Eddy Covariance Flux Tower Research Articles

Simulation of grassland productivity by the combination of ground and satellite data

This paper presents the assessment of a NDVI-based parametric model (C-Fix) and a bio-geochemical model (BIOME-BGC) for the simulation of semi-natural grassland primary productivity in Italy. The two models are first calibrated using the gross primary productivity (GPP) data of an eddy covariance flux tower placed over a Mediterranean-temperate hilly area in Central Italy. Next, they are applied to estimate the net primary productivity (NPP) of three independent areas representative of different eco-climatic zones. The first area shows a typical Alpine climate, while the other two are characterized by more or less pronounced Mediterranean features. The accuracy of the NPP estimates is assessed through comparison with destructive dry matter measurements taken in the three areas. The results obtained support the capability of the two models to predict spatial NPP differences across the various grassland sites. The greatest estimation errors are found in the mountain area, mostly due to inaccuracies in the meteorological input data. These errors affect particularly the outputs of the bio-geochemical model and are mitigated by the use of C-Fix, which exploits the remotely sensed information related to the seasonal evolution of green biomass.

Evaluation of optical remote sensing to estimate actual evapotranspiration and canopy conductance

We compared estimates of actual evapotranspiration (ET) produced with six different vegetation measures derived from the MODerate resolution Imaging Spectroradiometer (MODIS) and three contrasting estimation approaches using measurements from eddy covariance flux towers at 16 FLUXNET sites located over six different land cover types. The aim was to assess optimal approaches in using optical remote sensing to estimate ET. The first two approaches directly regressed various MODIS vegetation indices (VIs) and products such as leaf area index (LAI) and fraction of photosynthetically active radiation (fPAR) with ET and evaporative fraction (EF). In the third approach, the Penman–Monteith (PM) equation was inverted to obtain surface conductance (Gs), for dry plant canopies. The Gs values were then regressed against the MODIS data products and used to parameterize the PM equation for retrievals of ET. Jack-Knife cross-validation was used to evaluate the various regression models against observed ET. The PM-Gs approach provided the lowest root mean square error (RMSE), and highest determination coefficients (R2) across all sites, with an average RMSE=38Wm−2 and R2=0.72. Direct regressions of observed ET against the VIs resulted in an average RMSE=60Wm−2 and R2=0.22, while the EF regressions an average RMSE=42Wm−2 and R2=0.64. The MODIS LAI and fPAR product produced the poorest estimates of ET (RMSE>44Wm−2 and R2<0.6); while the VIs each performed best for some of the land cover types. The enhanced vegetation index (EVI) produced the best ET estimates for evergreen needleleaf forest (RMSE=28.4Wm−2, R2=0.66). The normalized difference vegetation index (NDVI) best estimated ET in grassland (RMSE=23.8Wm−2 and R2=0.68), cropland (RMSE=29.2Wm−2 and R2=0.86) and woody savannas (RMSE=25.4Wm−2 and R2=0.82), while the VI-based crop coefficient (Kc) yielded the best estimates for evergreen and deciduous broadleaf forests (RMSE=27Wm−2 and R2=0.7 in both cases). Using the ensemble-average of ET as estimated using NDVI, EVI and Kc we computed global grids of dry canopy conductance (Gc) from which annual statistics were extracted to characterise different functional types. The resulting Gc values can be used to parameterize land surface models.

A GPP assimilation model for the southeastern Tibetan Plateau based on CO2 eddy covariance flux tower and remote sensing data

