表征亚热带常绿林光合作用季节变化特征的多种植被指数

利用遥感方法可以在区域尺度反演地表植被的光合生理状况和生产力变化,但亚热带常绿林冠层结构季节变化较小,传统的光谱植被指数对植被光合作用难以准确捕捉。利用2014—2015年中国科学院广东省鼎湖山森林生态试验站多角度自动光谱观测系统的光谱反射数据,分别反演传统冠层结构型植被指数(NDVI)、光合生理生化型植被指数(CCI)和叶绿素荧光型植被指数(NDFI_(685)和NDFI_(760)),并利用不同类型植被指数的组合,构建多元线性回归模型。结果表明:亚热带常绿针阔混交林三种类型植被指数均与GPP的动态变化有显著的相关性,其中,NDVI是表征GPP较优的植被指数(R~2=0.60,P0.01),其次为CCI(R~2=0.55,P0.01),而NDFI能够作为辅助指数,有效提高NDVI(R~2=0.68,P0.001)和CCI(R~2=0.67,P0.001)表征GPP的程度。多个植被指数参与构建的多元回归模型能够有效提高亚热带地区常绿林GPP季节动态变化的拟合精度,提升遥感精确评估亚热带森林生产力的能力。     

Chlorophyll fluorescence tracks seasonal variations of photosynthesis from leaf to canopy in a temperate forest

Accurate estimation of terrestrial gross primary productivity (GPP) remains a challenge despite its importance in the global carbon cycle. Chlorophyll fluorescence (ChlF) has been recently adopted to understand photosynthesis and its response to the environment, particularly with remote sensing data. However, it remains unclear how ChlF and photosynthesis are linked at different spatial scales across the growing season. We examined seasonal relationships between ChlF and photosynthesis at the leaf, canopy, and ecosystem scales and explored how leaf‐level ChlF was linked with canopy‐scale solar‐induced chlorophyll fluorescence (SIF) in a temperate deciduous forest at Harvard Forest, Massachusetts, USA. Our results show that ChlF captured the seasonal variations of photosynthesis with significant linear relationships between ChlF and photosynthesis across the growing season over different spatial scales (R² = 0.73, 0.77, and 0.86 at leaf, canopy, and satellite scales, respectively; P < 0.0001). We developed a model to estimate GPP from the tower‐based measurement of SIF and leaf‐level ChlF parameters. The estimation of GPP from this model agreed well with flux tower observations of GPP (R² = 0.68; P < 0.0001), demonstrating the potential of SIF for modeling GPP. At the leaf scale, we found that leaf F_q’/F_m’, the fraction of absorbed photons that are used for photochemistry for a light‐adapted measurement from a pulse amplitude modulation fluorometer, was the best leaf fluorescence parameter to correlate with canopy SIF yield (SIF/APAR, R² = 0.79; P < 0.0001). We also found that canopy SIF and SIF‐derived GPP (GPP_SIF) were strongly correlated to leaf‐level biochemistry and canopy structure, including chlorophyll content (R² = 0.65 for canopy GPP_SIF and chlorophyll content; P < 0.0001), leaf area index (LAI) (R² = 0.35 for canopy GPP_SIF and LAI; P < 0.0001), and normalized difference vegetation index (NDVI) (R² = 0.36 for canopy GPP_SIF and NDVI; P < 0.0001). Our results suggest that ChlF can be a powerful tool to track photosynthetic rates at leaf, canopy, and ecosystem scales.     

Estimating soil respiration using spectral vegetation indices and abiotic factors in irrigated and rainfed agroecosystems

Aims

Our aims were to identify the primary factors involved in soil respiration (R_s) variability and the role that spectral vegetation indices played in R_s estimation in irrigated and rainfed agroecosystems during the growing season.

