Comparing global models of terrestrial net primary productivity (NPP): importance of vegetation structure on seasonal NPP estimates

Estimates of the seasonal absorbed fraction of photosynthetically active radiation (FPAR) and net primary productivity (NPP) are compared among four production efficiency models (PEMs) and seven terrestrial biosphere models simulating canopy development. In addition, the simulated FPARs of the models are compared to the FASIR-FPAR derived from NOAA-AVHRR satellite observations. All models reproduce observed summergreen phenology of temperate deciduous forests rather well, but perform less well for raingreen phenology of savannas. Some models estimate a much longer active canopy in savannas than indicated by satellite observations. As a result, these models estimate high negative monthly NPP during the dry season. For boreal and tropical evergreen ecosystems, several models overestimate LAI and FPAR. When the simulated canopy does respond to unfavourable periods, the seasonal NPP is largely determined by absorbed photosynthetically active radiation (APAR). When the simulated canopy does not respond to unfavourable periods, the light use efficiency (LUE) influences the seasonal NPP more. However, the relative importance of APAR and LUE can change seasonally.     

Diurnal and seasonal variations in light-use efficiency in an alpine meadow ecosystem: causes and implications for remote sensing

Aims Light-use efficiency (LUE) is an important tool for scaling up local CO₂ flux (F CO2) tower observations to regional and global carbon dynamics. Using a data set including F CO2 and environmental factors obtained from an alpine meadow on the Tibetan Plateau, we examined both diurnal and seasonal changes in LUE and the environmental factors controlling these changes. Our objectives were to (i) characterize the diurnal and daily variability of LUE in an alpine meadow, (ii) clarify the causes of this variability, and (iii) explore the possibility of applying the LUE approach to this alpine meadow by examining the relationship between daily LUE and hourly LUE at satellite visiting times.Methods First, we obtained the LUE—the ratio of the gross primary production (GPP) to the absorbed photosynthetically active radiation (APAR)—from the flux tower and meteorological observations. We then characterized the patterns of diurnal and seasonal changes in LUE, explored the environmental controls on LUE using univariate regression analyses and evaluated the effects of diffuse radiation on LUE by assigning weights through a linear programming method to beam photosynthetically active radiation (PAR) and diffuse PAR, which were separated from meteorological observations using an existing method. Finally, we examined the relationships between noontime hourly LUE and daily LUE and those between adjusted noontime hourly and daily LUE because satellites visit the site only once or twice a day, near noon.Important findings The results showed that (i) the LUE of the alpine meadow generally followed the diurnal and seasonal patterns of solar radiation but fluctuated with changes in cloud cover. (ii) The fraction of diffuse light played a dominant role in LUE variation. Daily minimum temperature and vapor pressure deficit also affected LUE variation. (iii) The adjusted APAR, defined as the weighted linear sum of diffuse APAR and beam APAR, was linearly correlated with GPP on different temporal scales. (iv) Midday adjusted LUE was closely related to daily adjusted LUE, regardless of the cloud cover. The results indicated the importance of considering radiation direction when developing LUE-based GPP-estimating models.     

Divergent long‐term trends and interannual variation in ecosystem resource use efficiencies of a southern boreal old black spruce forest 1999–2017

Long‐term trends in ecosystem resource use efficiencies (RUEs) and their controlling factors are key pieces of information for understanding how an ecosystem responds to climate change. We used continuous eddy covariance and microclimate data over the period 1999–2017 from a 120‐year‐old black spruce stand in central Saskatchewan, Canada, to assess interannual variability, long‐term trends, and key controlling factors of gross ecosystem production (GEP) and the RUEs of carbon (CUE = net primary production [NPP]/GEP), light (LUE = GEP/absorbed photosynthetic radiation [APAR]), and water (WUE = GEP/evapotranspiration [E]). At this site, annual GEP has shown an increasing trend over the 19 years (p < 0.01), which may be attributed to rising atmospheric CO₂ concentration. Interannual variability in GEP, aside from its increasing trend, was most strongly related to spring temperatures. Associated with the significant increase in annual GEP were relatively small changes in NPP, APAR, and E, so that annual CUE showed a decreasing trend and annual LUE and WUE showed increasing trends over the 19 years. The long‐term trends in the RUEs were related to the increasing CO₂ concentration. Further analysis of detrended RUEs showed that their interannual variation was impacted most strongly by air temperature. Two‐factor linear models combining CO₂ concentration and air temperature performed well (R²~0.60) in simulating annual RUEs. LUE and WUE were positively correlated both annually and seasonally, while LUE and CUE were mostly negatively correlated. Our results showed divergent long‐term trends among CUE, LUE, and WUE and highlighted the need to account for the combined effects of climatic controls and the ‘CO₂ fertilization effect’ on long‐term variations in RUEs. Since most RUE‐based models rely primarily on one resource limitation, the observed patterns of relative change among the three RUEs may have important implications for RUE‐based modeling of C fluxes.     

