Science, Irving K. Barber Faculty of (Okanagan)
Non UBC
Earth, Environmental and Geographic Sciences, Department of (Okanagan)
DSpace
Remote Sensing 16 (14): 2583 (2024)
Wu, Tong
Xu, Xiaoqian
Chen, Xinsen
Lyu, Shixuan
Zhang, Guotao
Kong, Dongdong
Zhang, Yongqiang
Tang, Yijuan
Chen, Yun
Zhang, Junlong
Phenology is a critical mirror reflecting vegetation growth and has a major impact on terrestrial ecosystems. The Loess Plateau (LP) is a paramount ecological zone in China that has experienced considerable vegetation changes. However, understanding the dynamics of vegetation phenology is limited by ambiguous vegetation interpretation and anthropogenic-induced forces. This study combined the multi-climatic and anthropogenic datasets to characterize the interactions between phenology shifts and environmental variables. The principal findings were as follows: (1) Phenological shifts exhibit spatial heterogeneity and an interannually increasing trend in greenness (R2 > 0.6, p < 0.05). Notably, SOS (the start of the growing season) advances while EOS (the end of the growing season) delays in both the southeastern and northwestern regions. (2) SOS and EOS, primarily in the range of 100–150 and 285–320 days, respectively. Phenological changes vary depending on vegetation types. The forest has an early SOS, within 80–112 days, and a delayed EOS, within 288–320 days. The SOS of shrub is mainly within 80–144 days. (3) EOS shows a strong response to the preseason of each climate variable. Precipitation (R = 0.76), soil moisture (R = −0.64), and temperature (R = 0.89) are the governing determinants in shaping vegetation phenology. In addition, agriculture and urbanization play a significant role in shaping the spatial variations of SOS. These findings provide a basis for a systematic understanding of the processes that affect vegetation growth, which is crucial for maintaining the health and sustainability of arid and semiarid ecosystems.
Citation: Wu, T.; Xu, X.; Chen, X.;Lyu, S.; Zhang, G.; Kong, D.;Zhang, Y.; Tang, Y.; Chen, Y.; Zhang, J.Characterizing Vegetation PhenologyShifts on the Loess Plateau over PastTwo Decades. Remote Sens. 2024, 16,2583. https://doi.org/10.3390/rs16142583Academic Editor: Jochem VerrelstReceived: 28 May 2024Revised: 2 July 2024Accepted: 11 July 2024Published: 14 July 2024Copyright: © 2024 by the authors.Licensee MDPI, Basel, Switzerland.This article is an open access articledistributed under the terms andconditions of the Creative CommonsAttribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).remote sensing ArticleCharacterizing Vegetation Phenology Shifts on the Loess Plateauover Past Two DecadesTong Wu 1, Xiaoqian Xu 1, Xinsen Chen 1, Shixuan Lyu 2, Guotao Zhang 3, Dongdong Kong 4 , Yongqiang Zhang 5 ,Yijuan Tang 6, Yun Chen 7 and Junlong Zhang 1,*1 College of Geography and Environment, Shandong Normal University, Jinan 250358, China;2022020779@stu.sdnu.edu.cn (T.W.); 2022302444@stu.sdnu.edu.cn (X.X.);202114000315@stu.sdnu.edu.cn (X.C.)2 Department of Earth, Environmental and Geographic Sciences, University of British Columbia,Kelowna, BC V1V 1V7, Canada; shixuan.lyu@ubc.ca3 Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and NaturalResources Research, Chinese Academy of Sciences (CAS), Beijing 100101, China; zhangguotao@igsnrr.ac.cn4 Department of Atmospheric Science, School of Environmental Studies, China University of Geosciences,Wuhan 430074, China; kongdongdong@cug.edu.cn5 Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences andNatural Resources Research, Chinese Academy of Sciences (CAS), Beijing 100101, China;zhangyq@igsnrr.ac.cn6 The Remote Sensing Surveying and Mapping Institute of the Ningxia Hui Autonomous Region,Yinchuan 750021, China; nxygytyj@163.com7 CSIRO Environment, Canberra, ACT 2601, Australia; yun.chen@csiro.au* Correspondence: junlong.zhang@sdnu.edu.cnAbstract: Phenology is a critical mirror reflecting vegetation growth and has a major impact onterrestrial ecosystems. The Loess Plateau (LP) is a paramount ecological zone in China that hasexperienced considerable vegetation changes. However, understanding the dynamics of vegetationphenology is limited by ambiguous vegetation interpretation and anthropogenic-induced forces.This study combined the multi-climatic and anthropogenic datasets to characterize the interactionsbetween phenology shifts and environmental variables. The principal findings were as follows:(1) Phenological shifts exhibit spatial heterogeneity and an interannually increasing trend in greenness(R2 > 0.6, p < 0.05). Notably, SOS (the start of the growing season) advances while EOS (the endof the growing season) delays in both the southeastern and northwestern regions. (2) SOS andEOS, primarily in the range of 100–150 and 285–320 days, respectively. Phenological changes varydepending on vegetation types. The forest has an early SOS, within 80–112 days, and a delayed EOS,within 288–320 days. The SOS of shrub is mainly within 80–144 days. (3) EOS shows a strong responseto the preseason of each climate variable. Precipitation (R = 0.76), soil moisture (R = −0.64), andtemperature (R = 0.89) are the governing determinants in shaping vegetation phenology. In addition,agriculture and urbanization play a significant role in shaping the spatial variations of SOS. Thesefindings provide a basis for a systematic understanding of the processes that affect vegetation growth,which is crucial for maintaining the health and sustainability of arid and semiarid ecosystems.Keywords: phenology; Loess Plateau; changing environment; climate change1. IntroductionA proverbial lyric “My home is on the Loess Plateau, where gales sweep across theslopes” alludes to the typical characteristics of the Loess Plateau (LP) in North China,cradling the development of regional agriculture and animal husbandry [1]. In recentyears, the LP has experienced a considerable change stemming from anthropogenic inter-ventions [2] and natural processes [3]. For example, massive afforestation implementedto reduce soil erosion [4] has had a profound influence on the emergence of ecologicalRemote Sens. 2024, 16, 2583. https://doi.org/10.3390/rs16142583 https://www.mdpi.com/journal/remotesensingRemote Sens. 2024, 16, 2583 2 of 18attributes, such as the distribution, diversity, function, and structural composition of vege-tation species. In terrestrial ecosystems, vegetation plays pivotal roles in the governanceof carbon cycling and energy conversion [5,6], sequestration of atmospheric CO2, andmodification of the land surface status and water cycle [7]. Therefore, quantitative analy-sis of vegetation change is important for assessing the ecological diversity and health ofvegetation in the changing environment of the LP [8].Vegetation phenology is a natural calendar [9] that reflects various phenomena of thelife cycle of plants (i.e., germination, greening, blossoming, fruiting, and senescence [10,11])and encompasses multiple indicators (e.g., the start of the growing season (SOS), the endof the growing season (EOS), and the length of the growing season (LOS) [12]). More-over, vegetation phenology is sensitive to the effects of climate change, which can induceprofound consequences on vegetation productivity, evapotranspiration [9,13,14], agron-omy, and human society [15], further affecting the environment by altering vegetationcycling activities.Various influences, such as climatic and anthropogenic factors, can affect vegetationphenology [16–19], which can cause rapid acceleration in vegetation growth and phenolog-ical shifts [5]. Considerable research attention has focused on phenology-related changesin vegetation on the LP, providing valuable insights for understanding the interactionbetween vegetation and environmental evolution [20–22]. However, consensus is lackingregarding the dynamic processes and main influencing factors owing to limitations inphenological detection and incomplete knowledge of the regulating laws for single seasonsand/or multiple seasons.Various methods have been developed to detect phenology-related processes [23–25],and phenological information can be obtained through field-based phenology observa-tions, using digital cameras, and applying phenological models that incorporate remotesensing data [15,26]. Field-based phenology observations provide a detailed and intuitiveperspective on vegetation cycling [27,28]. A digital camera represents a robust