Dynamics Of Gross Primary Production Research Articles

Spatial–temporal dynamics of gross primary productivity, evapotranspiration, and water-use efficiency in the terrestrial ecosystems of the Yangtze River Delta region and their relations to climatic variables

Moderate Resolution Imaging Spectroradiometer (MODIS) products and climate data collected from meteorological stations were used to characterize the spatial–temporal dynamics of gross primary productivity (GPP), evapotranspiration (ET), and water-use efficiency (WUE) in the Yangtze River Delta (YRD) region and the response of these three variables to meteorological factors. The seasonal patterns of GPP and WUE showed a bimodal distribution, with their peak values occurring in May and August, and April and October, respectively. By contrast, the seasonal variation of ET presented a unimodal pattern with its maximum in July or August. The spatial distribution of ET and GPP was similar to higher values occurring in the south. From 2001 to 2012, GPP in the eastern YRD decreased, while GPP in the western part increased. In comparison, over the 12 years, ET in the northern part of YRD decreased, while ET in the southern part increased. The spatial distribution and spatial variation of WUE were both similar to those of GPP. This implies that the changes in WUE are primarily controlled by the variations in GPP. The annual average WUE over vegetation types followed the order of: evergreen broadleaf forest (1.95 g C kg−1 H2O) > deciduous broadleaf forest (1.87 g C kg−1 H2O) > evergreen needle leaf forest (1.70 g C kg−1 H2O) > deciduous needle leaf forest (1.68 g C kg−1 H2O) > grassland (1.66 g C kg−1 H2O) > cropland (1.61 g C kg−1 H2O). Both GPP and ET increased with increasing annual mean temperature (Ta) and annual mean precipitation across all of the plant function types. WUE decreased as vapour pressure deficit (VPD) increased in all of the biomes. Interestingly, the relationship between WUE and VPD was the most significant in broadleaf forest. Whether this phenomenon is universal should be investigated in future studies.

Seasonal Changes in Metabolic Rates of Two Tropical Lakes in the Atlantic Forest of Brazil

We studied the importance of environmental drivers for the seasonal dynamics of gross primary production (GPP), ecosystem respiration (R), and net ecosystem production in surface waters of two tropical lakes in southeast Brazil (Carioca—CA; and Dom Helvecio—DH), 2011 and 2012, using high frequency measurements of dissolved oxygen. Metabolic rates were approximately twice as high during the fully mixed winter periods, compared to the summer periods. For both lakes, GPP was approximately 30% lower during the warmer and dryer spring of 2012 compared to 2011. Seasonal changes in GPP and R were negatively correlated to water column stability (GPP r = −0.82, p < 0.001; R r = −0.80, p < 0.001). Periods with high stability coincided with warm waters which reduced mixing and internal inputs of nutrients from hypolimnetic waters. GPP was accordingly suppressed in both lakes during summer due to a combination of nutrient depletion and photo-inhibition, which was more pronounced during summer. These conditions were more prevailing during the warm and dry year of 2012, indicating ecosystem responses in carbon cycling to the ongoing regional climate changes.

Large Differences in Terrestrial Vegetation Production Derived from Satellite-Based Light Use Efficiency Models

Terrestrial gross primary production (GPP) is the largest global CO2 flux and determines other ecosystem carbon cycle variables. Light use efficiency (LUE) models may have the most potential to adequately address the spatial and temporal dynamics of GPP, but recent studies have shown large model differences in GPP simulations. In this study, we investigated the GPP differences in the spatial and temporal patterns derived from seven widely used LUE models at the global scale. The result shows that the global annual GPP estimates over the period 2000–2010 varied from 95.10 to 139.71 Pg C∙yr−1 among models. The spatial and temporal variation of global GPP differs substantially between models, due to different model structures and dominant environmental drivers. In almost all models, water availability dominates the interannual variability of GPP over large vegetated areas. Solar radiation and air temperature are not the primary controlling factors for interannual variability of global GPP estimates for most models. The disagreement among the current LUE models highlights the need for further model improvement to quantify the global carbon cycle.