Satellite estimates of productivity and light use efficiency in United States agriculture, 1982–98

Remote sensing of net primary production (NPP) is a critical tool for assessing spatial and temporal patterns of carbon exchange between the atmosphere and biosphere. However, satellite estimates suffer from a lack of large‐scale field data needed for validation, as well as the need to parameterize plant light‐use efficiencies (LUEs). In this study, we estimated cropland NPP with the Carnegie‐Ames‐Stanford‐Approach (CASA), a biogeochemical model driven by satellite observations, and then compared these results with field estimates based on harvest data from United States Department of Agriculture National Agriculture Statistics Service (NASS) county statistics. Observed interannual variations in NPP over a 17‐year period were well modelled by CASA, with exceptions mainly due to occasional difficulties in estimating NPP from harvest yields. The role of environmental stressors in agriculture was investigated by running CASA with and without temperature and moisture down‐regulators, which are used in the model to simulate climate impacts on plant LUE. In most cases, correlations with NASS data were highest with modelled stresses, while the opposite was true for irrigated and temperature resistant crops. Analysis of the spatial variability in computed LUE revealed significantly higher values for corn than for other crops, suggesting a simple parameterization of LUE for future studies based on the fraction of area with corn. Absolute values of LUE were much lower than those reported in field trials, due to uncommonly high yields in most field trials, as well as overestimates of absorbed radiation in CASA attributed to bias from temporal compositing of satellite data. Total NPP for US croplands, excluding Alaska and Hawaii, was estimated as 0.62 Pg C year^?1, representing ～20% of total US NPP, and exhibited a positive trend of 3.7 Tg C year^?2. These results have several implications for large‐scale carbon cycle research that are discussed, and are especially relevant for studies of the role of agriculture in the global carbon balance.     

Developing an empirical model of stand GPP with the LUE approach: analysis of eddy covariance data at five contrasting conifer sites in Europe

This paper develops a statistical model for daily gross primary production (GPP) in boreal and temperate coniferous forests. The model applies the light use efficiency (LUE) approach, which estimates the conversion efficiency of daily absorbed photosynthetically active radiation (APAR) into daily GPP as a product of potential LUE and modifying factors. The latter were derived from daily total APAR and daily mean temperature, vapour pressure deficit (VPD) and soil water content (SWC). Modelling data came from five European eddy covariance measurement towers over 2–8 years. The model was tested against independent data from two AmeriFlux stations. The model with the APAR, temperature and VPD modifiers worked well in almost all the site–year combinations, but the SWC modifier only improved the fit in few cases. Geographical variation was found in the modifiers and potential LUE in site-specific models. When a model was fitted to pooled data, differences between sites could be explained by potential LUE, leaf area and environmental conditions. The test against the AmeriFlux data corroborated this finding. The potential LUE varied from 1.9 to 3.1 g C MJ⁻¹, and a weak correlation was found between foliar nitrogen concentration and potential LUE. Some year-to-year variation remained which could be captured by neither the pooled nor the site-specific models.     

