Gross Primary Production Measurements Research Articles

A novel algorithm to assess gross primary production for terrestrial ecosystems from MODIS imagery

AbstractQuantifying carbon fluxes at large spatial scales has attracted considerable scientific attentions. In this study, a novel approach was proposed to estimate the terrestrial ecosystem gross primary production (GPP) using imagery from the satellite‐borne Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. The new model (named Temperature and Greenness Rectangle, TGR) uses a combination of MODIS Enhanced Vegetation Index and Land Surface Temperature products as well as in situ measurement of photosynthetically active radiation to estimate GPP at a 16 day interval. Three major advantages are included in the model: (1) the model follows strictly the logic of the light use efficiency model and each parameter has physical meaning; (2) the model reduces the dependency on ground‐based meteorological measurements; and (3) the overlap of information in correlated explanatory variables is avoided. The model was calibrated with data from 17 sites within the Ameriflux network and validated at another 13 sites, covering a wide range of climates and eight major vegetation types. Results show that the TGR model explains reasonably well the tower‐based measurements of GPP for all vegetation types, except for the evergreen broadleaf forest, with the coefficient of determination in a range from 0.67 to 0.91 and the root mean square error from 9.0 to 31.9 g C/m2/16 days. Comparisons with other two models (the TG and GR model) show that the TGR model generally gives better GPP estimates in nearly all vegetation types, especially under dry climate conditions. These results indicate that the TGR model can be potentially used to estimate GPP at regional scale.

Assessing the capacity of three production efficiency models in simulating gross carbon uptake across multiple biomes in conterminous USA

Over the past decade, the use of production efficiency models (PEMs) to quantify terrestrial carbon exchange at regional to global scales has been on the rise. This has mainly been due to increased availability of remote sensing data to parameterise these models. However, these models are still subject to large uncertainties. Diagnosis of these uncertainties is necessary to correctly interpret their output and to suggest areas of improvement. In this study, three PEM models (i.e. Carnegie-CASA, C-Fix and MOD17 models) were run in their native format and their capacity to predict gross primary productivity (GPP) at five major biomes across conterminous USA was evaluated against eddy covariance flux tower GPP measurements. The influence of input datasets in the models output was also evaluated. Apart from the cropland biome, the Carnegie-CASA and C-Fix models predicted GPP which were slightly higher than in situ measurements in most of the evaluated biomes (i.e. the needle-leaf evergreen forests, deciduous broadleaf forests, Mediterranean savanna woodlands, and temperate grasslands). The MOD17 model on the other hand predicted lower GPP in most of the evaluated biomes. The overestimation of in situ GPP by the models was attributed to error propagation from the key vegetation biophysical used to drive the models (i.e. the FAPAR product). On the other hand, the low maximum light use efficiency (LUE) term prescribed by the models for particular biomes was responsible for most of the GPP underestimation by the models. Finally, it was noted that the differences in the models structural formulation also resulted in variation of their GPP predictions (e.g. the models which did not account for soil moisture performed poorly in predicting GPP in rain-driven biomes).

Gross and aboveground net primary production at Canadian forest carbon flux sites

Simultaneous biometric measurements of aboveground net primary production (ANPP) and eddy-covariance measurements of gross primary production (GPP) were made at 18 forest stands with 80 site-years of data across Canada – to assess the fraction of photosynthesis that is used to produce plant tissues and the consistency of carbon allocation patterns across forest ecosystems. The stands included boreal and temperate forests and spanned very young to mature stand ages. Across all sites, ANPP averaged 298±138gCm−2yr−1 (mean±1 s.d.), with the highest values for temperate white pine plantations (307–630gCm−2yr−1) and harvested Douglas-fir stands (219–459gCm−2yr−1), and the lowest values for boreal harvested jack pine stands (97–185gCm−2yr−1). ANPP more than doubled from newly established (≤12 years) to young (13–25 years) stands, then stabilized in young to mature (≥51 years) stands but with diverging trends among species. Inter-site variations in ANPP and GPP were closely related to site characteristics, in particular, to leaf area index, which explained 66% of the variation in ANPP and 80% of the variation in GPP, and absorbed photosynthetically active radiation, which explained 80% of the variation in ANPP and 82% of the variation in GPP. Both ANPP and GPP were also positively correlated with mean annual air temperature, mean annual precipitation, and total soil nitrogen in the upper 10cm of the mineral soil. ANPP was strongly, positively correlated with GPP and the ANPP/GPP ratio was relatively constant (0.29±0.06), with no consistent differences among species or age classes. The results support the use of a constant ANPP/GPP ratio as a reasonable assumption in models of forest productivity for boreal and northern temperate forests. A similar conclusion is reached for the NPP/GPP ratio when published values of belowground NPP are considered.

