}}-{\displaystyle \frac{1}{1+e^{-m_2\left(t-n_2\right)}}}\right)$

where f(t) is the fitted NDVI value at day t; v₁ and v₂ are the background and amplitude of NDVI over the entire year, respectively; the first sigmoid (${\displaystyle \frac{1}{1+e^{-m_1\left(t-n_1\right)}}}$) with pair-parameters of m₁ and n₁ captures the green-up phase of vegetation growth, and the second sigmoid (${\displaystyle \frac{1}{1+e^{-m_2\left(t-n_2\right)}}}$) parameters represent m₂ and n₂ of the senescence phase of vegetation. The above 6 parameters are estimated by a nonlinear regression function [69,70].

2.3.2. Phenological Trend Analysis

In phenological studies, Mann–Kendall and Thil–Sen trend analyses have been widely used in the analysis of time series changes in phenological periods [71,72]. The Mann–Kendall test is a non-parametric method used to assess the significance of a trend at a significance level of t < 0.05 [73].

(2)$S={{\displaystyle \sum }}_{i=1}^{n-1}{{\displaystyle \sum }}_{j=i+1}^{n}sgn\left(y_j-y_i\right)$

(3)$sgn\left(y_j-y_i\right)=\{\begin{array}{c}1,y_j-y_i>0\\ 1,y_j-y_i>0\\ -1,y_j-y_i<0\end{array}$

(4)$Z=\{\begin{array}{c}{\displaystyle \frac{S-1}{\sqrt{Var\left(S\right)}}}, S>0\\ 0, S=0\\ {\displaystyle \frac{S+1}{\sqrt{Var\left(S\right)}}}, S<0\end{array}$

where n represents the length of the time series, y_j and y_i are the observed values of the time series in jth and ith, respectively; 1985 ≤ i < j ≤ 2015, and sgn is the symbolic function; Z represents standardized test statistics, and Var(S) is the variance.

We estimate the monotonic variation in the SOS time series from 1982 to 2015 using the Theil–Sen median slope estimator [71], which calculates the slope of each pair of observation points (year and corresponding SOS) and selects the median as the robust estimate of the slope (Equation (5)). It is more robust than the traditional linear regression and can avoid the interference of outliers [74].

(5)$\beta =median\left({\displaystyle \frac{y_j-y_i}{x_j-x_i}}\right)$

where $\beta$ represents the Thil–Sen slope of SOS, and a positive value means that the SOS trend is delayed, 0 means no change, and a negative value means that the SOS trend is advanced.

2.3.3. Sensitivity Analysis

Different meteorological factors have spatial heterogeneity for the phenological period, and the same meteorological factor also has differences in different seasons [75]. In order to quantify the sensitivity of temperature to the SOS of the DBFs in Northern China, linear regression equations were employed to analyze the sensitivity of T_{day_max}, T_{night_min}, or DTR to SOS.

(6)$\Delta SOS=\beta ·\Delta T+ϵ$

$\Delta SOS$ represents the change in the start of the growing season (SOS) relative to the baseline year, measured in days; $\Delta T$ represents temperature change (°C); $\beta$ is the regression coefficient, indicating the number of days (days/°C) when SOS increases by 1 °C, and $ϵ$ denotes the regression residual term, representing unexplained variation.

2.4. Partial Correlation Between SOS and Preseason Temperature

The preseason length of phenology refers to the time preceding the multi-year average phenology period (e.g., SOS, EOS). It is determined by identifying the period in which the absolute maximum partial correlation coefficient between climate factors and vegetation phenology occurs relative to the multi-year mean phenology period [76,77]. Because the preseason time is inconsistent in different vegetation types or different regions, it is widely used in the study of the correlation between phenology and meteorological factors [78,79]. Since the DOY of the multi-year average SOS in this study was 119 days, we defined the pre-season periods as 1–4 months, and the pre-season length is defined as the length of time when the partial correlation coefficient between meteorological factors (T_{day_max}, T_{night_min}, and DTR) and the mean SOS reaches the maximum before the DOY of the multi-year mean SOS (with one month as the step). Reports have shown that precipitation has an impact on SOS [61,79,80], so we control precipitation to conduct partial correlation analysis between various temperature factors and SOS, and set the statistical significance level at p < 0.05.

3. Results

3.1. Spatio-Temporal Variations Patterns of SOS and Daytime and Nighttime Warming

We found that the SOS of the DBFs in Northern China ranged from DOY 89 to DOY 145, with a mean value of DOY 119. In terms of spatial distribution pattern, SOS was early in the south of the study area (such as GS, SN, SX, and HN), but late in the central (BJ, TJ, and HB) and northeastern regions (HL, JL, and LN) (Figure 2a).

Additionally, the temporal distribution trend of SOS was also analyzed. Over the 31-year period from 1985 to 2015, the SOS of DBFs in the south of the study area showed an advance trend, while that in the northeastern area showed both an advance and a delay, and the advanced area accounted for 75.98%, while the delayed area accounted for 24.02% (Figure 2b). On this basis, we conducted a significant test (p < 0.05 level) for the DOY of the time series SOS. Significantly advanced areas (37.06%) were mainly distributed in the southern part of the study area, and the northern part of NM and HL, while significantly delayed areas (6.98%) were distributed in the central and eastern parts of HL and JL (Figure 3a). Simultaneously, we used a linear regression to fit the SOS time series across all areas of DBFs in the study area, and found that the DOY of SOS advanced 1.7 days/decade⁻¹ from 1985 to 2015 (Figure 3b).

3.2. Relationships Between SOS and Preseason Temperature

We assessed the response of SOS to the variation in pre-season temperature (including T_{day_max}, T_{night_min}, and DTR) based on the partial correlation analysis of pre-season temperature and SOS. This approach has been widely used in the study of the effects of climate change on vegetation phenology [25,81]. In order to exclude the influence of precipitation on SOS, we controlled the accumulated precipitation and conducted a partial correlation analysis of SOS and preseason temperature (Figure 4).

