Impacts of snow cover duration on vegetation spring phenology over the Tibetan Plateau

Aims Snow cover occupies large percentage of land surface in Tibetan Plateau. Snow cover duration (SCD) during non-growing seasons plays a critical role in regulating alpine vegetation’s phenology by affecting the energy budgets of land surface and soil moisture conditions. Different period’s snow cover during non-growing season may have distinct effect on the vegetation’s phenology. Start of season (SOS) has been observed advanced under the ongoing climate change in the plateau, but it still remains unclear how the SCD alters the SOS. This study attempts to answer the following questions: (i) What is the pattern of spatial and temporal variations for SCD and grassland SOS? (ii) Which period’s SCD plays a critical role in grassland’s SOS?     

黄土高原植被物候变化及其对季节性气候变化的响应

受气候变化影响,全球范围内植被物候发生了显著变化,而目前针对不同植被分区类型下（荒漠草原区、典型草原区、森林草原区、落叶栎林区、落叶栎林亚区）植被物候变化及其对季节性气候变化响应的研究尚少。因此基于MODIS遥感归一化差值植被指数（MODIS NDVI：MOD13Q1）数据、中国植被区划数据及135个气象站点插值数据,利用Sen''s斜率估计、Hurst指数和高阶偏相关分析等方法,研究黄土高原2001-2018年植被物侯变化及其对季节性气候变化的响应。结果表明：（1）黄土高原植被生长季始期（SOS,Start of Growing Season）主要集中在第96-144天,子植被分区由西北向东南方向,逐渐呈现提前趋势,71.0%的像元植被SOS整体提前0-2 d/10a （α=0.05）,且在未来一段时间66%的像元植被SOS继续呈现提前趋势;植被生长季末期（EOS,End of Growing Season）主要集中在第288-304天,各子植被分区植被EOS变化基本保持一致,87.6％的像元植被EOS整体延迟0-3 d/10a （α=0.05）,且在未来一段时间有80%的像元植被EOS继续呈现推迟趋势。（2）黄土高原植被SOS主要受各季节温度的影响;当年春季降水导致植被SOS提前,主要分布在黄土高原中部;上年夏季和上年秋季降水增加会导致植被SOS推迟;当年春季、上年秋季和年初冬季的温度升高均会导致植被SOS提前;各子植被分区植被SOS对不同季节降水的响应存在差异,而对不同季节温度的响应具有一致性。（3）黄土高原植被EOS主要受各季节降水和秋季温度的影响;不同季节降水增加均会导致大部分植被EOS推迟;当年秋季温度导致整体区域植被EOS推迟,且各子植被区植被EOS对当年秋季温度响应具有一致性。该研究可为大尺度植被物候影响因素提供新的认识,也为植被适应未来气候变化提供借鉴。     

Spring phenology rather than climate dominates the trends in peak of growing season in the Northern Hemisphere

Shifts in plant phenology regulate ecosystem structure and function, which feeds back to the climate system. However, drivers for the peak of growing season (POS) in seasonal dynamics of terrestrial ecosystems remain unclear. Here, spatial–temporal patterns of POS dynamics were analyzed by solar-induced chlorophyll fluorescence (SIF) and vegetation index in the Northern Hemisphere over the past two decades from 2001 to 2020. Overall, a slow advanced POS was observed in the Northern Hemisphere, while a delayed POS distributed mainly in northeastern North America. Trends of POS were driven by the start of growing season (SOS) rather than pre-POS climate both at hemisphere and biome scale. The effect of SOS on the trends in POS was the strongest in shrublands while the weakest in evergreen broad-leaved forest. These findings highlight the crucial role of biological rhythms rather than climatic factors in exploring seasonal carbon dynamics and global carbon balance.     

北方地区典型林草地物候时空变化特征及其对气象因子的响应

为探究北方地区典型植被林地、草地物候特征及其对气候变化的响应,本文基于1982—2015年的GIMMS NDVI 3gv1数据集和气象资料,采用动态阈值法提取植被物候,应用线性回归和偏相关分析法分析林、草地物候时空变化特征及其与气候变化的关系。结果表明：(1)林地生长季长度(LOS)以0.32d/a的速率极显著延长,整体表现为生长季始期(SOS)以-0.18d/a极显著提前,生长季末期(EOS)以0.14d/a极显著推迟。林地SOS提前、EOS推迟和LOS延长的区域面积占比分别为93.3%、90.4%和96.3%。(2)草地LOS以-0.01d/a的速率不显著缩短,表现为SOS以-0.09d/a不显著提前,EOS以-0.10d/a不显著提前。SOS提前、EOS提前和LOS缩短的区域占比为67.5%、69.1%和50%。(3)林地SOS主要受冬末春初的气温升高和降水增加而提前,EOS受夏季气温升高、秋季以及冬末春初降水增加而推迟。(4)草地SOS主要受春季气温升高和短波辐射减少而提前,EOS受秋季气温升高和短波辐射减少而提前,草地物候与气象因子的响应时长小于林地。     