Continuous monitoring reveals multiple controls on ecosystem metabolism in a suburban stream

Summary1. Primary production and respiration in streams, collectively referred to as stream ecosystem metabolism, are fundamental processes that determine trophic structure, biomass and nutrient cycling. Few studies have used high‐frequency measurements of gross primary production (GPP) and ecosystem respiration (ER) over extended periods to characterise the factors that control stream ecosystem metabolism at hourly, daily, seasonal and annual scales.2. We measured ecosystem metabolism at 5‐min intervals for 23 months in Shepherd Creek, a small suburban stream in Cincinnati, Ohio (U.S.A.).3. Daily GPP was best predicted by a model containing light and its synergistic interaction with water temperature. Water temperature alone was not significantly related to daily GPP, rather high temperatures enhanced the capacity of autotrophs to use available light.4. The relationship between GPP and light was further explored using photosynthesis–irradiance curves (P–I curves). Light saturation of GPP was evident throughout the winter and spring and the P–I curve frequently exhibited strong counterclockwise hysteresis. Hysteresis occurred when water temperatures were greater in the afternoon than in the morning, although light was similar, further suggesting that light availability interacts synergistically with water temperature.5. Storm flows strongly depressed GPP in the spring while desiccation arrested aquatic GPP and ER in late summer and autumn.6. Ecosystem respiration was best predicted by GPP, water temperature and the rate of water exchange between the surface channel and transient storage zones. We estimate that c. 70% of newly fixed carbon was immediately respired by autotrophs and closely associated heterotrophs.7. Interannual, seasonal, daily and hourly variability in ecosystem metabolism was attributable to a combination of light availability, water temperature, storm flow dynamics and desiccation. Human activities affect all these factors in urban and suburban streams, suggesting stream ecosystem processes are likely to respond in complex ways to changing land use and climate.

Development of a two-leaf light use efficiency model for improving the calculation of terrestrial gross primary productivity

Gross primary productivity (GPP) is a key component of land–atmospheric carbon exchange. Reliable calculation of regional/global GPP is crucial for understanding the response of terrestrial ecosystems to climate change and human activity. In recent years, many light use efficiency (LUE) models driven by remote sensing data have been developed for calculating GPP at various spatial and temporal scales. However, some studies show that GPP calculated by LUE models was biased by different degrees depending on sky clearness conditions.In this study, a two-leaf light use efficiency (TL-LUE) model is developed based on the MOD17 algorithm to improve the calculation of GPP. This TL-LUE model separates the canopy into sunlit and shaded leaf groups and calculates GPP separately for them with different maximum light use efficiencies. Different algorithms are developed to calculate the absorbed photosynthetically active radiation for these two groups. GPP measured at 6 typical ecosystems in China was used to calibrate and validate the model. The results show that with the calibration using tower measurements of GPP, the MOD17 algorithm was able to capture the variations of measured GPP in different seasons and sites. But it tends to understate and overestimate GPP under the conditions of low and high sky clearness, respectively. The new TL-LUE model outperforms the MOD17 algorithm in reproducing measured GPP at daily and 8-day scales, especially at forest sites. The calibrated LUE of shaded leaves is 2.5–3.8 times larger than that of sunlit leaves. The newly developed TL-LUE model shows lower sensitivity to sky conditions than the MOD17 algorithm. This study demonstrates the potential of the TL-LUE model in improving GPP calculation due to proper description of differences in the LUE of sunlit and shaded leaves and in the transfer of direct and diffuse light beams within the canopy.