Estimating Vegetation Primary Production in the Heihe River Basin of China with Multi-Source and Multi-Scale Data

Estimating gross primary production (GPP) and net primary production (NPP) are significant important in studying carbon cycles. Using models driven by multi-source and multi-scale data is a promising approach to estimate GPP and NPP at regional and global scales. With a focus on data that are openly accessible, this paper presents a GPP and NPP model driven by remotely sensed data and meteorological data with spatial resolutions varying from 30 m to 0.25 degree and temporal resolutions ranging from 3 hours to 1 month, by integrating remote sensing techniques and eco-physiological process theories. Our model is also designed as part of the Multi-source data Synergized Quantitative (MuSyQ) Remote Sensing Production System. In the presented MuSyQ-NPP algorithm, daily GPP for a 10-day period was calculated as a product of incident photosynthetically active radiation (PAR) and its fraction absorbed by vegetation (FPAR) using a light use efficiency (LUE) model. The autotrophic respiration (Ra) was determined using eco-physiological process theories and the daily NPP was obtained as the balance between GPP and Ra. To test its feasibility at regional scales, our model was performed in an arid and semi-arid region of Heihe River Basin, China to generate daily GPP and NPP during the growing season of 2012. The results indicated that both GPP and NPP exhibit clear spatial and temporal patterns in their distribution over Heihe River Basin during the growing season due to the temperature, water and solar influx conditions. After validated against ground-based measurements, MODIS GPP product (MOD17A2H) and results reported in recent literature, we found the MuSyQ-NPP algorithm could yield an RMSE of 2.973 gC m^-2 d^-1 and an R of 0.842 when compared with ground-based GPP while an RMSE of 8.010 gC m^-2 d^-1 and an R of 0.682 can be achieved for MODIS GPP, the estimated NPP values were also well within the range of previous literature, which proved the reliability of our modelling results. This research suggested that the utilization of multi-source data with various scales would help to the establishment of an appropriate model for calculating GPP and NPP at regional scales with relatively high spatial and temporal resolution.     

Site-level evaluation of satellite-based global terrestrial gross primary production and net primary production monitoring

Operational monitoring of global terrestrial gross primary production (GPP) and net primary production (NPP) is now underway using imagery from the satellite‐borne Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. Evaluation of MODIS GPP and NPP products will require site‐level studies across a range of biomes, with close attention to numerous scaling issues that must be addressed to link ground measurements to the satellite‐based carbon flux estimates. Here, we report results of a study aimed at evaluating MODIS NPP/GPP products at six sites varying widely in climate, land use, and vegetation physiognomy. Comparisons were made for twenty‐five 1 km² cells at each site, with 8‐day averages for GPP and an annual value for NPP. The validation data layers were made with a combination of ground measurements, relatively high resolution satellite data (Landsat Enhanced Thematic Mapper Plus at ～30 m resolution), and process‐based modeling. There was strong seasonality in the MODIS GPP at all sites, and mean NPP ranged from 80 g C m^?2 yr^?1 at an arctic tundra site to 550 g C m^?2 yr^?1 at a temperate deciduous forest site. There was not a consistent over‐ or underprediction of NPP across sites relative to the validation estimates. The closest agreements in NPP and GPP were at the temperate deciduous forest, arctic tundra, and boreal forest sites. There was moderate underestimation in the MODIS products at the agricultural field site, and strong overestimation at the desert grassland and at the dry coniferous forest sites. Analyses of specific inputs to the MODIS NPP/GPP algorithm – notably the fraction of photosynthetically active radiation absorbed by the vegetation canopy, the maximum light use efficiency (LUE), and the climate data – revealed the causes of the over‐ and underestimates. Suggestions for algorithm improvement include selectively altering values for maximum LUE (based on observations at eddy covariance flux towers) and parameters regulating autotrophic respiration.     

