Estimates Of Net Ecosystem Exchange Research Articles

Influence of vegetation phenology on modelling carbon fluxes in temperate deciduous forest by exclusive use of MODIS time-series data

Understanding the influence of vegetation phenology on modelling primary productivity, biomass, and natural carbon dynamics, as well as the underlying mechanisms, is crucial for assessing the vulnerability of terrestrial carbon pools under future changing climate conditions. Considering that the component fluxes of carbon sequestration, gross primary production (GPP) and ecosystem respiration (Re), are dominant alternately during the course of the year, here we propose a new model for estimating the carbon sequestration of temperate deciduous forest exclusively based on Moderate Resolution Imaging Spectroradiometer (MODIS) time-series data, including land-surface temperature (LST), Terra night-time LST (LST′), enhanced vegetation index (EVI), land-surface water index (LSWI), fraction of absorbed photosynthetically active radiation (FPAR), and leaf area index (LAI). This study aims to reveal the main environmental control variables that contribute to the net ecosystem exchange (NEE) variations and to develop an improved model that accurately predicts NEE according to the growing and dormant seasons. The seasonality information was extracted from time series of MODIS NDVI data based on nonlinear least squares fits of asymmetric Gaussian model functions in the computer program, TIMESAT. The results suggest that the improved model could provide substantially better NEE estimates and well reflect the seasonal dynamics of the temperate deciduous forest. In addition, because both ecosystem photosynthesis and respiration are powerful during the growing season, all variables are strongly correlated with NEE at the 0.01 p-level, whereas only some parameters (temperature and water) are significant during the non-growing period due to dominant respiration and limited photosynthesis.

Regional inversion of CO<sub>2</sub> ecosystem fluxes from atmospheric measurements: reliability of the uncertainty estimates

Abstract. The Bayesian framework of CO2 flux inversions permits estimates of the retrieved flux uncertainties. Here, the reliability of these theoretical estimates is studied through a comparison against the misfits between the inverted fluxes and independent measurements of the CO2 Net Ecosystem Exchange (NEE) made by the eddy covariance technique at local (few hectares) scale. Regional inversions at 0.5° resolution are applied for the western European domain where ~ 50 eddy covariance sites are operated. These inversions are conducted for the period 2002–2007. They use a mesoscale atmospheric transport model, a prior estimate of the NEE from a terrestrial ecosystem model and rely on the variational assimilation of in situ continuous measurements of CO2 atmospheric mole fractions. Averaged over monthly periods and over the whole domain, the misfits are in good agreement with the theoretical uncertainties for prior and inverted NEE, and pass the chi-square test for the variance at the 30% and 5% significance levels respectively, despite the scale mismatch and the independence between the prior (respectively inverted) NEE and the flux measurements. The theoretical uncertainty reduction for the monthly NEE at the measurement sites is 53% while the inversion decreases the standard deviation of the misfits by 38%. These results build confidence in the NEE estimates at the European/monthly scales and in their theoretical uncertainty from the regional inverse modelling system. However, the uncertainties at the monthly (respectively annual) scale remain larger than the amplitude of the inter-annual variability of monthly (respectively annual) fluxes, so that this study does not engender confidence in the inter-annual variations. The uncertainties at the monthly scale are significantly smaller than the seasonal variations. The seasonal cycle of the inverted fluxes is thus reliable. In particular, the CO2 sink period over the European continent likely ends later than represented by the prior ecosystem model.