日光诱导叶绿素荧光对亚热带常绿针叶林物候的追踪

植被物候期(春季返青和秋季衰老)是表征生物响应和陆地碳循环的基础信息。由于常绿针叶林冠层绿度的季节变动较弱,遥感提取常绿针叶林的物候信息存在着较大的不确定性,是目前区域物候监测中的难点。利用MODIS植被指数(归一化植被指数NDVI和增强型植被指数EVI)、GOME-2日光诱导叶绿素荧光(SIF)和通量数据(总初级生产力GPP)估算2007—2011年亚热带常绿针叶林物候期,用来比较三类遥感指数估算常绿针叶林物候的差异。结果表明:基于表征光合作用物候的通量GPP数据估算得到5年内亚热带常绿针叶林生长季开始时间(SOS_(GPP))为第63天,生长季结束时间(EOS_(GPP))为第324天,生长季长度为272天;基于反映植被光合作用特征的SIF曲线获得物候信息要滞后GPP物候期,其中生长季开始时间滞后19天,生长季结束时间滞后2天;基于传统植被指数NDVI和EVI的物候期滞后GPP物候期的时间要大于SIF滞后期,其中植被指数SOS滞后SOS_(GPP)31天,植被指数EOS滞后EOS_(GPP)10—17天。虽然基于3种遥感指数估算的春季和秋季物候都滞后于通量GPP的物候期,但是卫星SIF的物候信息能够更好地捕捉常绿针叶林的生长阶段。同时,春季温度是影响森林生长季开始时间的最重要因素;秋季水分和辐射是影响生长季结束时间的关键因素。由此可见,SIF估算的亚热带常绿针叶林的春季和秋季物候的滞后时间要短于传统植被指数,能更好地追踪常绿林光合作用的季节性,为深入研究陆地生态系统碳循环及其对气候变化的响应提供重要的基础。     

北方典型农牧交错带草地开垦对地表辐射收支与水热平衡的影响

以北方典型农牧交错带草原和农田生态系统的涡度相关数据为基础,对比分析了生长季两种不同土地利用类型的辐射和水热通量之异同,揭示了草地开垦影响地表辐射收支与水热平衡的机制。结果表明:在植被生长季(5月—9月),草地开垦引起太阳总辐射增加了10.74%,短波反射辐射减少了14.20%,净辐射增加了35.16%;在水热通量方面,草地开垦引起潜热通量日积分平均值增加了0.20MJ/m~2,同时显热通量减少了0.09 MJ/m~2;生长季内地表反照率减小,表征地表吸收太阳辐射增加,有升高气温的趋势;非生长季内地表反照率增加,有降低气温趋势,此外地表反照率与土壤湿度存在负指数关系;波文比在植被生长早期和末期增加,生长旺期减小,说明草地开垦与影响着近地表大气状态,从而改变了区域气候。     

贺兰山地区植被冠层物候与树干形成层物候的关系

基于贺兰山地区98棵油松树轮样本的宽度数据、植被归一化指数(NDVI)数据以及土地覆被数据,采用VS-oscilloscope模型模拟的油松径向生长过程,研究植被冠层与树干形成层物候之间的联系。结果表明: 林地冠层与油松形成层生长结束期(EOS)显著相关,且高于草地与形成层之间的相关。油松生长开始期(SOS)和EOS分别与5—6月、8—9月的平均最低温度有关。5—6月的平均最低气温每升高1 ℃,SOS提前4.3 d;8—9月的平均最低气温每升高1 ℃,EOS推迟2.6 d。植被冠层物候与油松形成层物候的相关性受植被类型的影响;仅通过树轮生理模型模拟树木生长动态,结果可能存在偏差;利用遥感监测数据将冠层发育和形成层生长过程结合有助于更准确地了解树木生长动态。     

