Light-use Efficiency Model Research Articles

Comparison of solar-induced chlorophyll fluorescence, light-use efficiency, and process-based GPP models in maize.

Accurately quantifying cropland gross primary production (GPP) is of great importance to monitor cropland status and carbon budgets. Satellite-based light-use efficiency (LUE) models and process-based terrestrial biosphere models (TBMs) have been widely used to quantify cropland GPP at different scales in past decades. However, model estimates of GPP are still subject to large uncertainties, especially for croplands. More recently, space-borne solar-induced chlorophyll fluorescence (SIF) has shown the ability to monitor photosynthesis from space, providing new insights into actual photosynthesis monitoring. In this study, we examined the potential of SIF data to describe maize phenology and evaluated three GPP modeling approaches (space-borne SIF retrievals, a LUE-based vegetation photosynthesis model [VPM], and a process-based soil canopy observation of photochemistry and energy flux [SCOPE] model constrained by SIF) at a maize (Zea mays L.) site in Mead, Nebraska, USA. The result shows that SIF captured the seasonal variations (particularly during the early and late growing season) of tower-derived GPP (GPP_EC) much better than did satellite-based vegetation indices (enhanced vegetation index [EVI] and land surface water index [LSWI]). Consequently, SIF was strongly correlated with GPP_EC than were EVI and LSWI. Evaluation of GPP estimates against GPP_EC during the growing season demonstrated that all three modeling approaches provided reasonable estimates of maize GPP, with Pearson's correlation coefficients (r) of 0.97, 0.94, and 0.93 for the SCOPE, VPM, and SIF models, respectively. The SCOPE model provided the best simulation of maize GPP when SIF observations were incorporated through optimizing the key parameter of maximum carboxylation capacity (Vcmax). Our results illustrate the potential of SIF data to offer an additional way to investigate the seasonality of photosynthetic activity, to constrain process-based models for improving GPP estimates, and to reasonably estimate GPP by integrating SIF and GPP_EC data without dependency on climate inputs and satellite-based vegetation indices.

Monitoring Grassland Seasonal Carbon Dynamics, by Integrating MODIS NDVI, Proximal Optical Sampling, and Eddy Covariance Measurements

This study evaluated the seasonal productivity of a prairie grassland (Mattheis Ranch, in Alberta, Canada) using a combination of remote sensing, eddy covariance, and field sampling collected in 2012–2013. A primary objective was to evaluate different ways of parameterizing the light-use efficiency (LUE) model for assessing net ecosystem fluxes at two sites with contrasting productivity. Three variations on the NDVI (Normalized Difference Vegetation Index), differing by formula and footprint, were derived: (1) a narrow-band NDVI (NDVI680,800, derived from mobile field spectrometer readings); (2) a broad-band proxy NDVI (derived from an automated optical phenology station consisting of broad-band radiometers); and (3) a satellite NDVI (derived from MODIS AQUA and TERRA sensors). Harvested biomass, net CO2 flux, and NDVI values were compared to provide a basis for assessing seasonal ecosystem productivity and gap filling of tower flux data. All three NDVIs provided good estimates of dry green biomass and were able to clearly show seasonal changes in vegetation growth and senescence, confirming their utility as metrics of productivity. When relating fluxes and optical measurements, temporal aggregation periods were considered to determine the impact of aggregation on model accuracy. NDVI values from the different methods were also calibrated against fAPARgreen (the fraction of photosynthetically active radiation absorbed by green vegetation) values to parameterize the APARgreen (absorbed PAR) term of the LUE (light use efficiency) model for comparison with measured fluxes. While efficiency was assumed to be constant in the model, this analysis revealed hysteresis in the seasonal relationships between fluxes and optical measurements, suggesting a slight change in efficiency between the first and second half of the growing season. Consequently, the best results were obtained by splitting the data into two stages, a greening phase and a senescence phase, and applying separate fits to these two periods. By incorporating the dynamic irradiance regime, the model based on APARgreen rather than NDVI best captured the high variability of the fluxes and provided a more realistic depiction of missing fluxes. The strong correlations between these optical measurements and independently measured fluxes demonstrate the utility of integrating optical with flux measurements for gap filling, and provide a foundation for using remote sensing to extrapolate from the flux tower to larger regions (upscaling) for regional analysis of net carbon uptake by grassland ecosystems.