The gross primary production (GPP) at individual CO2 eddy covariance flux tower sites (GPPTower) in Dali (DL), Wenjiang (WJ) and Linzhi (LZ) around the southeastern Tibetan Plateau were determined by the net ecosystem exchange of CO2 (NEE) and ecosystem respiration (Re). The satellite remote sensing-VPM model estimates of GPP values (GPPMODIS) used the satellite-derived 8-day surface reflectance product (MOD09A1), including satellite-derived enhanced vegetation index (EVI) and land surface water index (LSWI). In this paper, we assembled a subset of flux tower data at these three sites to calibrate and test satellite-VPM model estimated GPPMODIS, and introduced the satellite data and site-level environmental factors to develop four new assimilation models. The new assimilation models’ estimates of GPP values were compared with GPPMODIS and GPPTower, and the final optimum model among the four assimilation models was determined and used to calibrate GPPMODIS. The results showed that GPPMODIS had similar temporal variations to the GPPTower, but GPPMODlS were commonly higher in absolute magnitude than GPPTower with relative error (RE) about 58.85%. While, the assimilation models’ estimates of GPP values (GPPMODEL) were much more closer to GPPTower with RE approximately 6.98%, indicating that the capacity of the simulation in the new assimilation model was greatly improved, the R2 and root mean square error (RMSE) of the new assimilation model were 0.57–4.90% higher and 0.74–2.47gCm−2s−1 lower than those of the GPPMODIS, respectively. The assimilation model was used to predicted GPP dynamics around the Tibetan Plateau and showed a reliable result compared with other researches. This study demonstrated the potential of the new assimilation model for estimating GPP around the Tibetan Plateau and the performances of site-level biophysical parameters in related to satellite-VPM model GPP.

Spatially explicit surface energy budget and partitioning with remote sensing and flux measurements in a boreal region of Interior Alaska

Extrapolating energy fluxes between the ground surface and the atmospheric boundary layer from point-based measurements to spatially explicit landscape estimation is critical to understand and quantify the energy balance components and exchanges in the hydrosphere, atmosphere, and biosphere. This information is difficult to quantify and are often lacking. Using a Landsat image (acquired on 5 August 2004), the flux measurements from three eddy covariance flux towers (a 1987 burn, a 1999 burn, and an unburned control site) and a customized satellite-based surface energy balance model of Mapping Evapotranspiration at High Resolution with Internalized Calibration (METRIC), we estimated net radiation, sensible heat flux (H), latent heat flux (LE), and soil heat flux (G) for the boreal Yukon River Basin of Interior Alaska. The model requires user selection of two extreme conditions present within the image area to calibrate and anchor the sensible flux output. One is the “hot” condition which refers to a bare soil condition with specified residual evaporation rates. Another one is the “cold” condition which refers to a fully transpiring vegetation such as full-cover agricultural crops. We selected one bare field as the “hot” condition while we explored three different scenarios for the “cold” pixel because of the absence of larger expanses of agricultural fields within the image area. For this application over boreal forest, selecting agricultural fields whose evapotranspiration was assumed to be 1.05 times the alfalfa-based reference evapotranspiration as the “cold” pixel could result in large errors. Selecting an unburned flux tower site as the “cold” pixel could achieve acceptable results, but uncertainties remain about the energy balance closure of the flux towers. We found that METRIC performs reasonably well in partitioning energy fluxes in a boreal landscape.

Comparing carbon flux and high-resolution spring phenological measurements in a northern mixed forest

Vegetative canopies play a crucial role in the energy balance and composition of the atmospheric boundary layer via biotic control over evapotranspiration and carbon sources/sinks. Accurately predicting the onset/increase of carbon uptake/transpiration during the spring leaf development period using coarse resolution tower-based and satellite-derived data alone is difficult. Thus, understanding stand-level spatial patterns of spring plant phenological development and the processes that drive them may be crucial for improving landscape level estimates of evapotranspiration and carbon accumulation.In this study, high-resolution spatial and temporal tree phenology data were recorded in field campaigns over approximately 5 weeks during spring 2006 and 2007 (within a 625m×275m area), and over similar periods during spring 2008, 2009, and 2010 (within two 625m×625m areas) near the WLEF eddy covariance flux tower site near Park Falls in northern Wisconsin. Our findings demonstrate that phenological variations between individual trees in a specific microclimate can be adequately represented with a sample of 30 or more individuals. Further, visual phenological observations can be generally related to under-canopy light levels, and for spring phenology measurements in similar microclimates, a sampling interval of every 4 days minimizes data uncertainty and field work expenses. An analysis of the relationships among phenology, climate, and gross primary productivity (GPP) during the spring indicate that the phenology of the dominant tree species is responsible for an overall positive trend in carbon assimilation, but climate is the cause of day-to-day variation.