Deriving land surface phenology indicators from CO2 eddy covariance measurements

Recent progress of CO2 eddy covariance (EC) technique and accumulation of measurements offer an unprecedented perspective to study the land surface phenology (LSP) in a more objective way than previously possible by allowing the actual photosynthesis measurement – gross primary productivity (GPP). Because of the spatial, temporal, and ecological complexity of processes controlling GPP time series, the extraction of important LSP dates from GPP has been elusive. Here, we present objective measures of several LSP metrics from GPP time series data. A case study based on long term GPP measurements over a mature boreal deciduous forest is provided together with LSP estimates from remote sensing data. Results show that most LSP metrics are interrelated within each season (spring and autumn) both from GPP and remote sensing based estimates. We provide simple mathematical derivatives of GPP time series to objectively estimate key LSP metrics such as: the start, end and length of growing season; end of greenup; start of browndown; length of canopy closure; start, end and length of peak; and peak of season. These key LSP metrics indicate the collective ecological responses to environmental changes over space and time.

Simulation of olive grove gross primary production by the combination of ground and multi-sensor satellite data

We developed and tested a methodology to estimate olive (Olea europaea L.) gross primary production (GPP) combining ground and multi-sensor satellite data. An eddy-covariance station placed in an olive grove in central Italy provided carbon and water fluxes over two years (2010–2011), which were used as reference to evaluate the performance of a GPP estimation methodology based on a Monteith type model (modified C-Fix) and driven by meteorological and satellite (NDVI) data. A major issue was related to the consideration of the two main olive grove components, i.e. olive trees and inter-tree ground vegetation: this issue was addressed by the separate simulation of carbon fluxes within the two ecosystem layers, followed by their recombination. In this way the eddy covariance GPP measurements were successfully reproduced, with the exception of two periods that followed tillage operations. For these periods measured GPP could be approximated by considering synthetic NDVI values which simulated the expected response of inter-tree ground vegetation to tillages.

Balanced plankton net community metabolism in the oligotrophic North Atlantic subtropical gyre from Lagrangian observations

Characterization of the microbial plankton metabolism in oligotrophic oceans is of relevance for the quantification of the global carbon balance; however whether the plankton community metabolism in oligotrophic gyres is net autotrophic or heterotrophic is still under debate. Discrepancies have been in part attributed to the difficulties of the standard snapshot estimations, based on in vitro measurements, to adequately represent the temporal scale of trophic processes. This work presents concurrent measurements of gross primary production and community respiration carried out in the North Atlantic Oligotrophic Gyre throughout two 7-day Lagrangian experiments that allowed us to investigate the effect of short term (daily) variability on the microbial metabolism quantification. Physicochemical and biological variables showed a low variability in each Lagrangian experiment and a balanced net plankton metabolism was found in 83% of the sampling days.

Spatial and Temporal Variability in the Ecosystem Metabolism of a High-elevation Lake: Integrating Benthic and Pelagic Habitats

