基于MOD09产品的水稻叶面积指数和叶绿素含量的遥感估算模型

基于中分辨率成像光谱仪MODIS (moderate resolution imaging spectroradiometer)反射率产品MOD09的同步野外实测水稻叶面积指数（LAI）和叶绿素含量（Chltot）相关数据,探寻用MOD09产品提取的植被指数(VIs)与水稻LAI和Chltot之间的相关性以及估算模型. 结果表明,MOD09计算的VI数值比MODIS前3个波段数值偏大,归一化植被指数NDVI (normalized difference vegetation index) 值普遍比增强性植被指数EVI(enhanced vegetation index) 值大. 通过4种不同植被指数与LAI相关性的比较,得出EVI与LAI的相关关系在水稻各个生育期优于其它植被指数,基于MOD09-EVI建立水稻LAI的遥感估算模型,经实际地面同步数据检验, 模型精度较高. 因而, MOD09-EVI较适用于水稻叶面积指数的实时遥感监测. MOD09红波段与Chltot之间的相关性在水稻前中期达到显著,并且优于其它植被指数,基于MOD09红波段建立了水稻前中期Chltot的估算模型并进行了精度检验. 除水稻孕穗期叶绿素含量估算模型的相关系数和F值通过了显著性检验外, 其余生育期估算模型都没有通过显著性检验.     

毛竹林总初级生产力年际变化及其驱动因素——以安吉县为例

森林生态系统碳通量的年际变化及其驱动因素分析是了解森林碳收支动态变化以及预测未来气候变化对森林碳收支影响的重要理论基础,对评估森林应对气候变化的贡献具有重要意义。结合MODIS叶面积指数(LAI)和归一化植被指数(NDVI)产品、MERRA气象数据和通量塔观测数据,采用光能利用率模型模拟2004—2011年安吉县毛竹林生态系统总初级生产力(GPP)空间分布,并分析GPP年际变化及其驱动因素。结果表明:(1)小年毛竹林GPP稍高于大年GPP;(2)2004—2011年安吉县毛竹林年日均GPP呈下降趋势,东部、西部和整个安吉县毛竹林年日均GPP变化速率分别为-0.064、-0.033和-0.045g C m-2W-1,年均温度持续下降是主要驱动因素;(3)LAI年际变化是GPP年际变化的主要驱动因素,主要原因是毛竹林大小年交替规律引起了有效LAI年际间差异;(4)西部GPP年际变化幅度大于东部,环境和生物因素对GPP年际变化的作用方向决定了毛竹林GPP年际变化的幅度。     

遥感GPP模型在中亚干旱区4个典型生态系统的适用性评价

总初级生产力(GPP)是全球生态系统碳循环的重要组成部分,对全球气候变化有重要影响。目前有多种遥感模型可以模拟总初级生产力,比较不同遥感模型在中亚干旱区上的适用性对推进全球干旱区碳收支估算具有重要意义。基于涡度协相关技术观测的四个地面站数据验证MOD17、VODCA2、VPM、TG、SANIRv五种模型的模拟精度。结果表明：(1)基于光能利用率理论的MOD17、VPM模型模拟咸海荒漠植被和阜康荒漠植被GPP的精度最高(R²分别为0.52和0.80),但在模拟草地、农田生态系统生产力时存在较明显的低估(RE>20%);基于植被指数的遥感模型TG模型、SANIRv模型模拟巴尔喀什湖草地生态系统和乌兰乌苏农田生态系统GPP的精度最高(R²分别为0.91和0.81),同时模拟值与实测值的相对误差也较低;基于微波的VODCA2模型模拟各生态系统生产力的效果最差。(2)水分亏缺是限制植被GPP的主要因素,因此是否合理考虑水分胁迫是影响GPP模型在中亚干旱区适用性的重要因素。研究揭示了遥感GPP模型在中亚干旱区的应用潜力,为推进全球植被碳通量的准确估...     

