A cross-biome comparison of daily light use efficiency for gross primary production

Vegetation light use efficiency is a key physiological parameter at the canopy scale, and at the daily time step is a component of remote sensing algorithms for scaling gross primary production (GPP) and net primary production (NPP) over regional to global domains. For the purposes of calibrating and validating the light use efficiency ( ε _g) algorithms, the components of ε _g– absorbed photosynthetically active radiation (APAR) and ecosystem GPP – must be measured in a variety of environments. Micrometeorological and mass flux measurements at eddy covariance flux towers can be used to estimate APAR and GPP, and the emerging network of flux tower sites offers the opportunity to investigate spatial and temporal patterns in ε _g at the daily time step. In this study, we examined the relationship of daily GPP to APAR, and relationships of ε _g to climatic variables, at four micrometeorological flux tower sites – an agricultural field, a tallgrass prairie, a deciduous forest, and a boreal forest. The relationship of GPP to APAR was close to linear at the tallgrass prairie site but more nearly hyperbolic at the other sites. The sites differed in the mean and range of daily ε _g, with higher values associated with the agricultural field than the boreal forest. ε_g decreased with increasing APAR at all sites, a function of mid‐day saturation of GPP and higher ε _g under overcast conditions. ε _g was generally not well correlated with vapor pressure deficit or maximum daily temperature. At the agricultural site, a ε _g decline towards the end of the growing season was associated with a decrease in foliar nitrogen concentration. At the tallgrass prairie site, a decline in ε _g in August was associated with soil drought. These results support inclusion of parameters for cloudiness and the phenological status of the vegetation, as well as use of biome‐specific parameterization, in operational ε _g algorithms.     

Remote sensing of sun-induced fluorescence to improve modeling of diurnal courses of gross primary production (GPP)

Terrestrial gross primary production (GPP) is an important parameter to explore and quantify carbon fixation by plant ecosystems at various scales. Remote sensing (RS) offers a unique possibility to investigate GPP in a spatially explicit fashion; however, budgeting of terrestrial carbon cycles based on this approach still remains uncertain. To improve calculations, spatio-temporal variability of GPP must be investigated in more detail on local and regional scales. The overarching goal of this study is to enhance our knowledge on how environmentally induced changes of photosynthetic light-use efficiency (LUE) are linked with optical RS parameters. Diurnal courses of sun-induced fluorescence yield ( F _Syield) and the photochemical reflectance index of corn were derived from high-resolution spectrometric measurements and their potential as proxies for LUE was investigated. GPP was modeled using Monteith's LUE-concept and optical-based GPP and LUE values were compared with synoptically acquired eddy covariance data. It is shown that the diurnal response of complex physiological regulation of photosynthesis can be tracked reliably with the sun-induced fluorescence. Considering structural and physiological effects, this research shows for the first time that including sun-induced fluorescence into modeling approaches improves their results in predicting diurnal courses of GPP. Our results support the hypothesis that air- or spaceborne quantification of sun-induced fluorescence yield may become a powerful tool to better understand spatio-temporal variations of fluorescence yield, photosynthetic efficiency and plant stress on a global scale.     

1980—2013年中国陆地生态系统总初级生产力对干旱的响应特征

干旱事件通过影响陆地生态系统的组成、结构和功能显著改变整个陆地生态系统碳循环。陆地生态系统总初级生产力(GPP)是全球陆地碳通量中最大的组成部分,反映了陆地生态系统的生产力水平。本研究利用基于过程模型模拟的GPP数据(DLM GPP)、基于通量观测升尺度的GPP数据(FLUXCOM GPP)和标准化降水蒸散指数(SPEI),量化分析了1980—2013年中国陆地生态系统GPP和干旱的时空格局,讨论了不同时间尺度上GPP对干旱的响应特征。结果表明:1980—2013年,两种不同GPP数据在中国地区呈现的时间变化趋势的空间分布格局较为一致,上升趋势主要分布在西南地区,下降趋势主要分布在东北大部分地区;中国干旱面积的长期时间变化趋势略有下降,其中干旱化趋势主要位于秦岭淮河以南地区,而西北内陆地区则呈现明显的湿润化趋势;时间尺度上,GPP与SPEI年际变化格局基本吻合,1986、1997、2001和2011年等干旱年份的GPP显著降低;空间尺度上,北方大部分地区的GPP与SPEI呈正相关,南方大部分地区呈负相关,干旱对GPP的影响在半干旱地区表现更加明显; GPP对干旱的响应格局与选取干旱指数...     