Modeling the influence of snow cover on low Arctic net ecosystem exchange

The Arctic net ecosystem exchange (NEE) of CO2 between the land surface and the atmosphere is influenced by the timing of snow onset and melt. The objective of this study was to examine whether uncertainty in model estimates of NEE could be reduced by representing the influence of snow on NEE using remote sensing observations of snow cover area (SCA). Observations of NEE and time-lapse images of SCA were collected over four locations at a low Arctic site (Daring Lake, NWT) in May–June 2010. Analysis of these observations indicated that SCA influences NEE, and that good agreement exists between SCA derived from time-lapse images, Landsat and MODIS. MODIS SCA was therefore incorporated into the vegetation photosynthesis respiration model (VPRM). VPRM was calibrated using observations collected in 2005 at Daring Lake. Estimates of NEE were then generated over Daring Lake and Ivotuk, Alaska (2004–2007) using VPRM formulations with and without explicit representations of the influence of SCA on respiration and/or photosynthesis. Model performance was assessed by comparing VPRM output against unfilled eddy covariance observations from Daring Lake and Ivotuk (2004–2007). The uncertainty in VPRM estimates of NEE was reduced when respiration was estimated as a function of air temperature when SCA ≤ 50% and as a function of soil temperature when SCA > 50%.

Subnivean Arctic and sub-Arctic net ecosystem exchange (NEE)

In the Arctic and sub-Arctic, up to half of annual net ecosystem exchange (NEE) occurs during the snow season. Subnivean soil respiration can persist at a greater rate when the overlying snowpack has a lower thermal conductivity, and the rate of photosynthetic uptake at the start and end of the snow season can be diminished by fractional snow cover. Although recent studies have indicated that uncertainty in model estimates of NEE can be reduced by representing the influence of a modeled snowpack on soil respiration, models of NEE have not represented the influence of snowpack dynamics on processes such as subnivean photosynthesis or CO2 diffusivity, and have not used remote sensing observations to characterize snow season processes. We therefore: (1) review snow season processes and their effects on NEE; (2) assess the suitability of cryospheric remote sensing approaches for models of NEE; and (3) suggest strategies for representing snow season processes in models of NEE. Strategies include: using observations of fractional snow cover in spring and fall to restrict estimates of photosynthetic uptake; combining observations of snow accumulation and soil freeze/thaw with observations of air temperature to generate more realistic estimates of soil temperature and soil respiration; and using observations of depth to estimate the influence of snow accumulation and tree wells on soil respiration. Including remote sensing observations of snow properties in models of NEE could reduce uncertainty in snow season estimates of NEE, resulting in a better understanding of the northern carbon cycle and how it is responding to climate-driven changes in the interconnected biospheric, atmospheric and cryospheric systems.

Carbon cycling of European croplands: A framework for the assimilation of optical and microwave Earth observation data

Worldwide, cropland ecosystems play a significant role in the global carbon (C) cycle. However, quantifying and understanding the cropland C cycle are complex, due to variable environmental drivers, varied management practices and often highly heterogeneous landscapes. Efforts to upscale processes using simulation models must resolve these challenges. In this study we show how data assimilation (DA) approaches can link C cycle modelling to Earth observation (EO) and reduce uncertainty in upscaling. We evaluate a framework for the assimilation of leaf area index (LAI) time-series, derived from EO optical and radar sensors, for state-updating a model of crop development and C fluxes. Sensors are selected with fine spatial resolutions (20–50m) to resolve variability across field sizes typically used in European agriculture (1.5–97.6ha). Sequential DA is used to improve the canopy development simulation, which is validated by comparing time-series of net ecosystem exchange (NEE) predictions to independent eddy covariance observations at multiple European cereal crop sites. From assimilating all EO LAI estimates, results indicated adjustments in LAI and, through an enhanced representation of C exchanges, the predicted at-harvest cumulative NEE was improved for all sites by an average of 69% when compared to the model without DA. However, using radar sensors, being relatively unaffected by cloud cover and more sensitive to the structural properties of crops, further improvements were achieved when compared to the combined, and individual, use of optical data. Specifically, when assimilating radar LAI estimates only, the cumulative NEE estimation was improved by 79% when compared to the simulation without DA. Future developments would include the assimilation of additional state variables, such as soil moisture.