基于FLUXNET的CLM模型总初级生产力模拟评价与误差分析

总初级生产力(gross primary productivity,GPP)的准确估计是陆地生态系统碳循环研究以及未来气候变化预测的基础。本文利用全球通量网的通量观测数据评估了CLM4.5的GPP模拟效果,结合叶面积指数(leaf area index,LAI)观测数据分析了CLM4.5模型GPP模拟误差的主要原因,并对CLM4.5模型主要的光合参数进行敏感性分析,探讨改善光合作用模拟精度的可能途径。结果表明:CLM4.5对GPP的模拟效果优于CLM4,月尺度和年尺度GPP模拟值的平均绝对偏差分别降低15%和29%。但与观测值相比,CLM4.5模拟的GPP年总量仍然具有较大偏差,平均偏差为366.06 g C·m-2·a-1。不同植被功能型GPP模拟误差具有不同的季节变化特征,误差主要发生在春季和夏季。冠层顶部比叶面积、叶片碳氮比和叶片氮含量中Rubisco氮含量所占比例是GPP模拟的3个敏感参数。未来应主要从物候期及LAI模拟的改进、磷循环过程的影响、生态系统水平光合参数集的构建等方面实现对GPP模拟精度的提高。     

双集合卡尔曼滤波LAI同化结合BEPS模型的竹林生态系统碳通量模拟

叶面积指数(LAI)是森林生态系统碳循环研究的重要观测数据,也是驱动森林生态系统模型模拟碳循环的重要参数.本文以毛竹林和雷竹林为研究对象,首先利用双集合卡尔曼滤波,同化两种竹林生态系统观测站点2014—2015年MODIS LAI时间序列数据,然后将同化的高质量毛竹LAI和雷竹LAI作为输入数据驱动BEPS模型,模拟两种竹林生态系统总初级生产力(GPP)、净生态系统碳交换量(NEE)和总生态系统呼吸(TER)等碳循环数据,并用通量站实际观测值评价模拟结果;另外,还对比不同质量LAI对碳循环模拟的影响.结果表明: 双集合卡尔曼滤波同化得到的毛竹林和雷竹林LAI与实测LAI之间的相关关系极为显著,R²分别为0.81和0.91,且均方根误差和绝对偏差均较小,极大地提高了MODIS LAI的产品精度;在同化得到的LAI驱动下,BEPS模型模拟的毛竹林GPP、NEE和TER与实际观测值之间的R²分别为0.66、0.47和0.64,雷竹林分别为0.66、0.45和0.73,模拟结果均好于三次样条帽盖算法平滑LAI模拟得到的GPP、NEE和TER,其中,毛竹林、雷竹林NEE的模拟精度提高幅度最大,分别为11.2%和11.8%.     

新疆喀纳斯国家自然保护区植被叶面积指数观测与遥感估算

叶面积指数(Leaf Area Index,LAI)是重要的植被结构参数,调控着植被与大气之间的物质与能量交换,在生态环境脆弱的我国西北部开展植被LAI的研究对阐明该地区植被对气候变化和人类活动的响应特征具有重要的科学意义.利用LAI-2200和TRAC仪器观测了新疆喀纳斯国家级自然保护区森林和草地的有效叶面积指数(LAIe)和真实LAI,构建了其遥感估算模型,生成了研究区LAIe和LAI的空间分布图.在此基础上,分析了LAI随地形因子(海拔、坡度、坡向)的变化特征,探讨了将其应用于估算研究区森林生物量密度的可行性,并评估了研究区MODIS LAI产品的精度.结果表明:研究区阔叶林、针阔混交林、针叶林、草地LAIe的平均值分别为4.40、3.18、2.57、1.76,LAI的平均值分别为4.76、3.93、3.27、2.30.LAIe和LAI的高值主要集中分布在湖泊和河流附近;植被LAI随海拔、坡度和坡向的变化表现出明显的垂直地带性的特点.LAI随海拔和坡度的增加呈现先增加后减小的变化趋势,坡向对针叶林和草地LAI的影响明显,但对阔叶林和针阔混交林LAI的影响较弱;森林生物量密度(BD)随LAI增加而线性增加(BD=44.396LAI-25.946,R2=0.83),研究区森林生物量密度平均值为120.3 t/hm2,估算的总生物量为5.0×l06 t;MODIS LAI产品与利用TM数据生成的LAI之间具有一定的相似性(森林R2=0.42,草地R2=0.53),但森林和草地的MODIS LAI产品分别比利用TM数据生成的LAI偏低16.5％和24.4％.     