Environmental controls on the light use efficiency of terrestrial gross primary production

Gross primary production (GPP) by terrestrial ecosystems is a key quantity in the global carbon cycle. The instantaneous controls of leaf-level photosynthesis are well established, but there is still no consensus on the mechanisms by which canopy-level GPP depends on spatial and temporal variation in the environment. The standard model of photosynthesis provides a robust mechanistic representation for C₃ species; however, additional assumptions are required to “scale up” from leaf to canopy. As a consequence, competing models make inconsistent predictions about how GPP will respond to continuing environmental change. This problem is addressed here by means of an empirical analysis of the light use efficiency (LUE) of GPP inferred from eddy covariance carbon dioxide flux measurements, in situ measurements of photosynthetically active radiation (PAR), and remotely sensed estimates of the fraction of PAR (fAPAR) absorbed by the vegetation canopy. Focusing on LUE allows potential drivers of GPP to be separated from its overriding dependence on light. GPP data from over 100 sites, collated over 20 years and located in a range of biomes and climate zones, were extracted from the FLUXNET2015 database and combined with remotely sensed fAPAR data to estimate daily LUE. Daytime air temperature, vapor pressure deficit, diffuse fraction of solar radiation, and soil moisture were shown to be salient predictors of LUE in a generalized linear mixed-effects model. The same model design was fitted to site-based LUE estimates generated by 16 terrestrial ecosystem models. The published models showed wide variation in the shape, the strength, and even the sign of the environmental effects on modeled LUE. These findings highlight important model deficiencies and suggest a need to progress beyond simple “goodness of fit” comparisons of inferred and predicted carbon fluxes toward an approach focused on the functional responses of the underlying dependencies.     

Analyzing the Ecosystem Carbon Dynamics of Four European Coniferous Forests Using a Biogeochemistry Model

This paper provides the first steps toward a regional-scale analysis of carbon (C) budgets. We explore the ability of the ecosystem model BIOME-BGC to estimate the daily and annual C dynamics of four European coniferous forests and shifts in these dynamics in response to changing environmental conditions. We estimate uncertainties in the model results that arise from incomplete knowledge of site management history (for example, successional stage of forest). These uncertainties are especially relevant in regional-scale simulations, because this type of information is difficult to obtain. Although the model predicted daily C and water fluxes reasonably well at all sites, it seemed to have a better predictive capacity for the photosynthesis-related processes than for respiration. Leaf area index (LAI) was modeled accurately at two sites but overestimated at two others (as a result of poor long-term climate drivers and uncertainties in model parameterization). The overestimation of LAI (and consequently gross photosynthetic production (GPP)), in combination with reasonable estimates of the daily net ecosystem productivity (NEP) of those forests, also illustrates the problem with modeled respiration. The model results suggest that all four European forests have been net sinks of C at the rate of 100–300 gC/m²/y and that this C sequestration capacity would be 30%–70% lower without increasing nitrogen (N) deposition and carbon dioxide (CO₂) concentrations. The magnitude of the forest responses was dependent not only on the rate of changes in environmental factors, but also on site-specific conditions such as climate and soil depth. We estimated that the modeled C exchange at the study sites was reduced by 50%–100% when model simulations were performed for climax forests rather than regrowing forests. The estimates of water fluxes were less sensitive to different initializations of state variables or environmental change scenarios than C fluxes.     