The need for “apples-to-apples” comparisons of carbon dioxide source and sink estimates

The global land-based carbon dioxide (CO2) sink can be derived from the difference between fossil fuel emissions and the sum of estimated increases of CO2 in the atmosphere and in the ocean [Houghton, 2010]. For the purposes of developing policy to limit CO2 emissions, it is necessary to refine scientific understanding of the land CO2 flux in terms of its spatial and temporal patterns, as well as the underlying drivers. Net ecosystem exchange (NEE) is the commonly used measure of the land flux and is defined as the net vertical exchange of CO2 between a specified horizontal surface and the atmosphere above it over a given period of time. NEE estimates are reported from the atmospheric perspective, such that a positive value represents emissions (a land source) and a negative value represents removals (a land sink). This term represents a seemingly simple concept, and it was given a clear definition as one of the key carbon cycle variables by Chapin et al. [2006]. However, considerable confusion still arises around its usage, and the ambiguity is traceable primarily to the suite of different approaches used to estimate it.

Net ecosystem exchange of CO2 and carbon balance for eight temperate organic soils under agricultural management

This study presents the first annual estimates of net ecosystem exchange (NEE) of CO2 and net ecosystem carbon balances (NECB) of contrasting Danish agricultural peatlands. Studies were done at eight sites representing permanent grasslands (PG) and rotational (RT) arable soils cropped to barley, potato or forage grasses in three geo-regional settings. Using an advanced flux-chamber technique, NEE was derived from modelling of ecosystem respiration (ER) and gross primary production (GPP) with temperature and photosynthetically active radiation as driving variables. At PG (n=3) and RT (n=5) sites, NEE (mean±standard error, SE) was 5.1±0.9 and 8.6±2.0MgCha−1yr−1, respectively, but with the overall lowest value observed for potato cropping (3.5MgCha−1yr−1). This was partly attributed to a short-duration vegetation period and drying of the soil especially in potato ridges. NECB, derived from NEE and C-removal in harvested biomass, was equivalent to 8.4±1.0 and 11.5±2.0MgCha−1 for the PG and RT land-use types, respectively. Means were not significantly different, P=0.214, and were comparable to a wider range of high-end emission factors for managed organic soils in boreal and temperate climate zones. It was stressed that evaluation of emission factors should explicitly differentiate between data representing net C balance from a soil perspective and CO2-C balance from an atmospheric perspective. Modelling of inter-annual variability in NEE for three selected sites during a 21-year meteorological period indicated a range of 18–67% (coefficients of variation). Yet, the robustness of these estimates and their importance for the derived emission factors needs to be substantiated experimentally.

Thinning effects on the net ecosystem carbon exchange of a Sitka spruce forest are temperature-dependent

Commercial forest plantations need to be actively managed, through tree removal, in order to improve wood quality, maintain productivity and provide an economic return, although this could compromise an important role for forests in carbon sequestration and greenhouse gas mitigation. The impact of forest thinning on net primary productivity (NPP) and net ecosystem exchange (NEE) was assessed using a combination of biometric and eddy covariance (EC) techniques. Two thinning operations were performed in close succession, which reduced the basal area of the stand by 17% and 11% and removed a timber volume of 48m3ha−1 and 50m3ha−1, respectively. Annual rates of NPP ranged from 13.24 (±3.96) to 18.94 (±4.88)tCha−1 and 13.22 (±3.72) to 17.77 (±5.30)tCha−1 for the pre- and post-thinning periods, respectively. Estimates of NEE varied between 8.44 (±1.34) to 8.87 (±1.48)tCha−1 and 6.75 (±1.19) to 10.33 (±1.41)tCha−1 in the pre- and post-thinning periods. Forest thinning did not have a significant impact on carbon stocks or fluxes when pre-thinning (2002–2006) and post-thinning (2007–2009) estimates of NPP and NEE were compared, however the range of inter-annual variability in NEE increased after thinning. The partitioning of annual NEE carbon budgets into gross primary productivity (GPP) and ecosystem respiration (Reco) together with an analysis of key physiological parameters suggested that the impacts of forest thinning are largely dependent on temperature. An expected decrease in GPP after the initial thinning in 2007 was not observed due, in part, to the higher mean annual air temperatures and incident photosynthetic active radiation (PAR) and a compensatory increase in photosynthesis by the remaining trees. A continual decline in Reco, was observed in the years subsequent to the first thinning and was attributed to both biomass removal and climatic factors.Inter-annual variations in climate had a significant impact on NEE, GPP and Reco. Annual mean air temperature, total precipitation and total incident PAR were all shown to influence the processes driving CO2 exchange. Overall, these results suggest that the impacts of the thinning practices, as implemented in this study, are dependent on climate and under similar conditions are unlikely, in the short-term, to compromise a role for forest ecosystems in carbon sequestration and greenhouse gas mitigation.