Comparison of Gross Primary Productivity Derived from GIMMS NDVI3g, GIMMS, and MODIS in Southeast Asia

Gross primary production (GPP) plays an important role in the net ecosystem exchange of CO2 between the atmosphere and terrestrial ecosystems. It is particularly important to monitor GPP in Southeast Asia because of increasing rates of tropical forest degradation and deforestation in the region in recent decades. The newly available, improved, third generation Normalized Difference Vegetation Index (NDVI3g) from the Global Inventory Modelling and Mapping Studies (GIMMS) group provides a long temporal dataset, from July 1981 to December 2011, for terrestrial carbon cycle and climate response research. However, GIMMS NDVI3g-based GPP estimates are not yet available. We applied the GLOPEM-CEVSA model, which integrates an ecosystem process model and a production efficiency model, to estimate GPP in Southeast Asia based on three independent results of the fraction of photosynthetically active radiation absorbed by vegetation (FPAR) from GIMMS NDVI3g (GPPNDVI3g), GIMMS NDVI1g (GPPNDVI1g), and the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD15A2 FPAR product (GPPMOD15). The GPP results were validated using ground data from eddy flux towers located in different forest biomes, and comparisons were made among the three GPPs as well as the MOD17A2 GPP products (GPPMOD17). Based on validation with flux tower derived GPP estimates the results show that GPPNDVI3g is more accurate than GPPNDVI1g and is comparable in accuracy with GPPMOD15. In addition, GPPNDVI3g and GPPMOD15 have good spatial-temporal consistency. Our results indicate that GIMMS NDVI3g is an effective dataset for regional GPP simulation in Southeast Asia, capable of accurately tracking the variation and trends in long-term terrestrial ecosystem GPP dynamics.

Modelling gross primary production of a tropical semi-deciduous forest in the southern Amazon Basin

Semi-deciduous forest in the Amazon Basin is sensitive to temporal variation in surface water availability that can limit seasonal rates of leaf and canopy gas exchange. We estimated the seasonal dynamics of gross primary production (GPP) over 3 years (2005–2008) using eddy covariance and assessed canopy spectral reflectance using MODIS imagery for a mature tropical semi-deciduous forest located near Sinop, Mato Grosso, Brazil. A light-use efficiency model, known as the Vegetation Photosynthesis Model (VPM), was used to estimate seasonal and inter-annual variations in GPP as a function of the enhanced vegetation index (EVI), the land surface water index (LSWI), and local meteorology. Our results indicate that the standard VPM was incapable of reproducing the seasonal variation in GPP, primarily because the model overestimated dry-season GPP. In the standard model, the scalar function that alters light-use efficiency (εg) as a function of water availability (Wscalar) is calculated as a linear function of the LSWI derived from MODIS; however, the LSWI is negatively correlated with several measures of water availability including precipitation, soil water content, and relative humidity (RH). Thus, during the dry season, when rainfall, soil water content, and RH are low, LSWI, and therefore, Wscalar, are at a seasonal maximum. Using previous research, we derived new functions for Wscalar based on time series of RH and photosynthetic photon flux density (PPFD) that significantly improved the performance of the VPM. Whether these new functions perform equally well in water stressed and unstressed tropical forests needs to be determined, but presumably unstressed ecosystems would have high cloud cover and humidity, which would minimize variations in Wscalar and GPP to spatial and/or temporal variation in water availability.

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.

Modeling gross primary production of two steppes in Northern China using MODIS time series and climate data

Abstract Terrestrial carbon cycle plays an important role in global climate change. As a key component of terrestrial carbon cycle, gross primary production (GPP) is a major determinant of the exchange of carbon between the atmosphere and terrestrial ecosystems. With rapid advancement of remote-sensing technology, it has become a common practice to utilize parameters derived from remote-sensing data to estimate GPP at a regional or global scale. In this study, a satellite-driven model, Vegetation Photosynthesis Model (VPM) was introduced to estimate GPP of two steppes, Xilinhot (XH, 43.5544°N, 116.6714°E) and Duolun (DL, 42.0467°N, 116.2836°E), at Inner Mongolia in Northern China, by integrating moderate resolution imaging spectral radiometer (MODIS) and meteorological measurements at the two flux towers. As defined by the input variables of VPM, two improved vegetation indices (enhanced vegetation index (EVI) and land surface water index (LSWI)) derived from the standard data product MOD09A1 of MODIS, air temperature and photosynthetic active radiation at the flux towers, were included for the model calculating. Canopy-level maximum light use efficiency, a key parameter for VPM, was estimated by using the observed CO2 flux data and photosynthetic active radiation (PAR). Observed GPP derived from flux data were then used to critically evaluate the performance of the model. The results indicate that the seasonal dynamics of GPP predicted by the VPM model agreed well with measured GPP by the flux towers. The determination coefficient (R2) of predicted GPP with measured GPP was 0.86 and 0.79 in 2006, 0.66 and 0.76 in 2007 for DL and XH, respectively. Further, time-series data for the EVI have a stronger linear relationship with the GPP than those for the Normalized Difference Vegetation Index. Results of this study demonstrate that the satellite-driven VPM has been potential for estimating site-level or regional grassland GPP, and might be an effective tool for scaling-up carbon fluxes.