Methods

We employed three vegetation indices [i.e., normalized difference vegetation index (NDVI), green edge chlorophyll index (CI_{green edge}) and enhanced vegetation index (EVI)] derived from the Moderate-resolution Imaging Spectroradiometer (MODIS) surface reflectance product as approximations of crop gross primary production (GPP) for R_s estimation. Different statistical models were used to analyze the dependencies of R_s on soil temperature, soil water content and plant photosynthesis, and accuracy of these models were compared in the irrigated and rainfed agroecosystems.

Results

The results demonstrated that a model based only on abiotic factors (e.g., soil temperature and soil water content) failed to describe part of the growing-season variability in R_s. Residual analysis indicated that R_s was influenced by a short-term gross primary production (GPP) and a longer-term (≥3 days) accumulated GPP in the irrigated and rainfed agroecosystems. Therefore, photosynthesis dependency of R_s should be included in the R_s model to describe the growing-season dynamics of R_s. Among the three VIs, CI_{green edge} showed generally better correlations with GPP at different cumulative times and canopy green leaf area index than EVI and NDVI. Adding the CI_{green edge} into the model considering only soil temperature and soil water content significantly improved the simulation accuracy of R_s.

Conclusions

Our results suggest that spectral vegetation index from remote sensing could be used to estimate R_s, which will be helpful for the development of a future R_s model over a large spatial scale.     

An improved approach for remotely sensing water stress impacts on forest C uptake

Given that forests represent the primary terrestrial sink for atmospheric CO₂, projections of future carbon (C) storage hinge on forest responses to climate variation. Models of gross primary production (GPP) responses to water stress are commonly based on remotely sensed changes in canopy ‘greenness’ (e.g., normalized difference vegetation index; NDVI). However, many forests have low spectral sensitivity to water stress (SSWS) – defined here as drought‐induced decline in GPP without a change in greenness. Current satellite‐derived estimates of GPP use a vapor pressure deficit (VPD) scalar to account for the low SWSS of forests, but fail to capture their responses to water stress. Our objectives were to characterize differences in SSWS among forested and nonforested ecosystems, and to develop an improved framework for predicting the impacts of water stress on GPP in forests with low SSWS. First, we paired two independent drought indices with NDVI data for the conterminous US from 2000 to 2011, and examined the relationship between water stress and NDVI. We found that forests had lower SSWS than nonforests regardless of drought index or duration. We then compared satellite‐derived estimates of GPP with eddy‐covariance observations of GPP in two deciduous broadleaf forests with low SSWS: the Missouri Ozark (MO) and Morgan Monroe State Forest (MMSF) AmeriFlux sites. Model estimates of GPP that used VPD scalars were poorly correlated with observations of GPP at MO (r² = 0.09) and MMSF (r² = 0.38). When we included the NDVI responses to water stress of adjacent ecosystems with high SSWS into a model based solely on temperature and greenness, we substantially improved predictions of GPP at MO (r² = 0.83) and for a severe drought year at the MMSF (r² = 0.82). Collectively, our results suggest that large‐scale estimates of GPP that capture variation in SSWS among ecosystems could improve predictions of C uptake by forests under drought.     

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)。     

Influence of stand structure on carbon-13 of vegetation, soils, and canopy air within deciduous and evergreen forests in Utah, United States

