松花江流域NPP时空演变及其对极端气候的响应机制

为探究全球气候变化条件下松花江流域陆地生态系统健康程度的变化特征,基于2000—2020年MODIS MOD17A3HGF数据集,采用趋势分析、相关性分析、M-K检验、地理探测器和相对重要性分析等方法,结合气象站点数据和土地利用数据,分析植被净初级生产力(net primary productivity,NPP)时空演变特征及其对极端气候事件的响应机制。结果表明:松花江流域年均NPP值为407.45 g/m^(2)(以C计,下同),以年均4.82 g/m的速率显著上升(p<0.01);极端降水事件对植被NPP空间分异性的影响强于极端气温事件,极端气候指数间交互作用的影响大于单一极端气候指数的影响,流域及农田和草地生态系统NPP主要受总降水量(PRCPTOT)与年平均最低气温(TMIN)交互作用的影响,森林、湿地和聚落生态系统NPP分别受中雨日数(R10 mm)与年平均最高气温(TMAX)交互作用、强降水量(R95P)与TMIN交互作用和R10 mm与暖夜日数(TN90P)交互作用的影响;时间尺度上PRCPTOT、TMAX和TMIN是植被NPP的主要影响因素,空间尺度上PRCPTOT和TMIN是多年平均NPP的主要影响因素。研究结果可为量化气候变化背景下区域生态系统健康程度和应对极端气候事件措施的制定提供科学依据。

Global patterns in above-ground net primary production and precipitation-use efficiency in grasslands

The above-ground net primary production(ANPP) and the precipitation-use efficiency(PUE) regulate the carbon and water cycles in grassland ecosystems, but the relationships among the ANPP, PUE and precipitation are still controversial. We selected 717 grassland sites with ANPP and mean annual precipitation(MAP) data from 40 publications to characterize the relationships ANPP–MAP and PUE–MAP across different grassland types. The MAP and ANPP showed large variations across all grassland types, ranging from 69 to 2335 mm and 4.3 to 1706 g m^(-2), respectively. The global maximum PUE ranged from 0.19 to 1.49 g m^(-2) mm^(-1) with a unimodal pattern. Analysis using the sigmoid function explained the ANPP–MAP relationship best at the global scale. The gradient of the ANPP–MAP graph was small for arid and semi-arid sites(MAP <400 mm). This study improves our understanding of the relationship between ANPP and MAP across dry grassland ecosystems. It provides new perspectives on the prediction and modeling of variations in the ANPP for different grassland types along precipitation gradients.

Aboveground biomass and net primary production of semi-evergreen tropical forest of Manipur,north-eastern India

The aboveground biomass dynamics and net primary productivity were investigated to assess the productive potential of Dipterocarpus forest in Manipur, Northeast India.Two forest stands(stand I and II) were earmarked randomly in the study site for the evaluation of biomass in the different girth classes of tree species by harvest method.The total biomass was 22.50 t·ha-1 and 18.27 t·ha-1 in forest stand I and II respectively.Annual aboveground net primary production varied from 8.86 to 10.43 t·ha-1 respectively in two forest stands(stand I and II).In the present study, the values of production efficiency and the biomass accumulation ratio indicate that the forest is at succession stage with high productive potential.

A process model for simulating net primary productivity (NPP) based on the interaction of water-heat process and nitrogen: a case study in Lantsang valley

Terrestrial carbon cycle and the global atmospheric CO2 budget are important foci in global climate change research. Simulating net primary productivity （NPP） of terrestrial ecosystems is important for carbon cycle research. In this study, a plant-atmosphere-soil continuum nitrogen （N） cycling model was developed and incorporated into the Boreal Ecosystem Productivity Simulator （BEPS） model. With the established database （leaf area index, land cover, daily meteorology data, vegetation and soil） at a 1 km resolution, daily maps of NPP for Lantsang valley in 2007 were produced, and the spatial-temporal patterns of NPP and mechanisms of its responses to soil N level were further explored. The total NPP and mean NPP of Lantsang valley in 2007 were 66.5 Tg C and 416 g？m-2？a-1 C, respectively. In addition, statistical analysis of NPP of different land cover types was conducted and investigated. Compared with BEPS model （without considering nitrogen effect）, it was inferred that the plant carbon fixing for the upstream of Lantsang valley was also limited by soil available nitrogen besides temperature and precipitation. However, nitrogen has no evident limitation to NPP accumulation of broadleaf forest, which mainly distributed in the downstream of Lantsang valley.