Environmental controls on the spatial variability of savanna productivity in the Northern Territory, Australia

Gross Primary Productivity (GPP) is a critical measure of the health and sustainability of natural ecosystems. Understanding the magnitude, spatial patterns and processes of GPP will underpin predictions of the impact of climate change on the carbon cycle. In Australia, savannas account for one third of the terrestrial carbon stores and therefore, estimating the magnitude of savanna GPP and studying the spatial relationship between GPP and environmental determinants at the regional scale is essential in understanding ecosystem responses to increasing atmospheric CO2 concentrations and climate change. In this study we employed an integrated approach combining in situ measurements, eddy covariance based flux tower data and remote sensing techniques to examine the role of environmental drivers in controlling the spatial variation in GPP of savannas in the Northern Territory (NT), Australia. We used field based light use efficiency (LUE), regional specific meteorology and Moderate Resolution Imaging Spectro-radiometer (MODIS) based fraction of absorbed Photosynthetically Active Radiation (fPAR) data to estimate GPP. The estimated GPP agreed quite well (only a 6% error) with GPP estimated from flux tower at the Howard Springs site. The spatial pattern of GPP along the Northern Australian Tropical Transect (NATT) was calculated and showed a strong gradient in GPP from the coast (12.50°S where rainfall was 1622mmyear−1) to inland (17.73°S where mean rainfall was 643mmyear−1) with a decrease of 77%. A decreasing trend in GPP with rainfall is noticed especially at the dry end of the transect studied. However, in the wet end and middle part of the transect (e.g. dominated by different Eucalyptus species), the response of GPP to changes in rainfall is reduced. This finding suggests that the influence of rainfall on various Eucalyptus species may be dampened by biotic factors. Our results suggest that future changes in precipitation driven by climate change may affect the future distribution and dynamics of GPP in northern Australia.

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.

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.

A data-model fusion approach for upscaling gross ecosystem productivity to the landscape scale based on remote sensing and flux footprint modelling

Abstract. In order to use the global available eddy-covariance (EC) flux dataset and remote-sensing measurements to provide estimates of gross primary productivity (GPP) at landscape (101–102 km2), regional (103–106 km2) and global land surface scales, we developed a satellite-based GPP algorithm using LANDSAT data and an upscaling framework. The satellite-based GPP algorithm uses two improved vegetation indices (Enhanced Vegetation Index – EVI, Land Surface Water Index – LSWI). The upscalling framework involves flux footprint climatology modelling and data-model fusion. This approach was first applied to an evergreen coniferous stand in the subtropical monsoon climatic zone of south China. The EC measurements at Qian Yan Zhou tower site (26°44´48" N, 115°04´13" E), which belongs to the China flux network and the LANDSAT and MODIS imagery data for this region in 2004 were used in this study. A consecutive series of LANDSAT-like images of the surface reflectance at an 8-day interval were predicted by blending the LANDSAT and MODIS images using an existing algorithm (ESTARFM: Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model). The seasonal dynamics of GPP were then predicted by the satellite-based algorithm. MODIS products explained 60% of observed variations of GPP and underestimated the measured annual GPP (= 1879 g C m−2) by 25–30%; while the satellite-based algorithm with default static parameters explained 88% of observed variations of GPP but overestimated GPP during the growing seasonal by about 20–25%. The optimization of the satellite-based algorithm using a data-model fusion technique with the assistance of EC flux tower footprint modelling reduced the biases in daily GPP estimations from about 2.24 g C m−2 day−1 (non-optimized, ~43.5% of mean measured daily value) to 1.18 g C m−2 day−1 (optimized, ~22.9% of mean measured daily value). The remotely sensed GPP using the optimized algorithm can explain 92% of the seasonal variations of EC observed GPP. These results demonstrated the potential combination of the satellite-based algorithm, flux footprint modelling and data-model fusion for improving the accuracy of landscape/regional GPP estimation, a key component for the study of the carbon cycle.

Modeling gross primary production of maize cropland and degraded grassland in northeastern China