Terrestrial gross primary production inferred from satellite fluorescence and vegetation models

Determining the spatial and temporal distribution of terrestrial gross primary production (GPP) is a critical step in closing the Earth's carbon budget. Dynamical global vegetation models (DGVMs) provide mechanistic insight into GPP variability but diverge in predicting the response to climate in poorly investigated regions. Recent advances in the remote sensing of solar‐induced chlorophyll fluorescence (SIF) opens up a new possibility to provide direct global observational constraints for GPP. Here, we apply an optimal estimation approach to infer the global distribution of GPP from an ensemble of eight DGVMs constrained by global measurements of SIF from the Greenhouse Gases Observing SATellite (GOSAT). These estimates are compared to flux tower data in N. America, Europe, and tropical S. America, with careful consideration of scale differences between models, GOSAT, and flux towers. Assimilation of GOSAT SIF with DGVMs causes a redistribution of global productivity from northern latitudes to the tropics of 7–8 Pg C yr^?1 from 2010 to 2012, with reduced GPP in northern forests (~3.6 Pg C yr^?1) and enhanced GPP in tropical forests (~3.7 Pg C yr^?1). This leads to improvements in the structure of the seasonal cycle, including earlier dry season GPP loss and enhanced peak‐to‐trough GPP in tropical forests within the Amazon Basin and reduced growing season length in northern croplands and deciduous forests. Uncertainty in predicted GPP (estimated from the spread of DGVMs) is reduced by 40–70% during peak productivity suggesting the assimilation of GOSAT SIF with models is well‐suited for benchmarking. We conclude that satellite fluorescence augurs a new opportunity to quantify the GPP response to climate drivers and the potential to constrain predictions of carbon cycle evolution.     

三江源地区人工草地的生态系统CO2净交换、总初级生产力及其影响因子

为了揭示三江源区垂穗披碱草(Elymus nutans)人工草地生态系统(100°26′-100°41′ E, 34°17′-34°25′ N, 海拔3 980 m)的净生态系统CO₂交换(NEE), 该研究利用2006年涡度相关系统观测的数据分析了该人工草地的NEE, 总初级生产力(GPP)、生态系统呼吸(R_eco)以及R_eco/GPP的变化特征及其影响因子。CO₂日最大吸收值为6.56 g CO₂·m^-2·d^-1, 最大排放值为4.87 g CO₂·m^-2·d^-1。GPP年总量为1 761 g CO₂·m^-2, 其中约90%以上被生态系统呼吸所消耗, CO₂的年吸收量为111 g CO₂·m^-2。5月的R_eco/GPP略高于生长季的其他月份, 为90%; 6月R_eco/GPP比值最低, 为79%。生态系统的呼吸商(Q₁₀)为4.81, 显著高于其他生态系统。该研究表明: 生长季的NEE主要受光量子通量密度(PPFD)、温度和饱和水汽压差(VPD)的影响, 生态系统呼吸则主要受土壤温度的控制。     

基于植被羰基硫通量估算陆地生态系统总初级生产力研究进展

羰基硫(COS)是大气中的长周期痕量气体,其分子结构、对流层大气混合比的昼夜和季节动态类似于二氧化碳(CO₂)。植物光合作用及其水解过程中,受扩散通路导度和酶活性影响,气孔的COS与CO₂吸收紧密相关,同时,植物自养呼吸并不释放COS。最新研究中,采用植被COS通量直接指示生态系统总初级生产力(GPP)。综述了植被COS通量与光合作用中碳固定过程的关联机制,以及采用涡度相关观测、整合大气COS监测和生态系统过程模型等方法开展植被COS通量与GPP研究的最新进展,探讨了关键生态过程和参数,发现方法存在以下瓶颈：(1)生理过程、尺度效应和解析效应影响了COS与CO₂的叶片相对吸收率,(2)观测与模拟手段有待进一步耦合,(3)全球COS观测密度限制了方法验证,(4)硫循环过程影响了多区域模拟精度。方法发展的前沿领域包括：(1)开展重点地区植被COS通量观测,(2)提高COS卫星柱浓度的覆盖范围,(3)完善生态系统过程模型的COS吸收机理。展望未来研究关注的科学问题是：对于亚热带等尚待开展COS连续观测的区域,采用植被COS通量...     