基于3S的自然植被光能利用率的时空分布特征的模拟

 光能利用率（LUE）直接影响植被各层中的能量分布和光合速率,在确定环境对光合和地上部生长分配的综合限制上十分有价值,是衡量系统功能的一个重要指标。本研究以遥感图像（TM）作为数据源,获取了影响植被LUE的重要变量——叶面积指数（LAI）;用程序语言编写了描述系统碳循环和水循环的景观尺度生态系统生产力过程模型（EPPML）,对长白山自然保护区的太阳总辐射、净初级生产力（NPP）和LUE等的季节动态和空间分布进行了模拟;并用地理信息系统（GIS）手段对空间数据进行处理、分析和显示,从而实现了将植物生理生态研究的结果从小尺度向中尺度进行拓展和转换。EPPML可以比较准确地模拟长白山自然保护区景观尺度上主要植被类型的NPP和太阳总辐射,对LUE的模拟结果也大多在我国森林的LUE范围之内,但对不同植被类型LUE的验证因实测数据不足,仅做了初步比较。模拟结果表明,长白山植被的LUE与NPP的季节进程十分近似,7月可达2.9%。春、夏、秋、冬四个季节植被LUE的模拟平均值分别为0.551%、2.680%、0.551%和0.047%。植被年LUE的模拟值平均为1.075%,在-3.272%～3.556%之间变化,阔叶红松(Pinus koraiensis)林最大（1.653%）,高山流砾滩草类最小（0.146%）。阔叶红松林的LUE虽然较高,但仍有很大的增长潜力。     

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

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 (ε) of transformation of APAR to organic matter, thus：NPP=(FPAR×PAR)×［ε*×σT×σE×σS×(1-Ym)×（1-Yg）］ . 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 6.13×109 t C·a-1in 1990 and the maximum NPP was 1 812.9 g C/m. 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.     

The importance of climate and soils for estimates of net primary production: a sensitivity analysis with the terrestrial ecosystem model

We used the Terrestrial Ecosystem Model (TEM) to investigate how alternative input data sets of climate (temperature/precipitation), solar radiation, and soil texture affect estimates of net primary productivity (NPP) for the conterminous United States. At the continental resolution, the climates of Cramer and Leemans (C&L) and of the Vegetation/ Ecosystem Modelling and Analysis Project (VEMAP) represent cooler and drier conditions for the United States in comparison to the Legates and Willmott (L&W) climate, and cause 5.2% and 2.3% lower estimates of NPP. Solar radiation derived from C&L and given in VEMAP is 32% and 60% higher than the solar radiation data derived from Hahn cloudiness. These differences cause ～ 8% and 10% lower NPP because of radiation-induced water stress. In comparison to the FAO/CSRC soil texture, which represents most biomes with loam soils, the soil textures are finer (more silt and clay) in the Zobler and VEMAP data sets. The use of VEMAP soil textures instead of FAO/CSRC soil textures causes ～ 3% higher NPP because enhanced volumetric soil moisture causes higher rates of nitrogen cycling, but use of the Zobler soil textures has little effect. In general, NPP estimates of TEM are more sensitive to alternative data sets at the biome and grid cell resolutions than at the continental resolution. At all spatial resolutions, the sensitivity of NPP estimates represents the impact of uncertainty among the alternative data sets we used in this study. The reduction of uncertainty in input data sets is required to improve the spatial resolution of NPP estimates by process-based ecosystem models, and is especially important for improving assessments of the regional impacts of global change.     

Sources of Variability in Net Primary Production Predictions at a Regional Scale: A Comparison Using PnET-II and TEM 4.0 in Northeastern US Forests

