Light Use Efficiency Model Based on Chlorophyll Content Better Captures Seasonal Gross Primary Production Dynamics of Deciduous Broadleaf Forests

Gross primary production(GPP)is a crucial indicator representing the absorption of atmospheric CO2 by vegetation.At present,the estimation of GPP by remote sensing is mainly based on leaf-related vegetation indexes and leaf-related biophysical para-meter leaf area index(LAI),which are not completely synchronized in seasonality with GPP.In this study,we proposed chlorophyll content-based light use efficiency model(CC-LUE)to improve GPP estimates,as chlorophyll is the direct site of photosynthesis,and only the light absorbed by chlorophyll is used in the photosynthetic process.The CC-LUE model is constructed by establishing a linear correlation between satellite-derived canopy chlorophyll content(Chlcanopy)and FPAR.This method was calibrated and validated utiliz-ing 7-d averaged in-situ GPP data from 14 eddy covariance flux towers covering deciduous broadleaf forest ecosystems across five dif-ferent climate zones.Results showed a relatively robust seasonal consistency between Chlcanopy with GPP in deciduous broadleaf forests under different climatic conditions.The CC-LUE model explained 88% of the in-situ GPP seasonality for all validation site-year and 56.0% of in-situ GPP variations through the growing season,outperforming the three widely used LUE models(MODIS-GPP algorithm,Vegetation Photosynthesis Model(VPM),and the eddy covariance-light use efficiency model(EC-LUE)).Additionally,the CC-LUE model(RMSE=0.50 g C/(m^(2)·d))significantly improved the underestimation of GPP during the growing season in semi-arid region,re-markably decreasing the root mean square error of averaged growing season GPP simulation and in-situ GPP by 75.4%,73.4%,and 37.5%,compared with MOD17(RMSE=2.03 g C/(m^(2)·d)),VPM(RMSE=1.88 g C/(m^(2)·d)),and EC-LUE(RMSE=0.80 g C/(m^(2)·d))model.The chlorophyll-based method proved superior in capturing the seasonal variations of GPP in forest ecosystems,thereby provid-ing the possibility of a more precise depiction of forest seasonal carbon uptake.

Seasonal Dynamics of Terrestrial Net Primary Production in Response to Climate Changes in China

Study on seasonal responses of terrestrial net primary production (NPP) to climate changes is to help understand feedback between climate systems and terrestrial ecosystems and mechanisms of increased NPP in the northern middle and high latitudes. In this study, time series dataset of normalized difference vegetation index (NDVI) and corresponding ground-based information on vegetation, climate, soil, and solar radiation, together with an ecological process model, were used to explore the seasonal trends of terrestrial NPP and their geographical differences in China from 1982 to 1999. As the results,. seasonal total NPP in China showed a significant increase for all four seasons (spring, summer, autumn and winter) during the past 18 years. The spring NPP indicated the largest increase rate, while the summer NPP was with the largest increase in magnitude. The response of NPP to climate changes varied with different vegetation types. The increased NPP was primarily led by an advanced growing season for broadleaf evergreen forest, needle-leaf evergreen forest, and needle-leaf deciduous forest, whilst that was mainly due to enhanced vegetation activity (amplitude of growth cycle) during growing season for broadleaf deciduous forest, broadleaf and needle-leaf mixed forest, broadleaf trees with groundcover, perennial grasslands, broadleaf shrubs with grasslands, tundra, desert, and cultivation. The regions with the largest increase in spring NPP appeared mainly in eastern China, while the areas with the largest increase in summer NPP occurred in most parts of Northwestern China, Qinghai-Xizang Plateau, Mts. Xiaoxinganling-Changbaishan, Sanjiang Plain, Songliao Plain, Sichuan Basin, Leizhou Peninsula, part of the middle and lower Yangtze River, and southeastern mountainous areas of China. In autumn, the largest NPP increase appeared in Yunnan Plateau-Eastern Xizang and the areas around Hulun Lake. Such different ways of the NPP responses depended on regional climate attributes and their changes.