tool with cus-tomizable precision and range suitable for recording variations in plant development [29].Digital cameras are usually deployed across multiple sites with different vegetation typesand linked by sensor networks [15]. The cameras are generally installed on towers orin visible observational locations [26], thereby allowing direct monitoring of vegetationgrowth and/or habitat conditions. They can also be used to assess canopy greennessand provide other physiological information [15]. Phenological models that incorporateremote sensing data generally have the advantage of being able to evaluate phenologicalstatus despite missing historical data, and they are capable of untangling the dynamicsand alterations in vegetation growth and development by offering long-term and/or large-scale perspectives [30]. However, the application of each method is limited by its specificdrawbacks. Field-based phenology observations are greatly affected by subjective factorsand are representative of only small-scale areas [31]. Moreover, the processes involved intheir acquisition are time-consuming and expensive [32]. Digital cameras require an ap-propriate communication network, and their placement and stabilization can lead to largeobservational errors [15]. Phenological models that incorporate remote sensing data haveshortcomings in obtaining a complete picture in earlier or later stages of vegetation physio-logical growth owing to coarse spatial scales [33]. For example, when testing the sensitivityof spring phenology to temperature, the performance of models in relation to an experimen-tal greenhouse does not translate well to natural environmental conditions [29,34,35]. Mostmodels are established using inputs from past data, which can limit extrapolation underfuture climate scenarios [15]. Overall, it is difficult to directly obtain a general picture of thephenology of LP vegetation using the above approaches. Fortunately, an advanced analysisplatform (i.e., Google’s cloud) and various related datasets are available to support aninvestigation of the phenological characteristics of vegetation in a changing environmenton the LP.Phenology affects the fluxes of various circulations that interact with the atmosphere,hydrosphere, and lithosphere [36]. Phenological alterations can have feedback to changesRemote Sens. 2024, 16, 2583 3 of 18in regional biological systems in terms of structure, function, habit, and diversity [37] andto the related environment [9]. However, determination of the dynamics of phenologyshifts is limited by the spatial variation of vegetation across large-scale regions. In thecontext of ecological and environmental transformations attributable to climate change andurbanization, as well as widespread ecohydrological and societal interests, the problemof how to best unravel the controlling factors/mechanisms behind seasonal phenologychange on the LP and to mitigate their impacts remains a challenge. Consequently, thereis an urgent need to conduct a thorough characterization of the phenological features ofvegetation on the LP.Although shifts in vegetation phenology within ecosystems have garnered significantattention, quantitative studies of phenological changes across different vegetation typesremain limited, particularly in the LP. The diverse triggers influencing vegetation develop-ment in arid and semi-arid regions are not well understood. This study aims to achieve acomprehensive and systematic understanding of the phenological shifts of vegetation onthe LP from varying perspectives, including phenological indicators (SOS, EOS, and LOS),seasonal characteristics, and vegetation type. The study investigates the impacts of climaticfactors (precipitation, temperature, and soil moisture) and anthropogenic influences (ur-banization, assessed using proxies such as annual nighttime light (ANL) and impervioussurface percentage (ISP)); the schematic illustration is shown in Figure 1. The specificobjectives of this study are as follows: (1) to analyze vegetation and phenological variabilityon the LP from 2003 to 2021; (2) to identify the phenological responses to evolving envi-ronmental shifts, both climatic and anthropogenic; (3) to assess the phenological changesacross different vegetation types; and (4) to determine the primary factors influencingphenological indicators. This study provides insights that deepen the understanding ofhydrological and agricultural systems in changing environments.Remote Sens. 2024, 16, 2583 3 of 20 are available to support an investigation of the phenological characteristics of vegetation in a changing environment on the LP. Phenology affects the fluxes of various circulations that interact with the atmosphere, hydrosphere, and lithosphere [36]. Phenological alterations can have feedback to changes in regional biological systems in terms of structure, function, habit, and diversity [37] and to the related environment [9]. However, determination of the dynamics of phenology shifts is limited by the spatial variation of vegetation across large-scale regions. In the context of ecological and environmental transformations attributable to climate change and urbanization, as well as widespread ecohydrological and societal interests, the prob-lem of how to best unravel the controlling factors/mechanisms behind seasonal phenology change on the LP and to mitigate their impacts remains a challenge. Consequently, there is an urgent need to conduct a thorough characterization of the phenological features of vegetation on the LP. Although shifts in vegetation phenology within ecosystems have garnered signifi-cant attention, quantitative studies of phenological changes across different vegetation types remain limited, particularly in the LP. The diverse triggers influencing vegetation development in arid and semi-arid regions are not well understood. This study aims to achieve a comprehensive and systematic understanding of the phenological shifts of veg-etation on the LP from varying perspectives, including phenological indicators (SOS, EOS, and LOS), seasonal characteristics, and vegetation type. The study investigates the im-pacts of climatic factors (precipitation, temperature, and soil moisture) and anthropogenic influences (urbanization, assessed using proxies such as annual nighttime light (ANL) and impervious surface percentage (ISP)); the schematic illustration is shown in Figure 1. The specific objectives of this study are as follows: (1) to analyze vegetation and phenological variability on the LP from 2003 to 2021; (2) to identify the phenological responses to evolv-ing environmental shifts, both climatic and anthropogenic; (3) to assess the phenological changes across different vegetation types; and (4) to determine the primary factors influ-encing phenological indicators. This study provides insights that deepen the understand-ing of hydrological and agricultural systems in changing environments. Figure 1. Schematic illustrations of the present study. 2. Study Area The LP in northwestern China covers an area of approximately 640,000 km2. It stretches to the Great Wall in the north, south to the Qinling Mountains, and from the Qilian Mountains in the west to the Taihang Mountains in the east (Figure 2). The eleva-tion of the land is higher in the west and lower in the east, and the relief also changes dramatically [38]. The primary landform categories of the LP comprise plains, hills, high plateaus, and mountainous terrain [39]. The LP features a temperate continental monsoon climate, with annual mean temperatures ranging from −6 to 16 °C. The highest (lowest) . t t .2. Study AreaThe LP in northwestern China covers an area of approximately 640,000 km2. Itstretches to the Great Wall in the north, south to the Qinling Mountains, and from the QilianMountains in the west to the Taihang Mountains in the east (Figure 2). The elevation of theland is higher in the west and lower in the east, and the relief also changes dramatically [38].The primary landform categories of the LP comprise plains, hills, high plateaus, andmountainous terrain [39]. The LP features a temperate continental monsoon climate, withannual mean temperatures ranging from −6 to 16 ◦C. The highest (lowest) temperaturesare usually recorded in July (January). Average annual precipitation is approximately100–800 mm, but rainfall occurs predominantly in the summer and autumn [40]. Riverdischarge