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1.
植被物候是气候变化和外界环境变化的感应器。鉴于当前植被物候研究多在生态区,城市相对较少,以北京为研究区,基于2001—2019年MOD13Q1植被指数产品,利用动态阈值法提取研究区内植被的物候参数,即生长季开始期(start of season,SOS)、生长季结束期(end of season,EOS)和生长季长度(length of season,LOS),揭示植被物候期的时空变化特征并利用相关分析法研究其对城市化的响应。研究表明:2001—2019年,北京出现植被生长季开始期提前、结束期推迟和长度延长的现象,城区和郊区亦然。植被物候在城郊方向上表现出明显的梯度现象,且城区生长季开始期最早、结束期最晚和长度最长。城市化的发展对植被生长季开始期提前、结束期推迟和长度延长具有重要作用,不同地区的植被物候受城市化影响具有差异性。  相似文献   

2.
新疆棉花物候时空变化遥感监测及气温影响分析   总被引:2,自引:0,他引:2  
基于2001~2016连续16a的MOD09Q1数据计算获取NDVI时序数据,利用Savitzky-Golay(S-G)滤波重构NDVI时序曲线,以动态阈值法提取新疆大地块棉花的生长季开始期、生长季结束期和生长季长度信息,并分析新疆棉花物候时空变化特征及其对气温变化的响应。结果表明:(1)南北疆棉花物候空间差异显著:生长季开始期由南向北逐渐推迟,南疆集中于第151d之前,北疆于第151~163d之间;生长季结束期逐渐提前,北疆大部分于第292d前结束生长,而南疆集中发生在第298d之后;生长季长度逐渐缩短,南疆普遍长于150d,北疆通常短于150d。(2)南北疆物候变化趋势不一致:阿克苏生长季开始期和结束期表现为"推迟—提前—推迟—提前"变化趋势,生长季长度不明显延长;沙湾县生长季开始期表现为微弱提前趋势,生长季结束期则为先推迟再提前趋势,生长季长度表现为缩短、延长、缩短的波动变化。(3)原因分析表明棉花物候主要受气温影响:生长季开始期与10℃初日、15℃初日、生长初期平均气温表现显著的相关性;生长季结束期与10℃终止日相关性较好。  相似文献   

3.
基于EVI2数据集提取青藏高原草地植被的物候信息,分析青藏高原草地返青期(Start of Growth Season,SOG)、枯黄期(End of Growth Season,EOG)和生长季长度(Length of Growth Season,LOG)的空间分布格局及近30 a来青藏高原草地物候的时空动态变化特征。结果表明:青藏高原的草地物候由东南向西北呈现出明显的区域性差异。其中,高原东部和西北部地区的草地植被返青时间早于中部和西南部地区,而枯黄时间却晚于中部和西南部地区,生长季长度较中部和西南部地区长。同时,青藏高原物候变化趋势在东西部地区的差异十分明显。草地植被返青提前的区域主要集中在高原的东部,提前速率为0.49 d/a(R~2=0.54)。草地植被物候分布和变化趋势在不同海拔和坡向上的差异也十分显著。海拔每升高1 000 m,草地SOG推迟4 d,EOG提前5 d,LOG缩短9 d。随海拔的升高,草地SOG的推迟速率逐渐增加,LOG变化速率呈现出逐渐减小的趋势。此外,南坡草地SOG较北坡晚,其LOG较北坡、东坡和西坡的短。北坡草地SOG平均推迟速率低于南坡。  相似文献   