Firstly, we conducted an analysis of the response of the SOS to variations in the T_{day_max} during the preseason period (Figure 4a). The results showed that approximately 60.80% of the area exhibited a 1-month response prior to the onset of the season. These areas were predominantly distributed in the southern (QH, GS, and HN) and northeastern regions. Meanwhile, about 18.43% of the area demonstrated a 2-month response during the preseason, with a prevailing concentration in the southern areas of the three northeastern provinces. Moreover, we delved into the impact of changes in the T_{night_min} on the SOS (Figure 4b). The analysis indicated that a preseason duration of 1 month accounted for 41.81% of the area, primarily concentrated in HL, JL, and NM. In contrast, a preseason duration of 2 months was more noticeable in provinces such as GS, SHA, HN, and SX. Areas exhibiting a longer preseason duration, ranging from 3 to 4 months, accounted for 27.85% of the total and were distributed across various parts of the study area. Lastly, we investigated the influence of asymmetric warming between daytime and nighttime temperatures on the SOS (Figure 4c). Areas characterized by a 1-month preseason DTR constituted 55% of the dataset, with a widespread distribution spanning the entire study area. On the other hand, areas with a preseason DTR duration of 2 to 4 months comprised a proportion of less than 45%, primarily concentrated in the northeastern region.

In the partial correlation analysis, the proportion of negative correlation among the three preseason temperature indicators was higher than that of positive correlation, and T_{day_max} (40.44%) accounted for the highest proportion of area at the level of moderate correlation and above (p > 0.4), followed by DTR (27.83%) and T_{night_min} (23.99%). In the partial correlation analysis of preseason T_{day_max} and SOS, around 85.12% of the area exhibited a negative correlation, and these areas were widely distributed in SHA, GS, HN, SX, and Northeast China, while positive correlation area (14.88%) were distributed in HL, LN, JL, and northeast NM (Figure 5a). The partial correlation coefficients of SOS and T_{night_min} were negative and accounted for 78.12%, which were widely distributed in southeast HL, eastern JL, central SHA, and western HN (Figure 5b). In addition, the area with positive correlation accounted for 21.88%, which was mainly distributed in southern SHA, northeastern NM, northern HL, and eastern LN. Likewise, the partial correlation coefficient between SOS and DTR was negative for 72.08% (Figure 5c). Geographically, the area of negative correlations was predominantly concentrated in the southern part of the study area, encompassing regions such as SHA, GS, HN, and the junction of HL and JL. In contrast, positive correlations constituted 27.92% of the relationship. These areas were primarily distributed across the eastern segment of the study area, covering areas like the northeastern part of NM, the northern region of HL, and the eastern portion of LN.

4. Discussion

4.1. Statistical Significance Test Analysis

Through the significance test of the partial correlation coefficient between the three preseason temperature indicators and SOS, it was found that the significant correlation (including extremely significant correlation) between T_{day_max} and SOS accounted for 53.31%, of which the significant negative correlation or above level accounted for 50.68% (Figure 6a). Similarly, the proportion of area that passed the partial correlation significance test of T_{night_min} and SOS was 38.03%, and the significant negative correlation was 34.02%, which was primarily concentrated in HL and JL provinces in the northeast of the study area, while it was less distributed in SHA and SX provinces in the south of the study area (Figure 6b). In addition, the proportion of area that passed the partial correlation significance test of DTR and SOS was 42.72% (significant negative correlation was 35.80%, significant positive correlation was 6.93%) (Figure 6c). Generally, compared with DTR and T_{night_min}, the proportion of a significant negative correlation between SOS and T_{day_max} is higher, supporting the conclusion that the greening of vegetation in the northern hemisphere is primarily driven by daytime maximum temperatures rather than nighttime minimum temperatures [20].

4.2. Spatial Variations in Sensitivity Between SOS and Preseason Temperature

At present, the advance of spring phenology due to global warming is still controversial [82]. Some studies suggest that the phenological advance is due to the increase in daytime temperature, but the rise in night temperature leads to the phenological delay [27,83], while some studies indicate that the increase in night temperature also leads to the phenological advance in most regions of the northern hemisphere [30,32]. However, the general conclusion is that the sensitivity of phenology to daytime temperature rise is decreasing and that of phenology to night temperature rise is increasing [27,32]. We selected the pixel with a significant correlation between the preseason temperature indicators and SOS for sensitivity analysis using linear regression, revealing that the annual average SOS in the whole study area advanced 1.8 days when the monthly mean of daytime maximum temperature increased by 1 °C from 1985 to 2015. The advanced trend area accounted for 95.06% of the total area, almost all over the study area, and the area 0–3 days in advance accounted for 83.58% of the total area (Figure 7a).

Similarly, the sensitivity analysis of SOS to nighttime minimum temperature found that during the study period, when the monthly mean of nighttime minimum temperature increased by 1 °C, SOS advanced 1.98 days (Figure 7b). It was proved that increasing the temperature at night could also advance phenology [29]. The advanced trend area accounted for 89.36% of the total area, and the area 0–3 days in advance accounted for 83.36% of the total area, mainly distributed in SHA, GS, HN, and SX; at the same time, the delayed area accounted for 10.63%, and the area with delayed days of 0 to 4.5 days accounted for 9.64%, mainly distributed in HL, NM, and JL, which also verified that the vegetation phenology in some areas was delayed due to the nighttime temperature rise [32]. In addition, an interesting phenomenon is that the significant correlation area between SOS and the minimum temperature of nighttime is significantly less than (especially in GS, SHA, and SX regions) that between SOS and the highest temperature during the day, indicating that the current phenology advance trend is mainly dominated by daytime warming [27].

To investigate the uncertainty in the impact of daytime and nighttime temperature anomalies on phenological trends, we further analyzed the sensitivity of DTR to DBF phenology in Northern China. The pixel of advanced accounted for 83.54%, and the delayed accounted for 16.45% (Figure 7c), and for every 1 °C increase in DTR, the phenology advances by 1.95 days. The delayed areas are mainly distributed in the three eastern provinces. In addition, studies have shown that when vegetation DTR increases by 1° in the northern hemisphere, phenology advances by 2.5 days [25]. However, due to the strong temperature seasonality in Northern China, SOS appears to be less sensitive to DTR, which may be related to the increased heat tolerance of vegetation [84].