1982-2013年内蒙古地区植被物候对干旱变化的响应

气候变化引起的植被物候变化正在大幅度改变生态系统,研究植被物候对干旱的响应对保护内蒙古的生态系统具有重要意义。根据1：100万植被区划,把内蒙古划分为8个植被分区,利用多时间尺度气象标准化降水蒸散指数（SPEI）和NDVI3g时序数据所反演的物候指标,分析内蒙古植被物候的时空变化及其对干旱的响应规律。结果显示：1）在1982年至2013年间,内蒙古植被受到不同时间尺度下干旱的高度控制,尤其是时间尺度干旱的影响（SPEI-3）;2）对于整个研究区,生长季开始（SOS）呈提前趋势,生长季结束（EOS）呈延后趋势,生长季长度（LOS）呈延长趋势,像元比例分别为63.79%、59.77%和62.83%;3）内蒙古除荒漠植被类型地区外,同年春季和夏季初期干旱对SOS均具有延迟作用,同年秋季干旱对EOS均具有延迟作用 ;4） 不同植被类型对干旱强度指数的响应程度存在差异,响应程度集中在-10d/0.1-10d/0.1（例如,1d/0.1表示干旱强度指数每增大0.1,会导致物候指数延迟1 d,而-1d/0.1表示干旱强度指数每增大0.1,会导致物候指数提前1 d）。     

祁连山不同植被类型的物候变化及其对气候的响应

基于1982—2006年GIMMS NDVI和2000—2014年MODIS NDVI遥感数据,利用double logistic拟合方法提取了1982—2014年祁连山区不同植被的生长季始期、生长季末期和生长季长度3个重要的物候参数,分析了不同植被物候期的时间变化趋势、空间分异特征及对气候因子的响应。结果表明:(1)祁连山区不同植被的生长季始期和生长季末期随年际变化表现出波动提前或推迟,其中沼泽植被的变化波动最大;草甸植被、灌丛植被、阔叶林植被和栽培植被生长季长度出现延长趋势;(2)祁连山区植被生长季始期集中在5月初,其中阔叶林植被生长季开始最早,荒漠植被生长季开始最晚,植被生长季末期集中在9月,栽培植被生长季结束较早,荒漠植被、沼泽植被生长季结束较晚,植被生长季长度集中在110—140 d,其中阔叶林植被、针叶林植被生长季长度较长,而荒漠植被、高山植被生长季长度较短;(3)祁连山植被物候期变化趋势的空间分布表明植被生长季始期、生长季末期主要表现为提前不明显和推迟不明显,生长季长度主要表现为缩短不明显和延长不明显;(4)物候要素与气候要素相关性表明前期温度的积累有利于植被的开始生长,但当年3月的降水量对植被生长季始期同样有重要作用,不同植被生长季末期与8月、9月温度相关性较大,而与10月、11月降水的相关性较大。     

Phenology and climate relationships in aspen (Populus tremuloides Michx.) forest and woodland communities of southwestern Colorado

Trembling aspen (Populus tremuloides Michx.) occurs over wide geographical, latitudinal, elevational, and environmental gradients, making it a favorable candidate for a study of phenology and climate relationships. Aspen forests and woodlands provide numerous ecosystem services, such as high primary productivity and biodiversity, retention and storage of environmental variables (precipitation, temperature, snow–water equivalent) that affect the spring and fall phenology of the aspen woodland communities of southwestern Colorado. We assessed the land surface phenology of aspen woodlands using two phenology indices, start of season time (SOST) and end of season time (EOST), from the U.S. Geological Survey (USGS) database of conterminous U.S. phenological indicators over an 11-year time period (2001–2011). These indicators were developed with 250 m resolution remotely sensed data from the Moderate Resolution Imaging Spectroradiometer processed to highlight vegetation response. We compiled data on SOST, EOST, elevation, precipitation, air temperature, and snow water equivalent (SWE) for selected sites having more than 80% cover by aspen woodland communities. In the 11-year time frame of our study, EOST had significant positive correlation with minimum fall temperature and significant negative correlation with fall precipitation. SOST had a significant positive correlation with spring SWE and spring maximum temperature.     