has notable seasonal variation and is characterized by apparent periods of highand low flow. The flood season has a very short duration, with frequent flood events mainlyoccurring between April and September [41]. The soil type within the region is mostly loess,Remote Sens. 2024, 16, 2583 4 of 18characterized by its loose and porous structure [40]. The vegetation distribution from theeast to the west reflects the following ecosystems: warm temperate deciduous broadleavedforests, forest-grassland transitional zones, typical grasslands, and desert grasslands [42].Remote Sens. 2024, 16, 2583 4 of 20 temperatures are usually recorded in July (January). Average annual precipitation is ap-proximately 100–800 mm, but rainfall occurs predominantly in the summer and autumn [40]. River discharge has notable seasonal variation and is characterized by apparent pe-riods of high and low flow. The flood season has a very short duration, with frequent flood events mainly occurring between April and September [41]. The soil type within the region is mostly loess, characterized by its loose and porous structure [40]. The vegetation distribution from the east to the west reflects the following ecosystems: warm temperate deciduous broadleaved forests, forest-grassland transitional zones, typical grasslands, and desert grasslands [42]. The LP plays an essential role in the economic development of the Yellow River Ba-sin. It previously featured abundant vegetation; however, since the end of the previous century, it has transformed into an ecologically fragile area with several environmental issues (e.g., vegetation degradation, soil erosion, and increasing ecological fragility) ow-ing to extensive human activities and inefficient and unsustainable resource exploitation [43]. At the end of the previous century, the Chinese government initiated the Grain for Green Program, following which the vegetation on the LP exhibited a remarkable expan-sion [44,45]. In 2019, a major strategy for the ecological conservation and high-quality de-velopment of the Yellow River Basin was proposed, which is a demand for continuous adaptation to socially stable development [46]. This substantially enhanced the potential for continuous devel pment in term of the ecological, social, and economic aspects of LP. Neverth less, the area sti l faces major challenges related to ecological sust inability and societal ransformation [47]. Figure 2. Location of the Loess Plateau and physical characteristics: (a) land use type, (b) multiyear average precipitation (P) during 2003–2022, and (c) multiyear average temperature (T) during 2003–2022. Figure 2. Location of the Loess Plateau and physical characteristics: (a) land use type, (b) ultiyearaverage precipitation (P) during 2003–2022, and (c) multiyear average temperature (T) during 2003–2022.The LP plays an essential role in the economic development of the Yellow River Basin.It previously featured abundant vegetation; however, since the end of the previous century,it has transformed into an ecologically fragile area with several environmental issues (e.g.,vegetation degradation, soil erosion, and increasing ecological fragility) owing to extensivehuman activities and inefficient and unsustainable resource exploitation [43]. At the end ofthe previous century, the Chinese government initiated the Grain for Green Program, fol-lowing which the vegetation on the LP exhibited a remarkable expansion [44,45]. In 2019, amajor strategy for the ecological conservation and high-quality development of the YellowRiver Basin was proposed, which is a demand for continuous adaptation to socially stabledevelopment [46]. This substantially enhanced the potential for continuous developmentin terms of the ecological, social, and economic aspects of the LP. Nevertheless, the area stillfaces major challenges related to ecological sustainability and societal transformation [47].3. Data and Methodology3.1. Data SourceThis study collected a series of meteorological, soil and urbanization datasets to in-vestigate how catchment characteristics influence the phenology shifts on the LP. It hasbeen evidenced that temperature and precipitation are essential elements affecting thedynamics of vegetation phenology in arid regions [48]. Additionally, soil moisture as aphysical property within the catchment also plays a key role in influencing phenology.Otherwise, ISP and ANL are representative urbanization indices have been widely em-ployed to unravel the influence of anthropogenic activities on landscape [49,50]. Thedatasets span 2003–2022 and were predominantly archived using the Google Earth Engine(https://earthengine.google.com/ (accessed on 21 January 2023)).Remote Sens. 2024, 16, 2583 5 of 18The UCSB-CHG/CHIRPS/DAILY dataset served as our primary source of precipita-tion data [51]. This dataset comprises a comprehensive time series of precipitation dataspanning more than 30 years. The daily mean station temperature dataset comprises datafrom automated and human-facilitated observation stations compiled in the Global Histori-cal Climatology Network-Daily database (National Centers for Environmental Information)and undergoes regular quality and consistency control.The enhanced vegetation index (EVI) was adopted as the proxy indicator of vegeta-tion. Its synthesis algorithm has undergone rigorous atmospheric and soil backgroundcorrection/enhancement, and it demonstrates superior performance particularly in termsof reducing saturation effects [52]. Thus, the EVI is particularly beneficial for monitoringvegetation growth in regions with high vegetation coverage [53].The surface soil moisture product [54] used has 1 km resolution, and the daily dataare known to exhibit superior and precise coverage, with excellent performance.Given the absence of ANL data spanning the entirety of the period 2003–2021, two ANLdatasets were combined: the Consistent and Corrected Nighttime Light dataset(BNU/FGS/CCNL/v1, band name: b1, from 2003 to 2012) [55] and the Annual Visible andInfrared Imaging Suite Nighttime Light data (NOAA/VIIRS/DNB/ANNUAL_V21, bandname: average, from 2013 to 2021) [56].This study also included an ISP and vegetation types dataset with a resolution of 30 mfor 2003–2021 [57], which can reveal the annual change in surface area.In the course of data processing, owing to varying levels of precision, all data werestandardized to spatial resolution of 250 m × 250 m. Further information is availablein Table 1.Table 1. Information on the datasets used in this study.Name Spatial Resolution Time Span LinkMODIS/061/MOD13Q EVINASA LP DAAC at the USGSEROS Center250 m × 250 m 2003–2022https://lpdaac.usgs.gov/products/mod13q1v061/ (accessed on18 February 2023)USB/CHG Precipitation 5.5 km × 5.5 km 2003–2021 https://chc.ucsb.edu/data/chirps(accessed on 10 September 2023)NOAA NCEI Temperature Station’s data 2003–2021https://www.climate.gov/maps-data/dataset/daily-temperature-and-precipitation-reports-data-tables(accessed on 20 September 2023)Daily all-weather surface soilmoisture data set with 1 kmresolution in China (SSM)1 km × 1 km 2003–2021https://data.tpdc.ac.cn/zh-hans/data/e1f24e35-6235-40b2-b3d7-677dfb249e39 (accessed on22 September 2023)Consistent and CorrectedNighttime Light dataset (CCNL) 1 km × 1 km 2003–2012https://zenodo.org/records/6644980(accessed on 26 September 2023)Annual Visible and InfraredImaging Suite Nighttime Lightdata (VNL)500 m × 500 m 2013–2021https://eogdata.mines.edu/products/vnl/#annual_v2 (accessedon 30 September 2023)Landsat-derived annual landcover product of China (CLCD)(impermeable surface andland use)30 m × 30 m 2003–2021https://zenodo.org/records/8176941(accessed on 2 October 2023)3.2. Phenology Extraction Design and ProcessingTIMESAT 3.3 is a software package used to derive phenological parameters. It providesthree methods for fitting smoothed time series to remote sensing data (EVI), i.e., theRemote Sens. 2024, 16, 2583 6 of 18Savitzky–Golay filtering (SG), double-logistic, and asymmetric Gaussian models, which arecapable of detecting outliers [58] when using the least squares method to generate indexes.The first model uses local polynomial functions for the fitting, whereas the other modelsuse the normal least squares method. The SG filter excels in preserving data integrityand capturing more details in vegetation index time series, while effectively filteringout noise [59]. Meanwhile, previous