4.3. Temporal Variations in Sensitivity Statistics

Phenological studies highlight the critical importance of determining the preseason [7,61,63]. However, previous studies lacked the sensitivity analysis of different preseason length temperature indicators to SOS. Through statistical analysis, it was found that when the preseason length was 4 months, the increase of T_{day_max}, T_{night_min}, and DTR would lead to the advance and delay of phenology, but with the continuous shortening of the pre-season length until the current season, the proportion of phenological advance area was increasing, while the proportion of delaying area was decreasing (Figure 8). In addition, from the perspective of sensitivity, when the three temperature indexes, respectively, rise by 1 °C, the pixel proportion of SOS advance 1.5 to 3 days is the largest, and the cumulative proportion is T_{day_max}: 185.74% (Figure 8a), T_{night_min}: 146.33% (Figure 8b), DTR:132.16% (Figure 8c), respectively. This result is similar to a 1 °C increase in DTR and a 2.7 day advance in SOS in the northern hemisphere [25].

4.4. Sensitivity Spatial Autocorrelation Analysis

Local Indicators of Spatial Association (LISA), including local spatial autocorrelation and global spatial autocorrelation, is an important tool often used to evaluate the spatial aggregation of geographical variables [85]. Spatial autocorrelation analysis can reveal whether there is a correlation between variables of geographical proximity units [86,87]. For details about LISA, see [88]. In this study, LISA was used to explore whether there was a spatial clustering of the multi-year mean SOS and the three preseason temperature indicators [89].

The annual average SOS spatial distribution showed high-high clusters and low-low clusters, and there were obvious regional differences. In other words, DBF with early phenology are mainly distributed in GS, SHA, SX, and HN, while those with late phenology are distributed in the Beijing–Tianjin–Hebei (JJJ) region and Northeast China (Figure 9a), which proves that phenology is related to latitude distribution [7]. In addition, the spatial autocorrelation analysis indicated that 39.68% of the pixels were statistically significant (p < 0.05), with highly significant clusters (p < 0.001) primarily located in the southern part of the study area and the northeastern region of NM (Figure 9b).

Our results also showed that the spatial autocorrelation of SOS sensitivity to pre-season temperature showed an obvious low-low cluster and high-high cluster. More precisely, the low-low distribution of SOS sensitivity to T_{day_max} was mainly concentrated in the southern part of the study area (SX, SHA, HN, GS, and LN), while the high-high clusters were distributed in northeastern NM and northern HL (Figure 9b). In addition, the spatial autocorrelation low-low cluster of SOS sensitivity to the T_{night_min} was mainly concentrated in LN and the southern part of the study area, while the high-high cluster was widely distributed in HL and JL (Figure 9c). Moreover, the spatial distribution of SOS sensitivity to DTR is similar to that of T_{day_max}, but the high-high clustering was widely distributed in LN (Figure 9d).

Previous studies primarily explored whether the three preseason temperature indicators of daytime maximum temperature, night minimum temperature, and DTR caused the advance or delay of phenology [25,30,83]. In this study, the spatial heterogeneity of phenological sensitivity of the above three indicators in different regions was explored, and it was clear that different indicators used to measure phenological changes in vegetation in the same region may have different results. Therefore, future research should focus on applying different preseason temperature influence indicators to explore the differences in different vegetation in different regions and the reasons for the differences, to provide scientific reference and explanation for the real response of vegetation in different regions to temperature change in the context of global warming.

5. Conclusions

This study provides new insights into the response of vegetation phenology to pre-seasonal temperature dynamics by integrating long-term GIMMS 3g NDVI data with a double logistic regression model. Our findings reveal a general advancement of the start of season (SOS) in deciduous broad-leaved forests (DBFs) across Northern China from 1985 to 2015. Partial correlation analysis indicates that a one-month preseason length has the most significant influence on phenological changes. Sensitivity analysis suggests no substantial difference in the effects of maximum daytime temperature (T_{day_max}), minimum nighttime temperature (T_{night_min}), and diurnal temperature range (DTR) on SOS, implying that diurnal warming does not exert asymmetric impacts on vegetation phenology. Additionally, spatial autocorrelation analysis highlights significant spatial heterogeneity in the aggregation of SOS sensitivity to different preseason temperature indicators. These findings underscore the complex and spatially variable nature of phenological responses to climate warming and provide a refined perspective on assessing the influence of preseason temperature variations on vegetation growth dynamics.

Author Contributions

Conceptualization, C.C.; methodology, S.H.; software, Q.K.; validation, Y.L. (Yujie Li); formal analysis, Y.L. (Yuying Liang); investigation, R.L.; writing—original draft preparation, S.H.; writing—review and editing, C.C.; visualization, Y.L. (Yujie Li); supervision, J.W.; funding acquisition, J.W. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

Data will be made available on request.

Conflicts of Interest

The authors declare no competing interests.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures

Figure 1 Distribution of deciduous broad-leaved forests (unchanged from 1985 to 2015) in Northern China.

Figure 2 Multi-year average and trends of the SOS over deciduous broad-leaved forest in Northern China from 1985 to 2015. (a) Spatial distribution of 31-year averaged SOS determined by the double logistic model. (b) Distribution of trends based on the Theil–Sen slope according to the average SOS.

Figure 3 Change trends of the SOS time series from 1985 to 2015. (a) Significance test of the SOS advance or delay trends. (b) Linear fitting of the SOS DOY variation trend from year to year.

Figure 4 Spatial and temporal distribution of preseason temperature. (a) Spatial distribution of the preseason length of preseason day max temperature (T_{day_max}) after controlling for accumulated precipitation. (b) Spatial distribution of the preseason length of preseason night min temperature (T_{night_min}) after controlling for accumulated precipitation. (c) Spatial distribution of the preseason length of the preseason diurnal temperature range (DTR) after controlling for accumulated precipitation.

Figure 5 Distribution of partial correlation (p) between SOS and pre-season temperature. (a) Partial correlation (P) between pre-season T_{day_max} and SOS, (b) partial correlation (p) between pre-season T_{night_min} and SOS, and (c) partial correlation (p) between pre-season DTR and SOS.