基于叶面积指数改进的直角双曲线模型在玉米农田生态系统中的应用

一般认为,生态系统的总初级生产力(GPP)对光合有效辐射(LAI)的响应曲线可以用直角双曲线来描述.研究表明,在不同的生长季进行模拟,模拟的直角双曲线的两个参数Amax和α值不同.为消除模型参数季节变化对模拟结果的影响,直角双曲线模型通常应用于较短的时间尺度(如半月、10d或5d),然而,这种在较短的时间尺度上进行模拟的方法过于繁琐,并且当通量数据缺失过多时,在短的时间模拟窗口上,少量的数据不足以拟合直角双曲线模型.在这种情况下,无法利用直角双曲线模型对生态系统的GPP进行准确的模拟,或者对缺失的碳通量数据进行插补.以玉米农田生态系统为例,旨在阐明生态系统的环境因子和生物因子在不同生长季对直角双曲线模型中两个参数Amax和α值的影响.结果表明,Amax与LAI具有显著的直线关系:Amax=a LAI+b(a=0.64,b=0.15,R=0.74,P=0.002).据此我们对直角双曲线模型进行了改进,用以预测半小时尺度的玉米农田生态系统GPP.与未改进的直角双曲线模型进行比较,在整个生长季进行模拟,改进的直角双曲线模型明显提高了模拟的精度;当在较短的时间窗口上进行模拟(半月时间尺度),改进的直角双曲线模型与之有着相似的精度.利用改进的双曲线模型不仅可以非常简捷地对生态系统GPP进行模拟,而且可以解释直角双曲线模型参数Amax值的连续变化,尤其是,当涡相关观测数据大量缺失时,可以很方便并且较为准确地插补缺失数据.     

中国东部森林最大总初级生产力的时空分布特征及其影响因子

生态系统碳循环对温度的响应是全球变化生态学的重要研究内容之一。总初级生产力(GPP)随着温度的升高而升高,在最适温下达到最大值(GPP_(max)),之后随温度的升高而保持不变甚至下降,因此GPP_(max)代表着最适温度下的植被光合潜力。然而,关于森林生态系统GPP_(max)的时空分布和影响因子仍不清楚。本文以中国东部南北森林样带(NSTEC)上的长白山温带针阔混交林、会同亚热带杉木人工林、千烟洲亚热带常绿针叶人工林、鼎湖山亚热带常绿针阔混交林和西双版纳热带季雨林等5种典型生态系统为对象,利用涡度相关技术分析森林GPP_(max)的时空规律及其主要影响因素。结果表明:在所有森林生态系统中,GPP对温度的响应模式均为单峰曲线,最适温下的GPP_(max)表现为长白山温带针阔混交林千烟洲亚热带常绿针叶人工林西双版纳热带季雨林会同亚热带杉木人工林鼎湖山亚热带常绿针阔混交林。在所有的站点中,温度是引起GPP_(max)空间变异的最主要因素,GPP_(max)随温度的增加而减少。此外,太阳辐射、降水和饱和蒸汽压差也显著影响GPP_(max)。在时间尺度上,对每个森林生态系统GPP_(max)年际变异的对比分析发现,温度是长白山温带针阔混交林GPP_(max)年际变化的主要控制因子,5 cm土壤含水量是影响会同、千烟洲和鼎湖山通量观测系统GPP_(max)年际变异的主要因子,未发现影响西双版纳热季雨林年际变异的主要因子。本研究有助于理解未来气候变暖背景下GPP的变化趋势,并为中国碳循环的准确模拟提供实验证据和参数依据。     

基于Biome-BGC模型和集合卡尔曼滤波方法的阔叶红松林生态系统水碳通量模拟

数据同化为模型与遥感观测结合提供了一条有效的途径,通过在模型运行过程中融入遥感观测数据,调整模型运行轨迹从而降低模型误差,提高模拟精度。本文利用集合卡尔曼滤波(En KF)算法同化生长季中分辨率成像光谱仪(MODIS)叶面积指数(LAI)与Biome-BGC模型模拟的LAI模拟长白山阔叶红松林的水碳通量。同时,通过改进模拟的雪面升华与土壤温度计算方法的参数,旨在降低冬季生态呼吸的模拟误差。结果表明,相对于原始模型,数据同化与模型改进后使得生态系统总初级生产力(GPP)的模拟值与观测值之间的相关系数提高0.06,中心化均方根误差(RMSE)降低0.48 g C·m~(-2)·d~(-1);生态系统呼吸(RE)的相关系数提高0.02,中心化均方根误差降低0.20 g C·m~(-2)·d~(-1);净生态系统碳交换量(NEE)相关系数提高0.35,中心化均方根误差降低0.50 g C·m~(-2)·d~(-1)。同时,数据同化对蒸散发(ET)的模拟精度没有显著影响,改进的模型提高了其相关系数。基于En KF算法的数据同化提高了长白山阔叶红松林碳通量模拟精度,对于精确估算区域碳通量有着重要的意义。     