studies shown that SG outperforms in practicalapplications [60,61]. In this study, SG filtering was utilized to reconstruct an EVI time series,from which the starting (when the EVI reaches 20% of the annual value on the ascendinglimb) and ending points (when the EVI drops to 20% of the annual value on the descendinglimb [62]) were extracted for SOS and EOS during 2003–2021. LOS can be determined bysubtracting SOS from EOS. The phenological information of vegetation types was extractedfollowing a similar procedure.3.3. Pixel-Wise Regression ModelA pixel-wise regression model was constructed to investigate the spatiotemporalvariation in the phenology of vegetation on the LP. The study performed a pixel-wisecorrelation analysis individually for the SOS, EOS, and LOS datasets. This involvedindependently applying regression models to each pixel in the long-term time series withmultiple images, which can facilitate the identification of distinct trends and relationshipsin phenological shifts at a fine spatial scale. In this study, the outcomes of the Pearsoncorrelation coefficient R (linear dependence between two random variables with a value inthe range of −1 to 1 that reflects the negative (<0) and positive (>0) impact of one variableon another [63]), slope (the linear trend of a variable that reflects a negative (<0) or positive(>0) trend of change), R2 (a measure of the degree of model fitting with a value in the rangeof 0 to 1; the larger the value, the better the fitting performance), and p-value (measureof the consistency between the sample and the hypothesis with a value in the range of0–1; a value of <0.05 indicates passing the significance test) of the pixel-wise regressionmodel were extracted to investigate the trend of change in phenology, and to assess theimpact and/or underlying mechanisms of the environmental factors (i.e., temperature,precipitation, and soil moisture) on phenological change. The Pearson correlation coefficientcan be used to assess the linear dependence between two random variables, such as theimpact of environmental factors (i.e., temperature, precipitation, and soil moisture) onphenological change.4. Result4.1. Delineate Trends in Vegetation Development and EvolutionThe EVI exhibits an overall upward tendency and a discernible spatial distributionwith a gradual decline from the southeast toward the northwest (Figure 3). It shows arelatively stable transformation/alteration in central and western regions during 2003–2011.After 2012, a steady increase is evident, with a noticeable reduction in regions where theEVI values fall below 0.4. Generally, southern areas consistently display the highest EVIvalues, while northwestern regions maintain the lowest values.The monthly mean EVI value is 0.225, with an anomaly of 0.182 (Figure 4). Positiveanomalies are chiefly concentrated during May–September, whereas negative anomaliesare distributed from October through to the subsequent April. Overall, there is a no-ticeable variability in the positive anomalies in comparison with the negative anomalies.Monthly averages generally show a rise, with slight variations. Peaks in July and Augustcontrast the troughs in January and February. The range between the highest (July 2018;0.371) and lowest (January 2008; 0.051) values is 0.32. Anomalies during May–September(2003–2010) show limited disparities. July 2018 marks the peak positive anomaly (0.189) andOctober 2021 marks the smallest positive anomaly (0.003). The highest negative anomalyis in January 2008 (−0.131), and the lowest negative anomaly is in October 2019 (−0.008).During October–April (2003–2007), the anomalies demonstrate stable fluctuation.Remote Sens. 2024, 16, 2583 7 of 18Remote Sens. 2024, 16, 2583 7 of 20 2011. After 2012, a steady increase is evident, with a noticeable reduction in regions where the EVI values fall below 0.4. Generally, southern areas consistently display the highest EVI values, while northwestern regions maintain the lowest values. Figure 3. Spatiotemporal distributions of maximum enhanced vegetation index (EVI) values during 2003–2022. The higher the value of the index, the more vigorous the vegetation growth. The monthly mean EVI value is 0.225, with an anomaly of 0.182 (Figure 4). Positive anomalies are chiefly concentrated during May–September, whereas negative anomalies are distributed from October through to the subsequent April. Overall, there is a noticea-ble variability in the positive anomalies in comparison with the negative anomalies. Monthly averages generally show a rise, with slight variations. Peaks in July and August contrast the troughs in January and February. The range between the highest (July 2018; 0.371) and lowest (January 2008; 0.051) values is 0.32. Anomalies during May–September (2003–2010) show limited disparities. July 2018 marks the peak positive anomaly (0.189) and October 2021 marks the smallest positive anomaly (0.003). The highest negative anomaly is in January 2008 (−0.131), and the lowest negative anomaly is in October 2019 (−0.008). During October–April (2003–2007), the anomalies demonstrate stable fluctuation. Figure 3. Spatiotemporal distributions of maximum enhanced vegetation index (EVI) values during2003–2022. The higher the value of the index, the more vigorous the vegetation growth.Remote Sens. 2024, 16, 2583 7 of 20 2011. After 2012, a steady increase is evident, with a noticeable reduction in regions where the EVI values fall below 0.4. Generally, southern areas consistently display the highest EVI values, while northwestern regions maintain the lowest values. Figure 3. Spatiotemporal distributions of maximum enhanced vegetation index (EVI) values during 2003–2022. The higher the value of the index, the more vigorous the vegetation growth. The monthly mean EVI value is 0.225, with an anomaly of 0.182 (Figure 4). Positive anomalies are chiefly concentrated during May–September, whereas negative anomalies are distributed from October through to the subsequent April. Overall, there is a noticea-ble variability in the positive anomalies in comparison with the negative anomalies. Monthly averages generally show a rise, with slight variations. Peaks in July and August contrast the troughs in January and February. The range between the highest (July 2018; 0.371) and lowest (January 2008; 0.051) values is 0.32. Anomalies during May–September (2003–2010) show limited disparities. July 2018 marks the peak positive anomaly (0.189) and October 2021 marks the smallest positive anomaly (0.003). The highest negative anomaly i in January 2008 (−0.131), d the lowest negative anomaly is in October 2019 (−0.008). During October–April (2003–2007), the anomalies demonstrate stable fluctuation. Figure 4. Anomalies of monthly mean enhanced vegetation index (EVI), the fitting lines and linerregression equations during 2003–2022. Green and red lines represent the trends of positive andnegative anomalies, respectively.4.2. Variability in Vegetation Phenological Changes of SOS and EOSAdvances in SOS span from the southeast to the northwest (Figure 5). In most areas,SOS occurs between 100–150 days; areas where the value exceeds 160 days are primarily innorthwestern and central parts of the LP. The variation is more pronounced in the norththan in the south, and a regular shift in the pattern is evident after 2010. The range of80–90 days has a trend of increase in the southern region.A divergent pattern in EOS is evident across various geographic zones of the LP(Figure 6). In most regions, EOS is concentrated within 285–320 days. Although centralparts have seen notable shift in EOS over the past two decades, a small portion of thesouthern region has seen minimal change, with EOS maintained at approximately 270 days.Notably, northern and central regions experienced advances in EOS during 2004–2006. TheEOS in northern regions in 2003 was notably prolonged compared with that in 2004 and2005. In 2012, EOS in most regions advanced in comparison with that of the preceding year.Remote Sens. 2024, 16, 2583 8 of 18Remote Sens. 2024, 16, 2583 8 of 20 Figure 4. Anomalies of monthly mean enhanced vegetation index (EVI), the fitting lines and liner regression equations during 2003–2022. Green and red lines represent the trends of positive and negative anomalies, respectively. 