4.
基于NDVI数据的三江平原农田物候监测   总被引:2,自引:0,他引:2  
物候现象被称为气候变化的积分仪,研究农田物候现象对农业生产有重要的指导意义。多时相遥感影像使区域物候监测成为可能。利用傅里叶级数对MODIS NDVI数据进行平滑,结合地面观测资料,采用动态阈值法提取物候信息,并与实际观测结果进行比较分析。研究结果表明:三江平原大部分农作物在第120~130 d开始生长,在第250~260 d左右停止生长,2003年三江平原农作物开始生长和结束的时间较早,2005年开始生长日期比2003年有所推迟,2007年农作物开始生长的日期早于2005年,但生长季结束的日期比2003年和2005年都晚,2007年生长季长度较长。采用MODIS NDVI数据获取的物候参数具有一定的可靠性,在农田大面积分布区域监测结果更为准确。
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5.
植被物候是监测陆地生态系统和全球气候变化的重要生物指标。基于经典遥感植被指数的陆表物候监测在不同植被类型的精确分析方面存在较大挑战,日光诱导叶绿素荧光(SIF)可以直接反映植被实际光合作用的动态变化,能够更精确地刻画出植被的年际变异。本研究基于2001~2020年GOSIF数据集,通过D-L拟合函数和动态阈值法提取东北地区植被物候参数,结合一元线性回归分析、稳定性和持续性分析,在多时空尺度下分析2001~2020年东北地区植被物候的时空演变特征,并探讨植被物候对气候变化的响应机制。结果表明:(1)植被生长季开始(Start of Season,SOS)、结束(EndofSeason,EOS)、生长季长度(LengthofSeason,LOS)和生长峰值(Position of Peak,POP)整体上分别呈现出提前、推迟、延长和提前趋势;(2)草丛SOS提前、EOS推迟趋势较为显著,针叶林EOS提前趋势显著;SOS提前、EOS推迟导致LOS延长,除针叶林外,所有植被类型LOS均呈现出延长趋势;除草丛和草原外,其余植被类型POP均呈提前趋势;(3)20年来植被SOS、EOS、LOS和PO...  相似文献   

6.
全球气候变化导致植被生长的季节性节律事件(如返青期、衰落期和生长峰值期等)发生显著变化。植被返青期、衰落期和生长季长度的变化已经得到广泛报道,植被生长峰值代表植被光合作用能力和对气候变化的响应,目前关于植被生长峰值特征(时间点和最大生长幅度)的时空变化和控制机理的研究相对较少,仍需在不同区域深入探讨。以植被覆盖度较好的中国东北地区为例,首先利用长时序遥感NDVI数据(GIMMS NDVI3g)和逻辑斯蒂法提取植被关键物候参数,然后分析了植被生长峰值关键特征(日期和幅度)的变化格局及对气候因子和返青期物候的响应,最后探索了生长峰值对植被生产力变化的贡献。结果表明:东北地区整体的生长峰值时间点和返青时间点呈现延迟趋势、生长幅度呈上升趋势(与MODIS EVI趋势验证一致),生长峰值存在一个约11 a的周期;季前气温和降水对生长幅度的影响范围和幅度较小,主要作用在草原区域,对峰值时间点的显著影响主要在北部森林区域;返青期对生长峰值的控制作用大于气温和降水因素,并且返青期在森林和草原区域主要影响生长幅度变化,在农作物区主要影响生长峰值时间点变化;植被最大生长幅度对自然植被生产力长期变化的影响显著。东北地区生长峰值及对气候、物候响应的不同时空格局反映了植被生长峰值对气候变化的适应能力的差别,并造成多样的植被固碳格局。生长峰值研究有助于深入理解植被光合作用的时空变化格局和气候变化情景下的碳循环过程,对我国东北地区的生态系统评估和管理也有参考意义。  相似文献   

7.
基于MODIS NDVI和多方法的青藏高原植被物候时空特征分析   总被引:1,自引:0,他引:1  
基于遥感的植被物候分析具有连续观测、覆盖面广的优点,已经成为全球气候变化背景下植被生理物理变化研究的重要手段。基于青藏高原96个气象站点和对应的MODIS NDVI时序数据,探索分析了2000~2014年间站点、典型植被覆盖和高原整体区域上的植被物候变化趋势及地理环境影响。首先采用三次样条(Spline)函数法、双逻辑斯蒂(D-L)函数法和奇异谱分析法(SSA)进行NDVI时序数据重构,其次利用导数法(Derivative method,Der)及阈值法(Threshold method,Trs)提取植被物候关键参数(生长季长度LOS、生长季开始点SOS、生长季结束点EOS),进而对比分析6种方法提取结果的差异和适用条件;再利用M-K test趋势分析法计算了各站点和区域的物候发展趋势,并探索其与海拔、降水、温度间的相关性;最后将本文遥感LOS与气温阈值法得到的生长期长度指标(GSL)进行了对比验证。结果表明:(1)阈值法有较强的稳定性及适应性,导数法结果差异较大,整体来看阈值法优于导数法。草原及森林覆被类型中3种物候参数使用SSA、Spline、D-L与阈值法的组合提取结果较优;(2)多种方法提取的多种物候参数趋势空间差异性较大,小尺度上趋势比较一致。青藏高原东南部湿润半湿润的灌木草原区及西北荒漠草原区,呈现SOS和EOS推迟、但LOS仍然延长的趋势;青藏高原西南部湿润区呈现SOS延后、EOS提前、LOS缩短的趋势;青藏高原分布广泛的草原区,各物候参数并未呈现显著变化的趋势特征;(3)温度与物候参数发展趋势呈相关关系,温度升高的情况下呈现SOS提前、EOS延后的现象。由于高原地貌、气候的复杂性,多数站点的物候发展趋势与海拔、降水的相关性并不显著,GSL与LOS的相关性也呈现相似的特征。本文证实在植被物候参数遥感提取中,多种方法差异较大,很少有在全部区域均适用的方法;站点尺度上青藏高原在近15年来植被物候并未呈现明显春季提前或秋季延后趋势。  相似文献   