Primary production estimation of Çankırı province’s rangelands using light use efficiency (LUE) model with satellite data and agrometshell module

In this study, monthly and annual gross primary production (GPP) of rangelands in Cankiri province for the period of 2000-2009 was calculated using light use efficiency (LUE) model with the inputs of satellite data and AgrometShell module. The average production of rangelands varied seasonally and annually (from 12630 to 37701 tons) and was approximately 17800 tons for the last ten years. The amount of rainfall and changing number of animal grazing in the region probably led to the variation. Model performance was tested with integrated normalized difference vegetation index (INDVI) approach which produced a moderate significant correlation (R2= 0.69, P 0.05 for 2008, r= 0.41, P>0.05 for 2009) due to some factors such as sampled plant type, scale differences between satellite data and ground sample size, and subjective sampling errors. This study indicates that LUE Model together with the inputs of AgrometShell module is suitable tool for estimation of rangeland primary production.

北京永定河沿河沙地杨树人工林光能利用效率

PDF HTML阅读 XML下载 导出引用 引用提醒 北京永定河沿河沙地杨树人工林光能利用效率 DOI: 10.5846/stxb201408241671 作者: 作者单位: 北京林业大学水土保持与荒漠化防治重点实验室,北京林业大学水土保持与荒漠化防治重点实验室,北京林业大学水土保持与荒漠化防治重点实验室,北京林业大学水土保持与荒漠化防治重点实验室 作者简介: 通讯作者: 中图分类号: S71 基金项目: 林业公益性行业科研专项"森林经营对生态系统碳水耦合变化的影响机理研究"(201204102);北京市教育委员会科学研究与研究生培养共建项目 The dynamics of light use efficiency at a poplar plantation in Beijing Author: Affiliation: College of Soil and Water Conservation at Beijing Forestry University,Key Lab. Of Soil and Water Conservation Desertification Combating,Ministry of Education,College of Soil and Water Conservation at Beijing Forestry University,Key Lab. Of Soil and Water Conservation Desertification Combating,Ministry of Education,College of Soil and Water Conservation at Beijing Forestry University,Key Lab. Of Soil and Water Conservation Desertification Combating,Ministry of Education,College of Soil and Water Conservation at Beijing Forestry University,Key Lab. Of Soil and Water Conservation Desertification Combating,Ministry of Education Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:光能利用效率(LUE)是影响生态系统生产力大小和质量的主要因素。以位于北京市大兴区永定河沿河沙地的杨树(欧美107/108, Populus euramericana cv.)人工林生态系统作为研究对象,依托涡度相关观测系统,对该生态系统的LUE进行研究,从而确定LUE在不同时间尺度上的影响因子,并确定最大光能利用利用效率(LUEmax)。结果表明:LUE存在明显的季节变化趋势,4月份生长季开始后LUE迅速升高,到7-8月达到最大,而后逐渐降低;在生长季不同阶段, LUE日动态的影响因子不同:4月份气温(Ta)、蒸散比(EF)和饱和水汽压差(VPD)是影响LUE日动态的主要因子,7、8月份光合有效辐射(PAR)和冠层导度(gc)是主要影响因子,5-6月与9-10月LUE日动态则与土壤水分(VWC)有较大关系;而LUE月动态则与月蒸散比(EFm)和月平均土壤温度(Tsm)有关。由于该人工林各月光能利用最适宜环境条件不同,各月LUEmax也各有差异,该生态系统年LUEmax为0.44 gC/MJ PAR,7、8月LUEmax最大,分别为0.66和0.69 gC/MJ PAR。研究结果表明,在利用光能利用模型进行区域乃至全球初级生产力估算时需要根据研究的不同时间尺度确定LUEmax。 Abstract:Light use efficiency (LUE) is a major limiting factor of gross ecosystem productivity (GPP). Various LUE models have been established to evaluate regional GPP. The various maximum light use efficiency (LUEmax) values used in these models are critical variables that influence model uncertainty. Since the dynamics and influential factors affecting LUE at different temporal resolutions vary, it is unclear whether the values of LUEmax at these temporal resolutions differ. Therefore, we examined the dynamics of LUE and LUEmax using data from a poplar plantation (Populus euramericana cv.) in the Daxing district, Beijing. Eddy covariance measurements were taken at this study site. A multiple stepwise regression procedure and recursive partitioning methods were applied at both monthly and annual scales. The results indicate that the averagely daily LUE values from 2006 to 2009 were (0.33 ± 0.16) gC/MJ, (0.35 ± 0.23) gC/MJ, (0.39 ± 0.16) gC/MJ, and (0.32 ± 0.19) gC/MJ, respectively. The daily LUE varied seasonally, with a rapid increase occurring in April and May, a peak from Jun to Aug, and a gradual decrease after September. The factors influencing daily LUE were different during different parts of the growing season. Air temperature (Ta), evaporative fraction (EF), and vapor pressure deficit (VPD) were the main factors in affecting LUE in April. In May, photosynthetically active radiation (PAR), volumetric water content (VWC), EF, and canopy conductance (gc) were the factors with the greatest influence. PAR, VWC, gc, and VPD had large impacts on LUE in June. In July and August, LUE was controlled by PAR and gc. In September, PAR, soil temperature (Ts), VWC, and EF were the main influencing factors, while PAR, VWC, EF, gc, and VPD influenced LUE in October. PAR was the most important factor regularizing LUE in the middle of the growing season, while moisture conditions were the main influencing factors early and late in the growing season. However, monthly PAR (PARm) was not a main factor affecting monthly LUE (LUEm). In contrast, 71% of LUEm variations were explained by the monthly evaporative fraction (EFm) and monthly soil temperature (Tsm). Because of various influential factors, LUEmax were not identical among temporal resolutions. Recursive partitioning analysis showed that EF=0.42 was the node for LUE in April. Correspondingly, LUEmax in Apr was 0.22 gC/MJ, when EF ≥ 0.42. PAR and EF were the nodes for LUE in May, LUEmax in May was 0.39 gC/MJ, when 17 ≤ PAR < 27 MJ and EF ≥ 0.77. In June, LUEmax was 0.38 gC/MJ when VPD < 1.2 kPa and PAR ≥ 21 MJ. From July to October, PAR was the main node for LUE, when LUEmax was 0.66 gC/MJ, 0.69 gC/MJ, 0.61 gC/MJ, and 0.44 gC/MJ, respectively. LUEmax in July, August, and September was slightly larger than that in other months. The average annual LUEmax was approximately 0.44. We concluded that iLUE models should incorporate different LUEmax at different temporal scales to better model GPP. 参考文献 相似文献 引证文献