Carbon isotope ratios (δ¹³C) were studied in evergreen and deciduous forest ecosystems in semi-arid Utah (Pinus contorta, Populus tremuloides, Acer negundo and Acer grandidentatum). Measurements were taken in four to five stands of each forest ecosystem differing in overstory leaf area index (LAI) during two consecutive growing seasons. The δ¹³C_leaf (and carbon isotope discrimination) of understory vegetation in the evergreen stands (LAI 1.5–2.2) did not differ among canopies with increasing LAI, whereas understory in the deciduous stands (LAI 1.5–4.5) exhibited strongly decreasing δ¹³C_leaf values (increasing carbon isotope discrimination) with increasing LAI. The δ¹³C values of needles and leaves at the top of the canopy were relatively constant over the entire LAI range, indicating no change in intrinsic water-use efficiency with overstory LAI. In all canopies, δ¹³C_leaf decreased with decreasing height above the forest floor, primarily due to physiological changes affecting c _i/c _a (> 60%) and to a minor extent due to δ¹³C of canopy air (< 40%). This intra-canopy depletion of δ¹³C_leaf was lowest in the open stand (1‰) and greatest in the denser stands (4.5‰). Although overstory δ¹³C_leaf did not change with canopy LAI, δ¹³C of soil organic carbon increased with increasing LAI in Pinus contorta and Populus tremuloides ecosystems. In addition, δ¹³C of decomposing organic carbon became increasingly enriched over time (by 1.7–2.9‰) for all deciduous and evergreen dry temperate forests. The δ¹³C_canopy of CO₂ in canopy air varied temporally and spatially in all forest stands. Vertical canopy gradients of δ¹³C_canopy, and [CO₂]_canopy were larger in the deciduous Populus tremuloides than in the evergreen Pinu contorta stands of similar LAI. In a very wet and cool year, ecosystem discrimination (Δ_e) was similar for both deciduous Populus tremulodies (18.0 ± 0.7‰) and evergreen Pinus contorta (18.3 ± 0.9‰) stands. Gradients of δ¹³C_canopy and [CO₂]_canopy were larger in denser Acer spp. stands than those in the open stand. However, ¹³C enrichment above and photosynthetic draw-down of [CO₂]_canopy below tropospheric baseline values were larger in the open than in the dense stands, due to the presence of a vigorous understory vegetation. Seasonal patterns of the relationship δ¹³C_canopy versus 1/[CO₂]_canopy were strongly influenced by precipitation and air temperature during the growing season. Estimates of Δ_e for Acer spp. did not show a significant effect of stand structure, and averaged 16.8 ± 0.5‰ in 1933 and 17.4 ± 0.7‰ in 1994. However, Δ_e varied seasonally with small fluctuations for the open stand (2‰), but more pronounced changes for the dense stand (5‰). Received: 15 April 1996 / Accepted: 19 October 1996     

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

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

Does the Diagnostic Accuracy of the 13C‐Urea Breath Test Vary with Age Even After the Application of Urea Hydrolysis Rate?

Background: Endogenous CO₂ production may be a possible explanation for higher false‐positive results reported for ¹³C‐urea breath test (UBT) in children below 6 years. In this study, we evaluated whether age affects the diagnostic accuracy of the ¹³C‐UBT even after the application of urea hydrolysis rate (UHR) in children. Methods: A total of 612 ¹³C‐UBTs and endoscopic biopsies were performed on children divided into two groups; children under 6 years (n = 126) and children aged 6–18 years (n = 486). For ¹³C‐UBT, 75 mg ¹³C‐urea was ingested, and breath sample was collected 30 minutes later. Delta over baseline (DOB) was determined, and UHR was calculated to normalize the DOB values for endogenous CO₂ production. Results: There was significant difference between the DOB values of children under 6 years and those of children over 6 years in H. pylori‐positive (p = .029) and ‐negative groups (p = .002). On applying the UHR, no significant difference was observed between the UHR values of children under 6 years and those of children over 6 years in H. pylori‐positive (p = .877) and ‐negative groups (p = .427). In 12.6% children under 6 years, false‐positive results were observed on applying the DOB, and in 9.0% on applying the UHR (p = .125). Conclusions: The ¹³C‐UBT is a noninvasive method exhibiting high diagnostic accuracy with both UHR as well as DOB. However, high false‐positive results for ¹³C‐UBT were noted in children below 6 years on applying both UHR as well as DOB. Thus, this may not only be due to the effects of endogenous CO₂ production but also due to other factors.     