Aspects of CO2 advection measurements

Observations of vegetation–atmosphere exchange of carbon dioxide (CO2) by the eddy covariance (EC) technique are limited by difficult conditions such as nighttime and heterogeneous terrain. Thus, advective flux components are included into the net ecosystem exchange (NEE) budget. However, advection measurements are experimentally challenging and do not always help to solve the night flux problem of the EC technique. This study investigates alternative methods for the observation of horizontal advection, in particular horizontal concentration gradients, as well as different approaches to coordinate rotation and vertical advection. Continuous high-frequency measurements of the horizontal CO2 concentration field are employed and compared to the often used discontinuous sequential sampling. Significant differences were found in the case of 30-min mean concentration values between the conventional discontinuous sampling approach and the complete observation of the time series by continuous sampling. Estimates of vertical advection rely on accurate estimates of vertical wind velocity (\(\emph{w}\)). Therefore, different approaches to the planar fit coordinate rotation have been investigated. Sector-wise rotation was able to eliminate directional dependencies of mean \(\emph{w}\). Furthermore, the effect of the data set length used for rotation (window length) was investigated and was found to have significant impact on estimates of vertical advection, with larger window lengths yielding about 50% larger vertical advection. A sequential planar fit with controlled window length is proposed to give reproducible results. The different approaches to the measurement and calculation of horizontal and vertical advection presented are applied to data obtained during the exchange processes in mountainous region experiment at the FLUXNET site Waldstein–Weidenbrunnen (DE-Bay). Estimates of NEE including advection are compared to NEE from turbulent and storage flux alone without advection. NEE including vertical advection with sector-wise planar fit rotation and controlled window length and including horizontal advection from continuous gradient measurements, which were comprehensively bias corrected by a new approach, did compare well with the expected night flux error, with meteorological drivers of the fluxes and with soil chamber measurements. Unrealistically large and noisy values of horizontal advection from the conventional discontinuous sampling approach, which lead to unrealistic values of NEE, could be eliminated by the alternative approaches presented. We therefore suggest the further testing of those approaches at other sites in order to improve the accuracy of advection measurements and, subsequently, estimates of NEE.

A comparison of different inverse carbon flux estimation approaches for application on a regional domain

Abstract. We have implemented six different inverse carbon flux estimation methods in a regional carbon dioxide (CO2) flux modeling system for the Netherlands. The system consists of the Regional Atmospheric Mesoscale Modeling System (RAMS) coupled to a simple carbon flux scheme which is run in a coupled fashion on relatively high resolution (10 km). Using an Ensemble Kalman filter approach we try to estimate spatiotemporal carbon exchange patterns from atmospheric CO2 mole fractions over the Netherlands for a two week period in spring 2008. The focus of this work is the different strategies that can be employed to turn first-guess fluxes into optimal ones, which is known as a fundamental design choice that can affect the outcome of an inversion significantly. Different state-of-the-art approaches with respect to the estimation of net ecosystem exchange (NEE) are compared quantitatively: (1) where NEE is scaled by one linear multiplication factor per land-use type, (2) where the same is done for photosynthesis (GPP) and respiration (R) separately with varying assumptions for the correlation structure, (3) where we solve for those same multiplication factors but now for each grid box, and (4) where we optimize physical parameters of the underlying biosphere model for each land-use type. The pattern to be retrieved in this pseudo-data experiment is different in nearly all aspects from the first-guess fluxes, including the structure of the underlying flux model, reflecting the difference between the modeled fluxes and the fluxes in the real world. This makes our study a stringent test of the performance of these methods, which are currently widely used in carbon cycle inverse studies. Our results show that all methods struggle to retrieve the spatiotemporal NEE distribution, and none of them succeeds in finding accurate domain averaged NEE with correct spatial and temporal behavior. The main cause is the difference between the structures of the first-guess and true CO2 flux models used. Most methods display overconfidence in their estimate as a result. A commonly used daytime-only sampling scheme in the transport model leads to compensating biases in separate GPP and R scaling factors that are readily visible in the nighttime mixing ratio predictions of these systems. Overall, we recommend that the estimate of NEE scaling factors should not be used in this regional setup, while estimating bias factors for GPP and R for every grid box works relatively well. The biosphere parameter inversion performs good compared to the other inversions at simultaneously producing space and time patterns of fluxes and CO2 mixing ratios, but non-linearity may significantly reduce the information content in the inversion if true parameter values are far from the prior estimate. Our results suggest that a carefully designed biosphere model parameter inversion or a pixel inversion of the respiration and GPP multiplication factors are from the tested inversions the most promising tools to optimize spatiotemporal patterns of NEE.