Ability of the Photochemical Reflectance Index to Track Light Use Efficiency for a Sub-Tropical Planted Coniferous Forest

Light use efficiency (LUE) models are widely used to estimate gross primary productivity (GPP), a dominant component of the terrestrial carbon cycle. Their outputs are very sensitive to LUE. Proper determination of this parameter is a prerequisite for LUE models to simulate GPP at regional and global scales. This study was devoted to investigating the ability of the photochemical reflectance index (PRI) to track LUE variations for a sub-tropical planted coniferous forest in southern China using tower-based PRI and GPP measurements over the period from day 101 to 275 in 2013. Both half-hourly PRI and LUE exhibited detectable diurnal and seasonal variations, and decreased with increases of vapor pressure deficit (VPD), air temperature (Ta), and photosynthetically active radiation (PAR). Generally, PRI is able to capture diurnal and seasonal changes in LUE. However, correlations of PRI with LUE varied dramatically throughout the growing season. The correlation was the strongest (R2 = 0.6427, p &lt; 0.001) in July and the poorest in May. Over the entire growing season, PRI relates better to LUE under clear or partially cloudy skies (clearness index, CI &gt; 0.3) with moderate to high VPD (&gt;20 hPa) and high temperatures (&gt;31 C). Overall, we found that PRI is most sensitive to variations in LUE under stressed conditions, and the sensitivity decreases as the growing conditions become favorable when atmosphere water vapor, temperature and soil moisture are near the optimum conditions.

Comparison of solar-induced chlorophyll fluorescence, light-use efficiency, and process-based GPP models in maize

Comparison of solar-induced chlorophyll fluorescence, light-use efficiency, and process-based GPP models in maize

Sun-induced chlorophyll fluorescence and photochemical reflectance index improve remote-sensing gross primary production estimates under varying nutrient availability in a typical Mediterranean savanna ecosystem