Because model predictions at continental and global scales are necessarily based on broad characterizations of vegetation, soils, and climate, estimates of carbon stocks and fluxes made by global terrestrial biosphere models may not be accurate for every region. At the regional scale, we suggest that attention can be focused more clearly on understanding the relative strengths of predicted net primary productivity (NPP) limitation by energy, water, and nutrients. We evaluate the sources of variability among model predictions of NPP with a regional-scale comparison between estimates made by PnET-II (a forest ecosystem process model previously applied to the northeastern region) and TEM 4.0 (a terrestrial biosphere model typically applied to the globe) for the northeastern US. When the same climate, vegetation, and soil data sets were used to drive both models, regional average NPP predictions made by PnET-II and TEM were remarkably similar, and at the biome level, model predictions agreed fairly well with NPP estimates developed from field measurements. However, TEM 4.0 predictions were more sensitive to regional variations in temperature as a result of feedbacks between temperature and belowground N availability. In PnET-II, the direct link between transpiration and photosynthesis caused substantial water stress in hardwood and pine forest types with increases in solar radiation; predicted water stress was relieved substantially when soil water holding capacity (WHC) was increased. Increasing soil WHC had little effect on TEM 4.0 predictions because soil water storage was already sufficient to meet plant demand with baseline WHC values, and because predicted N availability under baseline conditions in this region was not limited by water. Because NPP predictions were closely keyed to forest cover type, the relative coverage of low- versus high-productivity forests at both fine and coarse resolutions was an important determinant of regional NPP predictions. Therefore, changes in grid cell size and differences in the methods used to aggregate from fine to coarse resolution were important to NPP predictions insofar as they changed the relative proportions of forest cover. We suggest that because the small patches of high-elevation spruce-fir forest in this region are substantially less productive than forests in the remainder of the region, more accurate NPP predictions will result if models applied to this region use land cover input data sets that retain as much fine-resolution forest type variability as possible. The differences among model responses to variations in climate and soil WHC data sets suggest that the models will respond quite differently to scenarios of future climate. A better understanding of the dynamic interactions between water stress, N availability, and forest productivity in this region will enable models to make more accurate predictions of future carbon stocks and fluxes. Received 19 June 1998; accepted 25 June 1999.     

植被光能利用率研究进展

光能利用率是表征植物固定太阳能效率的指标,指植物通过光合作用将所截获/吸收的能量转化为有机干物质的效率,是植物光合作用的重要概念,也是区域尺度以遥感参数模型监测植被生产力的理论基础。传统的研究方法是通过生物量收获法分别确定植物生长和辐射量,求年或生长季比值;涡度相关技术作为目前直接测定植被冠层与大气间的CO2和水热交换量的唯一方法,使从冠层到景观水平的光能利用率估计成为可能。由于植被类型的差异和气候环境的综合影响使光能利用率表现出显著的空间异质性和时间动态性。在全球尺度上,利用耦合大气CO2观测、卫星遥感和大气辐射传输模型的反演模拟,发现净初级生产力的光能利用率存在明显的地理分异。影响光能利用率时空变异性的因子包括植物内在因素(如叶形、叶羧化酶含量)和外在环境因素。针对光能利用率的时空特征及其波动,建立在通量观测及模型分析基础上的跨尺度模拟,将成为今后该领域的研究重点。     

陆地植被净第一性生产力的研究

回顾了当前国内外陆地植被净第一性生产力（ＮＰＰ） 的研究现状，分析了３ 种生产力模型（ 气候相关模型、过程模型和光能利用率模型） 在应用于全球和区域生产力研究时的长处及不足：气候相关模型在气候变化研究中应用比较多，但计算的只是潜在ＮＰＰ；过程模型着重于植物生长的生理生态过程，但过于复杂，模型中的参数不易获得；光能利用率模型因为可直接利用遥感数据成为ＮＰＰ模型发展的一个主要方面．对国内ＮＰＰ的研究及遥感手段在ＮＰＰ研究中的应用进行了分析．     

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.     

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.     

Comparing global models of terrestrial net primary productivity (NPP): the importance of water availability

Given that neither absolute measures nor direct model validations of global terrestrial net primary productivity (NPP) are feasible, intercomparison of global NPP models provides an effective tool to check model consistency. For this study, we tested the assumption that water availability is the primary limiting factor of NPP in global terrestrial biospheric models. We compared a water balance coefficient (WBC), calculated as the difference of mean annual precipitation and potential evapotranspiration to NPP for each grid cell (0.5° × 0.5° longitude/latitude) in each of 14 models. We also evaluated different approaches used for introducing water budget limitations on NPP: (1) direct physiological control on evapotranspiration through canopy conductance; (2) climatological computation of constraints from supply/demand for ecosystem productivity; and (3) water limitation inferred from satellite data alone. Plots of NPP vs. WBC showed comparable patterns for the models using the same method for water balance limitation on NPP. While correlation plots revealed similar patterns for most global models, other environmental controls on NPP introduced substantial variability.     