8.
城市化过程使得土地利用、地表植被覆盖发生着显著的变化,如何定量化描述城市化对植被物候的影响越来越受到各方的关注。基于京津唐地区2001~2006年NDVI时间序列影像,得出了京津唐地区植被物候空间分布格局,计算出北京、天津、唐山3个核心城市城区的距离变量与平均植被物候,并分析了城市化对植被物候指标的影响趋势。结果表明:①2001~2006年,北京城市化使得城区及离城区较近的地方植被生长开始时间提前、结束时间推后、生长季周期变长、NDVI振幅减小;②天津和唐山的城市化使得城区及离城区较近的地方植被生长开始时间延后、结束时间提前、生长季周期变短\,NDVI振幅减小;③城市化对植被物候的影响与该地区城市扩张类型存在相关性关系。  相似文献   

9.
植物的物候与气候等环境因素息息相关,是指示气候与自然环境变化对生态影响的重要指标。目前,气候变暖日益为人所关注,使用遥感技术研究植物物候与气候变化之间的关系具有重要的意义。监测人口密度高和城市经济发达地区的植物物候对气候变暖的响应,可以揭示区域热环境变化及其产生的生态效应。本研究选取长江三角洲地区为研究区域,使用SPOT卫星VGT传感器的长时间NDVI数据序列,对经济发达区域森林植被的NDVI序列进行非对称性高斯函数拟合法平滑处理,并提取与研究其物候特征,发现①NDVI与气温具有较强相关性,随气候变暖,森林植被NDVI年均值有增加趋势;②森林植被生长活跃期起始日期提前,终止日期延后,时长有明显的延长趋势,生长活跃期内NDVI有所增加;③森林植被NDVI极大值与极小值出现日期均明显提前,NDVI极大值有增大趋势,而极小值呈下降趋势,年内极差增加,NDVI增长期缩短,衰落期延长;④森林植被在春、夏两季NDVI均值有所增长,秋季无明显变化,冬季略有降低。  相似文献   

10.
基于不同植被指数提取物候参数是分析长时间物候变化的重要基础。以多云雾的重庆地区为例,使用2010~2019年MODIS NDVI/EVI/EVI2共3种长时序的植被指数数据,通过D-L滤波方法分析了不同植被指数特征;并使用动态阈值法和趋势分析法,对比研究了基于3种植被指数提取的物候参数结果及其随不同地形因子的分异规律,结果如下:(1)EVI和EVI2的时间序列拟合曲线比NDVI的拟合曲线更加平滑,3种植被指数原始值与拟合值的差值主要分布为NDVI(0.05~0.18)、EVI(0.03~0.11)、EVI2(0.03~0.1)。(2)基于3种植被指数提取的物候参数在空间分布和变化趋势上呈现一致性,其中EVI和EVI2提取的植被指数参数皆相近,相差5d之内占比79%以上,SOSEVI2变化显著性区域所占比面积最高(16.36%),SOSNDVI最低为12.37%。(3)SOS随海拔升高而推迟,EOS随海拔升高先延后再提前,LOS随海拔升高先延长后缩短,且EOSNDVI、LOSNDVI随着海拔增加分别与EOSEVI/EOSEVI2、LOSEVI/LOSEVI2差异增大,不同植被类型上,EV...  相似文献   