We characterized spatial and temporal variability in net ecosystem production (NEP), community respiration (CR), and gross primary production (GPP) over an ice-free season in an oligotrophic high-elevation lake using high-frequency measurements of dissolved oxygen. We combined the use of free-water and incubation chamber measurements to compare pelagic and benthic habitats and estimate their relative contributions to whole-lake metabolism. Despite a brief period of predominant heterotrophy after snowmelt, both free-water and incubation chamber measurements confirmed autotrophy of the epilimnion in all habitats throughout the ice-free season. In contrast, benthic incubation chambers showed the benthos to be consistently heterotrophic. Although temperature was the strongest seasonal driver of benthic metabolism, bacterioplankton density and indexes of organic matter quality explained the most variability in pelagic metabolism. Driven largely by benthic metabolism, free-water measurements of GPP and CR were twice as high in littoral than pelagic habitats. However, rates of water column primary production overlying the littoral benthos were high enough to overcome net benthic heterotrophy, and seasonal mean NEP in littoral habitats remained positive and not significantly different from pelagic habitats. Benthic rates averaged about 25% of whole-lake metabolism. Pelagic metabolism measurements were affected by littoral rates about half the time, with the degree of isolation between the two a function of advection and water column stability. These results emphasize the importance of characterizing spatial and temporal variability in metabolism within the context of physical dynamics and challenge the notion that benthic metabolism will necessarily be larger than pelagic metabolism in oligotrophic lakes.

Advantages of visible-band spectral remote sensing at both satellite and near-surface scales for monitoring the seasonal dynamics of GPP in a Japanese larch forest

Remotely sensed vegetation indices such as the normalized difference vegetation index (NDVI) or enhanced vegetation index (EVI) have been used to scale up flux-based gross primary production (GPP) measurements. Recently, the use of visible-band (VIS) indices for estimation of GPP has been proposed, and VIS_indices derived from digital cameras have been used for detecting phenological changes. To confirm the utility of remotely sensed VIS_indices for the evaluation of GPP in a Japanese larch forest, we investigated the relationships between flux-based GPP measurements and indices derived from both moderate resolution imaging spectroradiometer (MODIS) data and tower-mounted digital camera images. We evaluated the suitability of both traditional (NDVI and EVI) and VIS_indices (the greenred vegetation index (GRVI) and green ratio (GR)) at both satellite and near-surface scales for GPP estimation. We also used the MODIS data to evaluate the sensitivity of the indices to the effects of a severe forest disturbance. The results showed that VIS_indices had several advantages over the traditional indices: (1) seasonal variations in VIS_indices were more strongly correlated with GPP variations; (2) the vegetation growing season could be easily discriminated from the winter dormant period, because ground surface conditions affect VIS_indices less than they affect traditional indices; (3) the seasonal dynamics of vegetation could be determined at a satellite scale from MODIS data, and possibly even at a canopy scale from digital camera images; and (4) inter-annual variations of VIS_indices were likely to be more sensitive to vegetation changes after a disturbance. These results demonstrate the utility of VIS_indices for estimating GPP at satellite scales and possibly at the canopy scale. We suggest that multi-scale visible-band remote sensing could help our understanding of the ecosystem by improving the temporal and spatial resolutions of satellite data.

Seasonal variability of primary production in a fjord ecosystem of the Chilean Patagonia: Implications for the transfer of carbon within pelagic food webs

We characterized the seasonal cycle of productivity in Reloncaví Fjord (41°30′S), Chilean Patagonia. Seasonal surveys that included measurements of gross primary production, community respiration, bacterioplankton secondary production, and sedimentation rates along the fjord were combined with continuous records of water-column temperature variability and wind forcing, as well as satellite-derived data on regional patterns of wind stress, sea surface temperatures, and surface chlorophyll concentrations. The hydrography and perhaps fjord productivity respond to the timing and intensity of wind forcing over a larger region. Seasonal changes in the direction and intensity of winds, along with a late-winter improvement in light conditions, may determine the timing of phytoplankton blooms and potentially modulate productivity cycles in the region. Depth-integrated gross primary production estimates were higher (0.4–3.8 g C m −2 d −1) in the productive season (October, February, and May), and lower (0.1–0.2 g C m −2 d −1) in the non-productive season (August). These seasonal changes were also reflected in community respiration and bacterioplankton production rates, which ranged, respectively, from 0.3 to 4.8 g C m −2 d −1 and 0.05 to 0.4 g C m −2 d −1 during the productive and non-productive seasons and from 0.05 to 0.6 g C m −2 d −1 and 0.05 to 0.2 g C m −2 d −1 during the same two periods. We found a strong, significant correlation between gross primary production and community respiration (Spearman, r=0.95; p<0.001; n=12), which suggests a high degree of coupling between the synthesis of organic matter and its usage by the planktonic community. Similarly, strong correlations were found between bacterioplankton secondary production and both gross primary production (Spearman, r=0.7, p<0.05, n=9) and community respiration (Spearman, r=0.8, p<0.05, n=9), indicating that bacterioplankton may be processing an important fraction (8–59%) of the organic matter produced by phytoplankton in Reloncaví Fjord. In winter, bacterial carbon utilization as a percentage of gross primary production was >100%, suggesting the use of allochthonous carbon sources by bacterioplankton when the levels of gross primary production are low. Low primary production rates were associated with a greater contribution of small cells to autotrophic biomass, highlighting the importance of small-sized plankton and bacteria for carbon cycling and fluxes during the less productive winter months. Fecal pellet sedimentation was minimal during this period, also suggesting that most of the locally produced organic carbon is recycled within the microbial loop. During the productive season, on the other hand, the area exhibited a great potential to export organic matter, be it to higher trophic levels or vertically towards the bottom.