Calculating CO2 and H2O eddy covariance fluxes from an enclosed gas analyzer using an instantaneous mixing ratio

AbstractEddy covariance flux research has relied on open‐ or closed‐path gas analyzers for producing estimates of net ecosystem exchange of carbon dioxide (CO2) and water vapor (H2O). The two instruments have had different challenges that have led to development of an enclosed design that is intended to maximize strengths and minimize weaknesses of both traditional designs. Similar to the closed‐path analyzer, the enclosed design leads to minimal data loss during precipitation events and icing, and it does not have surface heating issues. Similar to the open‐path design, the enclosed design has good frequency response due to small flux attenuation loss in the short intake tube, does not need frequent calibration, has minimal maintenance requirements, and can be used in a very low power configuration. Another important feature of such a design is the ability to output instantaneous mixing ratio, or dry mole fraction, so that instantaneous thermal and pressure‐related expansion and contraction, and water dilution of the sampled air have been accounted for. Thus, no density corrections should be required to compute fluxes during postprocessing. Calculations of CO2 and H2O fluxes via instantaneous mixing ratio from the new enclosed CO2/H2O gas analyzer were tested in nine field experiments during 2009–2010 in a wide range of ecosystems and setups. Fluxes computed via a mixing ratio output from the instrument without applying density corrections were compared to those computed the traditional way using density corrections. The results suggest that with proper temperature, water vapor, and pressure measurements in the cell, gas fluxes can be computed confidently from raw covariance of mixing ratio and vertical wind speed, multiplied by a frequency response correction. This has important implications for future flux measurements, because avoiding hourly density corrections could have the advantages of increasing flux measurement quality and temporal resolution, reducing the magnitude of minimum detectable flux, unifying data processing steps, and assuring better intercomparison between different sites and networks.

Estimating forest carbon fluxes for large regions based on process-based modelling, NFI data and Landsat satellite images

The aim of this study was to develop and evaluate a new approach for estimating forest carbon fluxes for large regions based on climate-sensitive process-based model, national forest inventory (NFI) data and satellite images. The approach was tested for Central Finland and Lapland with NFI field data and daily weather data from 2004 to 2008. The approach combines (1) a light use efficiency (LUE) model, (2) a process-based summary model for estimating gross primary production (GPP) and net primary production (NPP), and (3) the Yasso07 soil carbon model, which together allow the estimation of net ecosystem exchange (NEE). Landsat TM 5 satellite images were utilized to generalize the carbon fluxes obtained for field sample plots for all forested areas using the k-NN imputation method. The accuracy of the imputations was examined by leave-one-out cross validation and by comparing the imputed and simulated values with Eddy covariance (EC) measurements. RMSE of the k-NN imputations was slightly better in Central Finland than in Lapland, the bias staying at a similar level. Based on the EC comparisons, the approach seemed to work rather well with GPP estimates in both areas, but in the north the NEE estimates were remarkably biased. The main advantages of the approach include its applicability to basic NFI data and a high output resolution (30 m). The method proved to be a promising way to produce carbon flux estimates based on large-scale forest inventory data and could therefore be easily applied to the whole of Northern Europe. However, there are still drawbacks to the approach, such as lacking parameters for peat lands. One of the future goals is to integrate the approach with an interactive mapping framework, which could thereafter be utilized, for example, in climate change research.