基于植被遥感的西南喀斯特退耕还林工程效果评价——以贵州省毕节地区为例

中国西南喀斯特地区生态环境脆弱,人口压力和不合理的土地利用方式使得土地退化问题严峻。2000年以来,国家开始在该地区推行退耕还林等一系列生态工程。需要评估这些工程的效果,以期为进一步的生态建设工程决策提供科学依据。本文以贵州省毕节地区为例,利用SPOT-VGT NDVI遥感数据,以大规模开展退耕还林工程前的1998—2001年为基准,建立NDVI-气候响应模型,在此基础上结合残差法来分析2002—2008年以退耕工程为主的人为因素在当地生态恢复中的作用。结果表明,近年来开展的生态工程使得整个毕节地区植被条件得到了明显的改善,但在东部大方、黔西一些区域土地退化的趋势仍未扭转,需要今后进一步的政策引导和开展后续生态工程。     

Discrepancies in vegetation phenology trends and shift patterns in different climatic zones in middle and eastern Eurasia between 1982 and 2015

Changes in vegetation phenology directly reflect the response of vegetation growth to climate change. In this study, using the Normalized Difference Vegetation Index dataset from 1982 to 2015, we extracted start date of vegetation growing season (SOS), end date of vegetation growing season (EOS), and length of vegetation growing season (LOS) in the middle and eastern Eurasia region and evaluated linear trends in SOS, EOS, and LOS for the entire study area, as well as for four climatic zones. The results show that the LOS has significantly increased by 0.27 days/year, mostly due to a significantly advanced SOS (?0.20 days/year) and a slightly delayed EOS (0.07 days/year) over the entire study area from 1982 to 2015. The vegetation phenology trends in the four climatic zones are not continuous throughout the 34‐year period. Furthermore, discrepancies in the shifting patterns of vegetation phenology trend existed among different climatic zones. Turning points (TP) of SOS trends in the Cold zone, Temperate zone, and Tibetan Plateau zone occurred in the mid‐ or late 1990s. The advanced trends of SOS in the Cold zone, Temperate zone, and Tibetan Plateau zone exhibited accelerated, stalled, and reversed patterns after the corresponding TP, respectively. The TP did not occurred in Cold‐Temperate zone, where the SOS showed a consistent and continuous advance. TPs of EOS trends in the Cold zone, Cold‐Temperate zone, Temperate zone, and Tibetan Plateau zone occurred in the late 1980s or mid‐1990s. The EOS in the Cold zone, Cold‐Temperate zone, Temperate zone, and Tibetan Plateau zone showed weak advanced or delayed trends after the corresponding TP, which were comparable with the delayed trends before the corresponding TP. The shift patterns of LOS trends were primarily influenced by the shift patterns of SOS trends and were also heterogeneous within climatic zones.     

秦岭山区植被春季物候的海拔敏感性

植被物候作为自然界规律性、周期性的现象,对自然环境尤其是气候变化有着重要的指示作用,研究其时空变化特征对陆地植被生态环境监测具有重要意义。本研究采用Savitzky-Golay滤波法重建秦岭山区2001—2018年MODIS增强植被指数时间序列影像,利用动态阈值法提取研究区春季物候信息(返青期),并对返青期多年平均值和年际变化与海拔、坡度进行相关分析。结果表明: 海拔每升高100 m,植被返青期推迟1.82 d;返青期的年际变化趋势主要集中在0~5 d·(10 a)^-1。其中,呈推迟趋势的像元主要分布在低海拔地区,呈提前趋势的像元主要分布在高海拔地区。高海拔地区返青期的年际变化比低海拔地区复杂;秦岭山区植被返青期存在南北差异。北坡植被返青期多年平均值较南坡早2.9 d,南坡植被返青期的推迟程度大于北坡。南北坡植被返青期的年际变化在低海拔地区呈推迟趋势,且南北坡相差不大,而提前趋势在中高海拔地区存在显著差异。     

A new method of sample selections for optimizing phenology model based remote sensing data

 植被物候模型是生态系统模型的重要组成部分, 其精度对准确地模拟陆面和大气之间的能量和物质交换具有重要意义。利用遥感获取空间物候信息并与气候数据进行耦合分析是在中亚干旱区等地面物候观测数据缺乏的地区构建物候模型的重要方法。为减小混合植被像元和气候数据资料的内在误差及二者在空间尺度的不匹配对物候模型构建产生的影响, 该研究提出一种在气象站点周围选取满足规定规则集的“代表植被类型像元”作为样本点的选择方法, 以代表植被类型像元的遥感物候数据和气象站点数据为基础, 结合经典物候模型和改进物候模型, 在粒子群优化算法支持下, 分别以独立的拟合与评价样本数据, 完成了荒漠草原植被与落叶阔叶林的模型拟合与评价。研究发现中亚干旱区荒漠草原植被的最优模型为温度-降水修正模型, 落叶阔叶林的最优模型为替代模型。通过此方法模型总体精度在8–10 d左右。结果表明此方法在气候数据和植物物候空间匹配方面有改进, 有助于提高物候模型精度。     