Spatial variation in regional CO2 exchange for the Kuparuk River Basin, Alaska over the summer growing season

The spatial and temporal patterns in CO₂ flux for the Kuparuk River Basin, a 9200‐km² watershed located in NE Alaska were estimated using the Regional Arctic CO₂ Exchange Simulator (RACES) for the 1994–1995 growing seasons. RACES uses non‐linear models and a Geographical Information System database (GIS) consisting of the normalized difference vegetation index (NDVI) and dynamic temperature and radiation maps. The spatial and temporal patterns in the NDVI during both growing seasons suggest that ecosystem development occurred 2–4 weeks earlier and was relatively more rapid in the southern portion of the Kuparuk River Basin. Rates of gross primary production (GPP) and whole‐ecosystem respiration (R) were 2–4 fold higher in the southern basin than along the arctic coastal plain depending on time of year. The higher rate of GPP estimated for the southern basin was primarily due to higher NDVI values, while the higher R estimated for the southern basin was due in part to higher temperature and the NDVI. While GPP and R showed strong latitudinal trends, spatial and temporal trends in net ecosystem CO₂ exchange (NEE) were much more variable. Thus, while spatial trends in carbon gain (GPP) and loss (R) were highly correlated, small spatial and temporal differences in these large fluxes (GPP and/or R) lead to corresponding large spatial variations in the NEE.     

Is productivity of mesic savannas light limited or water limited? Results of a simulation study

A soil–plant–atmosphere model was used to estimate gross primary productivity (GPP) and evapotranspiration (ET) of a tropical savanna in Australia. This paper describes model modifications required to simulate the substantial C4 grass understory together with C3 trees. The model was further improved to include a seasonal distribution of leaf area and foliar nitrogen through 10 canopy layers. Model outputs were compared with a 5‐year eddy covariance dataset. Adding the C4 photosynthesis component improved the model efficiency and root‐mean‐squared error (RMSE) for total ecosystem GPP by better emulating annual peaks and troughs in GPP across wet and dry seasons. The C4 photosynthesis component had minimal impact on modelled values of ET. Outputs of GPP from the modified model agreed well with measured values, explaining between 79% and 90% of the variance and having a low RMSE (0.003–0.281 g C m^?2 day^?1). Approximately, 40% of total annual GPP was contributed by C4 grasses. Total (trees and grasses) wet season GPP was approximately 75–80% of total annual GPP. Light‐use efficiency (LUE) was largest for the wet season and smallest in the dry season and C4 LUE was larger than that of the trees. A sensitivity analysis of GPP revealed that daily GPP was most sensitive to changes in leaf area index (LAI) and foliar nitrogen (N_f) and relatively insensitive to changes in maximum carboxylation rate (V_cmax), maximum electron transport rate (J_max) and minimum leaf water potential (ψ_min). The modified model was also able to represent daily and seasonal patterns in ET, (explaining 68–81% of variance) with a low RMSE (0.038–0.19 mm day^?1). Current values of N_f, LAI and other parameters appear to be colimiting for maximizing GPP. By manipulating LAI and soil moisture content inputs, we show that modelled GPP is limited by light interception rather than water availability at this site.     

Spectral vegetation indices as the indicator of canopy photosynthetic productivity in a deciduous broadleaf forest

Aims Understanding of the ecophysiological dynamics of forest canopy photosynthesis and its spatial and temporal scaling is crucial for revealing ecological response to climate change. Combined observations and analyses of plant ecophysiology and optical remote sensing would enable us to achieve these studies. In order to examine the utility of spectral vegetation indices (VIs) for assessing ecosystem-level photosynthesis, we investigated the relationships between canopy-scale photosynthetic productivity and canopy spectral reflectance over seasons for 5 years in a cool, temperate deciduous broadleaf forest at 'Takayama' super site in central Japan.Methods Daily photosynthetic capacity was assessed by in situ canopy leaf area index (LAI), (LAI × V cmax [single-leaf photosynthetic capacity]), and the daily maximum rate of gross primary production (GPP max) was estimated by an ecosystem carbon cycle model. We examined five VIs: normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), green–red vegetation index (GRVI), chlorophyll index (CI) and canopy chlorophyll index (CCI), which were obtained by the in situ measurements of canopy spectral reflectance.Important findings Our in situ observation of leaf and canopy characteristics, which were analyzed by an ecosystem carbon cycling model, revealed that their phenological changes are responsible for seasonal and interannual variations in canopy photosynthesis. Significant correlations were found between the five VIs and canopy photosynthetic capacity over the seasons and years; four of the VIs showed hysteresis-type relationships and only CCI showed rather linear relationship. Among the VIs examined, we applied EVI–GPP max relationship to EVI data obtained by Moderate Resolution Imaging Spectroradiometer to estimate the temporal and spatial variation in GPP max over central Japan. Our findings would improve the accuracy of satellite-based estimate of forest photosynthetic productivity in fine spatial and temporal resolutions, which are necessary for detecting any response of terrestrial ecosystem to meteorological fluctuations.     