Improving remote sensing-based net primary production estimation in the grazed land with defoliation formulation model

Remote sensing(RS) technologies provide robust techniques for quantifying net primary productivity(NPP) which is a key component of ecosystem production management. Applying RS, the confounding effects of carbon consumed by livestock grazing were neglected by previous studies, which created uncertainties and underestimation of NPP for the grazed lands. The grasslands in Xinjiang were selected as a case study to improve the RS based NPP estimation. A defoliation formulation model(DFM) based on RS is developed to evaluate the extent of underestimated NPP between 1982 and 2011. The estimates were then used to examine the spatiotemporal patterns of the calculated NPP. Results show that average annual underestimated NPP was 55.74 gC·m^(-2)yr^(-1) over the time period understudied, accounting for 29.06% of the total NPP for the Xinjiang grasslands. The spatial distribution of underestimated NPP is related to both grazing intensity and time. Data for the Xinjiang grasslands show that the average annual NPP was 179.41 gC·m^(-2)yr^(-1), the annual NPP with an increasing trend was observed at a rate of 1.04 gC·m^(-2)yr^(-1) between 1982 and 2011. The spatial distribution of NPP reveals distinct variations from high to low encompassing the geolocations of the Tianshan Mountains, northern and southern Xinjiang Province and corresponding with mid-mountain meadow, typical grassland, desert grassland, alpine meadow, and saline meadow grassland types. This study contributes to improving RS-based NPP estimations for grazed land and provides a more accurate data to support the scientific management of fragile grassland ecosystems in Xinjiang.

Monitoring of Net Primary Production in California Rangelands Using Landsat and MODIS Satellite Remote Sensing

In this study, we present results from the CASA (Carnegie-Ames-Stanford Approach) model to estimate net primary production (NPP) in grasslands under different management (ranching versus unmanaged) on the Central Coast of California. The latest model version called CASA Express has been designed to estimate monthly patterns in carbon fixation and plant biomass production using moderate spatial resolution (30 m to 250 m) satellite image data of surface vegetation characteristics. Landsat imagery with 30 m resolution was adjusted by contemporaneous Moderate Resolution Imaging Spectroradiometer (MODIS) data to calibrate the model based on previous CASA research. Results showed annual NPP predictions of between 300 - 450 grams C per square meter for coastal rangeland sites. Irrigation increased the predicted NPP carbon flux of grazed lands by 59 grams C per square meter annually compared to unmanaged grasslands. Low intensity grazing activity appeared to promote higher grass regrowth until June, compared to the ungrazed grassland sites. These modeling methods were shown to be successful in capturing the differing seasonal growing cycles of rangeland forage production across the area of individual ranch properties.

基于改进CASA模型的陕西省植被NPP遥感估算

[目的]探究陕西省陆地生态系统植被群落生产状况,分析陕西省植被NPP时空格局变化及影响因素,为准确评估陕西省陆地生态系统碳源/汇,实现区域生态可持续发展,达成碳中和目标提供参考依据。[方法]基于温度—植被干旱指数(Temperature Vegetation Dryness Index, TVDI)对CASA(Carnegie-Ames-Stanford Approach)模型水分胁迫因子进行改进,从而估算陕西省2010—2020年植被NPP,并利用热点分析法、趋势分析法以及地理探测器对陕西省植被NPP进行空间分布格局、年际变化趋势和驱动因子研究。[结果](1)陕西省NPP空间分布呈现南高北低、冷热点区域差异明显的特征;(2)陕西省2010—2020年NPP平均值介于331.02~416.34 gC/(m^(2)·a),NPP均值在100~600 gC/(m^(2)·a)占比最大,最低值和最高值区间占比不足20%;(3)全省2010—2020年83.3%的面积植被NPP值无显著变化,4.2%的面积呈增加状态,12.5%的面积NPP值呈下降趋势;(4)降水是陕西省植被NPP变化的单因子主导驱动力,太阳辐射量及土地利用类型交互作用下对NPP变化解释力更强。[结论]基于TVDI改进的CASA模型能够有效量化区域植被NPP,且陕西省植被NPP南北分布差异明显,降水、土地利用类型及太阳辐射量是其主要影响因子。