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。     

Calibration of Terra/MODIS gross primary production over an irrigated cropland on the North China Plain and an alpine meadow on the Tibetan Plateau

This paper evaluated the MODerate resolution Imaging Spectroradiometer (MODIS) gross primary production (GPP) product (MOD17) by using estimated GPP from eddy‐covariance flux measurements over an irrigated winter wheat and maize double‐cropping field on the North China Plain in 2003–2004, and an alpine meadow on the Tibetan Plateau in 2002–2003. The mean annual GPP from MOD17 accounted for 1/2–2/3 of the surface estimated mean annual GPP for the alpine meadow, but only about 1/5–1/3 for the cropland. This underestimation was partly attributed to low estimates of leaf area index by a MODIS product (MOD15) because it is used to calculate absorbed photosynthetically active radiation in the MOD17 algorithm. The main reason is that the parameter maximum light use efficiency (ε_max) in the MOD17 algorithm was underestimated for the two biomes, especially for the cropland. Contrasted to the default, ε_max was optimized using surface measurements. The optimized ε_max for winter wheat, maize and meadow was 1.18, 1.81 and 0.73 g C/MJ, respectively. By using the surface measurements and optimized ε_max , the MOD17 algorithm significantly improved the accuracy of GPP estimates. The optimum MOD17 algorithm explained about 82%, 68%, and 79% of GPP variance for winter wheat, maize, and meadow, respectively. These results suggest that it is necessary to adjust the MOD17 parameters for the estimation of cropland and meadow GPP, particularly over cropland.     

科尔沁草甸生态系统水分利用效率及影响因素

生态系统水分利用效率(WUE)是衡量碳水循环耦合程度的重要指标。利用科尔沁温带草甸草地碳水通量观测数据,对该生态系统总初级生产力水分利用效率(WUEGPP)的日季变化规律及对环境和生理因子的响应进行分析。结果表明:(1)WUEGPP日变化呈下降-稳定-上升的变化趋势,最大值出现在日出后1—2 h,阴天条件下WUEGPP高于晴天,生长中期WUEGPP高于生长初期和末期;(2)总初级生产力、总蒸散和WUEGPP季节变化均呈夏季高、春秋低的形式,生长季平均值分别为0.57 mg m-2s-1、0.08 g m-2s-1和5.97 mg/g,最大值分别为1.49 mg m-2s-1、0.16 g m-2s1和13.62 mg/g;(3)总初级生产力与饱和差、气温和叶面积指数均呈二次曲线关系,与冠层导度呈对数曲线关系;总蒸散与气温呈二次曲线关系,与饱和差、叶面积指数和冠层导度相关性均不显著;(4)WUEGPP与饱和差、气温和叶面积指数均呈二次曲线关系,与冠层导度呈对数曲线关系,饱和差、冠层导度和叶面积指数分别为2.0 k Pa、0.0015 m/s和4.2是控制WUEGPP增加的阈值;(5)净生态系统生产力水分利用效率(WUENEP)和净初级生产力水分利用效率(WUENPP)季节变化规律与WUEGPP一致,均值分别为3.47和5.47 mg/g。     

Application of two remote sensing GPP algorithms at a semiarid grassland site of North China

Aims Estimation of gross primary production (GPP) from remote sensing data is an important approach to study regional or global carbon cycle. However, for a given algorithm, it usually has its limitation on applications to a wide range of vegetation types and/or under diverse environmental conditions. This study was conducted to compare the performance of two remote sensing GPP algorithms, the MODIS GPP and the vegetation photosynthesis model (VPM), in a semiarid temperate grassland ecosystem.Methods The study was conducted at a typical grassland site in Ujimuqin of Inner Mongolia, North China, over 2 years in 2006 and 2007. Environmental controls on GPP measured by the eddy covariance (EC) technique at the study site were first investigated with path analysis of meteorological and soil moisture data at a daily and 8-day time steps. The estimates of GPP derived from the MODIS GPP and the VPM with site-specific inputs were then compared with the values of EC measurements as ground truthing at the site. Site-specific ? max (α) was estimated by using rectangular hyperbola function based on the 7-day flux data at 30-min intervals over the peak period of the growing season (May to September).Important findings Between the two remote sensing GPP algorithms and various estimates of the fraction of absorbed photosynthetic active radiation (FPAR), the VPM based on FPAR derived from the enhanced vegetation index (EVI) works the best in predicting GPP against the ground truthing of EC GPP. A path analysis indicates that the EC GPP in this semiarid temperate grassland ecosystem is controlled predominantly by both soil water and temperature. The site water condition is slightly better simulated by the moisture multiplier in the VPM than in the MODIS GPP algorithm, which is a most probable explanation for a better performance of the VPM than MODIS GPP algorithm in this semiarid grassland ecosystem.     