A new assessment of European forests carbon exchanges by eddy fluxes and artificial neural network spatialization

Recently flux tower data have become available for a variety of ecosystems under different climatic and edaphic conditions. Although Flux tower data represent point measurements with a footprint of typically 1 km × 1 km they can be used to validate models and to spatialize biospheric fluxes at regional and continental scales. In this paper we present a study where biospheric flux data collected in the EUROFLUX project were used to train a neural network simulator to provide spatial (1 km × 1 km) and temporal (weekly) estimates of carbon fluxes of European forests at continental scale. The novelty of the approach is that flux data were used to constrain and parameterize the neural network structure using a limited number of input driving variables. The overall European carbon uptake from this analysis was 0.47 Gt C yr^?1 with distinctive differences between boreal and temperate regions. The length of the growing season is longer in the south of Europe (about 32 weeks), compared with north and central Europe, which have a similar length‐growing season (about 27 weeks). A peak in respiration was depicted in spring at continental scale as a coherent signal which parallel the construction respiration increase at the onset of the season as usually shown by leaf level measurements.     

Spatiotemporal pattern of gross primary productivity and its covariation with climate in China over the last thirty years

The uncertainties of China's gross primary productivity (GPP) estimates by global data‐oriented products and ecosystem models justify a development of high‐resolution data‐oriented GPP dataset over China. We applied a machine learning algorithm developing a new GPP dataset for China with 0.1° spatial resolution and monthly temporal frequency based on eddy flux measurements from 40 sites in China and surrounding countries, most of which have not been explored in previous global GPP datasets. According to our estimates, mean annual GPP over China is 6.62 ± 0.23 PgC/year during 1982–2015 with a clear gradient from southeast to northwest. The trend of GPP estimated by this study (0.020 ± 0.002 PgC/year² from 1982 to 2015) is almost two times of that estimated by the previous global dataset. The GPP increment is widely spread with 60% area showing significant increasing trend (p < .05), except for Inner Mongolia. Most ecosystem models overestimated the GPP magnitudes but underestimated the temporal trend of GPP. The monsoon affected eastern China, in particular the area surrounding Qinling Mountain, seems having larger contribution to interannual variability (IAV) of China's GPP than the semiarid northwestern China and Tibetan Plateau. At country scale, temperature is the dominant climatic driver for IAV of GPP. The area where IAV of GPP dominated by temperature is about 42%, while precipitation and solar radiation dominate 31% and 27% respectively over semiarid area and cold‐wet area. Such spatial pattern was generally consistent with global GPP dataset, except over the Tibetan Plateau and northeastern forests, but not captured by most ecosystem models, highlighting future research needs to improve the modeling of ecosystem response to climate variations.     

Carbon balance assessment of a Belgian winter wheat crop (Triticum aestivum L.)

The carbon balance of a winter wheat crop in Lonzée, Belgium, was assessed from measurements carried out at different spatial and temporal scales between November 2004 and August 2005. From eddy covariance measurements, the net ecosystem exchange was found to be ?0.63 kg C m^?2 and resulted from the difference between gross primary productivity (GPP) (?1.58 kg C m^?2) and total ecosystem respiration (TER) (0.95 kg C m^?2). The impact of the u_* threshold value on these fluxes was assessed and found to be very small. GPP assessment was partially validated by comparison with an estimation scaled up from leaf scale assimilation measurements. Soil respiration (SR), extrapolated from chamber measurements, was 0.52 kg C m^?2. Net primary productivity, assessed from crop sampling, was ?0.83 kg C m^?2. By combining these fluxes, the autotrophic and heterotrophic components of respiration were deduced. Autotrophic respiration dominated both TER and SR. The evolution of these fluxes was analysed in relation to wheat development.     