三峡库区消落带植被NPP估算——基于机器学习优化CASA模型

三峡库区蓄水后,其生态效应受到广泛关注。消落带植被固碳量作为衡量库区生态系统健康状态的重要指标,对库区碳循环与生态净化具有重要意义。针对消落带不同高程植被接受光照的时间有所差异,且受河流水位变化影响,传统的CASA模型在计算消落带植被固碳量时,存在对植物的光能利用率计算不够精确等问题。以三峡库区香溪河陡坡消落带为研究区域,提出了一种耦合RBFNN模型(Radial Basis Function Neural Network)与CASA模型(Carnegie-Ames-Stanford approach)的新方法(RBF-CASA)。基于RBFNN建立环境影响因子模型,借助高程数据及植被指数等特征计算适合消落带区域的环境影响因子。结合CASA模型中温度和水分胁迫因子,提高植被在像元尺度上的净初级生产力(Net Primary Productivity,NPP)的估算精度,并对反演结果进行验证。模型验证结果显示:RBF-CASA模型估算值与观测值的决定系数(Coefficient of determination,R^(2))为0.730(P<0.01,n=32)。对比原始CASA模型,平均绝对误差(Mean absolute error,MAE)降低10.991,均方根误差(Root mean square error,RMSE)降低了23.861,相对均方根误差(Relative root mean square error,RRMSE)降低5.10%,平均绝对百分误差(Mean absolute percentage error,MAPE)降低1.12%。使用提出的RBF-CASA模型在库区水位落干期(7—8月份)进行固碳量估算,结果表明:NPP月均值在66.234—134.144g C/m^(2)之间,NPP随着高程的增加呈现起伏变化,其总量在150—155m之间达到峰值,均值在170m以上区域最高。在2021年9月植被NPP均值为35.883g C/m^(2),2022年9月植被NPP均值为25.964g C/m^(2),由于降雨量减少、长江水位下降,在2021—2022年间植被恢复情况较差。研究结果可为库区碳循环、生态净化及生态修复等决策提供科学依据。

Linkages between the biomass of Scomber japonicus and net primary production in the southern East China Sea

Fish biomass is a critical component of fishery stock assessment and management and it is often estimated from ocean primary production（OPP）. However, the relationship between the biomass of a fish stock and OPP is always complicated due to a variety of trophic controls in the ecosystem. In this paper, we examine the quantitative relationship between the biomass of chub mackerel（Scomber japonicus） and net primary production（NPP） in the southern East China Sea（SECS）, using catch and effort data from the Chinese mainland large light-purse seine fishery logbook and NPP derived from remote sensing. We further discuss the mechanisms of trophic control in regulating this relationship. The results show a significant non-linear relationship exists between standardized CPUE（Catch-Per-Unit-Effort） and NPP（P〈0.05）. This relationship can be described by a convex parabolic curve, where the biomass of chub mackerel increases with NPP to a maximum and then decreases when the NPP exceeds this point. The results imply that the ecosystem in the SECS is subject to complex trophic controls. We speculate that the change in abundance of key species at intermediate trophic levels and/or interspecific competition might contribute to this complex relationship.

1982—2020年乌兰县植被NPP时空动态特征及驱动力量化分析

气候变化和人类活动对植被NPP(net primary productivity,净初级生产力)的驱动作用是全球气候变化背景下的研究热点,并且在不同的时空尺度上尚不能达成共识。中国西北寒旱牧区植被生态系统脆弱,对气候变化和人类活动的响应十分敏感,该研究以青海省乌兰县作为代表性研究区,采用GIMMS-NDVI and MOD-NDVI数据融合构建了长时序归一化植被指数(normalized difference vegetation index,NDVI)数据集,并结合CASA(carnegie-ames-stanford approach)模型获取了研究区1982—2020年植被生长季NPP,利用Sen+MK趋势分析方法探究了研究区植被生长季NPP的时空演变特征,同时采用构建的ADE+Sen量化归因方法对多种气候要素和人类活动的驱动作用进行了定量分析。结果表明,研究区植被生长季NPP多年均值为(205.9±11.5)g/(m^(2)·a)(以C计),年际变化无显著趋势,不同植被类型的生长季NPP年际波动过程与全域生长季NPP基本一致。在空间上,植被生长季NPP自西向东逐渐增加,年际变化趋势具有明显的空间异质性,且整体以退化为主,平均变化率为-0.151 g/(m^(2)·a2),其中表现出严重退化和轻度退化的面积占比分别达到了31.7%和29.5%。气候变化主导的植被面积占比达85.2%,其贡献值的绝对平均值为1.025 g/(m^(2)·a2),约是人类活动贡献值绝对平均值的2倍,太阳辐射、降水量、平均气温和平均风速均是影响植被生长季NPP动态的主要气候因素。该研究表明研究区植被整体表现为退化状态,气候变化是导致该现象的主导驱动因素。研究结果可为中国西北寒旱牧区植被生态系统的可持续利用和保护提供参考。