Improving the light use efficiency model for simulating terrestrial vegetation gross primary production by the inclusion of diffuse radiation across ecosystems in China

Qualification of gross primary production (GPP) of terrestrial ecosystem over large areas is important in understanding the response of terrestrial ecosystem to global climate change. While light use efficiency (LUE) models were widely used in regional carbon budget estimates, few studies consider the effect of diffuse radiation on LUE caused by clouds using a big leaf model. Here we developed a cloudiness index light use efficiency (CI-LUE) model based on the MOD17 model algorithm to estimate the terrestrial ecosystem GPP, in which the base light use efficiency encompassed the cloudiness index, maximum LUE and clear sky LUE. GPP measured at seven sites from 2003 to 2007 in China were used to calibrate and validate the CI-LUE model. The results showed that at forest sites and cropland site the CI-LUE model outperformed the Vegetation Photosynthesis Model (VPM), Terrestrial Ecosystem Carbon flux model (TEC), MOD17 model algorithm driven by in situ meteorological measurements and MODIS GPP products, especially the R² of simulated GPP against flux measurements at Dinghushan forest site increased from 0.17 (MODIS GPP products) to 0.61 (CI-LUE). Instead, VPM model had the best agreement with GPP measurements followed by CI-LUE model and lastly TEC model at two grassland sites. Meanwhile, GPP calculated by CI-LUE model has less underestimation under cloudy skies in comparison with MOD17 model. This study demonstrated the potential of the CI-LUE model in improving GPP simulations resulting from the inclusion of diffuse radiation in regulating the base light use efficiency and maximum light use efficiency.     

Inconsistencies of interannual variability and trends in long‐term satellite leaf area index products

Understanding the long‐term performance of global satellite leaf area index (LAI) products is important for global change research. However, few effort has been devoted to evaluating the long‐term time‐series consistencies of LAI products. This study compared four long‐term LAI products (GLASS, GLOBMAP, LAI3g, and TCDR) in terms of trends, interannual variabilities, and uncertainty variations from 1982 through 2011. This study also used four ancillary LAI products (GEOV1, MERIS, MODIS C5, and MODIS C6) from 2003 through 2011 to help clarify the performances of the four long‐term LAI products. In general, there were marked discrepancies between the four long‐term LAI products. During the pre‐MODIS period (1982–1999), both linear trends and interannual variabilities of global mean LAI followed the order GLASS>LAI3g>TCDR>GLOBMAP. The GLASS linear trend and interannual variability were almost 4.5 times those of GLOBMAP. During the overlap period (2003–2011), GLASS and GLOBMAP exhibited a decreasing trend, TCDR no trend, and LAI3g an increasing trend. GEOV1, MERIS, and MODIS C6 also exhibited an increasing trend, but to a much smaller extent than that from LAI3g. During both periods, the R² of detrended anomalies between the four long‐term LAI products was smaller than 0.4 for most regions. Interannual variabilities of the four long‐term LAI products were considerably different over the two periods, and the differences followed the order GLASS>LAI3g>TCDR>GLOBMAP. Uncertainty variations quantified by a collocation error model followed the same order. Our results indicate that the four long‐term LAI products were neither intraconsistent over time nor interconsistent with each other. These inconsistencies may be due to NOAA satellite orbit changes and MODIS sensor degradation. Caution should be used in the interpretation of global changes derived from the four long‐term LAI products.     

Terrestrial growth in China and its relationship with climate based on the MODIS data

The characteristics of terrestrial growth in China and its relationship with various climatic factors (e.g. temperature, precipitation and radiation) were investigated by using the data collected with the Moderate Resolution Imaging Spectroradiometer (MODIS). These data were collected once every 8 days during 2000–2003 and then again in 2005. The average annual gross primary production (GPP) in China during this period ranged from 0 to 3252.6 gC·m^?2·a^?1 with an average value of 491.1 gC·m^?2·a^?1. The maximum GPP values were observed over the regions of Yunnan, Hainan and Taiwan, and the southeast coastal areas, while the minimum values were observed in the cold and arid regions of the Tibetan Plateau, Xinjiang and Inner Mongolia. Correlation analysis showed that temperature was the primary factor influencing this terrestrial growth, while precipitation played a secondary role. However, only the terrestrial growth that occurred in southern China was affected by radiation. The correlation coefficients of GPP with temperature and precipitation decreased from northern to southern China and were negatively correlated with the distribution of the associated climatic factors within China. Furthermore, the correlation coefficient of GPP with cloud contents was observed to increase from northern to southern China.     