11.
Based on the third generation GIMMS NDVI time\|series datasets during 1982~2011,we extracted the start of growing season (SOS),end of growing season (EOS) and length of growing season (LOS) in the Mongolian Plateau using cumulative NDVI based logistic regression curves,change rate of curvature in NDVI logistic regression curves and change rate method of NDVI and further analyzed the spatio\|temporal changes of phenology.The results showed that the cumulative NDVI based logistic regression curves and change rate method of NDVI performed better predictions in SOS and EOS modeling,and the mean value of these two methods improved the extraction accuracy of phenology in the Mongolian Plateau.SOS in the Mongolian Plateau mostly started from the middle of April to the end of May and ended from the end of the September to the middle of the October.Most LOS ranged from 125 to 175 days.Spatially,the earlier SOS,later SOS and longer LOS occurred in the humid and sub\|humid area of the plateau,and later SOS,earlier EOS and shorter LOS occurred in arid and semi\|arid regions of the plateau.Temporally,during the 30\|year observation period,approximately,51.6% and 33.9% of the plateau experienced advanced and delayed SOS,respectively,and 21.2% and 12.4% of which are statistically significant;Approximately,35.6% and 49.8% of the study area experienced delayed EOS,respectively,and 8.2% and 12.0% of which are statistically significant;Accordingly,40.3% (17.8% are significant) and 44.8% (18.9% are significant) of the plateau showed shortening and lengthening of the LOS.  相似文献   

12.
Research in vegetation phenology change has been one heated topic of current ecological and climate change study. The Tibetan Plateau, as the highest plateau of the earth, is more vulnerable and sensitive to climate change than many other regions. In this region, shifts in vegetation phenology have been intensively studied during recent decades, primarily based on satellite-retrieved data. In this study, we explored the spatiotemporal changes of vegetation phenology for different land-cover types in the Tibetan Plateau and characterized their relationship with temperature and precipitation by using long-term time-series datasets of normalized difference vegetation index (NDVI) from 1982 to 2014. Diverse phenological changes were observed for different land-cover types, with an advancing start of growing season (SOS), delaying end of growing season (EOS) and increasing length of growing season (LOS) in the eastern Tibetan Plateau where meadow was the dominant vegetation type, but with the opposite changes in the steppe and sparse herbaceous or sparse shrub regions which are mostly located in the northwestern and western edges of the Tibetan Plateau. Correlation analysis indicated that sufficient preseason precipitation may delay the SOS of evergreen forests in the southeastern Plateau and advance the SOS of steppe and sparse herbaceous or sparse shrub in relatively arid areas, while the advance of SOS in meadow areas could be related to higher preseason temperature. For EOS, because it is less sensitive to climate change than SOS, the response of EOS for different land-cover types to precipitation and temperature were more complicated across the Tibetan Plateau.  相似文献   

13.
India has a diverse set of vegetation types ranging from tropical evergreen to dry deciduous. The phenology of these natural vegetation types is often controlled by climatic condition. Estimating phenological variables will help in understanding the response of tropical and subtropical vegetation to climate change. The study investigated the annual and inter-annual variation in vegetation phenology in India using satellite remote sensing. The study used time-series data of the only available satellite measured index of terrestrial chlorophyll content (MERIS Terrestrial Chlorophyll Index) from 2003 to 2007 at 4.6 km spatial resolution. A strong coincidence was observed with expected phenological pattern, in particular, in inter-annual and latitudinal variability of key phenological variables. For major natural vegetation type the onset of greenness had greater latitudinal variation compared to the end of senescence and there was a small or no leafless period. In the 2003-04 growing season a late start for the onset of greenness was detected at low-to-mid latitudes and it was attributed to the extreme cold weather during the early part of 2003. The length of growing season varied from east to west for the major cropping areas in the Indo-Gangetic plain, for both the first and second crops. For the first time, this study attempted to establish a broad regional phenological pattern for India using remotely sensed estimation of canopy chlorophyll content using five years of data. The overall patterns of phenological variables detected from this study broadly coincide with the pattern of natural vegetation phenology revealed in earlier community level studies. The results of this study suggest the need for an organised network combining ground and space observation which is at presently missing in India.  相似文献   