Figure 6 Distribution of partial correlation statistical significance test (Rp) between SOS and preseason temperature. (a) Statistical significance test (Rp) between pre-season T_{day_max} and SOS, (b) statistical significance test (Rp) between pre-season T_{night_min} and SOS, and (c) statistical significance test (Rp) between pre-season DTR and SOS.

Figure 7 Spatial distribution of sensitivity of SOS to preseason temperature after controlling for precipitation. (a) Sensitivity of SOS to preseason T_{day_max}. (b) Sensitivity of SOS to preseason T_{night_min}. (c) Sensitivity of SOS to preseason DTR.

Figure 8 Statistical analysis of the SOS sensitivity for different preseason temperatures after controlling precipitation. (a) Sensitivity statistics of different preseason lengths of T_{max_day}. (b) Sensitivity statistics of different preseason lengths of T_{night_min}. (c) Sensitivity statistics of different preseason lengths of DTR.

Figure 9 Distribution of significant LISAs. (a) LISAs of multi-year average SOS. (b) LISAs of sensitivity of T_{day_max} and SOS. (c) LISAs of sensitivity of T_{night_min} and SOS. (d) LISAs of sensitivity of DTR and SOS.

References

1. Yang, Y.; Guan, H.; Shen, M.; Liang, W.; Jiang, L. Changes in autumn vegetation dormancy onset date and the climate controls across temperate ecosystems in China from 1982 to 2010. Glob. Change Biol.; 2015; 21, pp. 652-665. [DOI: https://dx.doi.org/10.1111/gcb.12778] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/25430658]

2. Qiao, D.; Wang, N. Relationship between winter snow cover dynamics, climate and spring grassland vegetation phenology in Inner Mongolia, China. ISPRS Int. J. Geo-Inf.; 2019; 8, 42. [DOI: https://dx.doi.org/10.3390/ijgi8010042]

3. Wolkovich, E.M.; Cleland, E.E. The phenology of plant invasions: A community ecology perspective. Front. Ecol. Environ.; 2011; 9, pp. 287-294. [DOI: https://dx.doi.org/10.1890/100033]

4. Shen, X.; Liu, B.; Henderson, M.; Wang, L.; Wu, Z.; Wu, H.; Jiang, M.; Lu, X. Asymmetric effects of daytime and nighttime warming on spring phenology in the temperate grasslands of China. Agric. For. Meteorol.; 2018; 259, pp. 240-249. [DOI: https://dx.doi.org/10.1016/j.agrformet.2018.05.006]

5. Piao, S.; Liu, Q.; Chen, A.; Janssens, I.A.; Fu, Y.; Dai, J.; Liu, L.; Lian, X.; Shen, M.; Zhu, X. Plant phenology and global climate change: Current progresses and challenges. Glob. Change Biol.; 2019; 25, pp. 1922-1940. [DOI: https://dx.doi.org/10.1111/gcb.14619]

6. Piao, S.; Fang, J.; Zhou, L.; Ciais, P.; Zhu, B. Variations in satellite-derived phenology in China’s temperate vegetation. Glob. Change Biol.; 2006; 12, pp. 672-685. [DOI: https://dx.doi.org/10.1111/j.1365-2486.2006.01123.x]

7. Jeong, S.J.; HO, C.H.; GIM, H.J.; Brown, M.E. Phenology shifts at start vs. end of growing season in temperate vegetation over the Northern Hemisphere for the period 1982–2008. Glob. Change Biol.; 2011; 17, pp. 2385-2399. [DOI: https://dx.doi.org/10.1111/j.1365-2486.2011.02397.x]

8. Wang, L.; Tian, F.; Wang, Y.; Wu, Z.; Schurgers, G.; Fensholt, R. Acceleration of global vegetation greenup from combined effects of climate change and human land management. Glob. Change Biol.; 2018; 24, pp. 5484-5499. [DOI: https://dx.doi.org/10.1111/gcb.14369]

9. Caparros-Santiago, J.A.; Rodriguez-Galiano, V.; Dash, J. Land surface phenology as indicator of global terrestrial ecosystem dynamics: A systematic review. ISPRS J. Photogramm. Remote Sens.; 2021; 171, pp. 330-347.

10. Shen, M.; Wang, S.; Jiang, N.; Sun, J.; Cao, R.; Ling, X.; Fang, B.; Zhang, L.; Zhang, L.; Xu, X. . Plant phenology changes and drivers on the Qinghai–Tibetan Plateau. Nat. Rev. Earth Environ.; 2022; 3, pp. 633-651.

11. Li, X.; Ault, T.; Richardson, A.D.; Carrillo, C.M.; Lawrence, D.M.; Lombardozzi, D.; Frolking, S.; Herrera, D.A.; Moon, M. Impacts of shifting phenology on boundary layer dynamics in North America in the CESM. Agric. For. Meteorol.; 2023; 330, 109286.

12. Hufkens, K.; Filippa, G.; Cremonese, E.; Migliavacca, M.; D’Odorico, P.; Peichl, M.; Gielen, B.; Hörtnagl, L.; Soudani, K.; Papale, D. . Assimilating phenology datasets automatically across ICOS ecosystem stations. Int. Agrophysics/Int. Advert. Assoc.-N. Y.; 2018; 32, pp. 677-687.

13. Xie, Z.; Zhu, W.; Fu, Y. Key issues of remote sensing-based vegetation phenology monitoring. Natl. Remote Sens. Bull.; 2024; 28, pp. 2131-2143.

14. Yin, P.; Li, X.; Pellikka, P. Asymmetrical impact of daytime and nighttime warming on the interannual variation of urban spring vegetation phenology. Earth’s Future; 2024; 12, e2023EF004127. [DOI: https://dx.doi.org/10.1029/2023EF004127]

15. Brooks, B.-G.J.; Lee, D.C.; Pomara, L.Y.; Hargrove, W.W. Monitoring broadscale vegetational diversity and change across North American landscapes using land surface phenology. Forests; 2020; 11, 606. [DOI: https://dx.doi.org/10.3390/f11060606]

16. Chen, Y.; Lin, M.; Lin, T.; Zhang, J.; Jones, L.; Yao, X.; Geng, H.; Liu, Y.; Zhang, G.; Cao, X. . Spatial heterogeneity of vegetation phenology caused by urbanization in China based on remote sensing. Ecol. Indic.; 2023; 153, 110448.