Gross primary production estimation from MODIS data with vegetation index and photosynthetically active radiation in maize

Gross primary production (GPP) is a significant important parameter for carbon cycle and climate change research. Remote sensing combined with other climate and meteorological data offers a convenient tool for large‐scale GPP estimation. GPP was estimated as a product of vegetation indices (VIs) and photosynthetically active radiation (PAR). Four kinds of vegetation indices [the normalized difference vegetation index (NDVI), the weighted difference vegetation index, the soil‐adjusted vegetation index, and the enhanced vegetation index (EVI)] derived from the Moderate Resolution Imaging Spectroradiometer daily surface reflectance product were selected to test our method. The in situ GPP was calculated using the eddy covariance technique and the PAR data were acquired from meteorological measurements. Because VIs were found to be a reliable proxy of both light use efficiency (LUE) and the fraction of absorbed PAR (fAPAR; R2 of 0.63–0.87 for LUE and 0.69–0.76 for fAPAR), the product VI × VI × PAR is used as a measure of GPP according to Monteith logic. Moderate determination coefficients R2 from 0.65 for NDVI to 0.71 for EVI were obtained when GPP was estimated from a single index in maize. When testing our method, calculating GPP as a product of VI × VI × PAR, the determination coefficients R2 largely improved, fluctuating from 0.81 to 0.91. EVI × EVI × PAR provided the best estimation of GPP with the highest R2 of 0.91 because EVI was found to be the best indicator of both LUE and fAPAR (R2 of 0.87 and 0.76, respectively). These results will be helpful for the development of future GPP estimation models.

A continuous measure of gross primary production for the conterminous United States derived from MODIS and AmeriFlux data

The quantification of carbon fluxes between the terrestrial biosphere and the atmosphere is of scientific importance and also relevant to climate-policy making. Eddy covariance flux towers provide continuous measurements of ecosystem-level exchange of carbon dioxide spanning diurnal, synoptic, seasonal, and interannual time scales. However, these measurements only represent the fluxes at the scale of the tower footprint. Here we used remotely sensed data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to upscale gross primary productivity (GPP) data from eddy covariance flux towers to the continental scale. We first combined GPP and MODIS data for 42 AmeriFlux towers encompassing a wide range of ecosystem and climate types to develop a predictive GPP model using a regression tree approach. The predictive model was trained using observed GPP over the period 2000–2004, and was validated using observed GPP over the period 2005–2006 and leave-one-out cross-validation. Our model predicted GPP fairly well at the site level. We then used the model to estimate GPP for each 1 km × 1 km cell across the U.S. for each 8-day interval over the period from February 2000 to December 2006 using MODIS data. Our GPP estimates provide a spatially and temporally continuous measure of gross primary production for the U.S. that is a highly constrained by eddy covariance flux data. Our study demonstrated that our empirical approach is effective for upscaling eddy flux GPP data to the continental scale and producing continuous GPP estimates across multiple biomes. With these estimates, we then examined the patterns, magnitude, and interannual variability of GPP. We estimated a gross carbon uptake between 6.91 and 7.33 Pg C yr − 1 for the conterminous U.S. Drought, fires, and hurricanes reduced annual GPP at regional scales and could have a significant impact on the U.S. net ecosystem carbon exchange. The sources of the interannual variability of U.S. GPP were dominated by these extreme climate events and disturbances.