Spatiotemporal changes of gross primary productivity and its response to drought in the Mongolian Plateau under climate change

Gross primary productivity(GPP)of vegetation is an important constituent of the terrestrial carbon sinks and is significantly influenced by drought.Understanding the impact of droughts on different types of vegetation GPP provides insight into the spatiotemporal variation of terrestrial carbon sinks,aiding efforts to mitigate the detrimental effects of climate change.In this study,we utilized the precipitation and temperature data from the Climatic Research Unit,the standardized precipitation evapotranspiration index(SPEI),the standardized precipitation index(SPI),and the simulated vegetation GPP using the eddy covariance-light use efficiency(EC-LUE)model to analyze the spatiotemporal change of GPP and its response to different drought indices in the Mongolian Plateau during 1982-2018.The main findings indicated that vegetation GPP decreased in 50.53% of the plateau,mainly in its northern and northeastern parts,while it increased in the remaining 49.47%area.Specifically,meadow steppe(78.92%)and deciduous forest(79.46%)witnessed a significant decrease in vegetation GPP,while alpine steppe(75.08%),cropland(76.27%),and sandy vegetation(87.88%)recovered well.Warming aridification areas accounted for 71.39% of the affected areas,while 28.53% of the areas underwent severe aridification,mainly located in the south and central regions.Notably,the warming aridification areas of desert steppe(92.68%)and sandy vegetation(90.24%)were significant.Climate warming was found to amplify the sensitivity of coniferous forest,deciduous forest,meadow steppe,and alpine steppe GPP to drought.Additionally,the drought sensitivity of vegetation GPP in the Mongolian Plateau gradually decreased as altitude increased.The cumulative effect of drought on vegetation GPP persisted for 3.00-8.00 months.The findings of this study will improve the understanding of how drought influences vegetation in arid and semi-arid areas.

广西喀斯特地区植被NPP对高低温气象灾害的响应

高低温气象灾害对广西喀斯特地区石漠化治理和植被修复产生较大威胁。基于2000—2021年广西喀斯特地区植被净初级生产力指数(NetPrimary Productivity,NPP)和气温资料,采用线性趋势法、GIS空间分析法和相关性分析法,分析广西喀斯特地区植被NPP变化趋势和高低温气象灾害对植被NPP的影响。结果表明,2000—2021年,研究区植被NPP增加趋势明显,增长速率为84.7gC·m^(-2)/10a,增加区域面积占比为90.4%,东北和中部地区增加较明显。所有高温灾害指数,包括≥35℃日数、≥37℃日数、≥35℃积温、≥37℃积温和最高气温,对植被NPP均以弱负影响为主;低温灾害指数中,≤0℃积温和最低气温对植被NPP均以弱负影响为主,≤0℃日数以弱正影响为主。高温灾害对研究区中部植被NPP的负影响较强,低温灾害对植被NPP的负影响强度在不同指数间差异较大。高温灾害对各林种NPP的负影响强度和范围多大于低温灾害。发生高温灾害时,竹林NPP受其负影响的强度大于其他林种,桉树类NPP受其负影响的范围大于其他林种,平均面积占比约78.0%。发生低温灾害时,桉树类NPP受其负影响的强度大于其他林种,阔叶林NPP受其负影响的范围大于其他林种,平均面积占比约53.5%。

基于数据融合的植被NPP时空变化及驱动因素分析——以拜城盆地为例

植被净初级生产力(NPP)是区域生态系统保护及生态环境治理的重要参考指标,针对拜城盆地植被NPP时空变化特征及其与气候变化的响应关系不明这一问题,利用STARFM时空数据融合模型,估算拜城盆地30 m空间分辨率的植被NPP,同时使用Sen斜率估计及M-K检验,分析植被NPP的时空变化趋势特征,并通过偏相关系数法量化气候要素的影响程度。结果显示:时间上,研究区2000-2020年植被NPP均值为152.1 g/(m^(2)·a),总体呈不显著下降趋势;空间上,植被NPP值表现为南北高,中部河谷区域低,其中69.03%的区域呈不显著变化,11.44%呈显著增加趋势,19.53%呈显著减小趋势;研究区植被NPP变化与降雨总量、太阳辐射总量呈正相关,与平均气温呈现负相关关系,其中,太阳辐射是影响植被NPP变化的主导因素。研究结果表明:改进的CASA模型对于模拟研究区植被净初级生产力具有较好的适用性,有助于更好地揭示拜城盆地NPP的变化特征及驱动因素,并为估算与定期监测中小尺度区域的NPP提供了新方法。