Abstract. This study investigates the performances of different optical indices to estimate gross primary production (GPP) of herbaceous stratum in a Mediterranean savanna with different nitrogen (N) and phosphorous (P) availability. Sun-induced chlorophyll fluorescence yield computed at 760 nm (Fy760), scaled photochemical reflectance index (sPRI), MERIS terrestrial-chlorophyll index (MTCI) and normalized difference vegetation index (NDVI) were computed from near-surface field spectroscopy measurements collected using high spectral resolution spectrometers covering the visible near-infrared regions. GPP was measured using canopy chambers on the same locations sampled by the spectrometers. We tested whether light-use efficiency (LUE) models driven by remote-sensing quantities (RSMs) can better track changes in GPP caused by nutrient supplies compared to those driven exclusively by meteorological data (MM). Particularly, we compared the performances of different RSM formulations – relying on the use of Fy760 or sPRI as a proxy for LUE and NDVI or MTCI as a fraction of absorbed photosynthetically active radiation (fAPAR) – with those of classical MM. Results showed higher GPP in the N-fertilized experimental plots during the growing period. These differences in GPP disappeared in the drying period when senescence effects masked out potential differences due to plant N content. Consequently, although MTCI was closely related to the mean of plant N content across treatments (r2 = 0.86, p &lt; 0.01), it was poorly related to GPP (r2 = 0.45, p &lt; 0.05). On the contrary sPRI and Fy760 correlated well with GPP during the whole measurement period. Results revealed that the relationship between GPP and Fy760 is not unique across treatments, but it is affected by N availability. Results from a cross-validation analysis showed that MM (AICcv = 127, MEcv = 0.879) outperformed RSM (AICcv =140, MEcv = 0.8737) when soil moisture was used to constrain the seasonal dynamic of LUE. However, residual analyses demonstrated that GPP predictions with MM are inaccurate whenever no climatic variable explicitly reveals nutrient-related changes in the LUE parameter. These results suggest that RSM is a valuable means to diagnose nutrient-induced effects on the photosynthetic activity.

GPP and maximum light use efficiency estimates using different approaches over a rotating biodiesel crop

This paper presents: (a) results of gross primary production (GPP) 8-d estimated values using a light use efficiency model (LUE) in a non-irrigated rotating rapeseed crop in the upper Spanish plateau, and (b) inter-comparison results of observed GPP with those concurrently retrieved by MODIS. The rotation scheme over the four-year study comprised rapeseed, wheat, peas and rye. Rapeseed, peas and, in part, rye grew under well-watered conditions whereas wheat was dominated by drought.Input data for the LUE model were the fraction of PAR absorbed (FPAR) 8-d products supplied by MODIS (FPARMODIS), in situ photosynthetic active radiation (PAR) measurements and a scalar f varying between 0 and 1, to take into account the reduction of the maximum PAR conversion efficiency (ɛ0LUE) under limiting environmental conditions. In this study, f values were assumed to be dependent on air temperature (T) and the evaporative fraction which was considered a proxy of water availability. ɛ0LUE, a key parameter in LUE models, which varied according to land use, was derived through the results of a linear regression fit between observed GPP and concurrent GAPAR estimates defined as the product of PAR, FPARMODIS and f. Overall, the LUE model provided satisfactory results, R2=86.3%, significantly improving GPP MODIS estimates (GPPMODIS), R2=71.8%. GPPMODIS uncertainties have primarily been attributed to differences in the f stress factor involved in its formulation (fMODIS) depending on vapour pressure deficit and T which did not fully describe the environmental stress conditions at the measuring site.Overall, ɛ0LUE yielded 3.33±0.10gCMJ−1 although this varied depending on crop architecture, phenology and prevailing meteorological conditions. Crop-to-crop ɛ0LUE ranged from 2.74±0.17 to 3.95±0.19gCMJ−1 for peas and rye, respectively, yielding intermediate values for rapeseed and wheat, 2.92±0.18 and 2.86±0.23gCMJ−1, respectively. ɛ0MODIS, derived from the linear fit of GPP versus GPPMODIS estimates, yielded 2.13±0.10gCMJ−1 and crop-to-crop ranged from 1.28±0.17 to 2.41±0.12gCMJ−1 for wheat and rapeseed, respectively. The best linear fits corresponded to crops growing under well-watered conditions, rapeseed and peas, and the worst fits were for wheat, affected by drought. GPP annuals were 1680, 710, 730 and 1410gCm−2 for rapeseed, wheat, peas and rye, respectively.

A comparison of two photosynthesis parameterization schemes for an alpine meadow site on the Qinghai-Tibetan Plateau

Photosynthesis is a very important sub-process in the carbon cycle and is a crucial sub-modular function in carbon cycle models. In this study, two typical photosynthesis parameterization schemes were compared based on meteorological and eddy covariance (EC) observations at an alpine meadow site. The photosynthesis model parameters were estimated using the Markov Chain Monte Carlo (MCMC) method. The results indicated that the Farquhar-conductance coupled model better predicted the gross primary production (GPP) for the alpine meadow ecosystem at an hourly time scale than the light use efficiency (LUE) model even though the Farquhar-conductance coupled model has a lower computational efficiency than the LUE model. Compared to the Ball–Woodrow–Berry (BWB) stomatal conductance model, coupling the Farquhar model with the Leuning stomatal conductance model more accurately simulated GPP.