Terrestrial gross primary production: Using NIRV to scale from site to globe

Terrestrial photosynthesis is the largest and one of the most uncertain fluxes in the global carbon cycle. We find that near‐infrared reflectance of vegetation (NIR_V), a remotely sensed measure of canopy structure, accurately predicts photosynthesis at FLUXNET validation sites at monthly to annual timescales (R² = 0.68), without the need for difficult to acquire information about environmental factors that constrain photosynthesis at short timescales. Scaling the relationship between gross primary production (GPP) and NIR_V from FLUXNET eddy covariance sites, we estimate global annual terrestrial photosynthesis to be 147 Pg C/year (95% credible interval 131–163 Pg C/year), which falls between bottom‐up GPP estimates and the top‐down global constraint on GPP from oxygen isotopes. NIR_V‐derived estimates of GPP are systematically higher than existing bottom‐up estimates, especially throughout the midlatitudes. Progress in improving estimated GPP from NIR_V can come from improved cloud screening in satellite data and increased resolution of vegetation characteristics, especially details about plant photosynthetic pathway.     

基于叶面积指数估算植被总初级生产力

长时间序列的陆地碳通量数据在全球生态环境变化研究中具有重要意义。采用MODIS GPP(Gross Primary Productivity)算法,基于GIMMS LAI3g,MODIS15和Improved-MODIS15三种叶面积指数(LAI),估算了全球2000至2010年的植被总初级生产力(GPP)。该估算的GPP数值经过全球20个通量站点的验证,并结合MODIS17分析了它们在时空变化上的异同。结果表明:(1)4种GPP精度如下:GPP_(MOD17)GPP_(impro_MOD15)GPP_(LAI3g)GPP_(MOD15)。(2)4种GPP整体上具有一致的季节波动,冬季和夏季整体好于春季和秋季。GPP_(LAI3g)的4个季节精度较相近,而GPP_(MOD17)除了春秋季外其它季节都较好。(3)GPP_(LAI3g)在中等GPP值分布区的估值相对较高,其全球总GPP大体为(117±1.5)Pg C/a,GPP_(MOD17)和GPP_(impro_MOD15)相近且都低于该值。(4)GPP_(LAI3g)和GPP_(impro_MOD15)在大约63.29%的陆面上呈显著(P0.05)的正相关关系,它们和GPP_(MOD17)在LAI不确定性小的地区呈显著的正相关关系。GPP_(LAI3g)和GPP_(MOD15)正相关分布面积占比为40.61%。     

