Comparing global models of terrestrial net primary productivity (NPP): analysis of differences in light absorption and light-use efficiency

Twelve global net primary productivity (NPP) models were compared: BIOME3, CASA, CARAIB, FBM, GLO-PEM, HYBRID, KGBM, PLAI, SDBM, SIB2, SILVAN and TURC. These models all use solar radiation as an input, and compute either absorbed solar radiation directly, or the amount of leaves used to absorb solar radiation, represented by the leaf area index (LAI). For all models, we obtained or estimated photosynthetically active radiation absorbed by the canopy (APAR). We then computed the light use efficiency for NPP (LUE) on an annual basis as the ratio of NPP to APAR. We analysed the relative importance for NPP of APAR and LUE. The analyses consider the global values of these factors, their spatial patterns represented by latitudinal variations, and the overall grid cell by grid cell variability. Spatial variability in NPP within a model proved to be determined by APAR, and differences among models by LUE. There was a compensation between APAR and LUE, so that global NPP values fell within the range of ‘generally accepted values’. Overall, APAR was lower for satellite driven models than for the other models. Most computed values of LUE were within the range of published values, except for one model.     

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.     

落叶阔叶林冠层光合有效辐射分量的遥感模拟与分析

冠层绿色叶片(光合组分)的光合有效辐射分量(绿色FPAR)真实地反映了植被与外界进行物质和能量交换的能力,获取冠层光合组分吸收的太阳光合有效辐射,对生态系统生产力的遥感估算精度的提高具有重要的意义。研究以落叶阔叶林为例,基于SAIL模型模拟森林冠层光合组分和非光合组分吸收的光合有效辐射,研究冠层FPAR变化规律以及与植被指数的相关关系。结果表明,冠层结构的改变会影响冠层对PAR的吸收能力,冠层绿色FPAR的大小与植被面积指数及光合组分面积比相关;在高覆盖度植被区,冠层绿色FPAR占冠层总FPAR的80%以上,非光合组分的贡献较小,但在低植被覆盖区,当光合组分和非光合组分面积相同时,绿色FPAR不及冠层总FPAR的50%;相比于NDVI,北方落叶阔叶林冠层EVI与绿色FPAR存在更为显著的线性相关关系(R~20.99)。     

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．     

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.     

Major disturbance events in terrestrial ecosystems detected using global satellite data sets

Ecosystem scientists have yet to develop a proven methodology to monitor and understand major disturbance events and their historical regimes at a global scale. This study was conducted to evaluate patterns in an 18‐year record of global satellite observations of vegetation phenology from the Advanced Very High Resolution Radiometer (AVHRR) as a means to characterize major ecosystem disturbance events and regimes. The fraction absorbed of photosynthetically active radiation (FPAR) by vegetation canopies worldwide has been computed at a monthly time interval from 1982 to 1999 and gridded at a spatial resolution of 0.5° latitude/longitude. Potential disturbance events of large extent ( > 0.5 Mha) were identified in the FPAR time series by locating anomalously low values (FPAR‐LO) that lasted longer than 12 consecutive months at any 0.5° pixel. We find that nearly 400 Mha of the global land surface could be identified with at least one FPAR‐LO event over the 18‐year time series. The majority of these potential disturbance events occurred in tropical savanna and shrublands or in boreal forest ecosystem classes. Verification of potential disturbance events from our FPAR‐LO analysis was carried out using documented records of the timing of large‐scale wildfires at locations throughout the world. Disturbance regimes were further characterized by association analysis with historical climate anomalies. Assuming accuracy of the FPAR satellite record to characterize major ecosystem disturbance events, we estimate that nearly 9 Pg of carbon could have been lost from the terrestrial biosphere to the atmosphere as a result of large‐scale ecosystem disturbance over this 18‐year time series.     

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.     