Comparison of three models for predicting gross primary production across and within forested ecoregions in the contiguous United States

Gross primary production (GPP), the photosynthetic uptake of carbon, is an important variable in the global carbon cycle. Although continuous measurements of GPP are being collected from a network of micro-meteorological towers, each site represents a small area with records available for only a limited period. As a result, GPP is commonly modeled over forested landscapes as a function of climatic and soil variables, often supplemented with satellite-derived estimates of the vegetation's light-absorbing properties. Since the late 1990s, a number of models have been developed to provide seasonal and annual estimates of GPP across much of the Earth. Each model, however, contains different underlying assumptions and requires different amounts of data. As a result, predictions vary, sometimes significantly. In this paper we compare modeled estimates of GPP for forested areas across the U.S.A. derived from: NASA's MODIS Product (MOD17); the C-Fix model using SPOT-VGT satellite-derived vegetation data; and the Physiological Principles Predicting Growth from Satellites (3-PGS) model, a process-based model that requires information on both climate and soil properties. The models predicted average ecoregion values of forest GPP between 9.8 and 14.1 MgC ha − 1 y − 1 across the United States. 3-PGS predicted the lowest values while the C-Fix model, which included a CO 2 fertilization factor, produced the highest estimates. In the western part of the country, estimates of GPP within a given ecoregion varied by as much as 50%, whereas in the northeast, where topography and climate are less extreme, variation in GPP was less than 10%. Within ecoregions, 3PGS predicted the most variation, reflecting its sensitivity to variation in soil properties. We conclude that where model predictions disagree, an opportunity is presented to evaluate underlying assumptions through sensitivity analyses, additional data collection and where more detailed study is warranted.

Simulating Gross Primary Productivity of Humid‐Temperate Pastures

Although most pasture growth models simulate many above‐ and belowground components of the plant community, calibration and validation are usually based only on periodic measurements of aboveground forage yield. This research used daily measurements of gross primary productivity (GPP) to validate the photosynthesis subroutine of the Integrated Farm System Model (IFSM). The model was calibrated for a pasture grazed by beef cattle (Bos taurus) in 2003, then validated with data from 2004 through 2006. Predicted and observed annual yield differed by 14 ± 9%, whereas predicted GPP differed from observed GPP by only 7 ± 3%. Seasonal trends in GPP were also adequately simulated, although a slight overestimation in the spring and early‐summer and underestimation in the later half of the year occurred. Overestimation occurred when wintertime temperatures were above freezing or when N availability was high following fertilizer application. Late‐season underestimation was related to low soil N availability which resulted from excessive N uptake by plants earlier in the year. Only minor adjustments in model structure were needed to improve simulation of GPP. Most adjustments involved changes in parameter values, many of which are often difficult to find or lacking in the literature. Refinement of models to accurately simulate the seasonal distribution of physiological parameters such as GPP will help ensure that model structures correctly represent the true dynamics of C assimilation and pasture growth.