4.2. Variability in Vegetation Phenological Changes of SOS and EOS Advances in SOS span from the southeast to the northwest (Figure 5). In most areas, SOS occurs between 100–150 days; areas where the value exceeds 160 days are primarily in northwestern and central parts of the LP. The variation is more pronounced in the north than in the south, and a regular shift in the pattern is evident after 2010. The range of 80–90 days has a trend of increase in the southern region. Figure 5. Spatiotemporal distributions of the start of the growing season (SOS), 2003–2021. A divergent pattern in EOS is evident across various geographic zones of the LP (Fig-ure 6). In most regions, EOS is concentrated within 285–320 days. Although central parts have seen notable shift in EOS over the past two decades, a small portion of the southern region has seen minimal change, with EOS maintained at approximately 270 days. Nota-bly, northern and central regions experienced advances in EOS during 2004–2006. The EOS in northern regions in 2003 was notably prolonged compared with that in 2004 and 2005. In 2012, EOS in most regions advanced in comparison with that of the preceding year. Figure 5. Spatiotemporal distributions of the start of the growing season (SOS), 2003–2021.Remote Sens. 2024, 16, 2583 9 of 20 Figure 6. Spatiotemporal distributions of the end of the growing season (EOS), 2003–2021. 4.3. Phenological Pixel-Wise Regression Analysis Figure 7 shows the spatial distribution of the trend of change in vegetation phenology over the past two decades on the LP. There is an evident substantial trend of SOS advance-ment in the northwestern, southwestern, and southern regions during 2003–2021. Con-versely, northeastern, central, and certain southern areas exhibit a pattern of delay. The southeastern region displays a trend of advance (slope < 0), whereas the northern and northwestern regions show a trend of delay (slope > 0). There is also a trend of postpone-ment characterizing the overall EOS of vegetation phenology in the period 2003–2021. Specifically, the southern and northwestern regions demonstrate prominent delays in phenological change. Conversely, central and southern regions exhibit a trend of early occurrence (slope < 0), leading to EOS advancement. In the southeastern region, there is a trend of delay in EOS (slope > 0), whereas the central and western regions show advance-ment (slope < 0), coupled with a certain degree of temporal lag. Figure 6. Spatiotemporal distributions of the end of the growing season (EOS), 2003–2021.Remote Sens. 2024, 16, 2583 9 of 184.3. Phenological Pixel-Wise Regression AnalysisFigure 7 shows the spatial distribution of the trend of change in vegetation phenol-ogy over the past two decades on the LP. There is an evident substantial trend of SOSadvancement in the northwestern, southwestern, and southern regions during 2003–2021.Conversely, northeastern, central, and certain southern areas exhibit a pattern of delay.The southeastern region displays a trend of advance (slope < 0), whereas the northernand northwestern regions show a trend of delay (slope > 0). There is also a trend of post-ponement characterizing the overall EOS of vegetation phenology in the period 2003–2021.Specifically, the southern and northwestern regions demonstrate prominent delays inphenological change. Conversely, central and southern regions exhibit a trend of earlyoccurrence (slope < 0), leading to EOS advancement. In the southeastern region, thereis a trend of delay in EOS (slope > 0), whereas the central and western regions showadvancement (slope < 0), coupled with a certain degree of temporal lag.Remote Sens. 2024, 16, 2583 10 of 20 Figure 7. Statistical spatiality of vegetation phenology from pixel-wise regression model at the start of the growing season (SOS) and the end of the growing season (EOS) during 2003–2021. Variation rate and trend of SOS (a) and EOS (c). Spatial pattern of the effective linear fitting and statistically significant regions for SOS (b) and EOS (d). The study investigated the temporal variations of SOS and EOS for four vegetation types (Figures 8 and 9). The highest proportion of SOS is within 80–112 days. Distributions of SOS for cropland and grassland are higher than those for the other two vegetation types at <80 days. Over the past 20 years, grassland has seen notable fluctuation, with its largest (smallest) proportion in 2003 (2018). The proportion of forest with SOS within 80–112 days is higher than that of the other vegetation types. Among the five periods, the distribution of cropland has the highest proportion between 144 and 176 days; however, the opposite is true for forest. In the period of >176 days, the overall proportion is small; however, cropland in 2012 has the largest value, and forest has the same condition as in 2003. The proportion of shrubs is focused on the period of 80–144 days. Figure 8. Vegetation proportions at the start of the growing season (SOS) in different periods across varying vegetation types during 2003–2021. The five diagrams depict the distributions of vegetation proportions across various temporal intervals: (a) <80 days, (b) 80–112 days, (c) 112–144 days, (d) 144–176 days, and (e) >176 days. VT stands for vegetation type; C, F, S, and G represent cropland, forest, shrub, and grassland, respectively. Figure 7. Statistical spatiality of vegetation phenology fro pixel- ise regression odel at the startof the growing season (SOS) and the end of the growing season (EOS) during 2003–2021. Variationrate and trend of SOS (a) and EOS (c). Spatial pattern of the effective linear fitting and statisticallysignificant regions for SOS (b) and EOS (d).The study investigated the temporal variations of SOS and EOS for four vegetationtypes (Figures 8 and 9). The highest proportion of SOS is within 80–112 days. Distributionsof SOS for cropland and grassland are higher than those for the other two vegetation typesat <80 days. Over the past 20 years, grassland has seen notable fluctuation, with its largest(s allest) proportion in 2003 (2018). The proportion of forest with SOS within 80–112 daysis higher than that of the other vegetation types. A ong the five periods, the distributionof cropland has the highest proportion bet een 144 and 176 days; ho ever, the oppositeis true for forest. In the period of >176 days, the overall proportion is s all; ho ever,cro la i 2012 has the largest value, and forest has the sa e condition as in 2003. Theproportion of shrubs is focused on the period of 80–144 days.The phenological EOS shows a trend of delay and varies with vegetation type (Figure 9).The major range is concentrated within 288–320 days. In the period of <272 days, only crop-land exhibits an annual distribution, i.e., that of the other vegetation types in intermittent.The distributions of shrub and forest in 288–320 days are larger compared with those ofother periods. Within 272–288 days, the proportion of cropland shows little increase in2013 and 2018. During 288–304 days, grassland is minimal in comparison with the otherRemote Sens. 2024, 16, 2583 10 of 18vegetation types. The variability at 304–320 days of the four vegetation types is relativelylimited compared with that of the other periods.Remote Sens. 2024, 16, 2583 10 of 20 Figure 7. Statistical spatiality of vegetation phenology from pixel-wise regression model at the start of the growing season (SOS) and the end of the growing season (EOS) during 2003–2021. Variation rate and trend of SOS (a) and EOS (c). Spatial pattern of the effective linear fitting and statistically significant regions for SOS (b) and EOS (d). The study investigated the temporal variations of SOS and EOS for four vegetation types (Figures 8 and 9). The highest proportion of SOS is within 80–112 days. Distributions of SOS for cropland and grassland are higher than those for the other two vegetation types at <80 days. Over the past 20 years, grassland has seen notable fluctuation, with its largest (smallest) proportion in 2003 (2018). The proportion of forest with SOS within 80–112 days is higher than that of the other vegetation types. Among the five periods, the distribution of cropland has the highest proportion between 144 and 176 days; however, the opposite is true for forest. In the period of >176 days, the overall proportion is small; however, cropland i 2012 has the largest value, and forest has the same condition as in 2003. The proportion of shrubs is focused on t period of 80–144 days. Figure 8. Vegetation proportions at the start of