How forest age impacts on net primary productivity: Insights from future multi-scenarios

Forest net primary productivity(NPP)constitutes a key flux within the terrestrial ecosystem carbon cycle and serves as a significant indicator of the forests carbon sequestration capacity,which is closely related to forest age.Despite its significance,the impact of forest age on NPP is often ignored in future NPP projections.Here,we mapped forest age in Hunan Province at a 30-m resolution utilizing a combination of Landsat time series stack(LTSS),national forest inventory(NFI)data,and the relationships between height and age.Subsequently,NPP was derived from NFI data and the relationships between NPP and age was built for various forest types.Then forest NPP was predicted based on the NPP-age relationships under three future scenarios,assessing the impact of forest age on NPP.Our findings reveal substantial variations in forest NPP in Hunan Province under three future scenarios:under the age-only scenario,NPP peaks in 2041(133.56TgC·yr^(−1)),while NPP peaks three years later in 2044(141.14TgC·yr^(−1))under the natural development scenario.The maximum afforestation scenario exhibits the most rapid increase in NPP,with peaking in 2049(197.95TgC·yr^(−1)).However,with the aging of the forest,NPP is projected to then decrease by 7.54%,6.07%,and 7.47%in 2060,and 20.05%,19.74%,and 28.38%in 2100,respectively,compared to their peaks under the three scenarios.This indicates that forest NPP will continue to decline soon.Controlling the age structure of forests through selective logging,afforestation and reforestation,and encouraging natural regeneration after disturbance could mitigate this declining trend in forest NPP,but implications of these measures on the full forest carbon balance remain to be studied.Insights from the future multi-scenarios are expected to provide data to support sustainable forest management and national policy development,which will inform the achievement of carbon neutrality goals by 2060.

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.

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.

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.

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.

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.

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.

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 Diptero- carpus 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.

Decomposition and decoupling effect of energy eco-footprint based on global net primary productivity in urban agglomerations

Net primary productivity(NPP)is an important breakthrough point of current research on ecological footprint improvement.The energy eco-footprint(EEF)of the Four-City Area in Central China(FCACC)was measured by constructing an EEF-NPP model.This work has made the following efforts:(1)Gini coefficient was employed to analyze the degree of matching between the EEF and economic growth,population,and energy consumption.(2)LMDI decomposition method was used to explore the impacts of multiple factors on the EEF in the FCACC.(3)Tapio decoupling model was applied to verify the decoupling relationships between the above influencing factors and the EEF.(4)LMDI decomposition formula was embedded into the decoupling model to analyze the impacts of technical and non-technical factors on the decoupling elasticity of the above.The main findings show that from 2010 to 2020:(1)the degree of matching of EEF-GDP,EEF-population,and EEF-energy consumption increased.(2)energy intensity and per capita GDP were the main factors that affected the EEF.(3)the decoupling states between total energy consumption,energy consumption structure,energy intensity,per capita GDP,and population size with the EEF were expansive negative decoupling,expansive negative decoupling,strong negative decoupling,weak decoupling,and expansive negative decoupling,respectively.(4)the impact of non-technical factors was greater than that of technical factors,and their impacts were always in opposite directions.

Dynamic Variation of Vegetation NPP and Its Driving Forces in the Yellow River Basin, China

The productivity of vegetation is influenced by both climate change and human activities.Understanding the specific contributions of these influencing factors is crucial for ecological conservation and regional sustainability.This study utilized a combination of multi-source data to examine the spatiotemporal patterns of Net Primary Productivity(NPP)in the Yellow River Basin(YRB),China from 1982 to 2020.Additionally,a scenario-based approach was employed to compare Potential NPP(PNPP)with Actual NPP(ANPP)to determine the relative roles of climatic and human factors in NPP changes.The PNPP was estimated using the Lund-Potsdam-Jena General Ecosystem Simulator(LPJ-GUESS)model,while ANPP was evaluated by the Carnegie-Ames-Stanford Approach(CASA)model using different NDVI data sources.Both model simulations revealed that significant greening occurring in the YRB,with a gradual decrease observed from southeast to northwest.According to the LPJ_GUESS model simulations,areas experiencing an increasing trend in NPP accounted for 86.82% of the YRB.When using GIMMS and MODIS NDVI data with CASA model simulations,areas showing an increasing trend in NPP accounted for 71.42% and 97.02%,respectively.Furthermore,both climatic conditions and human factors had positive effects on vegetation restoration;approximated 41.15% of restored vegetation areas were influenced by both climate variation and human activities,while around 31.93% were solely affected by climate variation.However,it was found that human activities served as the principal driving force of vegetation degradation within the YRB,impacting 26.35% of degraded areas solely due to human activities.Therefore,effective management strategies encompassing both human activities and climate change adaptation are imperative for facilitating vegetation restoration within this region.These findings will valuable for enhancing our understanding in NPP changes and its underlying factors,thereby contributing to improved ecological management and the pursuit of regional carbon neutrality in China.

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.