FLUXNET and modelling the global carbon cycle

Measurements of the net CO₂ flux between terrestrial ecosystems and the atmosphere using the eddy covariance technique have the potential to underpin our interpretation of regional CO₂ source–sink patterns, CO₂ flux responses to forcings, and predictions of the future terrestrial C balance. Information contained in FLUXNET eddy covariance data has multiple uses for the development and application of global carbon models, including evaluation/validation, calibration, process parameterization, and data assimilation. This paper reviews examples of these uses, compares global estimates of the dynamics of the global carbon cycle, and suggests ways of improving the utility of such data for global carbon modelling. Net ecosystem exchange of CO₂ (NEE) predicted by different terrestrial biosphere models compares favourably with FLUXNET observations at diurnal and seasonal timescales. However, complete model validation, particularly over the full annual cycle, requires information on the balance between assimilation and decomposition processes, information not readily available for most FLUXNET sites. Site history, when known, can greatly help constrain the model‐data comparison. Flux measurements made over four vegetation types were used to calibrate the land‐surface scheme of the Goddard Institute for Space Studies global climate model, significantly improving simulated climate and demonstrating the utility of diurnal FLUXNET data for climate modelling. Land‐surface temperatures in many regions cool due to higher canopy conductances and latent heat fluxes, and the spatial distribution of CO₂ uptake provides a significant additional constraint on the realism of simulated surface fluxes. FLUXNET data are used to calibrate a global production efficiency model (PEM). This model is forced by satellite‐measured absorbed radiation and suggests that global net primary production (NPP) increased 6.2% over 1982–1999. Good agreement is found between global trends in NPP estimated by the PEM and a dynamic global vegetation model (DGVM), and between the DGVM and estimates of global NEE derived from a global inversion of atmospheric CO₂ measurements. Combining the PEM, DGVM, and inversion results suggests that CO₂ fertilization is playing a major role in current increases in NPP, with lesser impacts from increasing N deposition and growing season length. Both the PEM and the inversion identify the Amazon basin as a key region for the current net terrestrial CO₂ uptake (i.e. 33% of global NEE), as well as its interannual variability. The inversion's global NEE estimate of −1.2 Pg [C] yr⁻¹ for 1982–1995 is compatible with the PEM‐ and DGVM‐predicted trends in NPP. There is, thus, a convergence in understanding derived from process‐based models, remote‐sensing‐based observations, and inversion of atmospheric data. Future advances in field measurement techniques, including eddy covariance (particularly concerning the problem of night‐time fluxes in dense canopies and of advection or flow distortion over complex terrain), will result in improved constraints on land‐atmosphere CO₂ fluxes and the rigorous attribution of mechanisms to the current terrestrial net CO₂ uptake and its spatial and temporal heterogeneity. Global ecosystem models play a fundamental role in linking information derived from FLUXNET measurements to atmospheric CO₂ variability. A number of recommendations concerning FLUXNET data are made, including a request for more comprehensive site data (particularly historical information), more measurements in undisturbed ecosystems, and the systematic provision of error estimates. The greatest value of current FLUXNET data for global carbon cycle modelling is in evaluating process representations, rather than in providing an unbiased estimate of net CO₂ exchange.     

Constraining rooting depths in tropical rainforests using satellite data and ecosystem modeling for accurate simulation of gross primary production seasonality

Accurate parameterization of rooting depth is difficult but important for capturing the spatio-temporal dynamics of carbon, water and energy cycles in tropical forests. In this study, we adopted a new approach to constrain rooting depth in terrestrial ecosystem models over the Amazon using satellite data [moderate resolution imaging spectroradiometer (MODIS) enhanced vegetation index (EVI)] and Biome-BGC terrestrial ecosystem model. We simulated seasonal variations in gross primary production (GPP) using different rooting depths (1, 3, 5, and 10 m) at point and spatial scales to investigate how rooting depth affects modeled seasonal GPP variations and to determine which rooting depth simulates GPP consistent with satellite-based observations. First, we confirmed that rooting depth strongly controls modeled GPP seasonal variations and that only deep rooting systems can successfully track flux-based GPP seasonality at the Tapajos km67 flux site. Second, spatial analysis showed that the model can reproduce the seasonal variations in satellite-based EVI seasonality, however, with required rooting depths strongly dependent on precipitation and the dry season length. For example, a shallow rooting depth (1–3 m) is sufficient in regions with a short dry season (e.g. 0–2 months), and deeper roots are required in regions with a longer dry season (e.g. 3–5 and 5–10 m for the regions with 3–4 and 5–6 months dry season, respectively). Our analysis suggests that setting of proper rooting depths is important to simulating GPP seasonality in tropical forests, and the use of satellite data can help to constrain the spatial variability of rooting depth.     

Climate‐driven uncertainties in modeling terrestrial gross primary production: a site level to global‐scale analysis