An algorithm to derive the fraction of photosynthetically active radiation absorbed by photosynthetic elements of the canopy (FAPARps) from eddy covariance flux tower data

The fraction of absorbed photosynthetically active radiation (FAPAR) is a key vegetation biophysical variable in most production efficiency models (PEMs). Operational FAPAR products derived from satellite data do not distinguish between the fraction of photosynthetically active radiation (PAR) absorbed by nonphotosynthetic and photosynthetic components of vegetation canopy, which would result in errors in representation of the exact absorbed PAR utilized in photosynthesis. The possibility of deriving only the fraction of PAR absorbed by photosynthetic elements of the canopy (i.e. FAPAR(ps) ) was investigated. The approach adopted involved inversion of net ecosystem exchange data from eddy covariance measurements to calculate FAPAR(ps) . The derived FAPAR(ps) was then related to three vegetation indices (i.e. Normalized Difference Vegetation Index (NDVI), Medium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI) and Enhanced Vegetation Index (EVI)) in an attempt to determine their potential as surrogates for FAPAR(ps) . Finally, the FAPAR(ps) was evaluated against two operational satellite data-derived FAPAR products (i.e. MODIS and CYCLOPES products). The maximum FAPAR(ps) from the inversion approach ranged between 0.6 and 0.8. The inversion approach also predicted site-specific Q₁₀-modelled daytime respiration successfully (R² > 0.8). The vegetation indices were positively correlated (R² = 0.67-0.88) to the FAPAR(ps). Finally, the two operational FAPAR products overestimated the FAPAR(ps). This was attributed to the two products deriving FAPAR for the whole canopy rather than for only photosynthetic elements in the canopy.

Evaluating the two-source energy balance model using local thermal and surface flux observations in a strongly advective irrigated agricultural area

Application and validation of many thermal remote sensing-based energy balance models involve the use of local meteorological inputs of incoming solar radiation, wind speed and air temperature as well as accurate land surface temperature (LST), vegetation cover and surface flux measurements. For operational applications at large scales, such local information is not routinely available. In addition, the uncertainty in LST estimates can be several degrees due to sensor calibration issues, atmospheric effects and spatial variations in surface emissivity. Time differencing techniques using multi-temporal thermal remote sensing observations have been developed to reduce errors associated with deriving the surface-air temperature gradient, particularly in complex landscapes. The Dual-Temperature-Difference (DTD) method addresses these issues by utilizing the Two-Source Energy Balance (TSEB) model of Norman et al. (1995) [1], and is a relatively simple scheme requiring meteorological input from standard synoptic weather station networks or mesoscale modeling. A comparison of the TSEB and DTD schemes is performed using LST and flux observations from eddy covariance (EC) flux towers and large weighing lysimeters (LYs) in irrigated cotton fields collected during BEAREX08, a large-scale field experiment conducted in the semi-arid climate of the Texas High Plains as described by Evett et al. (2012) [2]. Model output of the energy fluxes (i.e., net radiation, soil heat flux, sensible and latent heat flux) generated with DTD and TSEB using local and remote meteorological observations are compared with EC and LY observations. The DTD method is found to be significantly more robust in flux estimation compared to the TSEB using the remote meteorological observations. However, discrepancies between model and measured fluxes are also found to be significantly affected by the local inputs of LST and vegetation cover and the representativeness of the remote sensing observations with the local flux measurement footprint.

A model‐data comparison of gross primary productivity: Results from the North American Carbon Program site synthesis

Accurately simulating gross primary productivity (GPP) in terrestrial ecosystem models is critical because errors in simulated GPP propagate through the model to introduce additional errors in simulated biomass and other fluxes. We evaluated simulated, daily average GPP from 26 models against estimated GPP at 39 eddy covariance flux tower sites across the United States and Canada. None of the models in this study match estimated GPP within observed uncertainty. On average, models overestimate GPP in winter, spring, and fall, and underestimate GPP in summer. Models overpredicted GPP under dry conditions and for temperatures below 0°C. Improvements in simulated soil moisture and ecosystem response to drought or humidity stress will improve simulated GPP under dry conditions. Adding a low‐temperature response to shut down GPP for temperatures below 0°C will reduce the positive bias in winter, spring, and fall and improve simulated phenology. The negative bias in summer and poor overall performance resulted from mismatches between simulated and observed light use efficiency (LUE). Improving simulated GPP requires better leaf‐to‐canopy scaling and better values of model parameters that control the maximum potential GPP, such asεmax (LUE), Vcmax (unstressed Rubisco catalytic capacity) or Jmax (the maximum electron transport rate).