17. Hu, Y.; Fu, B.; Michaelides, K.; De Kauwe, M.G.; Wang, J.; Shen, M.; Zhang, W.; Wang, Y.; Xiao, X.; Qin, Y. . Contrasting trends in onset of spring green-up between grasslands and forests in China. Earth’s Future; 2025; 13, e2024EF005379. [DOI: https://dx.doi.org/10.1029/2024EF005379]

18. Cui, H.; Chu, L.; Yin, Y.; Pan, Y.; Meng, H.; Wang, T. Spatio-temporal patterns of vegetation phenology and their evolutionary mechanisms in the Three Gorges Reservoir Area from 1990 to 2020. Acta Ecol. Sin.; 2024; 44, pp. 3775-3790. [DOI: https://dx.doi.org/10.20103/j.stxb.202303150487]

19. Liu, Q.; Piao, S.; Fu, Y.H.; Gao, M.; Peñuelas, J.; Janssens, I.A. Climatic warming increases spatial synchrony in spring vegetation phenology across the Northern Hemisphere. Geophys. Res. Lett.; 2019; 46, pp. 1641-1650.

20. Piao, S.; Tan, J.; Chen, A.; Fu, Y.H.; Ciais, P.; Liu, Q.; Janssens, I.A.; Vicca, S.; Zeng, Z.; Jeong, S.-J. . Leaf onset in the northern hemisphere triggered by daytime temperature. Nat. Commun.; 2015; 6, 6911.

21. Xiong, T.; Du, S.; Zhang, H.; Zhang, X. Satellite observed reversal in trends of spring phenology in the middle-high latitudes of the Northern Hemisphere during the global warming hiatus. Glob. Change Biol.; 2023; 29, pp. 2227-2241. [DOI: https://dx.doi.org/10.1111/gcb.16580] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/36602438]

22. Wang, S.; Yang, B.; Yang, Q.; Lu, L.; Wang, X.; Peng, Y. Temporal trends and spatial variability of vegetation phenology over the Northern Hemisphere during 1982–2012. PLoS ONE; 2016; 11, e0157134. [DOI: https://dx.doi.org/10.1371/journal.pone.0157134]

23. Liu, Y.; Wu, C.; Wang, X.; Jassal, R.S.; Gonsamo, A. Impacts of global change on peak vegetation growth and its timing in terrestrial ecosystems of the continental US. Glob. Planet. Change; 2021; 207, 103657. [DOI: https://dx.doi.org/10.1016/j.gloplacha.2021.103657]

24. Zhang, R.; Guo, J.; Liang, T.; Feng, Q. Grassland vegetation phenological variations and responses to climate change in the Xinjiang region, China. Quat. Int.; 2019; 513, pp. 56-65. [DOI: https://dx.doi.org/10.1016/j.quaint.2019.03.010]

25. Huang, Y.; Jiang, N.; Shen, M.; Guo, L. Effect of preseason diurnal temperature range on the start of vegetation growing season in the Northern Hemisphere. Ecol. Indic.; 2020; 112, 106161. [DOI: https://dx.doi.org/10.1016/j.ecolind.2020.106161]

26. Gao, M.; Wang, X.; Meng, F.; Liu, Q.; Li, X.; Zhang, Y.; Piao, S. Three-dimensional change in temperature sensitivity of northern vegetation phenology. Glob. Change Biol.; 2020; 26, pp. 5189-5201. [DOI: https://dx.doi.org/10.1111/gcb.15200]

27. Meng, F.; Zhang, L.; Zhang, Z.; Jiang, L.; Wang, Y.; Duan, J.; Wang, Q.; Li, B.; Liu, P.; Hong, H. . Opposite effects of winter day and night temperature changes on early phenophases. Ecology; 2019; 100, e02775. [DOI: https://dx.doi.org/10.1002/ecy.2775]

28. Shen, M.; Piao, S.; Chen, X.; An, S.; Fu, Y.H.; Wang, S.; Cong, N.; Janssens, I.A. Strong impacts of daily minimum temperature on the green-up date and summer greenness of the Tibetan Plateau. Glob. Change Biol.; 2016; 22, pp. 3057-3066. [DOI: https://dx.doi.org/10.1111/gcb.13301] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/27103613]

29. Wang, Y.; Luo, Y.; Shafeeque, M. Interpretation of vegetation phenology changes using daytime and night-time temperatures across the Yellow River Basin, China. Sci. Total Environ.; 2019; 693, 133553. [DOI: https://dx.doi.org/10.1016/j.scitotenv.2019.07.359]

30. Zhu, G.; Wang, X.; Xiao, J.; Zhang, K.; Wang, Y.; He, H.; Li, W.; Chen, H. Daytime and nighttime warming has no opposite effects on vegetation phenology and productivity in the northern hemisphere. Sci. Total Environ.; 2022; 822, 153386. [DOI: https://dx.doi.org/10.1016/j.scitotenv.2022.153386]

31. Li, X.; Guo, W.; Li, S.; Zhang, J.; Ni, X. The different impacts of the daytime and nighttime land surface temperatures on the alpine grassland phenology. Ecosphere; 2021; 12, e03578.

32. Wang, J.; Xi, Z.; He, X.; Chen, S.; Rossi, S.; Smith, N.G.; Liu, J.; Chen, L. Contrasting temporal variations in responses of leaf unfolding to daytime and nighttime warming. Glob. Change Biol.; 2021; 27, pp. 5084-5093. [DOI: https://dx.doi.org/10.1111/gcb.15777] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34263513]

33. Deng, G.; Zhang, H.; Zhao, J.; Guo, X.; Biniyaz, E.; Zhao, H.; Guo, D.; Wu, R.; Li, H. Diverse variations in middle and high latitudes of the Northern Hemisphere spring phenology sensitivity to diurnal temperature during 1982–2015. Int. J. Climatol.; 2022; 42, pp. 9385-9403.