新疆植被物候时空变化特征

基于MODIS-NDVI数据,提取新疆2001—2016年典型植被物候期,分析新疆不同生态分区的山地-绿洲系统植被物候期的时空演变趋势和空间分异特征,并结合同期气象数据,探讨植被物候与气候变化的响应关系。结论为:(1)新疆植被物候具有明显的纬向分布和垂直地带性分布特征,海拔在物候的地域分异中扮演着重要作用。新疆植被生长季开始时间(Start of season,SOS)集中于3月中旬至5月上旬,生长季结束时间(End of season,EOS)集中于10月中旬至12月下旬。(2)与全球大背景下典型植被物候特征变化趋势相反,新疆植被SOS呈推迟趋势,推迟幅度为1.9d/10a;EOS呈提前趋势,提前幅度为3.66d/10a;生长季长度(Length of season,LEN)呈缩短趋势,缩短幅度为5.6d/10a。除东疆地区外,全疆及不同分区均呈现出绿洲及平原SOS较早,山地区域较迟;全疆及不同分区均呈现出山地EOS结束较早,绿洲结束较迟;除东疆地区外,全疆及不同分区的LEN均为绿洲及平原区域山地,同样显示出垂直地带性分布的特征。(3)通过冗余分析(Redundancy analysis,RDA)解释了物候特征与气象因子关系的绝大部分信息,生长季开始时间受春季气温、前一年冬季降水量和日照时数的显著影响。夏季和秋季降水量是新疆植被生长季结束时间的重要影响因素,在总体上受气温和日照时数的影响较小。     

A Multilevel Analysis of the Impact of Land Use on Interannual Land-Cover Change in East Africa

Abstract The aim of this study was to characterize the short-term land-cover change processes that were detected in Eastern Africa, based on a set of change metrics that allow for the quantification of interannual changes in vegetation productivity, changes in vegetation phenology and a combination of both. We tested to what extent land use, fire activity and livestock grazing modified the vegetation response to short-term rainfall variability in East Africa and how this is reflected in change metrics derived from MODerate Imaging Spectrometer (MODIS) time series of remote sensing data. We used a hierarchical approach to disentangle the contribution of human activities and climate variability to the patterns of short-term vegetation change in East Africa at different levels of organization. Our results clearly show that land use significantly influences the vegetation response to rainfall variability as measured by time series of MODIS data. Areas with different types of land use react in a different way to interannual climate variability, leading to different values of the change indices depending on the land use type. The impact of land use is more reflected in interannual variability of vegetation productivity and overall change in the vegetation, whereas changes in phenology are mainly driven by climate variability and affect most vegetation types in similar ways. Our multilevel approach led to improved models and clearly demonstrated that climate influence plays at a different scale than land use, fire and herbivore grazing. It helped us to understand dynamics within and between biomes in the study area and investigate the relative importance of different factors influencing short-term variability in change indices at different scales.     

A systematic review of vegetation phenology in Africa

The study of vegetation phenology is important because it is a sensitive indicator of climate changes and it regulates carbon, energy and water fluxes between the land and atmosphere. Africa, which has 17% of the global forest cover, contributes significantly to the global carbon budget and has been identified as potentially highly vulnerable to climate change impacts. In spite of this, very little is known about vegetation phenology across Africa and the factors regulating vegetation growth and dynamics. Hence, this review aimed to provide a synthesis of studies of related Africa's vegetation phenology and classify them based on the methods and techniques used in order to identify major research gaps. Significant increases in the number of phenological studies in the last decade were observed, with over 70% of studies adopting a satellite-based remote sensing approach to monitor vegetation phenology. Whereas ground based studies that provide detailed characterisation of vegetation phenological development, occurred rarely in the continent. Similarly, less than 14% of satellite-based remote sensing studies evaluated vegetation phenology at the continental scale using coarse spatial resolution datasets. Even more evident was the lack of research focusing on the impacts of climate change on vegetation phenology. Consequently, given the importance and the uniqueness of both methods of phenological assessment, there is need for more ground-based studies to enable greater understanding of phenology at the species level. Likewise, finer spatial resolution satellite sensor data for regional phenological assessment is required, with a greater focus on the relationship between climate change and vegetation phenological changes. This would contribute greatly to debates over climate change impacts and, most importantly, climate change mitigation strategies.     