Estimating canopy photosynthetic parameters in maize field based on multi-spectral remote sensing

Aims Determination of canopy photosynthetic parameters is key to accurate simulation of ecosystem function by using remote sensing methods. Currently, remote estimation of vegetation canopy structure characteristics has been widely adopted. However, directly estimating photosynthetic variables (photosynthetic capacity and efficiency) at canopy scale based on field spectrometry combined with CO₂ flux measurements is rare.
Methods In this study, we remotely estimated solar radiation use efficiency (εN, net ecosystem CO₂ exchange/absorbed photosynthetically active radiation (NEE_CO2/APAR); εG, gross primary productivity/absorbed photosynthetically active radiation (GPP/APAR); α, apparent quantum efficiency) and photosynthetic capacity (P_max) based on in situ measurements of spectral reflectance and ecosystem CO₂ fluxes, along with observational data on micrometeorological factors during the entire growing season for a maize canopy in Northeast China.
Important findings Results showed that the seasonal variations in P_max and α exhibited a single peak; whereas the values of εN and εG were higher at the start of vegetative stage and then rapidly decreased with the development of maize until displaying a single peak at the intermediate and late stages of the growing season, coinciding with the occurrence of peak values in P_max. A comparison was made on the predictive performance based on normalized difference vegetation index (NDVI), ratio vegetation index (RVI), wide dynamic range vegetation index (WDRVI), 2-band enhanced vegetation index (EVI2), and chlorophyll index (CI) in estimating four canopy photosynthetic parameters with any combination of two separate wavelengths at the range of 400–1 300 nm, which showed that EVI2 was most closely and linearly related to photosynthetic capacity and efficiency. This study demonstrates that multi-spectral remote sensing information is sensitive to the variations in canopy photosynthetic parameters in maize field and can be used to quantitatively monitor seasonal dynamics of canopy photosynthesis, and to accurately assess crop productivity and ecosystem CO₂ exchange capacity.     

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.     

Remote estimation of the fraction of absorbed photosynthetically active radiation for a maize canopy in Northeast China

Aims Accurate remote estimation of the fraction of absorbed photosynthetically active radiation (fAPAR) is essential for the light use efficiency (LUE) models. Currently, one challenge for the LUE models is lack of knowledge about the relationship between fAPAR and the normalized difference vegetation index (NDVI). Few studies have tested this relationship against field measurements and evaluated the accuracy of the remote estimation method. This study aimed to reveal the empirical relationship between NDVI and fAPAR and to improve algorithms for remote estimation of fAPAR.Methods To investigate the method of remote estimation of fAPAR seasonal dynamics, the CASA (Carnegie–Ames–Stanford Approach) model and spectral vegetation indices (VIs) were used for in situ measurements of spectral reflectance and fAPAR during the growing season of a maize canopy in Northeast China.Important findings The results showed that the fAPAR increased rapidly with the day of year during the vegetative stage, it remained relatively stable at the stage of reproduction, and finally decreased slowly during the senescence stage. In addition, fAPAR green [fAPAR green = fAPAR × (green LAI/green LAI max)] showed clearer seasonal trends than fAPAR. The NDVI, red-edge NDVI, wide dynamic range vegetation index, red-edge position (REP) and REP with Sentinel-2 bands derived from hyperspectral remote sensing data were all significantly positively related to fAPAR green during the entire growing season. In a comparison of the predictive performance of VIs for the whole growing season, REP was the most appropriate spectral index, and can be recommended for monitoring seasonal dynamics of fAPAR in a maize canopy.     

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.     

Modeling the Sensitivity of the Seasonal Cycle of GPP to Dynamic LAI and Soil Depths in Tropical Rainforests

The seasonality of pan-tropical wet forests has been highlighted by recent remote sensing and eddy flux measurements that have recorded both increased and sustained dry-season gross primary productivity (GPP). These observations suggest that wet tropical forests are primarily light limited and that the mechanisms for resilience to drought and projected climate change must be considered in ecosystem model development. Here we investigate two proposed mechanisms for drought resilience of tropical forests, deep soil water access and the seasonality of phenology, using the LPJmL Dynamic Global Vegetation Model. We parameterize a new seasonal phenology module for tropical evergreen trees using remotely sensed leaf area index (LAI) and incoming solar radiation data from the Terra Earth Observing System. Simulations are evaluated along a gradient of dry-season length (DSL) in South America against MODIS GPP estimates. We show that deep soil water access is critical for maintaining dry-season GPP, whereas implementing a seasonal LAI did not enhance simulated dry-season GPP. The Farquhar-Collatz photosynthesis scheme used in LPJmL optimizes leaf nitrogen allocation according to light conditions, causing maximum photosynthetic capacity in the dry season. High LAI, characteristic of tropical forests, also dampens the seasonal amplitude of the fraction of photosynthetically active radiation (FPAR). Given the relatively high uncertainty in tropical phenology observations and their corresponding proximate drivers, we recommend that ecosystem model development focus on belowground processes. An improved representation of soil depths and rooting distributions is necessary for modeling the dynamics of dry-season tropical forest functioning and may have important impacts for modeling tropical forest vulnerability to climate change. Author Contributions  BP conceived of the study, analyzed data, and wrote the paper. UH designed study and contributed new methods. WC designed study and contributed to paper.     