The potential of the satellite derived green chlorophyll index for estimating midday light use efficiency in maize, coniferous forest and grassland

Light use efficiency (LUE) is an important variable in carbon cycle and climate change research. We present an investigation of remotely estimating midday LUE using the green chlorophyll index (CI_green) derived from the cloud-free Moderate Resolution Imaging Spectroradiometer (MODIS) images in maize, coniferous forest and grassland. Similar temporal patterns are observed in both canopy chlorophyll content and midday LUE which indicates that the chlorophyll content in the maize canopy servers as a proxy of midday LUE (R² = 0.736, p < 0.001). Therefore, the CI_green, tested as a good indicator of canopy chlorophyll content (R² = 0.840, p < 0.001), has been demonstrated to be a reliable candidate in providing reasonable estimates of midday LUE with determination coefficient R² equals to 0.820 and a root mean square error (RMSE) of 0.002 mol CO₂ per mol incident photosynthetic photon flux density (PPFD). Further validation of the prediction model derived from the maize site demonstrates that the CI_green has potential to be applied in the coniferous forest and grassland ecosystems with RMSE of 0.005 and 0.004 mol CO₂ mol⁻¹ PPFD, respectively. A comparison analysis between different vegetation types is included and these results could be helpful in the development of future LUE and terrestrial models.     

An improved indicator of simulated grassland production based on MODIS NDVI and GPP data: A case study in the Sichuan province,China

Grassland monitoring is important for both global change research and regional sustainable development. Gross primary production (GPP) is one of the key factors for understanding grass growing conditions. Methods for estimating GPP are plentiful, and the light use efficiency (LUE) model based on remote sensing data is widely used. The MODIS GPP product, which is employed by the National Aeronautics and Space Administration (NASA), is calculated using the LUE model and the surface reflection data from the Moderate Resolution Imaging Spectroradiometer onboard the Terra/Aqua satellite. The MODIS GPP product harbors its own uncertainties arising from the sources and parameters, such as FPAR and light use efficiency (ɛ). In this study, we propose an improved indicator for monitoring grassland based on MODIS GPP and NDVI data. Fractional vegetation coverage and the percentage of grass area (1 km²) were used to reduce the mixed pixel effect. A function of NDVI was used to simulate the light use efficiency and FPAR. The modified GPP data were calculated and validated with in situ measured data from the Sichuan province, China, 2011. The results indicated that the modified GPP data were a more accurate indicator for monitoring grassland than previous indicators, and the precision of grass production simulated by SsGPP_ndvi reached 85.6%. Spatial statistic results were consistent with the practical condition in most cases. Since MODIS data are available twice a day, the improved indicator can meet the actual requirement of grassland monitoring at regional scale.     