Using ecosystem CO2 measurements to estimate the timing and magnitude of greenhouse gas mitigation potential of forest bioenergy

AbstractForest bioenergy opportunities may be hindered by a long greenhouse gas (GHG) payback time. Estimating this payback time requires the quantification of forest‐atmosphere carbon exchanges, usually through process‐based simulation models. Such models are prone to large uncertainties, especially over long‐term carbon fluxes from dead organic matter pools. We propose the use of whole ecosystem field‐measured CO2 exchanges obtained from eddy covariance flux towers to assess the GHG mitigation potential of forest biomass projects as a way to implicitly integrate all field‐level CO2 fluxes and the inter‐annual variability in these fluxes. As an example, we perform the evaluation of a theoretical bioenergy project that uses tree stems as bioenergy feedstock and include multi‐year measurements of net ecosystem exchange (NEE) from forest harvest chronosequences in the boreal forest of Canada to estimate the time dynamics of ecosystem CO2 exchanges following harvesting. Results from this approach are consistent with previous results using process‐based models and suggest a multi‐decadal payback time for our project. The time for atmospheric carbon debt repayment of bioenergy projects is highly dependent on ecosystem‐level CO2 exchanges. The use of empirical NEE measurements may provide a direct evaluation of, or at least constraints on, the GHG mitigation potential of forest bioenergy projects.

Remote sensing-based estimation of gross primary production in a subalpine grassland

Abstract. This study investigates the performances in a terrestrial ecosystem of gross primary production (GPP) estimation of a suite of spectral vegetation indexes (VIs) that can be computed from currently orbiting platforms. Vegetation indexes were computed from near-surface field spectroscopy measurements collected using an automatic system designed for high temporal frequency acquisition of spectral measurements in the visible near-infrared region. Spectral observations were collected for two consecutive years in Italy in a subalpine grassland equipped with an eddy covariance (EC) flux tower that provides continuous measurements of net ecosystem carbon dioxide (CO2) exchange (NEE) and the derived GPP. Different VIs were calculated based on ESA-MERIS and NASA-MODIS spectral bands and correlated with biophysical (Leaf area index, LAI; fraction of photosynthetically active radiation intercepted by green vegetation, fIPARg), biochemical (chlorophyll concentration) and ecophysiological (green light-use efficiency, LUEg) canopy variables. In this study, the normalized difference vegetation index (NDVI) was the index best correlated with LAI and fIPARg (r = 0.90 and 0.95, respectively), the MERIS terrestrial chlorophyll index (MTCI) with leaf chlorophyll content (r = 0.91) and the photochemical reflectance index (PRI551), computed as (R531-R551)/(R531+R551) with LUEg (r = 0.64). Subsequently, these VIs were used to estimate GPP using different modelling solutions based on Monteith's light-use efficiency model describing the GPP as driven by the photosynthetically active radiation absorbed by green vegetation (APARg) and by the efficiency (ε) with which plants use the absorbed radiation to fix carbon via photosynthesis. Results show that GPP can be successfully modelled with a combination of VIs and meteorological data or VIs only. Vegetation indexes designed to be more sensitive to chlorophyll content explained most of the variability in GPP in the ecosystem investigated, characterised by a strong seasonal dynamic of GPP. Accuracy in GPP estimation slightly improves when taking into account high frequency modulations of GPP driven by incident PAR or modelling LUEg with the PRI in model formulation. Similar results were obtained for both measured daily VIs and VIs obtained as 16-day composite time series and then downscaled from the compositing period to daily scale (resampled data). However, the use of resampled data rather than measured daily input data decreases the accuracy of the total GPP estimation on an annual basis.

Assessment of MODIS imagery to track light-use efficiency in a water-limited Mediterranean pine forest

Daily values of gross primary production (GPP) derived from an eddy-covariance flux tower have been used to analyze the information content of the MODIS Photochemical Reflectance Index (PRI) on the light-use efficiency (ε). The study has been conducted in a Mediterranean Pinus pinaster forest showing summer water stress. Advanced processing techniques have been used to analyze the effect of various external factors on ε and PRI temporal variations. The intra-annual correlation between these two variables has been found to be mostly attributable to concurrent variations in sun and view zenith angles. The PRI has been normalized from these angular effects (NPRI), and its ability to track ecosystem ε response to prolonged summer water limitations has been analyzed. The observed shift between ε and NPRI reveals that, for the study area and at MODIS spatial resolution, NPRI is informative on changes in pigments and canopy structure related to the vegetation response to prolonged water stress.