14.
Land surface phenology is defined as the seasonal timing of life cycle events of vegetated land surface on local or global scale.Most studies of vegetation phenology in China’s temperate zone are focused on single vegetation type in certain area,the studies about long-time vegetation phenology on large scale is rare.The influence of vegetation phenology on GPP(gross primary productivity) remains to be determined.Using Moderate Resolution Imaging Spectroradiometer(MODIS) MCD12Q2 data from 2001 to 2014,start of growing season(SOS),end of growing season(EOS) and length of growing season(LOS) in temperate China(>30°N) are obtained.GPP from MODIS MOD17A3 data for the same period is also obtained.Using regression analysis and correlation analysis methods,spatial and temporal patterns of SOS,EOS and LOS are analyzed.The impacts of SOS,EOS and LOS on interannual variability of GPP are also analyzed.Results show that the average and standard deviation of SOS,EOS and LOS from 2001 to 2014 are 121±10,270±12 and 153±12 days,respectively.The trend of earlier SOS,delayed EOS and increased LOS are not significant(p>0.05),but LOS shows positively correlated to GPP.The spatial distribution of annual average LOS and GPP from 2001 to 2014 presents an increase trend from northwest to southeast.Regions with significant interannual variation(p<0.05) of SOS,EOS and LOS are 13%,21% and 13.2%,respectively.Regions of significant correlation(p<0.05) of SOS,EOS and LOS to GPP account for 8.31%,9.33% and 8.72% of the study area.GPP has mainly medium correlations(p<0.05,0.5<|r|<0.8) to SOS,EOS and LOS.  相似文献   

15.
Plant phenology is influenced by various climatic factors such as temperature, precipitation, insolation, and humidity, etc. Among these factors, temperature and precipitation are proved to be the most important. However, the relative importance of these two factors is different among various phenophases and regions and is seldom discussed along environmental gradients. Based on normalized difference vegetation index (NDVI) data from the NDVI3g dataset and using the mid-point method, we extracted the start date of the growing season (SOG) and the end date of the growing season (EOG) in northern China during 1982–2012. To determine which climate factor was more influential on plant phenology, partial correlation analysis was applied to analyse the spatial difference between the response of SOG and EOG to temperature and precipitation. Finally, we calculated the temperature and precipitation sensitivities of the SOG and EOG. The results showed that: (1) SOG displayed an advancing trend in most regions, while EOG was delayed for all the vegetation types during 1982–2012. (2) SOG was mainly triggered by preseason temperature. The increase in temperature caused an overall advance in SOG. However, the relationship between SOG and precipitation varied among different vegetation types. Regarding EOG, precipitation had greater impacts than temperature in relatively arid environments, such as deserts, steppes and meadow biomes. (3) The response of vegetation phenology (both SOG and EOG) to temperature became stronger with increasing preseason precipitation across space. The response of EOG to precipitation became weaker from arid regions to relatively humid regions. These results provide a better understanding of the spatial pattern of the phenological response along the precipitation gradient and a reference for assessing impacts of future climate change on vegetation phenology, especially in transitional and fragile zones.  相似文献   

16.
In the context of global climate change,vegetation phenology analysis based on remote sensing has become an critical method for studying the characteristics of physical and physiological changes of vegetation.This paper uses the MODIS NDVI time\|series data of 96 meteorological stations over the Tibetan Plateau during 2000\|2014 to explore the development trend of vegetation phenological and geographical environment factors of each meteorological station,typical vegetation coverage and the whole plateau region.Firstly,using three cubic spline function method (Spline),double logistic function method(D\|L)and singular spectrum analysis (SSA),NDVI time\|series data is reconstructed,then using the derivative method (Der)and threshold method (Trs),the key parameters of phenological information is extracted,after that differences and application conditions between the six methods are analyzed and compared.Secondly,using M\|K test trend analysis method,the phenological development trend of each site and area were calculated,the relationship between phenological development trend and altitude,precipitation,temperature is studied.Finally,by the Growing season length(GSL)obtained by temperature threshold method,LOS is compared and verified.in grassland and forest land cover types,SSA,Spline,D\|L combined with threshold method to get the Start of Season(SOS),end of season(EOS),Length of season (SOS)respectively is a good combination strategy.(2)The spatial differences of various phenological parameters extracted by different methods are large,and the trend is relatively consistent at small scales.Southeast humid and semi\|humid shrub steppe region and northwestern desert steppe in the Tibetan Plateau,SOS and EOS delayed,but LOS prolonged;southwestern humid region,SOS and EOS delayed,LOS shortened;widely distributed grassland,the phenological parameters did not show significant tendency.(3)Temperature is related to the development trend of phenological parameters.With temperature increasing,the phenomena of SOS advance,EOS lag are presented.Because of the complexity of the plateau landform and climate,there was no significant relationship between phenological development trend for most of the site with the altitude and precipitation,only a few sites have strong correlation,the correlation between GSL and LOS also showed similar characteristics.For remote sensing based phonological analyses,our study identify there is no method existing here that is a adaptive across all the Tibetan Plateau.in addition,at point scale the phenological parameters do not represent a significant earlier or later trend.  相似文献   