the growing season (SOS) in different periods across varying vegetation types during 2003–2021. The five diagrams depict the distributions of vegetation proportions across various temporal intervals: (a) <80 days, (b) 80–112 days, (c) 112–144 days, (d) 144–176 days, and (e) >176 days. VT stands for vegetation type; C, F, S, and G represent cropland, forest, shrub, and grassland, respectively. Figure 8. Vegetation proportions at the start of the gro ing season (SOS) in different periods acrossvarying vegetation types during 2003–2021. The five diagrams depict the distributions of vegetationproportions across various temporal intervals: (a) <80 days, (b) 80–112 days, (c) 112–144 days,(d) 144–176 days, and (e) >176 days. VT stands for vegetation type; C, F, S, and G represent cropland,forest, shrub, and grassland, respectively.Remote Sens. 2024, 16, 2583 11 of 20 The phenological EOS shows a trend of delay and varies with vegetation type (Figure 9). The major range is concentrated within 288–320 days. In the period of <272 days, only cropland exhibits an annual distribution, i.e., that of the other vegetation types in inter-m ttent. The distributions f shrub and forest in 288–320 days are larger compared with those of other period . W thin 272–288 days, the proportion of cropland sh ws li tle in-crease in 2013 and 2018. During 288–304 days, grassland is minimal in comparison with the other vegetation types. The variability at 304–320 days of the four v getation types is relatively limited compared with that of the other periods. Figure 9. Vegetation proportions at the end of the growing season (EOS) in different periods across varying vegetation types during 2003–2021. The five diagrams depict the distributions of vegetation proportions across various temporal intervals: (a) <272 days, (b) 272–288 days, (c) 288–304 days, (d) 304–320 days, and (e) >320 days. VT stands for vegetation type: C, F, S, and G represent cropland, forest, shrub, and grassland, respectively. 4.4. Response of Phenology Shifts to Changing Environment To evaluate the factors related to changes in vegetation phenology, this study ana-lyzed the association of factors and phenological indicator quantiles (Figures 10 and 11). EOS shows a significantly higher correlation with temperature than EOS50 (R = 0.89). SOS90 is more sensitive to PRE_SOS than SOS. Temperature and precipitation in SOS pe-riod are positive with ISP (R = 0.67 and 0.28). SM_LOS shows a remarkable relationship with TEM_EOS, EOS and SM_SOS (R = −0.69 and 0.85). The annual phenological indicators and ANL were individually investigated (Figure 11). It shows that the proportion of positive is greater than negative correlations in both SOS and EOS (Figure 11a,b), and exhibits a spatial distribution of positive correlations in the northwest and southeast for SOS, and southeast and northern regions for EOS. In con-trast to LOS, the relationship shows a relatively even distribution (Figure 11c). Figure 9. Vegetation proportions at the end of the growing season (EOS) in different periods acrossvarying vegetation types during 2003–2021. The five diagrams depict the distributions of vegetationproportions across various temporal intervals: (a) <272 days, (b) 272–288 days, (c) 288–304 days,(d) 304–320 days, and (e) >320 days. VT stands for vegetation type: C, F, S, and G represent cropland,forest, shrub, and grassland, respectively.4.4. Response of Phenology Shifts to hanging nviron entTo l t t e factors related to changes in vegetation phenology, this study anal zedth associati n of factors and phenologica indicator quantiles (Figures 10 and 11). EOSshows a significa tly higher corr lati n wi h tempera ure than EOS50 (R = 0.89). SOS90 ismore sensitive to PRE_SOS than SOS. Temperature and p cipitation in SOS period arepositive with ISP (R = 0.67 and 0.28). SM_LOS shows a remarkable elationship withTEM_ OS, EOS and SM_SOS (R = −0.69 and 0.85).Remote Sens. 2024, 16, 2583 11 of 18Remote Sens. 2024, 16, 2583 12 of 20 Figure 10. Correlation between phenology and influencing factors. SOS, EOS, and LOS are the start of the growing season, end of the growing season, and length of the growing season, respectively. PRE_SOS, TEM_SOS, and SM_SOS are the mean temperatures in April and May; PRE_EOS, TEM_EOS, and SM_EOS are the mean temperatures in November; and PRE_LOS, TEM_LOS, and SM_LOS are the mean temperatures from March to November. ISP is impervious surface percent-age. Attached numerals of 10, 50, and 90 represent the corresponding percentile. Note: *** means p < 0.001, ** means p < 0.01, * means p < 0.05. Figure 11. Correlation coefficient between phenology characteristics and annual nighttime light (ANL) based on pixel-wise regression: (a) start of the growing season (SOS), (b) end of the growing season (EOS), and (c) length of the growing season (LOS). The upper-left corner of each plot shows the frequency distribution of the relation results. Blue and red bars represent negative and positive values, respectively. 5. Discussion 5.1. Spatiotemporal Characteristics of Phenology Shifts Phenological shifts are the combined result of changes in vegetation growth and en-vironmental conditions. Vegetation on the LP has consistently expanded and demon-strated a strong upward trend over the past two decades. This expansion can be attributed to the combined dynamics associated with human activities and changes in natural cli-matic conditions [64]. Previous studies showed that human activities make a greater con-tribution (≥50%) to vegetation recovery. The Grain for Green Program, as a reforestation and ecological restoration project, has made a substantial contribution to the prevention Figure 10. Correlation between phenology and influencing factors. SOS, EOS, and LOS are thestart of the growing season, end of the growing season, and length of the growing season, respec-tively. PRE_SOS, TEM_SOS, and SM_SOS are the mean temperatures in April and May; PRE_EOS,TEM_EOS, and SM_EOS are the mean temperatures in November; and PRE_LOS, TEM_LOS, a dSM_LOS ar the ean temperatures from Ma ch to November. ISP s impervious surface percentage.Attached numerals of 1 , 50, and 90 represent the corresponding percentile. Note: *** means p < 0.001,** means p < 0.01, * means p < 0.05.Remote Sens. 2024, 16, 2583 12 of 20 Figure 10. Correlation between phenology and influencing factors. SOS, EOS, and LOS are the start of the growing season, end of the growing season, and le gth of t e growing season, respectively. PRE_SOS, TEM_SOS, and SM_SOS are th m an temperatures in April and ay; , r t t r t r s i r; , , n S _LOS are the mean temperatures from March to November. ISP is impervious surface percent-age. Attached numerals of 10, 50, and 90 represent the corresponding percentile. Note: *** means p < 0.001, ** means p < 0.01, * means p < 0.05. Figure 11. Correlation coefficient between phenology characteristics and annual nighttime light (ANL) based on pixel-wise regression: (a) start of the growing season (SOS), (b) end of the growing season (EOS), and (c) length of the growing season (LOS). The upper-left corner of each plot shows the frequency distribution of the relation results. Blue and red bars represent negative and positive values, respectively. 