34. Davy, R.; Esau, I.; Chernokulsky, A.; Outten, S.; Zilitinkevich, S. Diurnal asymmetry to the observed global warming. Int. J. Climatol.; 2017; 37, pp. 79-93. [DOI: https://dx.doi.org/10.1002/joc.4688]

35. Xiao, S.; Wang, C.; Yu, K.; Liu, G.; Wu, S.; Wang, J.; Niu, S.; Zou, J.; Liu, S. Enhanced CO₂ uptake is marginally offset by altered fluxes of non-CO₂ greenhouse gases in global forests and grasslands under N deposition. Glob. Change Biol.; 2023; 29, pp. 5829-5849.

36. Elmore, A.J.; Guinn, S.M.; Minsley, B.J.; Richardson, A.D. Landscape controls on the timing of spring, autumn, and growing season length in mid-A tlantic forests. Glob. Change Biol.; 2012; 18, pp. 656-674.

37. Berra, E.F.; Gaulton, R. Remote sensing of temperate and boreal forest phenology: A review of progress, challenges and opportunities in the intercomparison of in-situ and satellite phenological metrics. For. Ecol. Manag.; 2021; 480, 118663.

38. Wang, L.; Tian, F.; Huang, K.; Wang, Y.; Wu, Z.; Fensholt, R. Asymmetric patterns and temporal changes in phenology-based seasonal gross carbon uptake of global terrestrial ecosystems. Glob. Ecol. Biogeogr.; 2020; 29, pp. 1020-1033. [DOI: https://dx.doi.org/10.1111/geb.13084]

39. Keenan, T.F.; Gray, J.; Friedl, M.A.; Toomey, M.; Bohrer, G.; Hollinger, D.Y.; Munger, J.W.; O’Keefe, J.; Schmid, H.P.; Wing, I.S. . Net carbon uptake has increased through warming-induced changes in temperate forest phenology. Nat. Clim. Change; 2014; 4, pp. 598-604. [DOI: https://dx.doi.org/10.1038/nclimate2253]

40. Mack, M.C.; Walker, X.J.; Johnstone, J.F.; Alexander, H.D.; Melvin, A.M.; Jean, M.; Miller, S.N. Carbon loss from boreal forest wildfires offset by increased dominance of deciduous trees. Science; 2021; 372, pp. 280-283.

41. Deng, L.; Liu, G.b.; Shangguan, Z.p. Land-use conversion and changing soil carbon stocks in C hina’s ‘Grain-for-Green’Program: A synthesis. Glob. Change Biol.; 2014; 20, pp. 3544-3556. [DOI: https://dx.doi.org/10.1111/gcb.12508] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/24357470]

42. Su, Y.; Guo, Q.; Hu, T.; Guan, H.; Jin, S.; An, S.; Chen, X.; Guo, K.; Hao, Z.; Hu, Y. . An updated vegetation map of China (1:1,000,000). Sci. Bull.; 2020; 65, pp. 1125-1136. [DOI: https://dx.doi.org/10.1016/j.scib.2020.04.004]

43. Montibeller, B.; Jaagus, J.; Mander, Ü.; Uuemaa, E. Evapotranspiration intensification over unchanged temperate vegetation in the Baltic countries is being driven by climate shifts. Front. For. Glob. Change; 2021; 4, 663327. [DOI: https://dx.doi.org/10.3389/ffgc.2021.663327]

44. Yan-ling, M.; Wen-long, Z.; Ya-xiong, C.; Chao-jie, J.; Guo-jun, S. Potential distribution of the forest in northern China and their responses to future climate change. J. Lanzhou Univ. (Nat. Sci.); 2010; 46, pp. 25-32. [DOI: https://dx.doi.org/10.13885/j.issn.0455-2059.2010.s1.020]

45. Zhang, X.; Sun, S.; Yong, S.; Zhou, Z.; Wang, R. Vegetation Map of the People’s Republic of China (1:1,000,000); Geological Publishing House: Beijing, China, 2007.

46. Tucker, C.J.; Pinzon, J.E.; Brown, M.E.; Slayback, D.A.; Pak, E.W.; Mahoney, R.; Vermote, E.F.; El Saleous, N. An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data. Int. J. Remote Sens.; 2005; 26, pp. 4485-4498. [DOI: https://dx.doi.org/10.1080/01431160500168686]

47. Wu, W.; Sun, Y.; Xiao, K.; Xin, Q. Development of a global annual land surface phenology dataset for 1982–2018 from the AVHRR data by implementing multiple phenology retrieving methods. Int. J. Appl. Earth Obs. Geoinf.; 2021; 103, 102487. [DOI: https://dx.doi.org/10.1016/j.jag.2021.102487]

48. Guay, K.C.; Beck, P.S.; Berner, L.T.; Goetz, S.J.; Baccini, A.; Buermann, W. Vegetation productivity patterns at high northern latitudes: A multi-sensor satellite data assessment. Glob. Change Biol.; 2014; 20, pp. 3147-3158.

49. Rogers, B.M.; Solvik, K.; Hogg, E.H.; Ju, J.; Masek, J.G.; Michaelian, M.; Berner, L.T.; Goetz, S.J. Detecting early warning signals of tree mortality in boreal North America using multiscale satellite data. Glob. Change Biol.; 2018; 24, pp. 2284-2304. [DOI: https://dx.doi.org/10.1111/gcb.14107]

50. Dardel, C.; Kergoat, L.; Hiernaux, P.; Mougin, E.; Grippa, M.; Tucker, C. Re-greening Sahel: 30 years of remote sensing data and field observations (Mali, Niger). Remote Sens. Environ.; 2014; 140, pp. 350-364. [DOI: https://dx.doi.org/10.1016/j.rse.2013.09.011]

51. Zhang, J.; Zhao, J.; Wang, Y.; Zhang, H.; Zhang, Z.; Guo, X. Comparison of land surface phenology in the Northern Hemisphere based on AVHRR GIMMS3g and MODIS datasets. ISPRS J. Photogramm. Remote Sens.; 2020; 169, pp. 1-16.