Impact of estimated solar radiation on gross primary productivity simulation in subtropical plantation in southeast China

Sunshine duration is widely used to estimate solar radiation, but this estimated inherently contains some uncertainties, limiting its applications. This study investigated the impacts of the estimated solar radiation on simulated gross primary productivity (GPP), which were obtained using ecosystem models – light use efficiency model (LUE) and process-based model – Boreal Ecosystem Productivity Simulator (BEPS) at an evergreen coniferous forest ecosystem in southeast China. The models for solar radiation and diffuse radiation estimation were calibrated through observation data from nearby meteorological stations. The results showed that the established model could be successfully used to estimate solar radiation with high coefficient of determination (0.92) and low root mean square error (2.18MJm−2day−1), but the solar radiation was overestimated when the clearness index was less than 0.15 and underestimated when it was within the range of 0.2–0.35 or greater than 0.6. The estimated solar radiation has significant influence on the diffuse radiation estimation and GPP simulation comparing with using observations. The two ecosystem models reacted differently to the errors of estimated solar radiation. For the LUE model, the estimated solar radiation led to the underestimated GPP in growing season (May–October), and overestimated GPP during non-growing season (November–April) with the bias ranged from −11% to 10% depending on the month of a year. For the BEPS model, estimated solar radiation resulted in overestimated GPP in most months with the bias ranged from −6% to 20%. The difference between the simulated GPP based on these two sources of solar radiation could be counteracted to some extent at the annual scale, especially for LUE model.

Reviews and Syntheses: optical sampling of the flux tower footprint

Abstract. The purpose of this review is to address the reasons and methods for conducting optical remote sensing within the flux tower footprint. Fundamental principles and conclusions gleaned from over 2 decades of proximal remote sensing at flux tower sites are reviewed. The organizing framework used here is the light-use efficiency (LUE) model, both because it is widely used, and because it provides a useful theoretical construct for integrating optical remote sensing with flux measurements. Multiple ways of driving this model, ranging from meteorological measurements to remote sensing, have emerged in recent years, making it a convenient conceptual framework for comparative experimental studies. New interpretations of established optical sampling methods, including the photochemical reflectance index (PRI) and solar-induced chlorophyll fluorescence (SIF), are discussed within the context of the LUE model. Multi-scale analysis across temporal and spatial axes is a central theme because such scaling can provide links between ecophysiological mechanisms detectable at the level of individual organisms and broad patterns emerging at larger scales, enabling evaluation of emergent properties and extrapolation to the flux footprint and beyond. Proper analysis of the sampling scale requires an awareness of sampling context that is often essential to the proper interpretation of optical signals. Additionally, the concept of optical types, vegetation exhibiting contrasting optical behavior in time and space, is explored as a way to frame our understanding of the controls on surface–atmosphere fluxes. Complementary normalized difference vegetation index (NDVI) and PRI patterns across ecosystems are offered as an example of this hypothesis, with the LUE model and light-response curve providing an integrating framework. I conclude that experimental approaches allowing systematic exploration of plant optical behavior in the context of the flux tower network provides a unique way to improve our understanding of environmental constraints and ecophysiological function. In addition to an enhanced mechanistic understanding of ecosystem processes, this integration of remote sensing with flux measurements offers many rich opportunities for upscaling, satellite validation, and informing practical management objectives ranging from assessing ecosystem health and productivity to quantifying biospheric carbon sequestration.

Application of the photosynthetic light-use efficiency model in a northern Great Plains grassland