Recent History of Large-Scale Ecosystem Disturbances in North America Derived from the AVHRR Satellite Record

Ecosystem structure and function are strongly affected by disturbance events, many of which in North America are associated with seasonal temperature extremes, wildfires, and tropical storms. This study was conducted to evaluate patterns in a 19-year record of global satellite observations of vegetation phenology from the advanced very high resolution radiometer (AVHRR) as a means to characterize major ecosystem disturbance events and regimes. The fraction absorbed of photosynthetically active radiation (FPAR) by vegetation canopies worldwide has been computed at a monthly time interval from 1982 to 2000 and gridded at a spatial resolution of 8–km globally. Potential disturbance events were identified in the FPAR time series by locating anomalously low values (FPAR-LO) that lasted longer than 12 consecutive months at any 8-km pixel. We can find verifiable evidence of numerous disturbance types across North America, including major regional patterns of cold and heat waves, forest fires, tropical storms, and large-scale forest logging. Summed over 19 years, areas potentially influenced by major ecosystem disturbances (one FPAR-LO event over the period 1982–2000) total to more than 766,000 km². The periods of highest detection frequency were 1987–1989, 1995–1997, and 1999. Sub-continental regions of the Pacific Northwest, Alaska, and Central Canada had the highest proportion (>90%) of FPAR-LO pixels detected in forests, tundra shrublands, and wetland areas. The Great Lakes region showed the highest proportion (39%) of FPAR-LO pixels detected in cropland areas, whereas the western United States showed the highest proportion (16%) of FPAR-LO pixels detected in grassland areas. Based on this analysis, an historical picture is emerging of periodic droughts and heat waves, possibly coupled with herbivorous insect outbreaks, as among the most important causes of ecosystem disturbance in North America.     

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.     

基于遥感的光合有效辐射吸收比率(FPAR) 估算方法综述

光合有效辐射吸收比率(FPAR)是反映植被生长过程的重要生理参数,是陆地生态系统模型的关键参数,是反映全球气候变化的重要因子。基于遥感的FPAR估算方法是获取区域乃至全球尺度FPAR的有效方法。目前,主要形成了植被指数法和机理法两类方法,植被指数法是建立FPAR与植被指数的经验统计模型,简单、计算效率高;机理法则从物理模型上进行FPAR的求解与反演,机理明晰、可行性强。然而,由于FPAR本身的复杂性以及环境因素、遥感数据质量的影响,导致了估算方法面临诸多不确定性问题。为了解决这些不确定性问题以及满足生态过程深入研究的需求,将进一步注重FPAR的机理研究、先验知识的获取与积累,构建长时间序列FPAR以及高时空的FPAR算法研究。     

Leaf dynamics of a deciduous forest canopy: no response to elevated CO<Subscript>2</Subscript>

Leaf area index (LAI) and its seasonal dynamics are key determinants of terrestrial productivity and, therefore, of the response of ecosystems to a rising atmospheric CO₂ concentration. Despite the central importance of LAI, there is very little evidence from which to assess how forest LAI will respond to increasing [CO₂]. We assessed LAI and related leaf indices of a closed-canopy deciduous forest for 4 years in 25-m-diameter plots that were exposed to ambient or elevated CO₂ (542 ppm) in a free-air CO₂ enrichment (FACE) experiment. LAI of this Liquidambar styraciflua (sweetgum) stand was about 6 and was relatively constant year-to-year, including the 2 years prior to the onset of CO₂ treatment. LAI throughout the 1999–2002 growing seasons was assessed through a combination of data on photosynthetically active radiation (PAR) transmittance, mass of litter collected in traps, and leaf mass per unit area (LMA). There was no effect of [CO₂] on any expression of leaf area, including peak LAI, average LAI, or leaf area duration. Canopy mass and LMA, however, were significantly increased by CO₂ enrichment. The hypothesized connection between light compensation point (LCP) and LAI was rejected because LCP was reduced by [CO₂] enrichment only in leaves under full sun, but not in shaded leaves. Data on PAR interception also permitted calculation of absorbed PAR (APAR) and light use efficiency (LUE), which are key parameters connecting satellite assessments of terrestrial productivity with ecosystem models of future productivity. There was no effect of [CO₂] on APAR, and the observed increase in net primary productivity in elevated [CO₂] was ascribed to an increase in LUE, which ranged from 1.4 to 2.4 g MJ^–1. The current evidence seems convincing that LAI of non-expanding forest stands will not be different in a future CO₂-enriched atmosphere and that increases in LUE and productivity in elevated [CO₂] are driven primarily by functional responses rather than by structural changes. Ecosystem or regional models that incorporate feedbacks on resource use through LAI should not assume that LAI will increase with CO₂ enrichment of the atmosphere.     