We used a land surface model to quantify the causes and extents of biases in terrestrial gross primary production (GPP) due to the use of meteorological reanalysis datasets. We first calibrated the model using meteorology and eddy covariance data from 25 flux tower sites ranging from the tropics to the northern high latitudes and subsequently repeated the site simulations using two reanalysis datasets: NCEP/NCAR and CRUNCEP. The results show that at most sites, the reanalysis‐driven GPP bias was significantly positive with respect to the observed meteorology‐driven simulations. Notably, the absolute GPP bias was highest at the tropical evergreen tree sites, averaging up to ca. 0.45 kg C m^?2 yr^?1 across sites (ca. 15% of site level GPP). At the northern mid‐/high‐latitude broadleaf deciduous and the needleleaf evergreen tree sites, the corresponding annual GPP biases were up to 20%. For the nontree sites, average annual biases of up to ca. 20–30% were simulated within savanna, grassland, and shrubland vegetation types. At the tree sites, the biases in short‐wave radiation and humidity strongly influenced the GPP biases, while the nontree sites were more affected by biases in factors controlling water stress (precipitation, humidity, and air temperature). In this study, we also discuss the influence of seasonal patterns of meteorological biases on GPP. Finally, using model simulations for the global land surface, we discuss the potential impacts of site‐level reanalysis‐driven biases on the global estimates of GPP. In a broader context, our results can have important consequences on other terrestrial ecosystem fluxes (e.g., net primary production, net ecosystem production, energy/water fluxes) and reservoirs (e.g., soil carbon stocks). In a complementary study (Barman et al., 2013 ), we extend the present analysis for latent and sensible heat fluxes, thus consistently integrating the analysis of climate‐driven uncertainties in carbon, energy, and water fluxes using a single modeling framework.     

2001—2018年中国总初级生产力时空变化的遥感研究

总初级生产力（GPP）是绿色植被吸收大气中CO₂进行光合作用生产的有机质,是陆地生态系统碳循环研究的一个关键参数。利用遥感数据和气象数据驱动的双叶光能利用率DTEC模型计算了2001-2018年中国逐月GPP,并结合日光诱导叶绿素荧光（SIF）反演的GOSIF GPP数据集,分析了中国陆地生态系统2001-2018年GPP的时空变化特征。结果表明：（1） GOSIF和DTEC模拟的中国多年GPP平均值分别为7.23 Pg C和6.93 Pg C,在空间分布上呈现东南部高西北部低的特征;（2）2001-2018年,中国GPP呈显著增长（P<0.01）,年增长幅度分别为0.094 PgC/a （GOSIF）和0.073 PgC/a （DTEC）。而已有研究估计的中国GPP年增长幅度约为0.02-0.057 PgC/a,低估了GPP增长趋势。（3）在中国通量网6个通量站的GPP验证表明,两种模型精度高、表现好,都能较好地模拟观测站的GPP季节变化。（4） GOSIF GPP的精度优于DTEC GPP模型,这可能是由于SIF与GPP存在直接机理联系。GOSIF GPP算法能客观地反映植被生产力状况,而DTEC模型更适合自然条件下植被生产力的模拟。     

Estimation of gross primary production of irrigated maize using Landsat-8 imagery and Eddy Covariance data

A study was conducted to understand the potential of Landsat-8 in the estimation of gross primary production (GPP) and to quantify the productivity of maize crop cultivated under hyper-arid conditions of Saudi Arabia. The GPP of maize crop was estimated by using the Vegetation Photosynthesis Model (VPM) utilizing remote sensing data from Landsat-8 reflectance (GPP_VPM) as well as the meteorological data provided by Eddy Covariance (EC) system (GPP_EC), for the period from August to November 2015. Results revealed that the cumulative GPP_EC for the entire growth period of maize crop was 1871 g C m⁻². However, the cumulative GPP determined as a function of the enhanced vegetation index – EVI (GPP_EVI) was 1979 g C m⁻², and that determined as a function of the normalized difference vegetation index – NDVI (GPP_NDVI) was 1754 g C m⁻². These results indicated that the GPP_EVI was significantly higher than the GPP_EC (R² = 0.96, P = 0.0241 and RMSE = 12.6%). While, the GPP_NDVI was significantly lower than the GPP_EC (R² = 0.93, P = 0.0384 and RMSE = 19.7%). However, the recorded relative error between the GPP_EC and both the GPP_EVI and the GPP_NDVI was −6.22% and 5.76%, respectively. These results demonstrated the potential of the landsat-8 driven VPM model for the estimation of GPP, which is relevant to the productivity and carbon fluxes.