Abstract The light-use efficiency (LUE) model of photosynthesis is widely used to estimate ecosystem photosynthesis and net primary production from remote sensing measurements. The fraction of absorbed photosynthetically active radiation ( f APAR ) is a dominant term in this model, and it is fundamentally important for model calculations of ecosystem productivity across large areas. The LUE term is sometimes considered a constant, but may be best represented as a variable scalar under stress conditions. The main objective of this study was to better understand factors influencing f APAR , its relationship with seasonal variation in canopy greenness (Normalized Difference Vegetation Index (NDVI)), and the consequences of potential seasonal changes in the NDVI- f APAR relationship for LUE model calculations of ecosystem photosynthesis in a semi-arid grassland. We used two approaches to determine f APAR , (i) direct incoming and outgoing radiation measurements above and below the canopy, and (ii) an inversion approach based on incident photosynthetically active radiation and the light response curve of net ecosystem productivity measured by eddy covariance at low light levels. The two approaches resulted in f APAR values that were very strongly correlated during the initial development of the canopy until peak leaf area index (LAI) was reached. During this time, a strong linear relationship also occurred between f APAR and NDVI calculated from spectral reflectance measurements of the grassland canopy. After peak LAI, there was hysteresis in the NDVI– f APAR relationship, and the two f APAR estimates diverged. Light-use efficiency model calculations of ecosystem photosynthesis made using f APAR values were strongly correlated with chamber CO 2 exchange measurements during the initial development of the canopy leaf area. After peak LAI, a stress function, based on either soil moisture or vapour pressure deficit (VPD) measurements, was necessary to reduce quantum yield and model calculations of ecosystem photosynthesis during periods of relatively low soil moisture and higher VPD later in the growing season. Both stress functions were similarly effective in improving the correlation between modeled and measured ecosystem photosynthesis values, and indicated reduced LUE under late season conditions. Modulating LUE based on the Photochemical Reflectance Index or the Water Band Index (both proposed as possible indicators of LUE) was not effective to improve the correlation between modeled and measured ecosystem photosynthesis values in this ecosystem.

Uncertainty in simulating gross primary production of cropland ecosystem from satellite-based models

Accurate estimates of gross primary production (GPP) for croplands are needed to assess carbon cycle and crop yield. Satellite-based models have been developed to monitor spatial and temporal GPP patterns. However, there are still large uncertainties in estimating cropland GPP. This study compares three light use efficiency (LUE) models (MODIS-GPP, EC-LUE, and VPM) with eddy-covariance measurements at three adjacent AmeriFlux crop sites located near Mead, Nebraska, USA. These sites have different crop-rotation systems (continuous maize vs. maize and soybean rotated annually) and water management practices (irrigation vs. rainfed). The results reveal several major uncertainties in estimating GPP which need to be sufficiently considered in future model improvements. Firstly, the C4 crop species (maize) shows a larger photosynthetic capacity compared to the C3 species (soybean). LUE models need to use different model parameters (i.e., maximal light use efficiency) for C3 and C4 crop species, and thus, it is necessary to have accurate species-distribution products in order to determine regional and global estimates of GPP. Secondly, the 1km sized MODIS fPAR and EVI products, which are used to remotely identify the fraction of photosynthetically active radiation absorbed by the vegetation canopy, may not accurately reflect differences in phenology between maize and soybean. Such errors will propagate in the GPP model, reducing estimation accuracy. Thirdly, the water-stress variables in the remote sensing models do not fully characterize the impacts of water availability on vegetation production. This analysis highlights the need to improve LUE models with regard to model parameters, vegetation indices, and water-stress inputs.

Insect herbivory alters impact of atmospheric change on northern temperate forests.

Stimulation of forest productivity by elevated concentrations of CO2 is expected to partially offset continued increases in anthropogenic CO2 emissions. However, multiple factors can impair the capacity of forests to act as carbon sinks; prominent among these are tropospheric O3 and nutrient limitations(1,2). Herbivorous insects also influence carbon and nutrient dynamics in forest ecosystems, yet are often ignored in ecosystem models of forest productivity. Here we assess the effects of elevated levels of CO2 and O3 on insect-mediated canopy damage and organic matter deposition in aspen and birch stands at the Aspen FACE facility in northern Wisconsin, United States. Canopy damage was markedly higher in the elevated CO2 stands, as was the deposition of organic substrates and nitrogen. The opposite trends were apparent in the elevated O3 stands. Using a light-use efficiency model, we show that the negative impacts of herbivorous insects on net primary production more than doubled under elevated concentrations of CO2, but decreased under elevated concentrations of O3. We conclude that herbivorous insects may limit the capacity of forests to function as sinks for anthropogenic carbon emissions in a high CO2 world.

Productivity, absorbed photosynthetically active radiation, and light use efficiency in crops: Implications for remote sensing of crop primary production