Productivity cycles in the coastal upwelling area off Concepción: The importance of diatoms and bacterioplankton in the organic carbon flux

Recurrent coastal upwelling is recognized as one of the main factors promoting the exceptionally high productivity of the Humboldt Current System. Herein, we study time series data of gross primary production (2003–2006) and its fluctuation in relation to seasonal changes in the light and nutrient field of the Concepción upwelling ecosystem. Concurrent measurements of gross primary production, community respiration, bacterial secondary production, and sedimentation rates allowed a characterization of the main carbon fluxes and pathways in the study area. The integrated values of gross primary production were higher during the upwelling period (>1 g C m −2 d −1; October–April; that is, early spring to early austral fall). Seasonal changes in the system were also reflected in community respiration, organic matter sedimentation, and bacterial production rates, which varied along with the gross primary production. The significant correlation between gross primary production and community respiration (Spearman, r = 0.7; p < 0.05; n = 18) reflected an important degree of coupling between organic matter formation and its usage by the microplanktonic community during periods when gross primary production/community respiration were highly similar. Higher gross primary production values (>6 g C m −2 d −1) were consistently associated with maximum biomass levels of Skeletonema costatum and Thalassiosira subtilis. We observed a positive correlation between gross primary production and the sedimentation of intact diatom cells (Spearman, r = 0.5, p < 0.05, n = 17). Our data suggest that, in the Concepción upwelling ecosystem, bacteria utilize an important fraction of the gross primary production. If our interpretations are correct, they leave unanswered the question of how the system supports the extremely high fish biomass levels, therein pointing out the system’s limited capacity to buffer the evasion of CO 2 following upwelling.

Assimilation of global MODIS leaf area index retrievals within a terrestrial biosphere model

We investigate the capability of global leaf area index (LAI) retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) to be assimilated within a process‐oriented biosphere model in order to constrain the distribution of carbon fluxes. This is achieved by implementing a sequential data assimilation procedure within a Dynamic Global Vegetation Model. Assimilating two years (2000–2001) of satellite LAI retrievals advances the onset and the end of the growing season at high northern latitudes, by 20 and 40 days respectively. This reduces the growing season length and leads to lower estimates of global annual gross primary productivity (GPP) and net primary productivity (NPP), respectively by 5 and 3% with large variations from one vegetation biome to another. In situ measurements of monthly GPP from eddy flux towers provide an independent check on the performance of the assimilation procedure, resulting in an improvement of 25% in terms of root mean square error between modelled and observed GPP over the model grid points where the flux towers are located.

Developing a continental-scale measure of gross primary production by combining MODIS and AmeriFlux data through Support Vector Machine approach

Remote sensing is a potentially powerful technology with which to extrapolate eddy covariance-based gross primary production (GPP) to continental scales. In support of this concept, we used meteorological and flux data from the AmeriFlux network and Support Vector Machine (SVM), an inductive machine learning technique, to develop and apply a predictive GPP model for the conterminous U.S. In the following four-step process, we first trained the SVM to predict flux-based GPP from 33 AmeriFlux sites between 2000 and 2003 using three remotely-sensed variables (land surface temperature, enhanced vegetation index (EVI), and land cover) and one ground-measured variable (incident shortwave radiation). Second, we evaluated model performance by predicting GPP for 24 available AmeriFlux sites in 2004. In this independent evaluation, the SVM predicted GPP with a root mean squared error (RMSE) of 1.87 gC/m 2/day and an R 2 of 0.71. Based on annual total GPP at 15 AmeriFlux sites for which the number of 8-day averages in 2004 was no less than 67% (30 out of a possible 45), annual SVM GPP prediction error was 32.1% for non-forest ecosystems and 22.2% for forest ecosystems, while the standard Moderate Resolution Imaging Spectroradiometer GPP product (MOD17) had an error of 50.3% for non-forest ecosystems and 21.5% for forest ecosystems, suggesting that the regionally tuned SVM performed better than the standard global MOD17 GPP for non-forest ecosystems but had similar performance for forest ecosystems. The most important explanatory factor for GPP prediction was EVI, removal of which increased GPP RMSE by 0.85 gC/m 2/day in a cross-validation experiment. Third, using the SVM driven by remote sensing data including incident shortwave radiation, we predicted 2004 conterminous U.S. GPP and found that results were consistent with expected spatial and temporal patterns. Finally, as an illustration of SVM GPP for ecological applications, we estimated maximum light use efficiency ( e max), one of the most important factors for standard light use efficiency models, for the conterminous U.S. by integrating the 2004 SVM GPP with the MOD17 GPP algorithm. We found that e max varied from ∼ 0.86 gC/MJ in grasslands to ∼ 1.56 gC/MJ in deciduous forests, while MOD17 e max was 0.68 gC/MJ for grasslands and 1.16 gC/MJ for deciduous forests, suggesting that refinements of MOD17 e max may be beneficial.