Advances in upscaling of eddy covariance measurements of carbon and water fluxes

Eddy covariance flux towers provide continuous measurements of ecosystem‐level net exchange of carbon, water, energy, and other trace gases between land surface and the atmosphere. The upscaling of flux observations from towers to broad regions provides a new and independent approach for quantifying these fluxes over regions, continents, or the globe. The seven contributions of this special section reflect the most recent advances in the upscaling of fluxes from towers to these broad regions. The section mainly stems from presentations at the recent North American Carbon Program (NACP), FLUXNET, and AGU meetings. These studies focus on different aspects of upscaling: (1) assessing the representativeness of flux networks; (2) upscaling fluxes from towers to broad spatial scales; (3) examining the magnitude, distribution, and interannual variability of fluxes over regions, continents, or the globe; and (4) evaluating the impacts of spatial heterogeneity and parameter variability on flux estimates. Collectively, this special issue provides a timely update on upscaling science and also generates gridded flux data that can be used for model evaluations. Future upscaling studies are expected to advance toward incorporating the impacts of disturbance on ecosystem carbon dynamics, quantifying uncertainties associated with gridded flux estimates, and comparing various upscaling methods and the resulting gridded flux fields.

Relation of chlorophyll fluorescence sensitive reflectance ratios to carbon flux measurements of montanne grassland and norway spruce forest ecosystems in the temperate zone.

We explored ability of reflectance vegetation indexes (VIs) related to chlorophyll fluorescence emission (R 686/R 630, R 740/R 800) and de-epoxidation state of xanthophyll cycle pigments (PRI, calculated as (R 531 − R 570)/(R 531 − R 570)) to track changes in the CO2 assimilation rate and Light Use Efficiency (LUE) in montane grassland and Norway spruce forest ecosystems, both at leaf and also canopy level. VIs were measured at two research plots using a ground-based high spatial/spectral resolution imaging spectroscopy technique. No significant relationship between VIs and leaf light-saturated CO2 assimilation (A MAX) was detected in instantaneous measurements of grassland under steady-state irradiance conditions. Once the temporal dimension and daily irradiance variation were included into the experimental setup, statistically significant changes in VIs related to tested physiological parameters were revealed. ΔPRI and Δ(R 686/R 630) of grassland plant leaves under dark-to-full sunlight transition in the scale of minutes were significantly related to A MAX (R 2 = 0.51). In the daily course, the variation of VIs measured in one-hour intervals correlated well with the variation of Gross Primary Production (GPP), Net Ecosystem Exchange (NEE), and LUE estimated via the eddy-covariance flux tower. Statistical results were weaker in the case of the grassland ecosystem, with the strongest statistical relation of the index R 686/R 630 with NEE and GPP.

Seasonal patterns of carbon dioxide and water fluxes in three representative tundra ecosystems in northern Alaska

Understanding the carbon dioxide and water fluxes in the Arctic is essential for accurate assessment and prediction of the responses of these ecosystems to climate change. In the Arctic, there have been relatively few studies of net CO2, water, and energy exchange using micrometeorological methods due to the difficulty of performing these measurements in cold, remote regions. When these measurements are performed, they are usually collected only during the short summer growing season. We established eddy covariance flux towers in three representative Alaska tundra ecosystems (heath tundra, tussock tundra, and wet sedge tundra), and have collected CO2, water, and energy flux data continuously for over three years (September 2007–May 2011). In all ecosystems, peak CO2 uptake occurred during July, with accumulations of ∼51–95 g C/m2 during June–August. The timing of the switch from CO2 source to sink in the spring appears to be regulated by the number of growing degree days early in the season, indicating that warmer springs may promote increased net CO2 uptake. However, this increased uptake in the spring may be lost through warmer temperatures in the late growing season that promote respiration, if this respiration is not impeded by large amounts of precipitation or cooler temperatures. Net CO2 accumulation during the growing season was generally lost through respiration during the snow covered months of September–May, turning the ecosystems into net sources of CO2 over measurement period. The water balance from June to August at the three ecosystems was variable, with the most variability observed in the heath tundra, and the least in the tussock tundra. These findings underline the importance of collecting data over the full annual cycle and across multiple types of tundra ecosystems in order to come to a more complete understanding of CO2 and water fluxes in the Arctic.