Vegetation productivity metrics such as gross primary production (GPP) at the canopy scale are greatly affected by the efficiency of using absorbed radiation for photosynthesis, or light use efficiency (LUE). Thus, close investigation of the relationships between canopy GPP and photosynthetically active radiation absorbed by vegetation is the basis for quantification of LUE. We used multiyear observations over irrigated and rainfed contrasting C3 (soybean) and C4 (maize) crops having different physiology, leaf structure, and canopy architecture to establish the relationships between canopy GPP and radiation absorbed by vegetation and quantify LUE. Although multiple LUE definitions are reported in the literature, we used a definition of efficiency of light use by photosynthetically active “green” vegetation (LUEgreen) based on radiation absorbed by “green” photosynthetically active vegetation on a daily basis. We quantified, irreversible slowly changing seasonal (constitutive) and rapidly day-to-day changing (facultative) LUEgreen, as well as sensitivity of LUEgreen to the magnitude of incident radiation and drought events. Large (2–3-fold) variation of daily LUEgreen over the course of a growing season that is governed by crop physiological and phenological status was observed. The day-to-day variations of LUEgreen oscillated with magnitude 10–15% around the seasonal LUEgreen trend and appeared to be closely related to day-to-day variations of magnitude and composition of incident radiation. Our results show the high variability of LUEgreen between C3 and C4 crop species (1.43gC/MJ vs. 2.24gC/MJ, respectively), as well as within single crop species (i.e., maize or soybean). This implies that assuming LUEgreen as a constant value in GPP models is not warranted for the crops studied, and brings unpredictable uncertainties of remote GPP estimation, which should be accounted for in LUE models. The uncertainty of GPP estimation due to facultative and constitutive changes in LUEgreen can be considered as a critical component of the total error budget in the context of remotely sensed based estimations of GPP. The quantitative framework of LUEgreen estimation presented here offers a way of characterizing LUEgreen in plants that can be used to assess their phenological and physiological status and vulnerability to drought under current and future climatic conditions and is essential for calibration and validation of globally applied LUE algorithms.

Comparison of four light use efficiency models for estimating terrestrial gross primary production

Light use efficiency (LUE) models that with different structures (i.e., methods to address environmental stresses on LUE) have been widely used to estimate terrestrial gross primary production (GPP) because of their theoretical soundness and practical conveniences. However, a systematic validation of those models with field observations across diverse ecosystems is still lacking and whether the model can be further improved by structural optimization remains unclear. Using GPP estimates at global 51 eddy covariance flux towers that cover a wide climate range and diverse vegetation types, we evaluated the performances of the four major LUE models (i.e., Carnegie-Ames-Stanford approach (CASA), Global Production Efficiency Model (GLO-PEM), Vegetation Photosynthesis Model (VPM), and Eddy Covariance-Light Use Efficiency (EC-LUE)) and examined the possible further improvement of the better-performed model(s) via model structural optimization. Our results showed that the GLO-PEM, VPM, and EC-LUE exhibited the similar capabilities in simulating GPP (explained around 68% of the total variations) and overall performed better than CASA (58%). Nevertheless, the EC-LUE and VPM were the optimal ones because they required less model inputs than the GLO-PEM. For the two optimal models, we found that the minimum method is better than the multiplication approach to integrate multiple environmental stresses on LUE. Moreover, we found that the VPM can be further improved by incorporating the constraint of water vapor deficit (VPDs). We suggested that a modified VPM by using minimum method and adding VPDs may be the best model in estimating large-scale GPP if high-quality remote sensing data available, otherwise, the modified models with the water stress reflected by VPDs only is optimal.

Assessment of the gross primary production dynamics of a Mediterranean holm oak forest by remote sensing time series analysis

Agroforestry ecosystems have a significant social, economic and environmental impact on the development of many regions in the world. On the Iberian Peninsula the Mediterranean agroforestry oak forest known as the dehesa or montado (usually formed by species of the genus Quercus) is considered to be the extreme case of transformation of a Mediterranean forest by human management to provide a wide range of natural resources. The great variability of the Mediterranean climate and the different extensive management practices carried out by humans on the dehesa produces a high spatial and temporal variability in the dynamics of the ecosystem. This leads to a complex pattern of CO2 exchange between the atmosphere and the ecosystem that can act as a sink or as a source of CO2 over the years, depending on the various factors interacting with them. It is thus essential to assess the carbon cycle on the dehesa in order to obtain the maximum economic benefits and ensure environmental sustainability. The availability of high-frequency remote sensing time series allows the evolution of an ecosystem to be assessed at different temporal and spatial scales. In this study our overall objective is to assess the gross primary production (GPP) dynamics of a dehesa ecosystem in Central Spain by analysing the time series (2004–2008) of two models: (1) GPP provided by remote sensing images from the MODIS sensor (MOD17A2 product); and (2) GPP estimated by the implementation of a site-specific light-use efficiency model taking into account local ecological and meteorological parameters. Both models were compared to the production provided by an eddy covariance flux tower located in our study area. Dynamic relationships between models of GPP and precipitation and soil water content were investigated by means of cross-correlations and Granger causality tests. Our results indicate that both models of GPP show a typical dehesa dynamic where there are primarily two layers, the arboreal and the herbaceous strata. However, MODIS underestimates the production of the dehesa in a Mediterranean climate, while our site-specific model produced more similar values and dynamics to those of the eddy covariance tower. The analysis of the dynamic relationships corroborated the strong dynamic link between GPP and available water for plant growth. In conclusion, we succeeded in avoiding the main source of underestimation of the MODIS model by the implementation of a site-specific model. It therefore appears that the different ecological and meteorological parameters used in the MODIS model are primarily responsible for this underestimation. Finally, the Granger causality tests indicate that GPP prediction can be improved by including precipitation or soil water in the models.