52. Liu, Z.; Wu, C.; Liu, Y.; Wang, X.; Fang, B.; Yuan, W.; Ge, Q. Spring green-up date derived from GIMMS3g and SPOT-VGT NDVI of winter wheat cropland in the North China Plain. ISPRS J. Photogramm. Remote Sens.; 2017; 130, pp. 81-91.

53. Fensholt, R.; Proud, S.R. Evaluation of earth observation based global long term vegetation trends—Comparing GIMMS and MODIS global NDVI time series. Remote Sens. Environ.; 2012; 119, pp. 131-147. [DOI: https://dx.doi.org/10.1016/j.rse.2011.12.015]

54. Buitenwerf, R.; Rose, L.; Higgins, S.I. Three decades of multi-dimensional change in global leaf phenology. Nat. Clim. Change; 2015; 5, pp. 364-368.

55. Peng, J.; Wu, C.; Zhang, X.; Wang, X.; Gonsamo, A. Satellite detection of cumulative and lagged effects of drought on autumn leaf senescence over the Northern Hemisphere. Glob. Change Biol.; 2019; 25, pp. 2174-2188.

56. Yang, J.; Dong, J.; Xiao, X.; Dai, J.; Wu, C.; Xia, J.; Zhao, G.; Zhao, M.; Li, Z.; Zhang, Y. . Divergent shifts in peak photosynthesis timing of temperate and alpine grasslands in China. Remote Sens. Environ.; 2019; 233, 111395. [DOI: https://dx.doi.org/10.1016/j.rse.2019.111395]

57. Zhang, X.; Liu, L.; Chen, X.; Gao, Y.; Xie, S.; Mi, J. GLC_FCS30: Global land-cover product with fine classification system at 30 m using time-series Landsat imagery. Earth Syst. Sci. Data; 2021; 13, pp. 2753-2776.

58. Zheng, Q.-H.; Chen, W.; Li, S.-L.; Yu, L.; Zhang, X.; Liu, L.-F.; Singh, R.P.; Liu, C.-Q. Accuracy comparison and driving factor analysis of LULC changes using multi-source time-series remote sensing data in a coastal area. Ecol. Inform.; 2021; 66, 101457. [DOI: https://dx.doi.org/10.1016/j.ecoinf.2021.101457]

59. Liu, L.; Zhang, X.; Gao, Y.; Chen, X.; Shuai, X.; Mi, J. Finer-resolution mapping of global land cover: Recent developments, consistency analysis, and prospects. J. Remote Sens.; 2021; 2021, 5289697. [DOI: https://dx.doi.org/10.34133/2021/5289697]

60. He, J.; Yang, K.; Tang, W.; Lu, H.; Qin, J.; Chen, Y.; Li, X. The first high-resolution meteorological forcing dataset for land process studies over China. Sci. Data; 2020; 7, 25.

61. Du, J.; He, Z.; Piatek, K.B.; Chen, L.; Lin, P.; Zhu, X. Interacting effects of temperature and precipitation on climatic sensitivity of spring vegetation green-up in arid mountains of China. Agric. For. Meteorol.; 2019; 269–270, pp. 71-77. [DOI: https://dx.doi.org/10.1016/j.agrformet.2019.02.008]

62. Li, X.; Zhou, Y.; Meng, L.; Asrar, G.R.; Lu, C.; Wu, Q. A dataset of 30 m annual vegetation phenology indicators (1985–2015) in urban areas of the conterminous United States. Earth Syst. Sci. Data; 2019; 11, pp. 881-894. [DOI: https://dx.doi.org/10.5194/essd-11-881-2019]

63. Liu, Q.; Fu, Y.H.; Zeng, Z.; Huang, M.; Li, X.; Piao, S. Temperature, precipitation, and insolation effects on autumn vegetation phenology in temperate China. Glob. Change Biol.; 2016; 22, pp. 644-655. [DOI: https://dx.doi.org/10.1111/gcb.13081]

64. Xin, Q.; Li, J.; Li, Z.; Li, Y.; Zhou, X. Evaluations and comparisons of rule-based and machine-learning-based methods to retrieve satellite-based vegetation phenology using MODIS and USA National Phenology Network data. Int. J. Appl. Earth Obs. Geoinf.; 2020; 93, 102189.

65. Chen, X.; Wang, D.; Chen, J.; Wang, C.; Shen, M. The mixed pixel effect in land surface phenology: A simulation study. Remote Sens. Environ.; 2018; 211, pp. 338-344. [DOI: https://dx.doi.org/10.1016/j.rse.2018.04.030]

66. Zhang, X.; Tarpley, D.; Sullivan, J.T. Diverse responses of vegetation phenology to a warming climate. Geophys. Res. Lett.; 2007; 34, L19405.

67. Zhang, X.; Friedl, M.A.; Schaaf, C.B.; Strahler, A.H.; Hodges, J.C.; Gao, F.; Reed, B.C.; Huete, A. Monitoring vegetation phenology using MODIS. Remote Sens. Environ.; 2003; 84, pp. 471-475. [DOI: https://dx.doi.org/10.1016/S0034-4257(02)00135-9]

68. Jonsson, P.; Eklundh, L. Seasonality extraction by function fitting to time-series of satellite sensor data. IEEE Trans. Geosci. Remote Sens.; 2002; 40, pp. 1824-1832. [DOI: https://dx.doi.org/10.1109/TGRS.2002.802519]

69. Li, X.; Zhou, Y.; Asrar, G.R.; Meng, L. Characterizing spatiotemporal dynamics in phenology of urban ecosystems based on Landsat data. Sci. Total Environ.; 2017; 605, pp. 721-734. [DOI: https://dx.doi.org/10.1016/j.scitotenv.2017.06.245]

70. Levenberg, K. A method for the solution of certain non-linear problems in least squares. Q. Appl. Math.; 1944; 2, pp. 164-168. [DOI: https://dx.doi.org/10.1090/qam/10666]

71. Zhou, J.; Cai, W.; Qin, Y.; Lai, L.; Guan, T.; Zhang, X.; Jiang, L.; Du, H.; Yang, D.; Cong, Z. . Alpine vegetation phenology dynamic over 16 years and its covariation with climate in a semi-arid region of China. Sci. Total Environ.; 2016; 572, pp. 119-128.