Assessing Net Primary Production in Montane Wetlands from Proximal, Airborne, and Satellite Remote Sensing

In this study, several vegetation indices were examined in order to determine the most sensitive vegetation index for monitoring southern Appalachian wetlands. Three levels of platforms (in situ, airborne, and satellite) for sensors were also examined in conjunction with vegetation indices. Net primary production (NPP) data were gathered to use as a measure of wetland function. Along with the in situ radiometers, National Agricultural Imagery Program (NAIP) data and Landsat 8 Operational Land Imager (OLI) data were gathered in order to calculate vegetation indices at three platforms. At the in situ level, VARI700 was the most sensitive vegetation index in terms of NPP (r2 = 0.65, p < 0.05). At the airborne level, the NDVI was the most sensitive vegetation index to NPP (r2 = 0.35, p = 0.11). At the satellite level, the DVI appeared to have a positive relationship with NPP. For most indices there was a drop in the coefficient of determination with NPP when the platform altitude increased, with the exception of NDVI when increasing altitude from in situ to airborne. This study provides a novel methodology comparing reflectance and vegetation indices at three platform levels.

基于GEE的粤北典型石漠化地区耕地NPP年际时空演变研究

耕地是保障粮食安全和国民经济高质量发展的宝贵资源。喀斯特地区的耕地变化监测对促进耕地资源可持续利用,评估石漠化治理成效与区域生态安全具有重要意义。研究基于Google Earth Engine,调用土地覆被、NPP数据集与地形高程数据,分析2018-2022年耕地面积、NPP的时空演变。研究表明:①2018~2022年粤北典型石漠化地区耕地面积总体略有缩减,集中于研究区南北两侧及缓坡与中坡地带;②总体NPP在镇域尺度与所有坡型均呈现递增趋势,表明生态环境的整体改善;③耕地NPP在不同坡型均体现为递增趋势,但在镇域尺度呈现出现南北两侧递减与中南部递增的差异化趋势。表明在退耕还林、封山育林等石漠化综合治理措施取得成效,且尽管耕地面积有所缩减仍保证了现有耕地的生产能力,部分地区呈现出的递减趋势可能是治理过程中产生的暂时性下降。表明韶关典型石漠化地区综合治理取得成效,有效改善了区域生态和耕作条件。

2000—2018年海南岛NPP时空变化及气候驱动力

海南岛作为国家生态文明实验区,针对其植被净初级生产力(NPP)变化趋势及气候驱动力尚未明确的问题,基于MODIS数据分析了海南岛2000—2018年NPP、植被呼吸(Re)和植被初级生产力(GPP)的时空变化趋势,并利用多元线性回归分析量化并识别海南岛NPP的主要气候驱动力。结果表明,GPP、NPP和Re均呈现上升趋势,NPP的增长趋势最小(增速为0.016 kg C·m^(-2)·a^(-1))且仅在海南岛中部偏北地区呈不显著的下降趋势,而其余地区呈上升趋势。多元线性回归分析表明:太阳辐射主导着海南岛大部分地区NPP变化;海南岛中部偏北地区NPP变化由降水和温度共同驱动,中部偏南地区则由降水和太阳辐射共同驱动;温度驱动着海南岛西南部和南部地区的NPP变化。

草原区露天煤矿开发对草地植被NPP的影响研究

为了定量化评价草原区露天矿开发对草地生态系统影响,以伊敏露天煤矿为例,选择植被净初级生产力(NPP)指标,采用1985—2018年间21期卫星遥感数据进行长时间系列的NPP计算,分析露天矿开采对周边草地生态系统的影响程度及排土场生态恢复效果。结果表明,伊敏露天矿开发影响导致影响区草地NPP损失约10%~30%。随着恢复时间的增加,排土场植被不断向自然植被演替,NPP也不断增加并趋于稳定,稳定后的外排土场NPP已达到未受影响区历年NPP均值的90%左右,植被生产力接近自然植被生产力水平。但恢复时间较短的内排土场、北外排土场NPP均值仅达到未受影响区历年NPP均值的50%左右。露天矿生态重建与恢复是一个长期过程,外排土场的植被演替达到稳定状态需要20年以上,必须与露天开采活动同步推进,并长期坚持。