Spatio-temporal distribution of net primary productivity along the northeast China transect and its response to climatic change

An improved Carnegie Ames Stanford Approach model （CASA model） was used to estimate the net primary productivity （NPP） of the Northeast China Transect （NECT） every month from 1982 to 2000. The spatial-temporal distribution of NPP along NECT and its response to climatic change were also analyzed. Results showed that the change tendency of NPP spatial distribution in NECT is quite similar to that of precipitation and their spatial correlation coefficient is up to 0.84 （P 〈 0.01）. The inter-annual variation of NPP in NECT is mainly affected by the change of the aestival NPP every year, which accounts for 67.6% of the inter-annual increase in NPP and their spatial correlation coefficient is 0.95 （P 〈 0.01）. The NPP in NECT is mainly cumulated between May and September, which accounts for 89.8% of the annual NPP. The NPP in summer （June to August） accounts for 65.9% of the annual NPP and is the lowest in winter. Recent climate changes have enhanced plant growth in NECT. The mean NPP increased 14.3% from 1980s to 1990s. The inter-annual linear trend of NPP is 4.6 gC·m^-2·a^-1, and the relative trend is 1.17%, which owns mainly to the increasing temperature.

Responses of Vegetation and Primary Production in North-South Transect of Eastern China to Global Change Under Land Use Constraint

A regional model of vegetation dynamics was revised to include land use as a constraint to vegetation dynamics and primary production processes. The model was applied to a forest transect in eastern China (NSTEC, North-South transect of eastern China) to investigate the responses of the transect to possible future climatic change. The simulation result indicated that land use has profound effects on vegetation transition and primary production. In particular, land use reduced competition among vegetation classes and tended to result in less evergreen broadleaf forests but more shrubs and grasses in the transect area. The simulation runs with land use constraint also gave much more realistic estimation about net primary productivity as well as responses of the productivity to future climatic change along the transect. The simulations for future climate scenarios projected by general circulation models (GCM) with doubled atmospheric CO2 concentration predicted that deciduous broadleaf forests would increase, but conifer forests, shrubs and grasses would decrease. The overall effects of doubling CO2 and climatic changes on NSTEC were to produce an increased net primary productivity (NPP) at equilibrium for all seven GCM scenarios. The predicted range of NPP variation in the north is much larger than that in the south.

Estimation of Net Primary Productivity of Terrestrial Vegetation in China by Remote Sensing

Among the many approaches for studying the net primary productivity (NPP), a new method by using remote sensing was introduced in this paper. With spectral information source (the visible band, near infrared band and thermal infrared band) of NOAA-AVHRR, we can get the relative index and parameters, which can be used for estimating NPP of terrestrial vegetation. By means of remote sensing, the estimation of biomass and NPP is mainly based on the models of light energy utilization. In other words, the biomass and NPP can be calculated from the relation among NPP, absorbed photosynthetical active radiation (APAR) and the rate (epsilon) of transformation of APAR to organic matter, thus: NPP = ( FPAR x PAR) x [epsilon * x sigma (T) x sigma (E) x sigma (S) x (1 - Y-m) x (1 - Y-g)]. Based upon remote sensing ( RS) and geographic information system (GIS), the NPP of terrestrial vegetation in China in every ten days was calculated, and the annual NPP was integrated. The result showed that the total NPP of terrestrial vegetation in China was 6.13 x 10(9) t C . a(-1) in 1990 and the maximum NPP was 1 812.9 g C/m(2). According to this result, the spatio-temporal distribution of NPP was analyzed. Comparing to the statistical models, the RS model, using area object other than point one, can better reflect the distribution of NPP, and match the geographic distribution of vegetation in China.

Storage of biomass and net primary productivity in desert shrubland of Artemisia ordosica on Ordos Plateau of Inner Mongolia, China

Biomass and net primary productivity （NPP） are two important parameters in determining ecosystem carbon pool and carbon sequestration. The biomass storage and NPP in desert shrubland of Artemisia ordosica on Ordos Plateau were investigated with method of harvesting standard size shrub in the growing season （June-October） of 2006. Results indicated that above- and belowground biomass of the same size shrubs showed no significant variation in the growing season （p〉0.1）, but annual biomass varied significantly （p〈 0.01）. In the A. ordosica community, shrub biomass storage was 699.76-1246.40 g.m^-2 and annual aboveground NPP was 224.09 g-m^-2·a^-1. Moreover, shrub biomass and NPP were closely related with shrub dimensions （cover and height） and could be well predicted by shrub volume using power regression.

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。

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.