Light Use Efficiency over Two Temperate Steppes in Inner Mongolia, China

Vegetation light use efficiency (LUE) is a key parameter of Production Efficiency Models (PEMs) for simulating gross primary production (GPP) of vegetation, from regional to global scales. Previous studies suggest that grasslands have the largest inter-site variation of LUE and controlling factors of grassland LUE differ from those of other biomes, since grasslands are usually water-limited ecosystems. Combining eddy covariance flux data with the fraction of photosynthetically active radiation absorbed by the plant canopy from MODIS, we report LUE on a typical steppe and a desert steppe in Inner Mongolia, northern China. Results show that both annual average LUE and maximum LUE were higher on the desert steppe (0.51 and 1.13 g C MJ(-1)) than on the typical steppe (0.34 and 0.88 g C MJ(-1)), despite the higher GPP of the latter. Water availability was the primary limiting factor of LUE at both sites. Evaporative fraction (EF) or the ratio of actual evapotranspiration to potential evapotranspiration (AET/PET) can explain 50-70% of seasonal LUE variations at both sites. However, the slope of linear regression between LUE and EF (or AET/PET) differed significantly between the two sites. LUE increased with the diffuse radiation ratio on the typical steppe; however, such a trend was not found for the desert steppe. Our results suggest that a biome-dependent LUE(max) is inappropriate, because of the large inter-site difference of LUE(max) within the biome. EF could be a promising down-regulator on grassland LUE for PEMs, but there may be a site-specific relationship between LUE and EF.     

森林生态系统生物物理参数遥感反演研究进展

论述了利用遥感技术反演推算森林生态系统主要生物物理参数：叶面积指数、吸收光合有效辐射、净第一性生产力和生物量的技术、方法和模型及各个参量之间的相互关系研究进展,阐述了各研究方法、模型的特点、优势及局限性。特别论述了净第一性生产力的森林冠层光合作用理论模型基础、微波遥感估算森林生物量的应用优势和理论模型,展示了遥感技术在森林生态学研究中广阔的应用前景,同时也指出现有研究中各生物物理参数的定量遥感估算还有待进一步深入研究。     

Can we measure terrestrial photosynthesis from space directly,using spectral reflectance and fluorescence?

Attempts to estimate photosynthetic rate or gross primary productivity from remotely sensed absorbed solar radiation depend on knowledge of the light use efficiency (LUE). Early models assumed LUE to be constant, but now most researchers try to adjust it for variations in temperature and moisture stress. However, more exact methods are now required. Hyperspectral remote sensing offers the possibility of sensing the changes in the xanthophyll cycle, which is closely coupled to photosynthesis. Several studies have shown that an index (the photochemical reflectance index) based on the reflectance at 531 nm is strongly correlated with the LUE over hours, days and months. A second hyperspectral approach relies on the remote detection of fluorescence, which is a directly related to the efficiency of photosynthesis. We discuss the state of the art of the two approaches. Both have been demonstrated to be effective, but we specify seven conditions required before the methods can become operational.     

中国陆地植被净初级生产力遥感估算

该文在综合分析已有光能利用率模型的基础上,构建了一个净初级生产力(NPP)遥感估算模型,该模型体现了3方面的特色:1)将植被覆盖分类引入模型,并考虑植被覆盖分类精度对NPP估算的影响,由它们共同决定不同植被覆盖类型的归一化植被指数(NDVI)最大值;2)根据误差最小的原则,利用中国的NPP实测数据,模拟出各植被类型的最大光能利用率,使之更符合中国的实际情况;3)根据区域蒸散模型来模拟水分胁迫因子,与土壤水分子模型相比,这在一定程度上对有关参数实行了简化,使其实际的可操作性得到加强。模拟结果表明,1989~1993年中国陆地植被NPP平均值为3.12 Pg C (1 Pg=10¹⁵ g),NPP模拟值与观测值比较接近,690个实测点的平均相对误差为4.5%;进一步与其它模型模拟结果以及前人研究结果的比较表明,该文所构建的NPP遥感估算模型具有一定的可靠性,说明在区域及全球尺度上,利用地理信息系统技术将遥感数据和各种观测数据集成在一起,并对NPP模型进行参数校正,基本上可以实现全球范围不同生态系统NPP的动态监测。