Estimating canopy gross primary production by combining phloem stable isotopes with canopy and mesophyll conductances

Gross primary production (GPP) is a key component of the forest carbon cycle. However, our knowledge of GPP at the stand scale remains uncertain, because estimates derived from eddy covariance (EC) rely on semi-empirical modelling and the assumptions of the EC technique are sometimes not fully met. We propose using the sap flux/isotope method as an alternative way to estimate canopy GPP, termed GPP_iso/SF, at the stand scale and at daily resolution. It is based on canopy conductance inferred from sap flux and intrinsic water-use efficiency estimated from the stable carbon isotope composition of phloem contents. The GPP_iso/SF estimate was further corrected for seasonal variations in photosynthetic capacity and mesophyll conductance. We compared our estimate of GPP_iso/SF to the GPP derived from PRELES, a model parameterized with EC data. The comparisons were performed in a highly instrumented, boreal Scots pine forest in northern Sweden, including a nitrogen fertilized and a reference plot. The resulting annual and daily GPP_iso/SF estimates agreed well with PRELES, in the fertilized plot and the reference plot. We discuss the GPP_iso/SF method as an alternative which can be widely applied without terrain restrictions, where the assumptions of EC are not met.     

基于MODIS的中国陆地植被生长及其与气候的关系

基于MODIS2000~2005(缺2004年)5a每8d的GPP资料,分析了中国陆地植被生长特征,讨论了我国气候因子(温度、降水以及辐射)对同期植被生长的影响。研究结果显示,中国陆地植被的年均GPP分布范围在0~3252.6gC.m-.2a-1之间,5a平均值为491.1gC.m-.2a-1;年均GPP最大的地方主要分布于云南南部、海南、台湾以及东南沿海部分地区,最小值主要出现在青藏高原、新疆以及内蒙古西部等高寒、干旱地区。温度是影响同期我国陆地植被生长的主要因子,其次为降水,辐射对我国陆地植被的影响比较小,主要影响区域在华南地区。陆地植被GPP与温度和降水的相关系数在我国表现为自北向南逐渐减小的分布特征,与对应气候因子的分布呈反向关系;而GPP与云量的相关系数则自北向南逐渐增大。     

Is productivity of mesic savannas light limited or water limited? Results of a simulation study

A soil–plant–atmosphere model was used to estimate gross primary productivity (GPP) and evapotranspiration (ET) of a tropical savanna in Australia. This paper describes model modifications required to simulate the substantial C4 grass understory together with C3 trees. The model was further improved to include a seasonal distribution of leaf area and foliar nitrogen through 10 canopy layers. Model outputs were compared with a 5‐year eddy covariance dataset. Adding the C4 photosynthesis component improved the model efficiency and root‐mean‐squared error (RMSE) for total ecosystem GPP by better emulating annual peaks and troughs in GPP across wet and dry seasons. The C4 photosynthesis component had minimal impact on modelled values of ET. Outputs of GPP from the modified model agreed well with measured values, explaining between 79% and 90% of the variance and having a low RMSE (0.003–0.281 g C m^?2 day^?1). Approximately, 40% of total annual GPP was contributed by C4 grasses. Total (trees and grasses) wet season GPP was approximately 75–80% of total annual GPP. Light‐use efficiency (LUE) was largest for the wet season and smallest in the dry season and C4 LUE was larger than that of the trees. A sensitivity analysis of GPP revealed that daily GPP was most sensitive to changes in leaf area index (LAI) and foliar nitrogen (N_f) and relatively insensitive to changes in maximum carboxylation rate (V_cmax), maximum electron transport rate (J_max) and minimum leaf water potential (ψ_min). The modified model was also able to represent daily and seasonal patterns in ET, (explaining 68–81% of variance) with a low RMSE (0.038–0.19 mm day^?1). Current values of N_f, LAI and other parameters appear to be colimiting for maximizing GPP. By manipulating LAI and soil moisture content inputs, we show that modelled GPP is limited by light interception rather than water availability at this site.