Carbon and Nitrogen Cycling in Snow-Covered Environments

The last two decades have seen significant advances in understanding the cycling of carbon and nutrients in ecosystems characterized by seasonal snow cover. This paper reviews and summarizes work on the interactions between seasonal snow cover, soil physico-chemical characteristics, biological activity, and plot- to ecosystem-scale carbon and nitrogen cycling. The magnitude of winter biogeochemical activity is considerable. For example, including these winter fluxes into annual estimates of net ecosystem exchange reduces annual carbon uptake by 50% or more in many ecosystems. The primary climatic control on these fluxes is the amount and timing of precipitation, especially the formation of a consistent seasonal snow cover. Consistent snow cover limits frost damage and controls both the timing and amount of liquid water in soil and the availability of labile carbon substrates. Together, liquid water and labile carbon control the magnitude of in situ activity, exchanges of CO2 and trace gases, and export of dissolved nutrients. The importance of snow cover to biogeochemical fluxes has led a renewed interest in how spatial variability in vegetation structure influences snow cover through shading, wind sheltering, and interception. Changes in snow cover associated with ongoing changes in both temperature and precipitation have the potential to profoundly impact the soil environment during winter and spring with unclear effects on annual and longer-term patterns of carbon and nitrogen cycling.

Uncertainty in predictions of forest carbon dynamics: separating driver error from model error

We present an analysis of the relative magnitude and contribution of parameter and driver uncertainty to the confidence intervals on estimates of net carbon fluxes. Model parameters may be difficult or impractical to measure, while driver fields are rarely complete, with data gaps due to sensor failure and sparse observational networks. Parameters are generally derived through some optimization method, while driver fields may be interpolated from available data sources. For this study, we used data from a young ponderosa pine stand at Metolius, Central Oregon, and a simple daily model of coupled carbon and water fluxes (DALEC). An ensemble of acceptable parameterizations was generated using an ensemble Kalman filter and eddy covariance measurements of net C exchange. Geostatistical simulations generated an ensemble of meteorological driving variables for the site, consistent with the spatiotemporal autocorrelations inherent in the observational data from 13 local weather stations. Simulated meteorological data were propagated through the model to derive the uncertainty on the CO2 flux resultant from driver uncertainty typical of spatially extensive modeling studies. Furthermore, the model uncertainty was partitioned between temperature and precipitation. With at least one meteorological station within 25 km of the study site, driver uncertainty was relatively small ( 10% of the total net flux), while parameterization uncertainty was larger, 50% of the total net flux. The largest source of driver uncertainty was due to temperature (8% of the total flux). The combined effect of parameter and driver uncertainty was 57% of the total net flux. However, when the nearest meteorological station was > 100 km from the study site, uncertainty in net ecosystem exchange (NEE) predictions introduced by meteorological drivers increased by 88%. Precipitation estimates were a larger source of bias in NEE estimates than were temperature estimates, although the biases partly compensated for each other. The time scales on which precipitation errors occurred in the simulations were shorter than the temporal scales over which drought developed in the model, so drought events were reasonably simulated. The approach outlined here provides a means to assess the uncertainty and bias introduced by meteorological drivers in regional-scale ecological forecasting.

Simulating seasonal and inter-annual variations in energy and carbon exchanges and forest dynamics using a process-based atmosphere–vegetation dynamics model