5. Discussion 5.1. Spatiotemporal Characteristics of Phenology Shifts Phenological shifts are the combined result of changes in vegetation growth and en-vironmental conditions. Vegetation on the LP has consistently expanded and demon-strated a strong upward trend over the past two decades. This expansion can be attributed to the combined dynamics associated with human activities and changes in natural cli-matic conditions [64]. Previous studies showed that human activities make a greater con-tribution (≥50%) to vegetation recovery. The Grain for Green Program, as a reforestation and ecological restoration project, has made a substantial contribution to the prevention Figure 11. C rrelation coefficient between phenology charact ristics and annual nighttime light(ANL) based on pixel-wise regression: (a) start of the growing season (SOS), (b) end of the growingseason (EOS), and (c) length of the growing season (LOS). The upper-left corner of each plot showsthe frequency distribution of the relation results. Blue and red bars represent negative and positivevalues, respectively.The annual phenological indicators and ANL were individually investigated (Figure 11).It shows that the proportion of positive is greater than negative correlations in both SOS andEOS (Figure 11a,b), and exhibits a spatial distrib tion of positive correlations in the northwestand southe st for SOS, and sou heast a d northern regi s for EOS. In contrast to LOS, therel tionship shows a relatively even distribution (Figure 11c).Remote Sens. 2024, 16, 2583 12 of 185. Discussion5.1. Spatiotemporal Characteristics of Phenology ShiftsPhenological shifts are the combined result of changes in vegetation growth and envi-ronmental conditions. Vegetation on the LP has consistently expanded and demonstrateda strong upward trend over the past two decades. This expansion can be attributed tothe combined dynamics associated with human activities and changes in natural climaticconditions [64]. Previous studies showed that human activities make a greater contribu-tion (≥50%) to vegetation recovery. The Grain for Green Program, as a reforestation andecological restoration project, has made a substantial contribution to the prevention offurther degradation of the ecological environment of the LP [17,65]. Findings show that themajority of vegetation greening on the LP is concentrated in low-elevation and relativelyflat regions, consistent with the results of Li et al. [7].Phenology varies with vegetation type, and different types of vegetation do not shareidentical growth cycles; thus, they are present in various proportions during different SOSand EOS periods. Changes in vegetation phenology can influence energy generation (e.g.,adjustment of leaf reflectance and emission of organic gases into the atmosphere) [9] andare associated with the inherent characteristics of specific vegetation types. For example,one of the important contributors to phenological change stems from gene expression [66],functional traits, and physical distinctions between herbaceous and woody plants [67–69].Additionally, physical drivers represent other controlling factors that can influence vegeta-tion changes. On the LP, there is a latitudinal gradient in vegetation reduction from lowerto higher latitudes. For example, the relatively flat topography and favorable moistureand thermal conditions in southern areas facilitate rapid extension of the growth period ofvegetation [70].Results also found that cropland is a significant contributor to SOS and EOS shifts. Apart of SOS begins later, which has a larger proportion of 144–176d than the other threevegetation types. It has been evidenced that temperature requirements for crop greeningare distinct from those of natural vegetation [71,72]. Moreover, seed variety and typesalso have an impact on it, causing more complicated crop phenology shifts in a changingenvironment. This is consistent with the study by Zhao et al. [73].5.2. Patterns and Mechanisms of LOSThe phenology of vegetation on the LP exhibits noticeable annual and seasonal vari-ation. Typically, SOS and EOS are mainly distributed within 100–150 and 285–320 days,respectively. However, EOS demonstrates greater change than SOS and has a higher sensi-tivity to climate change [20]. To gain insight into how SOS and EOS influence vegetationgrowth, the study calculated LOS (i.e., EOS minus SOS), whereby LOS is the outcome of themutual relationship between SOS and EOS (i.e., the advance or delay of SOS and/or EOS).Thus, LOS represents the temporal span across the entire process of vegetation growth anddevelopment [74,75]. On the LP, the regional vegetation exhibits a tendency toward SOSadvance and EOS delay associated with climate change (slope > 0, R2 > 0.6).In central, eastern, southern, and southwestern areas of the LP, EOS exhibits an up-ward trend (Figure 12; slope > 0). In contrast, in some northwestern and southern regions,a declining trend is noticeable (slope < 0). In high-elevation regions, LOS change is lesspronounced than in low-elevation areas, and this is attributed to the varying impacts ofthe climate and human activities at different elevations [76,77]. Regions at lower latitudesand lower elevations tend to have relatively high temperatures with early SOS and de-layed EOS, which contribute to the prolonged LOS. It has been demonstrated that thecombination of elevated temperatures and increased precipitation is conducive for thegrowth and maturation of vegetation [78]. LOS shows a positive association with the netprimary productivity of vegetation because prolonged LOS increases the overall potentialfor vegetative production [10]. Previous research indicated that extending LOS by a singleday could increase the daily gross primary productivity and net primary productivity by0.6% and 2.8 g Cm−2 yr−1, respectively [79].Remote Sens. 2024, 16, 2583 13 of 18Remote Sens. 2024, 16, 2583 14 of 20 Figure 12. Same as Figure 7 but for the length of the growing season (LOS). 5.3. Drivers of Phenology Linked to Environment Change The controls on SOS and EOS vary greatly among environmental factors. Based on relevance, the study found that temperature has a greater influence on EOS (R = 0.89) than on SOS. Keenan and Richardson [80] revealed that temperature and the photoperiod pre-dominantly contribute to the regulation of autumn phenology for deciduous trees, and another study found that increasing temperatures play an essential role in delaying EOS [81]. The reasons for this might reflect the complexity of the influencing factors of leaf coloration and leaf drop and the protracted characteristics of autumn events that make the mechanism between climate and phenology change in terms of EOS more obscure than that of SOS [82]. Changes in autumn temperatures can impact vegetation and its sus-ceptibility to disasters such as frost damage because elevated temperatures might reduce the likelihood of the occurrence of such events [81]. Rising temperatures can increase the activity of photosynthetic enzymes, thereby facilitating vegetation growth and signifi-cantly impacting terrestrial cyclic events [83,84]. A higher maximum temperature can ac-celerate plant senescence by increasing evaporation, leading to reduced moisture levels [37,85]. In contrast, a higher minimum temperature can delay vegetation development by slowing down physiological processes [86]. In addition, EOS has a significant response to soil moisture, precipitation, and temperature during the SOS period. It is shown that high preseason temperatures can promote and/or delay vegetation dynamics through con-trolled photosynthesis, thus changing autumn phenology [6]. In LP, an arid and semiarid area, drought frequency was intensified in post-spring and pre-summer under global warming [87]. This potentially leads to an earlier EOS to some extent. Earlier flowering in spring also has an impact on earlier EOS, which is associated with the increasing risk of frost and pest outbreaks in this stage, leading to earlier senescence in autumn [88]. The study observed that SOS (R = 0.76) is more sensitive than EOS (R = 0.22) to the impact of precipitation. Zeppel et al. [89] suggested that seasonal changes in precipitation in cold seasons might have a greater effect than similar changes in warm seasons. This is partially because precipitation is the main factor controlling vegetation growth in arid regions [78]. In spring, when vegetation is recovering from its dormancy period, a reduc-tion in precipitation triggers water stress in plants, thereby retarding the development of vegetation; however, there is no apparent effect in autumn or winter [89]. Findings show that the phenology of vegetation with late SOS has a stronger negative correlation with soil moisture (R = −0.64), consistent with previous research [90]. Soil mois-ture throughout the entire growing season is positively correlated with LOS (R = 0.67). The growth of vegetation entails substantial depletion of soil water, which is essential to sustain the burgeoning stages, particularly during the transition to the flowering stage [13]. The earlier onset of vegetation growth induces moisture decline, which exacerbates the reduction in surface evaporative cooling [13]. The observed SOS shifts on the LP are associated with the changes in soil moisture resulting from spring sowing, which is re-sponsible for substantial water consumption in arid and semiarid regions. Moreover, this sowing pattern can coincide with limited precipitation (i.e., severe drought in spring) in Figure 12. Same as Figure 7 but for the length