Simulation of tree-ring widths with a model for primary production, carbon allocation, and growth

Abstract. We present a simple, generic model of annual tree growth, called "T". This model accepts input from a first-principles light-use efficiency model (the "P" model). The P model provides values for gross primary production (GPP) per unit of absorbed photosynthetically active radiation (PAR). Absorbed PAR is estimated from the current leaf area. GPP is allocated to foliage, transport tissue, and fine-root production and respiration in such a way as to satisfy well-understood dimensional and functional relationships. Our approach thereby integrates two modelling approaches separately developed in the global carbon-cycle and forest-science literature. The T model can represent both ontogenetic effects (the impact of ageing) and the effects of environmental variations and trends (climate and CO2) on growth. Driven by local climate records, the model was applied to simulate ring widths during the period 1958–2006 for multiple trees of Pinus koraiensis from the Changbai Mountains in northeastern China. Each tree was initialised at its actual diameter at the time when local climate records started. The model produces realistic simulations of the interannual variability in ring width for different age cohorts (young, mature, and old). Both the simulations and observations show a significant positive response of tree-ring width to growing-season total photosynthetically active radiation (PAR0) and the ratio of actual to potential evapotranspiration (α), and a significant negative response to mean annual temperature (MAT). The slopes of the simulated and observed relationships with PAR0 and α are similar; the negative response to MAT is underestimated by the model. Comparison of simulations with fixed and changing atmospheric CO2 concentration shows that CO2 fertilisation over the past 50 years is too small to be distinguished in the ring-width data, given ontogenetic trends and interannual variability in climate.

Impacts of diffuse radiation on light use efficiency across terrestrial ecosystems based on Eddy covariance observation in China.

Ecosystem light use efficiency (LUE) is a key factor of production models for gross primary production (GPP) predictions. Previous studies revealed that ecosystem LUE could be significantly enhanced by an increase on diffuse radiation. Under large spatial heterogeneity and increasing annual diffuse radiation in China, eddy covariance flux data at 6 sites across different ecosystems from 2003 to 2007 were used to investigate the impacts of diffuse radiation indicated by the cloudiness index (CI) on ecosystem LUE in grassland and forest ecosystems. Our results showed that the ecosystem LUE at the six sites was significantly correlated with the cloudiness variation (0.24≤R2≤0.85), especially at the Changbaishan temperate forest ecosystem (R2 = 0.85). Meanwhile, the CI values appeared more frequently between 0.8 and 1.0 in two subtropical forest ecosystems (Qianyanzhou and Dinghushan) and were much larger than those in temperate ecosystems. Besides, cloudiness thresholds which were favorable for enhancing ecosystem carbon sequestration existed at the three forest sites, respectively. Our research confirmed that the ecosystem LUE at the six sites in China was positively responsive to the diffuse radiation, and the cloudiness index could be used as an environmental regulator for LUE modeling in regional GPP prediction.

The need for a common basis for defining light-use efficiency: Implications for productivity estimation

A primary focus of this short communication is to show how the operational definition of light use efficiency (LUE) influences the results and interpretation of the LUE model. Our study was motivated by the observation that multiple LUE definitions are reported in the literature. The temporal behavior of three operational definitions of LUE, based on (i) incident radiation, (ii) total absorbed radiation and (iii) radiation absorbed by photosynthetically active/green vegetation was examined for two contrasting crops (soybean and maize) having different physiologies, leaf structures and canopy architectures. Over the course of a growing season, the behavior of these three contrasting LUE definitions was strikingly dissimilar, and the degree of dissimilarity varied with contrasting crops (corn and soybean). This demonstrates that LUE model behavior would vary strongly with the LUE definition used, with resulting implications both for the estimated seasonal productivity, and for the interpretation of the underlying mechanism. Based on these findings, we recommend a standard definition of the LUE model based on radiation absorbed by green vegetation. We also discuss the practical and theoretical implications of using this simple conceptual model on a dynamic biological system.