Annual balances and extended seasonal modelling of carbon fluxes from a temperate fen cropped to festulolium and tall fescue under two‐cut and three‐cut harvesting regimes

This study reports the annual carbon balance of a drained riparian fen under two‐cut or three‐cut managements of festulolium and tall fescue. CO₂ fluxes measured with closed chambers were partitioned into gross primary production (GPP) and ecosystem respiration (ER) for modelling according to environmental factors (light and temperature) and canopy reflectance (ratio vegetation index, RVI). Methodological assessments were made of (i) GPP models with or without temperature functions (Ft) to adjust GPP constraints imposed by low temperature (<10 °C) and (ii) ER models with RVI or GPP parameters as biomass proxies. The sensitivity of the models was also tested on partial datasets including only alternate measurement campaigns and on datasets only from the crop growing period. Use of Ft in GPP models effectively corrected GPP overestimation in cold periods, and this approach was used throughout. Annual fluxes obtained with ER models including RVI or GPP parameters were similar, and also annual GPP and ER fluxes obtained with full and partial datasets were similar. Annual CO₂ fluxes and biomass yield were not significantly different in the crop/management combinations although the individual collars (n = 12) showed some variations in GPP (?1818 to ?2409 g CO₂‐C m^?2), ER (1071 to 1738 g CO₂‐C m^?2), net ecosystem exchange (NEE, ?669 to ?949 g CO₂‐C m^?2) and biomass yield (556 to 1044 g CO₂‐C m^?2). Net ecosystem carbon balance (NECB), as the sum of NEE and biomass carbon export, was only slightly negative to positive in all crop/management combinations. NECBs, interpreted as emission factors, tended to favour the least biomass producing systems as the best management options in relation to climate saving carbon balances. Yet, considering the down‐stream advantages of biomass for fossil fuel replacement, yield‐scaled carbon fluxes are suggested to be given additional considerations for comparison of management options in terms of atmospheric impact.     

Modeling winter wheat phenology and carbon dioxide fluxes at the ecosystem scale based on digital photography and eddy covariance data

Recent studies have shown that the greenness index derived from digital camera imagery has high spatial and temporal resolution. These findings indicate that it can not only provide a reasonable characterization of canopy seasonal variation but also make it possible to optimize ecological models. To examine this possibility, we evaluated the application of digital camera imagery for monitoring winter wheat phenology and modeling gross primary production (GPP).By combining the data for the green cover fraction and for GPP, we first compared 2 different indices (the ratio greenness index (green-to-red ratio, G/R) and the relative greenness index (green to sum value, G%)) extracted from digital images obtained repeatedly over time and confirmed that G/R was best suited for tracking canopy status. Second, the key phenological stages were estimated using a time series of G/R values. The mean difference between the observed phenological dates and the dates determined from field data was 3.3 days in 2011 and 4 days in 2012, suggesting that digital camera imagery can provide high-quality ground phenological data.Furthermore, we attempted to use the data (greenness index and meteorological data in 2011) to optimize a light use efficiency (LUE) model and to use the optimal parameters to simulate the daily GPP in 2012. A high correlation (R² = 0.90) was found between the values of LUE-based GPP and eddy covariance (EC) tower-based GPP, showing that the greenness index and meteorological data can be used to predict the daily GPP. This finding provides a new method for interpolating GPP data and an approach to the estimation of the temporal and spatial distributions of photosynthetic productivity.In this study, we expanded the potential use of the greenness index derived from digital camera imagery by combining it with the LUE model in an analysis of well-managed cropland. The successful application of digital camera imagery will improve our knowledge of ecosystem processes at the temporal and spatial levels.     

Modelling the discrimination of 13CO2 above and within a temperate broad-leaved forest canopy on hourly to seasonal time scales