The present paper shows simulated results of seasonal and inter-annual variations in energy and carbon exchanges and forest dynamics in a sub-boreal deciduous forest using a fully coupled atmosphere–vegetation interaction model [multilayered integrated numerical model of surface physics-growing plants interaction (MINoSGI)]. With careful adjustment of site-specific eco-physiological parameters, MINoSGI reproduced successfully stand biomass–tree density relationship based on the forest inventory data for 7 years (1999–2005) and seasonal and inter-annual variations in energy and CO2 fluxes measured by means of eddy covariance technique for 3 years (2003–2005) in the sub-boreal forest, northern Japan. In addition, MINoSGI estimated annual evapotranspiration (Evt) at 328.6 ± 25.8 mm year−1, net primary production (NPP) at 372.1 ± 31.5 gC m−2 year−1 and net ecosystem exchange (NEE) at −224.2 ± 32.2 gC m−2 year−1. We found the estimate of annual NEE in our site lies among the estimates at other forest stands with the almost same climatic conditions in northern Japan, although the tree species and stand age of these forests are different from those of our site. Overall, MINoSGI was found useful to present simultaneous simulations of forest dynamics, surface energy, and carbon exchanges of a forest stand in the future from micro-meteorological and ecophysiological points of view.

Cropland carbon fluxes in the United States: increasing geospatial resolution of inventory‐based carbon accounting

Net annual soil carbon change, fossil fuel emissions from cropland production, and cropland net primary production were estimated and spatially distributed using land cover defined by NASA's moderate resolution imaging spectroradiometer (MODIS) and by the USDA National Agricultural Statistics Service (NASS) cropland data layer (CDL). Spatially resolved estimates of net ecosystem exchange (NEE) and net ecosystem carbon balance (NECB) were developed. The purpose of generating spatial estimates of carbon fluxes, and the primary objective of this research, was to develop a method of carbon accounting that is consistent from field to national scales. NEE represents net on-site vertical fluxes of carbon. NECB represents all on-site and off-site carbon fluxes associated with crop production. Estimates of cropland NEE using moderate resolution (approximately 1 km2) land cover data were generated for the conterminous United States and compared with higher resolution (30-m) estimates of NEE and with direct measurements of CO2 flux from croplands in Illinois and Nebraska, USA. Estimates of NEE using the CDL (30-m resolution) had a higher correlation with eddy covariance flux tower estimates compared with estimates of NEE using MODIS. Estimates of NECB are primarily driven by net soil carbon change, fossil fuel emissions associated with crop production, and CO2 emissions from the application of agricultural lime. NEE and NECB for U.S. croplands were -274 and 7 Tg C/yr for 2004, respectively. Use of moderate- to high-resolution satellite-based land cover data enables improved estimates of cropland carbon dynamics.

Application of remote sensing, an artificial neural network leaf area model, and a process-based simulation model to estimate carbon storage in Florida slash pine plantations

Carbon sequestration in forests is of great interest due to concerns about global climate change. Carbon storage rates depend on ecosystem fluxes (photosynthesis and ecosystem respiration), typically quantified as net ecosystem exchange (NEE). Methods to estimate forest NEE without intensive Site sampling are needed to accurately assess rates of carbon sequestration at stand-level and larger scales. We produced spatially-explicit estimates of NEE for 9770ha of slash pine (Pinus elliottii) plantations in North-Central Florida for a single year by coupling remote sensing-based estimates of leaf area index (LAI) with a process-based growth simulation model. LAI estimates produced from a neural-network modeling of ground plot and Landsat TM satellite data had a mean of 1.06 (range 0-3.93, including forest edges). Using the neural network LAI values as inputs, the slash pine simulation model (SPM2) estimates of NEE ranged from -5.52 to 11.06Mg•ha^(-1)-a^(-1) with a mean of 3.47Mg•ha^(-1)-a^(-1) Total carbon storage for the year was 33920t, or about 3.5 tons per hectare. Both estimated LAI and NEE were highly sensitive to fertilization.

Assessing the effects of agricultural management practices on carbon fluxes: Spatial variation and the need for replicated estimates of Net Ecosystem Exchange