72. Gonsamo, A.; D’Odorico, P.; Chen, J.M.; Wu, C.; Buchmann, N. Changes in vegetation phenology are not reflected in atmospheric CO 2 and 13C/12C seasonality. Glob. Change Biol.; 2017; 23, pp. 4029-4044. [DOI: https://dx.doi.org/10.1111/gcb.13646] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/28142209]

73. Mann, H.B. Nonparametric tests against trend. Econom. J. Econom. Soc.; 1945; 13, pp. 245-259. [DOI: https://dx.doi.org/10.2307/1907187]

74. Tang, Q.; Hua, L.; Cao, Y.; Jiang, L.; Cai, C. Human activities are the key driver of water erosion changes in northeastern China. Land Degrad. Dev.; 2024; 35, pp. 62-75. [DOI: https://dx.doi.org/10.1002/ldr.4897]

75. Zhu, W.; Zhang, X.; Zhang, J.; Zhu, L. A comprehensive analysis of phenological changes in forest vegetation of the Funiu Mountains, China. J. Geogr. Sci.; 2019; 29, pp. 131-145. [DOI: https://dx.doi.org/10.1007/s11442-019-1588-z]

76. Zhao, Z.; Wang, X.; Li, R.; Luo, W.; Wu, C. Impacts of climate extremes on autumn phenology in contrasting temperate and alpine grasslands in China. Agric. For. Meteorol.; 2023; 336, 109495. [DOI: https://dx.doi.org/10.1016/j.agrformet.2023.109495]

77. Zeng, Z.; Wu, W.; Ge, Q.; Li, Z.; Wang, X.; Zhou, Y.; Zhang, Z.; Li, Y.; Huang, H.; Liu, G. . Legacy effects of spring phenology on vegetation growth under preseason meteorological drought in the Northern Hemisphere. Agric. For. Meteorol.; 2021; 310, 108630. [DOI: https://dx.doi.org/10.1016/j.agrformet.2021.108630]

78. Wang, J.; Liu, D.; Ciais, P.; Peñuelas, J. Decreasing rainfall frequency contributes to earlier leaf onset in northern ecosystems. Nat. Clim. Change; 2022; 12, pp. 386-392. [DOI: https://dx.doi.org/10.1038/s41558-022-01285-w]

79. Shen, M.; Piao, S.; Cong, N.; Zhang, G.; Jassens, I.A. Precipitation impacts on vegetation spring phenology on the T ibetan P lateau. Glob. Change Biol.; 2015; 21, pp. 3647-3656. [DOI: https://dx.doi.org/10.1111/gcb.12961]

80. Zhang, Q.; Kong, D.; Shi, P.; Singh, V.P.; Sun, P. Vegetation phenology on the Qinghai-Tibetan Plateau and its response to climate change (1982–2013). Agric. For. Meteorol.; 2018; 248, pp. 408-417. [DOI: https://dx.doi.org/10.1016/j.agrformet.2017.10.026]

81. Du, Q.; Yi, G.; Zhou, X.; Zhang, T.; Li, J.; Xie, H.; Hu, J. Analysis of asymmetry in diurnal warming and its impact on vegetation phenology in the Qinghai-Tibetan Plateau using MODIS remote sensing data. J. Appl. Remote Sens.; 2021; 15, 028502. [DOI: https://dx.doi.org/10.1117/1.JRS.15.028502]

82. Huang, J.G.; Zhang, Y.; Wang, M.; Yu, X.; Deslauriers, A.; Fonti, P.; Liang, E.; Mäkinen, H.; Oberhuber, W.; Rathgeber, C.B. . A critical thermal transition driving spring phenology of Northern Hemisphere conifers. Glob. Change Biol.; 2023; 29, pp. 1606-1617.

83. Meng, L.; Zhou, Y.; Li, X.; Asrar, G.R.; Mao, J.; Wanamaker, A.D., Jr.; Wang, Y. Divergent responses of spring phenology to daytime and nighttime warming. Agric. For. Meteorol.; 2020; 281, 107832.

84. National Forestry and Grassland Administration. First in the World in Plantation Area! A Quarter of the World in Recent 20 Years, Green Is by the China Contribution. 12 March 2024. Available online: https://www.forestry.gov.cn/lyj/1/lcdt/20240312/550136.html (accessed on 3 January 2025).

85. Shi, K.; Chang, Z.; Chen, Z.; Wu, J.; Yu, B. Identifying and evaluating poverty using multisource remote sensing and point of interest (POI) data: A case study of Chongqing, China. J. Clean. Prod.; 2020; 255, 120245.

86. Li, L.; Tang, H.; Lei, J.; Song, X. Spatial autocorrelation in land use type and ecosystem service value in Hainan Tropical Rain Forest National Park. Ecol. Indic.; 2022; 137, 108727.

87. Bone, C.; Wulder, M.A.; White, J.C.; Robertson, C.; Nelson, T.A. A GIS-based risk rating of forest insect outbreaks using aerial overview surveys and the local Moran’s I statistic. Appl. Geogr.; 2013; 40, pp. 161-170. [DOI: https://dx.doi.org/10.1016/j.apgeog.2013.02.011]

88. Anselin, L. Local indicators of spatial association—LISA. Geogr. Anal.; 1995; 27, pp. 93-115. [DOI: https://dx.doi.org/10.1111/j.1538-4632.1995.tb00338.x]

89. Li, J.; He, J.; Liu, Y.; Wang, D.; Rafay, L.; Chen, C.; Hong, T.; Fan, H.; Lin, Y. Spatial autocorrelation analysis of multi-scale damaged vegetation in the Wenchuan earthquake-affected area, Southwest China. Forests; 2019; 10, 195. [DOI: https://dx.doi.org/10.3390/f10020195]