Fluxes and concentrations of carbon dioxide and ¹³CO₂ provide information about ecosystem physiological processes and their response to environmental variation. The biophysical model, CANOAK, was adapted to compute concentration profiles and fluxes of ¹³CO₂ within and above a temperate deciduous forest (Walker Branch Watershed, Tennessee, USA). Modifications to the model are described and the ability of the new model (CANISOTOPE) to simulate concentration profiles of ¹³CO₂, its flux density across the canopy–atmosphere interface and leaf‐level photosynthetic discrimination against ¹³CO₂ is demonstrated by comparison with field measurements. The model was used to investigate several aspects of carbon isotope exchange between a forest ecosystem and the atmosphere. During the 1998 growing season, the mean photosynthetic discrimination against ¹³CO₂, by the deciduous forest canopy (Δ_canopy), was computed to be 22·4‰, but it varied between 18 and 27‰. On a diurnal basis, the greatest discrimination occurred during the early morning and late afternoon. On a seasonal time scale, the greatest diurnal range in Δ_canopy occurred early and late in the growing season. Diurnal and seasonal variations in Δ_canopy resulted from a strong dependence of Δ_canopy on photosynthetically active radiation and vapour pressure deficit of air. Model calculations also revealed that the relationship between canopy‐scale water use efficiency (CO₂ assimilation/transpiration) and Δ_canopy was positive due to complex feedbacks among fluxes, leaf temperature and vapour pressure deficit, a finding that is counter to what is predicted for leaves exposed to well‐mixed environments.     

Differences in diffuse photosynthetically active radiation effects on cropland light use efficiency calculated via contemporary remote sensing and crop production models

Diffuse photosynthetically active radiation (PAR_diff) is instrumental to the light use efficiency (LUE) of vegetation. Accurately assessing the impact of PAR_diff on crop LUE can better our understanding of the carbon cycle in cropland ecosystems. LUE estimates from six remote sensing models (including four big-leaf models and two two-leaf models) and two crop production models were compared with measured FLUXNET LUE data from cropland sites under different PAR_diff fraction (FDIFF_PAR) intervals. Compared with the FLUXNET observations, the Eddy Covariance-Light Use Efficiency (EC-LUEa) model exhibited the best LUE estimation (R² = 0.250, RMSE = 0.868 gC·MJ⁻¹, and Bias = −0.005 gC·MJ⁻¹) owing to the use of more accurate calculation scheme of environmental stress factors. LUEs calculated from FLUXNET observational data were positively correlated with FDIFF_PAR, but only LUEs simulated by the Moderate Resolution Imaging Spectroradiometer Photosynthesis (MOD17) and Two-Leaf Light Use Efficiency (TL-LUE) models increased with increasing FDIFF_PAR. This is attributed to the fact that the MOD17 model divides the crop growth types into cereal and broadleaf, while the TL-LUE model considers the change of light interception with increased FDIFF_PAR. Furthermore, the maximum LUE (LUE_max) increased with FDIFF_PAR at FLUXNET observational sites, but the eight models could not capture the effects of PAR_diff on the crop LUE_max. Among the eight models, the LUE_max–FDIFF_PAR relationship simulated by the two-leaf models fluctuated because the crops were divided into sunlit and shaded leaves, while the big-leaf and crop production models used a constant LUE_max and showed a constant LUE_max–FDIFF_PAR relationship. Additionally, big-leaf models performed better than two-leaf models for gross primary production (GPP) simulation in the cropland ecosystem, which is related to the planting density and vegetation structure. These results demonstrate the importance of considering the impact of FDIFF_PAR on LUE_max in LUE modeling.     

Seasonal variability in the effect of elevated CO2 on ecosystem leaf area index in a scrub-oak ecosystem