Estimation of FPAR and FPAR profile for maize canopies using airborne LiDAR

FPAR (fraction of photosynthetically active radiation) and FPAR profile (vertical FPAR distribution) are important parameters for characterizing the vegetation growth status and studying global climate change. Few studies have been carried out to estimate FPAR and FPAR profile using waveform LiDAR data. This research explored the potential of airborne small-footprint full-waveform LiDAR in the estimation of FPAR and FPAR profile of the maize canopy in Huailai County of Hebei Province, China. First, the maize growing area was identified by a simple decision tree model. Second, raw waveform data were processed to extract LiDAR-derived energy ratio and energy ratio profile. Third, FPAR and FPAR profile were estimated from LiDAR-derived metrics. Finally, we analyzed the FPAR and FPAR profile estimation results and assessed the model validity using the leave-one-out cross-validation (LOOCV) method. The comparative analyses found that the LiDAR-derived energy ratio profile and field-measured FPAR profile had the same trend and similar change rate for all maize layers. The accuracy assessments indicated that the FPAR and FPAR profile were estimated well by the LiDAR waveform data, with the high R² (0.90 for the whole canopy, and 0.95, 0.90, 0.93, 0.92, and 0.97 for layers 1–5) and low RMSEs (0.042 for the whole canopy, and 0.033, 0.035, 0.039, 0.043, and 0.044 for layers 1–5). The spatial distribution map of FPAR was produced to describe the maize growth status of the whole study area, and the map showed that the FPAR distributed relatively uniformly. This study suggested that airborne small-footprint full-waveform LiDAR was useful in accurately measuring FPAR and FPAR profile of the maize canopy and in effectively mapping the maize FPAR spatial distribution.     

Mathematical models of the photosynthetic response of tree stands to rising CO2 concentrations and temperatures

Two published models of canopy photosynthesis, MAESTRO and BIOMASS, are simulated to examine the response of tree stands to increasing ambient concentrations of carbon dioxide (C_a) and temperatures. The models employ the same equations to described leaf gas exchange, but differ considerably in the level of detail employed to represent canopy structure and radiation environment. Daily rates of canopy photosynthesis simulated by the two models agree to within 10% across a range of CO₂ concentrations and temperatures. A doubling of C_a leads to modest increases of simulated daily canopy photosynthesis at low temperatures (10% increase at 10°C), but larger increases at higher temperatures (60% increase at 30°C). The temperature and CO₂ dependencies of canopy photosynthesis are interpreted in terms of simulated contributions by quantum-saturated and non-saturated foliage. Simulations are presented for periods ranging from a diurnal cycle to several years. Annual canopy photosynthesis simulated by BIOMASS for trees experiencing no water stress is linearly related to simulated annual absorbed photosynthetically active radiation, with light utilization coefficients for carbon of ?= 1.66 and 2.07g MJ^?1 derived for C_a of 350 and 700 μmol mol^?1, respectively.     

2010—2012年北京植被光合有效辐射吸收比例的时空变化

利用MODIS产品中分辨率为1 km的光合有效辐射吸收比例(FPAR)数据,结合植被功能型分类,分析2010—2012年北京植被FPAR的空间分布特征,以及各种植被类型FPAR的多年变化,并进一步探讨了FPAR与叶面积指数(LAI)之间的相关性.结果表明: 研究期间,北京植被的FPAR空间分布均呈现出东北部、西南部高,并向中心城区逐渐递减的分布特征.通过FPAR的叠加分析还发现,各种植被类型FPAR年平均值的波动均较小,针叶树、阔叶树、草地、作物FPAR的年均值波动范围仅分别在0.42～0.44、0.38～0.39、0.32～0.33、0.21～0.22,但各种植被类型FPAR的年内变化范围均较大.各种植被类型的FPAR与LAI也具有较好的线性或对数关系.经过Timesat软件中的Savitzky-Golay平滑滤波后,各种植被类型FPAR的季节性变化特征更加明显.     

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.