of the growing season (LOS).5.3. ri ers of e olo y i ked to nviron ent Changee controls on S S and EOS vary greatly among environmental factors. Base onrele a c , t st found that temperature has a greater influence on EOS (R = 0.89)than on SOS. Keenan and Richardson [80] revealed that temperature and t e photoperiodpredominantly contribute to the regulation of autumn phenology for deciduous trees,and another study found that increasing temperatures play an essential role in delayingEOS [81]. The reasons for this might reflect the complexity of the influencing factors of leafcoloration and leaf drop and the protracted characteristics of autu n events that akethe mechanism between climate and phenology change in terms of EOS more obscurethan that of SOS [82]. Changes in autumn temperatures can impact vegetation and itssusceptibility to disasters such as frost damage because elevated temperatures might reducethe likelihood of the occurrence of such events [81]. Rising temperatures can increase theactivity of photosynthetic enzymes, thereby facilitating vegetation growth and significantlyimpacting terrestrial cyclic events [83,84]. A higher maximum temperature can accelerateplant senescence by increasing evaporation, leading to reduced moisture levels [37,85]. Incontrast, a higher minimum temperature can delay vegetation development by slowingdown physiological processes [86]. In addition, EOS has a significant response to soil mois-ture, precipitation, and temperature during the SOS period. It is shown that high preseasontemperatures can promote and/or delay vegetation dynamics through controlled photo-synthesis, thus changing autumn phenology [6]. In LP, an arid and semiarid area, droughtfrequency was intensified in post-spring and pre-summer under global warming [87]. Thispotentially leads to an earlier EOS to some extent. Earlier flowering in spring also hasan impact on earlier EOS, which is associated with the increasing risk of frost and pestoutbreaks in this stage, leading to earlier senescence in autumn [88].The study observed that SOS (R = 0.76) is more sensitive than EOS (R = 0.22) to theimpact of precipitation. Zeppel et al. [89] suggested that seasonal changes in precipitationin cold seasons might have a greater effect than similar changes in warm seasons. Thisis partially because precipitation is the main factor controlling vegetation growth in aridregions [78]. In spring, when vegetation is recovering from its dormancy period, a reductionin precipitation triggers water stress in plants, thereby retarding the development ofvegetation; however, there is no apparent effect in autumn or winter [89].Findings show that the phenology of vegetation with late SOS has a stronger negativecorrelation with soil moisture (R = −0.64), consistent with previous research [90]. Soilmoisture throughout the entire growing season is positively correlated with LOS (R = 0.67).The growth of vegetation entails substantial depletion of soil water, which is essential tosustain the burgeoning stages, particularly during the transition to the flowering stage [13].The earlier onset of vegetation growth induces moisture decline, which exacerbates thereduction in surface evaporative cooling [13]. The observed SOS shifts on the LP are associ-ated with the changes in soil moisture resulting from spring sowing, w ich is responsiblefor substantial water consumption in arid and semiarid regions. Moreover, this sowing pat-tern can coincide with limited precipitation (i.e., severe rought in spring) in the northernRemote Sens. 2024, 16, 2583 14 of 18regions of the LP, resulting in enhanced soil moisture decline as reported by Lian et al. [13].In turn, these SOS shifts can consequently influence the accuracy of forecasts of summerclimactic change.This study selected two indicators (i.e., ANL and ISP) to investigate the influenceof anthropogenic factors on phenological change (Figures 11 and 12). It is noted thaturbanization has a stronger impact on SOS compared to EOS and LOS. This is consistentwith the study investigated by Yao et al. [91]. High-correlation areas are concentratedalong the riverbanks (e.g., the Fenhe and Weihe Rivers), coinciding with significant humanactivity. This confirms that urbanization and agriculture are the major anthropogenicfactors that contribute to phenological change by altering local climatic conditions (e.g.,the urban heat island effect), land surface features, and hydrological and environmentalcycles [92,93]. It has been proven that the rate of warming depends on the variationof urban and rural circumstances in most areas, the changes in urban temperature andprecipitation associated with anthropogenic gas emissions, building density, and populationdistribution [94]. Regional urbanization-related processes alter the underlying surfaces andlead to a heterogeneous spatial distribution of vegetation types across the land surface [95],thereby manifesting distinct phenological responses.6. ConclusionsThis study investigated the vegetation phenology changes on the Loess Plateau during2003–2022 and elucidated the primary drivers. It shows a noticeable vegetation green-ing with an evident spatiotemporal characteristic. SOS and EOS were mainly within100–150 and 285–320 days, respectively. Clear variations of phenological shifts emerged atthe regional scale; the southeastern and northwestern regions exhibited a notable changewith advanced SOS, delayed EOS, and prolonged LOS. Overall, the cropland proportions ofSOS and EOS indicate significant seasonal shift changes in Loess Plateau. Grasslands withan SOS of less than 80 days have exhibited the most significant changes over the past twentyyears. The primary controlling factors that influence SOS, EOS, and LOS are precipitation,temperature, and soil moisture, respectively. Rising temperatures and precipitation canresult in earlier SOS and later EOS. Preseason climatic conditions have a lagged effect onphenology. Anthropogenic-induced drivers also have a role, i.e., urbanization contributesto the spatial changes of vegetation phenology, and specific mechanisms are needed tofurther investigate.This study enhances our knowledge of how vegetation phenology responds to thetemporal dynamics across ecosystems on the Loess Plateau. However, there are someuncertainties associated with the methods and data utilized in this study. Despite the EVIundergoing atmospheric correction, it can still contain uncertainties in areas with cloudcover or substantial atmospheric variations. Moreover, the resolution of the data usedmight be inadequate for detecting subtle variations in vegetation growth at smaller scalesand/or over short timescales. In future work, comprehensive integration of a suite ofobservational and remote sensing data will build up an understanding of the patterns anddrivers of vegetation phenology in a changing environment.Author Contributions: Methodology, T.W.; software, T.W., X.X., X.C. and S.L.; data processing, T.W.,X.C. and X.X.; writing—original draft, T.W.; writing—review and editing, T.W., X.X., X.C., S.L., D.K.,Y.Z., Y.T., Y.C., J.Z. and G.Z.; conceptualization, S.L., J.Z. and G.Z. All authors have read and agreedto the published version of the manuscript.Funding: This research is jointly funded by the National Natural Science Foundation of China(42101038 and 42201086), the Natural Science Foundation of Shandong Province (ZR2019BD059),and the Natural Science Foundation of the Ningxia Hui Autonomous Region (2022AAC03668). Theauthors thank the China Scholarship Council (202308060113) for sponsoring Shixuan Lyu.Data Availability Statement: The data presented in this study are available on request from thecorresponding author (junlong.zhang@sdnu.edu.cn).Remote Sens. 2024, 16, 2583 15 of 18Acknowledgments: We thank the associate editor, and the three anonymous reviewers and editorsfor their thoughtful comments and suggestions regarding our article. We also thank language editorfor editing the English text of a draft of this manuscript.Conflicts of Interest: The authors declare no conflicts of interest.References1. Hao, H.; Li, Y.; Zhang, H.; Zhai, R.; Liu, H. 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phenology
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changing environment
climate change
Characterizing Vegetation Phenology Shifts on the Loess Plateau over Past Two Decades
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