We report effects of elevated atmospheric CO₂ concentration (C_a) on leaf area index (LAI) of a Florida scrub‐oak ecosystem, which had regenerated after fire for between three and five years in open‐top chambers (OTCs) and was yet to reach canopy closure. LAI was measured using four nondestructive methods, calibrated and tested in experiments performed in calibration plots near the OTCs. The four methods were: PAR transmission through the canopy, normalized difference vegetation index (NDVI), hemispherical photography, and allometric relationships between plant stem diameter and plant leaf area. Calibration experiments showed: (1) Leaf area index could be accurately determined from either PAR transmission through the canopy or hemispherical photography. For LAI determined from PAR transmission through the canopy, ecosystem light extinction coefficient (k) varied with season and was best described as a function of PAR transmission through the canopy. (2) A negative exponential function described the relationship between NDVI and LAI; (3) Allometric relationships overestimated LAI. Throughout the two years of this study, LAI was always higher in elevated C_a, rising from, 20% during winter, to 55% during summer. This seasonality was driven by a more rapid development of leaf area during the spring and a relatively greater loss of leaf area during the winter, in elevated C_a. For this scrub‐oak ecosystem prior to canopy closure, increased leaf area was an indirect mechanism by which ecosystem C uptake and canopy N content were increased in elevated C_a. In addition, increased LAI decreased potential reductions in canopy transpiration from decreases in stomatal conductance in elevated C_a. These findings have important implications for biogeochemical cycles of C, N and H₂O in woody ecosystems regenerating from disturbance in elevated C_a.     

Developing a diagnostic model for estimating terrestrial vegetation gross primary productivity using the photosynthetic quantum yield and Earth Observation data

This article develops a new carbon exchange diagnostic model [i.e. Southampton CARbon Flux (SCARF) model] for estimating daily gross primary productivity (GPP). The model exploits the maximum quantum yields of two key photosynthetic pathways (i.e. C₃ and C₄) to estimate the conversion of absorbed photosynthetically active radiation into GPP. Furthermore, this is the first model to use only the fraction of photosynthetically active radiation absorbed by photosynthetic elements of the canopy (i.e. FAPAR_ps) rather than total canopy, to predict GPP. The GPP predicted by the SCARF model was comparable to in situ GPP measurements (R² > 0.7) in most of the evaluated biomes. Overall, the SCARF model predicted high GPP in regions dominated by forests and croplands, and low GPP in shrublands and dry‐grasslands across USA and Europe. The spatial distribution of GPP from the SCARF model over Europe and conterminous USA was comparable to those from the MOD17 GPP product except in regions dominated by croplands. The SCARF model GPP predictions were positively correlated (R² > 0.5) to climatic and biophysical input variables indicating its sensitivity to factors controlling vegetation productivity. The new model has three advantages, first, it prescribes only two quantum yield terms rather than species specific light use efficiency terms; second, it uses only the fraction of PAR absorbed by photosynthetic elements of the canopy (FAPAR_ps) hence capturing the actual PAR used in photosynthesis; and third, it does not need a detailed land cover map that is a major source of uncertainty in most remote sensing based GPP models. The Sentinel satellites planned for launch in 2014 by the European Space Agency have adequate spectral channels to derive FAPAR_ps at relatively high spatial resolution (20 m). This provides a unique opportunity to produce global GPP operationally using the Southampton CARbon Flux (SCARF) model at high spatial resolution.     

日光诱导叶绿素荧光对亚热带常绿针叶林物候的追踪

植被物候期(春季返青和秋季衰老)是表征生物响应和陆地碳循环的基础信息。由于常绿针叶林冠层绿度的季节变动较弱,遥感提取常绿针叶林的物候信息存在着较大的不确定性,是目前区域物候监测中的难点。利用MODIS植被指数(归一化植被指数NDVI和增强型植被指数EVI)、GOME-2日光诱导叶绿素荧光(SIF)和通量数据(总初级生产力GPP)估算2007—2011年亚热带常绿针叶林物候期,用来比较三类遥感指数估算常绿针叶林物候的差异。结果表明:基于表征光合作用物候的通量GPP数据估算得到5年内亚热带常绿针叶林生长季开始时间(SOS_GPP)为第63天,生长季结束时间(EOS_GPP)为第324天,生长季长度为272天;基于反映植被光合作用特征的SIF曲线获得物候信息要滞后GPP物候期,其中生长季开始时间滞后19天,生长季结束时间滞后2天;基于传统植被指数NDVI和EVI的物候期滞后GPP物候期的时间要大于SIF滞后期,其中植被指数SOS滞后SOS_GPP31天,植被指数EOS滞后EOS_GPP10—17天。虽然基于3种遥...