On the temporal upscaling of evapotranspiration from instantaneous remote sensing measurements to 8-day mean daily-sums

The regular monitoring of evapotranspiration from satellites has been limited because of discontinuous temporal coverage, resulting in snapshots at a particular point in space and time. We developed a temporal upscaling scheme using satellite-derived instantaneous estimates of evapotranspiration to produce a daily-sum evapotranspiration averaged over an 8-day interval. We tested this scheme against measured evapotranspiration data from 34 eddy covariance flux towers covering seven plant functional types from boreal to tropical climatic zones. We found that the ratio of a half-hourly-sum of potential solar radiation (extraterrestrial solar irradiance on a plane parallel to the Earth's surface) between 10:00 hh and 14:00 hh to a daily-sum of potential solar radiation provides a robust scaling factor to convert a half-hourly measured evapotranspiration to an estimate of a daily-sum; the estimated and measured daily sum evapotranspiration showed strong linear relation ( r 2 = 0.92) and small bias (−2.7%). By comparison, assuming a constant evaporative fraction (the ratio of evapotranspiration to available energy) during the daytime, although commonly used for temporal upscaling, caused 13% underestimation of evapotranspiration on an annual scale. The proposed temporal upscaling scheme requires only latitude, longitude and time as input. Thus it will be useful for developing continuous evapotranspiration estimates in space and time, which will improve continuous monitoring of hydrological cycle from local to global scales.

Upscaling carbon fluxes from towers to the regional scale: Influence of parameter variability and land cover representation on regional flux estimates

[1] Quantifying the current carbon cycle of terrestrial ecosystems requires that we translate spatially sparse measurements into consistent, gridded flux estimates at the regional scale. This is particularly challenging in heterogeneous regions such as the northern forests of the United States. We use a network of 17 eddy covariance flux towers deployed across the Upper Midwest region of northern Wisconsin and Michigan and upscale flux observations from towers to the regional scale. This region is densely instrumented and provides a unique test bed for regional upscaling. We develop a simple Diagnostic Carbon Flux Model (DCFM) and use flux observations and a data assimilation approach to estimate the model parameters. We then use the optimized model to produce gridded flux estimates across the region. We find that model parameters vary not only across plant functional types (PFT) but also within a given PFT. Our results show that the parameter estimates from a single site are not representative of the parameter values of a given PFT; cross-site (or joint) optimization using observations from multiple sites encompassing a range of site and climate conditions considerably improves the representativeness and robustness of parameter estimates. Parameter variability within a PFT can result in substantial variability in regional flux estimates. We also find that land cover representation including land cover heterogeneity and the spatial resolution and accuracy of land cover maps can lead to considerable uncertainty in regional flux estimates. In heterogeneous, complex regions, detailed and accurate land cover maps are essential for accurate estimation of regional fluxes.

Modeling gross primary production of irrigated and rain-fed maize using MODIS imagery and CO2 flux tower data

Information on gross primary production (GPP) of maize croplands is needed for assessing and monitoring maize crop conditions and the carbon cycle. A number of studies have used the eddy covariance technique to measure net ecosystem exchange (NEE) of CO2 between maize cropland fields and the atmosphere and partitioned NEE data to estimate seasonal dynamics and interannual variation of GPP in maize fields having various crop rotation systems and different water management practices. How to scale up in situ observations from flux tower sites to regional and global scales is a challenging task. In this study, the Vegetation Photosynthesis Model (VPM) and satellite images from the Moderate Resolution Imaging Spectroradiometer (MODIS) are used to estimate seasonal dynamics and interannual variation of GPP during 2001–2005 at five maize cropland sites located in Nebraska and Minnesota of the U.S.A. These sites have different crop rotation systems (continuously maize vs. maize and soybean rotated annually) and different water management practices (irrigation vs. rain-fed). The VPM is based on the concept of light absorption by chlorophyll and is driven by the Enhanced Vegetation Index (EVI) and the Land Surface Water Index (LSWI), photosynthetically active radiation (PAR), and air temperature. The seasonal dynamics of GPP predicted by the VPM agreed well with GPP estimates from eddy covariance flux tower data over the period of 2001–2005. These simulation results clearly demonstrate the potential of the VPM to scale-up GPP estimation of maize cropland, which is relevant to food, biofuel, and feedstock production, as well as food and energy security.