基于碳汇法与NPP法的安徽省能源足迹影响因素研究

文章采用碳汇法和净初级生产力(net primary productivity,NPP)法,以安徽省为例计算能源足迹;将非化石能源消费也纳入能源足迹的计算中,以便更准确测度能源足迹;采用对数平均迪氏指数(logarithmic mean Divisia index,LMDI)分解法分析能源足迹的影响因素,并使用Pearson相关系数分析法探索非化石能源足迹对能源足迹总量变化率的影响。结果表明:与碳汇法相比,NPP法考虑区域综合碳吸收能力和土地利用变化的影响,计算结果更准确;基于NPP法计算含非化石能源的能源足迹,2009—2016年安徽省能源足迹累计增长511.25×10^(4)hm^(2),年均增长率为5.02%;能源强度在抑制能源足迹增长因素中占94.35%,贡献度为-1.57;经济发展在促进能源足迹增长的因素中占95.95%,贡献度为2.55;以经济发展为主的正效应大于以能源强度为主的负效应,两者比值为1.74∶1.00;非化石能源足迹、煤炭足迹的贡献度与总量变化率分别呈-0.54负相关和0.44正相关。因此,调整能源结构、大力发展非化石能源,能有效降低能源足迹。

Variation of net primary productivity and its drivers in China’s forests during 2000-2018

Background:Net primary productivity(NPP)in forests plays an important role in the global carbon cycle.However,it is not well known about the increase rate of China’s forest NPP,and there are different opinions about the key factors controlling the variability of forest NPP.Methods:This paper established a statistics-based multiple regression model to estimate forest NPP,using the observed NPP,meteorological and remote sensing data in five major forest ecosystems.The fluctuation values of NPP and environment variables were extracted to identify the key variables influencing the variation of forest NPP by correlation analysis.Results:The long-term trends and annual fluctuations of forest NPP between 2000 and 2018 were examined.The results showed a significant increase in forest NPP for all five forest ecosystems,with an average rise of 5.2 gC·m-2·year-1 over China.Over 90%of the forest area had an increasing NPP range of 0-161 gC·m-2·year-1.Forest NPP had an interannual fluctuation of 50-269 gC.m-2·year-1 for the five major forest ecosystems.The evergreen broadleaf forest had the largest fluctuation.The variability in forest NPP was caused mainly by variations in precipitation,then by temperature fluctuations.Conclusions:All five forest ecosystems in China exhibited a significant increasing NPP along with annual fluctuations evidently during 2000-2018.The variations in China’s forest NPP were controlled mainly by changes in precipitation.

Assessing the Dynamics of Grassland Net Primary Productivity in Response to Climate Change at the Global Scale

Understanding the net primary productivity(NPP) of grassland is crucial to evaluate the terrestrial carbon cycle. In this study, we investigated the spatial distribution and the area of global grassland across the globe. Then, we used the Carnegie-Ames-Stanford Approach(CASA) model to estimate global grassland NPP and explore the spatio-temporal variations of grassland NPP in response to climate change from 1982 to 2008. Results showed that the largest area of grassland distribution during the study period was in Asia(1737.23 × 104 km^2), while the grassland area in Europe was relatively small(202.83 × 10~4 km^2). Temporally, the total NPP increased with fluctuations from 1982 to 2008, with an annual increase rate of 0.03 Pg C/yr. The total NPP experienced a significant increasing trend from 1982 to 1995, while a decreasing trend was observed from 1996 to 2008. Spatially, the grassland NPP in South America and Africa were higher than the other regions, largely as a result of these regions are under warm and wet climatic conditions. The highest mean NPP was recorded for savannas(560.10 g C/(m^2·yr)), whereas the lowest was observed in open shrublands with an average NPP of 162.53 g C/(m^2·yr). The relationship between grassland NPP and annual mean temperature and annual precipitation(AMT, AP, respectively) varies with changes in AP, which indicates that, grassland NPP is more sensitive to precipitation than temperature.