Replicated measurements of Net Ecosystem CO 2 Exchange (NEE) were made on four arable plots using two eddy covariance (EC) towers that were moved at regular intervals. Replicated plots were created by dividing a large arable field into four approximately equal areas (∼2.5 ha). Regular adjustments of the EC measurement height (1.5–1.9 m) due to seasonal changes in canopy height ensured that the analyzed fluxes were derived from the individual plots. This was sufficient to make high-quality flux measurements with the energy balance accounting for up to 96% of the available energy in the system. The replicated experiment included two treatment plots managed with conventional tillage practices (CON) and two plots managed with non-inversion tillage (NIT). The mean annual NEE (aNEE) over all plots and years showed a carbon uptake of –163 g [C] m −2 y −1. Plot to plot variability ranged between 9 and 70 g [C] m −2 y −1. Differences in leaf area index (LAI) were the most likely cause of plot to plot variability. For the majority of the experiment spatial variation was greater than treatment effects and provided over half the total annual uncertainty, demonstrating the need to address spatial variability when assessing aNEE using EC techniques. Interannual variation in the measured fluxes was greater than the plot-to-plot or the treatment variations, most likely due to differences in water availability, although this could be confounded by changes in spatial variability due to other environmental factors or management operations. The proportionally large uncertainty associated with spatial and interannual variability when compared to the measured fluxes, suggests that NEE estimates are, in the short term, unlikely to result in sufficiently accurate estimates of net biome productivity (NBP) for assessing the slow accumulation/loss of SOC associated with altered tillage management without a much larger number of replicated plots. Whilst the approach used has limited statistical power it could be modified to accommodate a large number of replicates to provide more robust estimates of treatment differences.

Seven years of recent European net terrestrial carbon dioxide exchange constrained by atmospheric observations

AbstractWe present an estimate of net ecosystem exchange (NEE) of CO2 in Europe for the years 2001–2007. It is derived with a data assimilation that uses a large set of atmospheric CO2 mole fraction observations (∼70 000) to guide relatively simple descriptions of terrestrial and oceanic net exchange, while fossil fuel and fire emissions are prescribed. Weekly terrestrial sources and sinks are optimized (i.e., a flux inversion) for a set of 18 large ecosystems across Europe in which prescribed climate, weather, and surface characteristics introduce finer scale gradients. We find that the terrestrial biosphere in Europe absorbed a net average of −165 Tg C yr−1 over the period considered. This uptake is predominantly in non‐EU countries, and is found in the northern coniferous (−94 Tg C yr−1) and mixed forests (−30 Tg C yr−1) as well as the forest/field complexes of eastern Europe (−85 Tg C yr−1). An optimistic uncertainty estimate derived using three biosphere models suggests the uptake to be in a range of −122 to −258 Tg C yr−1, while a more conservative estimate derived from the a‐posteriori covariance estimates is −165±437 Tg C yr−1. Note, however, that uncertainties are hard to estimate given the nature of the system and are likely to be significantly larger than this. Interannual variability in NEE includes a reduction in uptake due to the 2003 drought followed by 3 years of more than average uptake. The largest anomaly of NEE occurred in 2005 concurrent with increased seasonal cycles of observed CO2. We speculate these changes to result from the strong negative phase of the North Atlantic Oscillation in 2005 that lead to favorable summer growth conditions, and altered horizontal and vertical mixing in the atmosphere. All our results are available through http://www.carbontracker.eu

Validating an integrated strategy to model net land carbon exchange against aircraft flux measurements

Aircraft eddy covariance technique is a modern and powerful means to directly measure net ecosystem exchange (NEE) over relatively large land areas. The NEE measurements taken by a specifically developed aircraft platform (Sky Arrow ERA) over a transect in Central Italy during an 18-month period are used to validate a recently proposed modeling strategy. The strategy is based on the integration of the outputs from a NDVI-driven parametric model, C-Fix, and a model of ecosystem processes, BIOME-BGC, and can simulate both gross and net land carbon fluxes over different spatial and temporal scales. The application of this strategy to 1-km resolution ground and remotely sensed data descriptive of the study area enables the production of NEE estimates comparable to the aircraft measurements. The agreement between the two data series is high, especially when averaging the modeling outputs over areas consistent with the Sky Arrow footprint (i.e. 1–2 pixels apart from the flight line). Fractional forest cover and NDVI are the two model driving variables which explain most spatial variability of the aircraft NEE measurements. The modeling strategy yields the best performances for spring–summer seasons, when vegetation photosynthetic and respiratory processes are higher and easier to simulate. These performances are finally commented with specific emphasis on the contribution brought by remote sensing information.