17.
Vegetation phenology tracks plants' lifecycle events, revealing the response of vegetation to global climate changes. Changes in vegetation phenology also influence fluxes of carbon, water, and energy at local and global scales. In this study, we analysed a time series of Ku-band radar backscatter measurements from the SeaWinds scatterometer on board the Quick Scatterometer (QuickSCAT) to examine canopy phenology from 2003 to 2005 across China. The thaw season SeaWinds backscatter and Moderate Resolution Imaging Spectroradiometer (MODIS) leaf area index (LAI) time series were significantly correlated in 20 of the 22 sites (p < 0.05). A weighted curve-fitting method was applied to detect the start of season and end of season from both data sets. The SeaWinds scatterometer generally detected earlier timing of spring leaf-out and later fall senescence than the MODIS LAI data sets. The SeaWinds backscatter detected phenological metrics in 75.85% of mainland China. Similar spatial patterns were observed from the SeaWinds backscatter and MODIS LAI time series; however, the average standard deviation of the scatterometer-detected metrics was lower than that of MODIS LAI products. Overall, the phenological information from the SeaWinds scatterometer could provide an alternative view on the growth dynamics of land-surface vegetation.  相似文献   

18.
利用1980~2019年中国长时间序列的AVHRR逐日无云积雪面积产品和气象站实测雪深资料计算积雪日数、积雪初日、积雪终日、积雪期、雪深等积雪物候参数,研究积雪物候的时空分布变化,同时结合ECMWF-ERA5再分析资料和GIMMS NDVI3g数据集分别提取气象因子(气温、降水)和植被因子(返青期、枯黄期、生长期),探究北疆积雪物候变化对气象因子和植被因子的响应。结果表明:北疆近40 a间的平均积雪日数为81.62 d/a,73%的区域为稳定积雪区,积雪初日在11月、终日在3月,积雪期为每年11月初至次年3月底4月初;空间上呈现不均匀分布,其中阿勒泰山地区、天山地区、大部分塔城盆地和额尔齐斯谷地区为主要积雪区,1980~2019年间北疆积雪覆盖面积比例、积雪日数和积雪期逐年降低,积雪初日基本没变,但积雪终日显著提前;ECMWF-ERA5再分析资料表明1980~2019年北疆积雪期降水量无明显变化,但积雪覆盖面积比例显著降低,说明降雪区雪深可能增加,这与北疆气象站实测雪深逐渐增加结果相吻合;平均气温与积雪期积雪覆盖面积比例、积雪日数、积雪期长度相关性较大,呈现显著负相关,积雪期降水量与积雪物候参数呈现正相关;积雪物候及其气候效应引起北疆自然植被返青期显著提前,植被生长期延长的特征。  相似文献   

19.
Land surface phenology dynamics reflect the response of the Earth's biosphere to inter‐ and intra‐annual dynamics of the Earth's climate and hydrologic regimes. Investigations of land surface phenology dynamics and its relation to long‐term climate variation could help us to detect the response of regional vegetation to climate variation. The present study developed a new algorithm for detecting regional land surface phenology dynamics (ARLSPD) and demonstrated it in detecting the vegetation response to inter‐annual climate variability in the North East China Transect (NECT), a mid‐latitude semi‐arid terrestrial transect with strong gradients in environmental conditions and vegetation formations. The spatial–temporal patterns of greenup‐onset date, maturity date, and senescence date during the period of 1982–2000 are presented. The resultant spatial–temporal patterns of land surface phenology were quite consistent with the land‐cover characteristics, moisture, and temperature gradients. The relations between inter‐annual variations in phenology and seasonal climate were investigated. It was found that besides human disturbance, land surface phenology depended primarily on the combined effects of preseason temperature and precipitation. The relative influence of preseason temperature and precipitation on land surface phenology was changing, which led to the different responses of land surface dynamics to climate variation along the moisture gradient in the NECT. In the arid and semi‐arid region of NECT, the dates of onset for phonological events in temperate typical grassland were most significantly related to the precipitation during the preceding 2–4 months. Temperature‐induced drought stress during the preceding months could delay greenup onset in cropland/grassland mosaic, and advance senescence in temporal typical grassland, and in cropland/grassland mosaic. The regional phenology algorithm, theoretically also applicable for complex ecosystems characterized by annual multiple growth cycles, is expected to couple with large‐scale biogeochemical models to regulate dynamically land surface phenology.  相似文献   

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