Variability of the start of the growing season in Fennoscandia, 1982–2002

Fennoscandia is characterized by a large degree of climatic diversity. Vegetation phenology may respond differently to climate change according to the climatic gradients within the region. To map the annual and spatial variability of the start of the growing season (SOS) in Fennoscandia, the twice-monthly GIMMS-NDVI satellite dataset was used. The data set has an 8 × 8 km² spatial resolution and covers the period from 1982 to 2002. The mapping was done by applying pixel-specific threshold values to the NDVI data. These threshold values were determined form surface phenology data on birch (Betula sp.). Then, we produced NDVI based maps of SOS for each of the 21 years. Finally, the time differences between the SOS and the last day of snow cover, as well as dates of passing different temperatures, were analyzed for 21 meteorological stations. The analyses showed that 1985 was the most extreme year in terms of late SOS. In terms of early SOS, the year 1990 was by far the most extreme. Locally, the SOS has an average range of 1 month between the earliest and latest recorded SOS, with a trend towards a bigger range in the oceanic parts. The results indicate that a 1°C increase in spring temperatures in general corresponds to an advancement of 5–6 days in SOS. However, there is a clear trend according to the degree of oceanity, with a 1°C increase in the most oceanic parts corresponding roughly to 7–9 days earlier SOS, compared to less than 5 days earlier in the continental parts.     

Strong contribution of autumn phenology to changes in satellite‐derived growing season length estimates across Europe (1982–2011)

Land Surface Phenology (LSP) is the most direct representation of intra‐annual dynamics of vegetated land surfaces as observed from satellite imagery. LSP plays a key role in characterizing land‐surface fluxes, and is central to accurately parameterizing terrestrial biosphere–atmosphere interactions, as well as climate models. In this article, we present an evaluation of Pan‐European LSP and its changes over the past 30 years, using the longest continuous record of Normalized Difference Vegetation Index (NDVI) available to date in combination with a landscape‐based aggregation scheme. We used indicators of Start‐Of‐Season, End‐Of‐Season and Growing Season Length (SOS, EOS and GSL, respectively) for the period 1982–2011 to test for temporal trends in activity of terrestrial vegetation and their spatial distribution. We aggregated pixels into ecologically representative spatial units using the European Landscape Classification (LANMAP) and assessed the relative contribution of spring and autumn phenology. GSL increased significantly by 18–24 days decade^?1 over 18–30% of the land area of Europe, depending on methodology. This trend varied extensively within and between climatic zones and landscape classes. The areas of greatest growing‐season lengthening were the Continental and Boreal zones, with hotspots concentrated in southern Fennoscandia, Western Russia and pockets of continental Europe. For the Atlantic and Steppic zones, we found an average shortening of the growing season with hotspots in Western France, the Po valley, and around the Caspian Sea. In many zones, changes in the NDVI‐derived end‐of‐season contributed more to the GSL trend than changes in spring green‐up, resulting in asymmetric trends. This underlines the importance of investigating senescence and its underlying processes more closely as a driver of LSP and global change.     

The extent of shifts in vegetation phenology between rural and urban areas within a human‐dominated region

Urbanization is one of the major environmental challenges facing the world today. One of its particularly pressing effects is alterations to local and regional climate through, for example, the Urban Heat Island. Such changes in conditions are likely to have an impact on the phenology of urban vegetation, which will have knock‐on implications for the role that urban green infrastructure can play in delivering multiple ecosystem services. Here, in a human‐dominated region, we undertake an explicit comparison of vegetation phenology between urban and rural zones. Using satellite‐derived MODIS‐EVI data from the first decade of the 20th century, we extract metrics of vegetation phenology (date of start of growing season, date of end of growing season, and length of season) for Britain's 15 largest cities and their rural surrounds. On average, urban areas experienced a growing season 8.8 days longer than surrounding rural zones. As would be expected, there was a significant decline in growing season length with latitude (by 3.4 and 2.4 days/degree latitude in rural and urban areas respectively). Although there is considerable variability in how phenology in urban and rural areas differs across our study cities, we found no evidence that built urban form influences the start, end, or length of the growing season. However, the difference in the length of the growing season between rural and urban areas was significantly negatively associated with the mean disposable household income for a city. Vegetation in urban areas deliver many ecosystem services such as temperature mitigation, pollution removal, carbon uptake and storage, the provision of amenity value for humans and habitat for biodiversity. Given the rapid pace of urbanization and ongoing climate change, understanding how vegetation phenology will alter in the future is important if we wish to be able to manage urban greenspaces effectively.