Constraining ecosystem processes from tower fluxes and atmospheric profiles

The planetary boundary layer (PBL) provides an important link between the scales and processes resolved by global atmospheric sampling/modeling and site-based flux measurements. The PBL is in direct contact with the land surface, both driving and responding to ecosystem processes. Measurements within the PBL (e.g., by radiosondes, aircraft profiles, and flask measurements) have a footprint, and thus an integrating scale, on the order of 1-100 km. We use the coupled atmosphere-biosphere model (CAB) and a Bayesian data assimilation framework to investigate the amount of biosphere process information that can be inferred from PBL measurements. We investigate the information content of PBL measurements in a two-stage study. First, we demonstrate consistency between the coupled model (CAB) and measurements, by comparing the model to eddy covariance flux tower measurements (i.e., water and carbon fluxes) and also PBL scalar profile measurements (i.e., water, carbon dioxide, and temperature) from Canadian boreal forest. Second, we use the CAB model in a set of Bayesian inversions experiments using synthetic data for a single day. In the synthetic experiment, leaf area and respiration were relatively well constrained, whereas surface albedo and plant hydraulic conductance were only moderately constrained. Finally, the abilities of the PBL profiles and the eddy covariance data to constrain the parameters were largely similar and only slightly lower than the combination of both observations.

Relating the X-band opacity of a tropical tree canopy to sapflow, rain interception and dew formation

During summer and autumn 2007, a 11 GHz microwave radiometer was deployed in an experimental tree plantation in Sardinilla, Panama. The opacity of the tree canopy was derived from incoming brightness temperatures received on the ground. A collocated eddy-covariance flux tower measured water vapor fluxes and meteorological variables above the canopy. In addition, xylem sapflow of trees was measured within the flux tower footprint. We observed considerable diurnal differences between measured canopy opacities and modeled theoretical opacities that were closely linked to xylem sapflow. It is speculated that dielectric changes in the leaves induced by the sapflow are causing the observed diurnal changes. In addition, canopy intercepted rain and dew formation also modulated the diurnal opacity cycle. With an enhanced canopy opacity model accounting for water deposited on the leaves, we quantified the influence of canopy stored water (i.e. intercepted water and dew) on the opacity. A time series of dew formation and rain interception was directly monitored during a period of two weeks. We found that during light rainfall up to 60% of the rain amount is intercepted by the canopy whereas during periods of intense rainfall only 4% were intercepted. On average, 0.17 mm of dew was formed during the night. Dew evaporation contributed 5% to the total water vapor flux measured above the canopy.

Parameterization of a diagnostic carbon cycle model for continental scale application

Diagnostic carbon cycle models depend on parameterization to establish model sensitivity to climate variables and site factors. Here we acquired meteorological and carbon flux data from a diverse set (N = 18) of eddy covariance (EC) flux towers, along with MODIS data on FPAR (the fraction of incident photosynthetically active radiation that is absorbed by the plant canopy) at the sites, and used the data to develop a parameter set for the application of a diagnostic carbon cycle model over North America. The parameter optimization approach relied on goodness of fit between model simulations and tower estimates of gross primary production and net ecosystem production (NEP). Parameters such as light use efficiency (LUE) and base rate of heterotrophic respiration varied widely between sites representing different plant functional types (PFTs), thus supporting the value of stratification by PFT when parameterizing the model. Where multiple EC sites were available within a PFT, overall prediction error and bias in mean NEP was reduced by cross-site optimization as opposed to reliance on a single site. Optimization with the MODIS Enhanced Vegetation Index (EVI) instead of MODIS FPAR resulted in a similar goodness of fit, however, LUE values were pushed to levels that were not physiologically realistic when using EVI. The increasing availability of gap-filled EC tower data is rapidly improving the opportunities for direct coupling of satellite and ground observational data for parameterizing of diagnostic carbon flux models.