The contribution of gross primary production of understory dwarf bamboo, Sasa senanensis, in a cool-temperate deciduous broadleaved forest in central Japan

Temperate deciduous forests often consist of the mixed species and multi-layered canopies that include taller vegetation layers (overstory trees) and subordinate layers (understory vegetations). We attempted to describe the seasonal changes in gross primary production (GPP) in a cool-temperate deciduous broadleaf forest in central Japan using a process-based model, and to determine the contribution of the understory vegetation to whole ecosystem carbon balance. Leaf phenology and seasonal trends in photosynthetic characteristics differed among species. GPP of overstory trees was reflected by an increase in the higher maximum photosynthetic rate and the amount of foliage. On the other hand, understory Sasa, which grew under light-limited conditions, was associated with high photosynthetic capacity even at a low air temperature, allowing it to take full advantage of leafless periods of overstory canopies. Additionally, understory Sasa maintained healthy growth by physiological acclimation, having a higher apparent quantum yield, which allowed maximized utilization of the available sunlight. Such growth patterns were well suited to their strategy of niche occupancy in the forest ecosystem. Consequently, the annual GPP of the overstory trees and understory Sasa were estimated as 78.1 ± 34.2 and 26.1 ± 6.8 mol m −2 year −1, respectively. The contribution of GPP of understory Sasa to the entire forest was approximately 25%. This result indicates that understory Sasa is as important as overstory trees in determining the sink/source function of forest ecosystems. In order to validate the modeling approach, we compared the modeled GPP values with independent measurements obtained using the eddy covariance technique at the same site. Despite differences in the methods and analytical techniques, GPP values at the stand level were in good agreement ( r 2 = 0.83, P < 0.0001, n = 195). This demonstrates the potential of the process-based model for scaling-up GPP measurements obtained at the CO 2 flux tower.

Evaluation of MODIS NPP and GPP products across multiple biomes

Estimates of daily gross primary production (GPP) and annual net primary production (NPP) at the 1 km spatial resolution are now produced operationally for the global terrestrial surface using imagery from the MODIS (Moderate Resolution Imaging Spectroradiometer) sensor. Ecosystem-level measurements of GPP at eddy covariance flux towers and plot-level measurements of NPP over the surrounding landscape offer opportunities for validating the MODIS NPP and GPP products, but these flux measurements must be scaled over areas on the order of 25 km 2 to make effective comparisons to the MODIS products. Here, we report results for such comparisons at 9 sites varying widely in biome type and land use. The sites included arctic tundra, boreal forest, temperate hardwood forest, temperate conifer forest, tropical rain forest, tallgrass prairie, desert grassland, and cropland. The ground-based NPP and GPP surfaces were generated by application of the Biome-BGC carbon cycle process model in a spatially-distributed mode. Model inputs of land cover and leaf area index were derived from Landsat data. The MODIS NPP and GPP products showed no overall bias. They tended to be overestimates at low productivity sites — often because of artificially high values of MODIS FPAR (fraction of photosynthetically active radiation absorbed by the canopy), a critical input to the MODIS GPP algorithm. In contrast, the MODIS products tended to be underestimates in high productivity sites — often a function of relatively low values for vegetation light use efficiency in the MODIS GPP algorithm. A global network of sites where both NPP and GPP are measured and scaled over the local landscape is needed to more comprehensively validate the MODIS NPP and GPP products and to potentially calibrate the MODIS NPP/GPP algorithm parameters.