Measurements from individual CO 2 eddy flux sites provide valuable information on the seasonal dynamics of gross primary production (GPP). In this study, we estimated seasonal dynamics of GPP from 3 years (2004–2006) of the eddy covariance observations at maize cropland and degraded grassland in a semi-arid area of Tongyu county (44.5667°N, 122.8833°E), Northeast China. The biophysical performance of vegetation indices (EVI, NDVI, and LSWI) derived from the 8-day Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product and their relations to GPP dynamics were evaluated. The quantitative relationships between the vegetation indices and CO 2 flux data clearly demonstrated the improvement of EVI over NDVI, in terms of the phase and magnitude of photosynthesis. Canopy-level maximum light use efficiency, ɛ 0, was estimated for both maize and grassland by using the observed CO 2 flux data and Photosynthetically Active Radiation (PAR) data from eddy flux tower sites. For maize cropland, the ɛ 0 value was 0.56 g C/mol PAR, and for degraded grassland, the ɛ 0 value was 0.37 g C/mol PAR. We conducted a simulation of the Vegetation Photosynthesis Model (VPM) using the Enhanced Vegetation Index (EVI) and the Land Surface Water Index (LSWI) derived from the 8-day (MODIS) surface reflectance product, as well as site-specific climate data. The comparison between simulated GPP and estimated GPP from tower CO 2 flux data showed good agreement in both maize cropland and degraded grassland. This study highlighted the biophysical performance of improved vegetation indices in relation to GPP and demonstrated the potential of the satellite-driven VPM model for scaling-up of GPP of maize cropland and grassland in semi-arid ecosystems.

Global estimates of evapotranspiration and gross primary production based on MODIS and global meteorology data

article i nfo The simulation of gross primary production (GPP) at various spatial and temporal scales remains a major challenge for quantifying the global carbon cycle. We developed a light use efficiency model, called EC-LUE, driven by only four variables: normalized difference vegetation index (NDVI), photosynthetically active radiation (PAR), air temperature, and the Bowen ratio of sensible to latent heat flux. The EC-LUE model may have the most potential to adequately address the spatial and temporal dynamics of GPP because its parameters (i.e., the potential light use efficiency and optimal plant growth temperature) are invariant across the various land cover types. However, the application of the previous EC-LUE model was hampered by poor prediction of Bowen ratio at the large spatial scale. In this study, we substituted the Bowen ratio with the ratio of evapotranspiration (ET) to net radiation, and revised the RS-PM (Remote Sensing-Penman Monteith) model for quantifying ET. Fifty-four eddy covariance towers, including various ecosystem types, were selected to calibrate and validate the revised RS-PM and EC-LUE models. The revised RS-PM model explained 82% and 68% of the observed variations of ET for all the calibration and validation sites, respectively. Using estimated ET as input, the EC-LUE model performed well in calibration and validation sites, explaining 75% and 61% of the observed GPP variation for calibration and validation sites respectively. Global patterns of ET and GPP at a spatial resolution of 0.5° latitude by 0.6° longitude during the years 2000- 2003 were determined using the global MERRA dataset (Modern Era Retrospective-Analysis for Research and Applications) and MODIS (Moderate Resolution Imaging Spectroradiometer). The global estimates of ET and GPP agreed well with the other global models from the literature, with the highest ET and GPP over tropical forests and the lowest values in dry and high latitude areas. However, comparisons with observed GPP at eddy flux towers showed significant underestimation of ET and GPP due to lower net radiation of MERRA dataset. Applying a procedure to correct the systematic errors of global meteorological data would improve global estimates of GPP and ET. The revised RS-PM and EC-LUE models will provide the alternative approaches making it possible to map ET and GPP over large areas because (1) the model parameters are invariant across various land cover types and (2) all driving forces of the models may be derived from remote sensing data or existing climate observation networks.

Modeling gross primary production of a temperate grassland ecosystem in Inner Mongolia, China, using MODIS imagery and climate data

Carbon fluxes in temperate grassland ecosystems are characterized by large inter-annual variations due to fluctuations in precipitation and land water availability. Since an eddy flux tower has been in operation in the Xilin Gol grassland, which belongs to typical temperate grassland in North China, in this study, observed eddy covariance flux data were used to critically evaluate the biophysical performance of different remote sensing vegetation indices in relation to carbon fluxes. Furthermore, vegetation photosynthesis model (VPM) was introduced to estimate gross primary production (GPP) of the grassland ecosystem for assessing its dependability. As defined by the input variables of VPM, Moderate Resolution Imaging Spectroradimeter (MODIS) and standard data product MOD09A1 were downloaded for calculating enhanced vegetation index (EVI) and land surface water index (LSWI). Measured air temperature (Ta) and photosynthetically active radiation (PAR) data were also included for model simulating. Field CO 2 flux data, during the period from May, 2003 to September, 2005, were used to estimate the "observed" GPP (GPP obs ) for validation. The seasonal dynamics of GPP predicted from VPM (GPP VPM ) was compared quite well (R 2 =0.903, N =111, p VPM for the study period was 641.5 g C&middotm -2 , representing a μ6% over-estimation, compared with GPP obs . Additionally, GPP predicted from other two typical production efficiency model (PEM) represents either higher overestimation or lower underestimation to GPP obs . Results of this study demonstrate that VPM has potential for estimating site-level or regional grassland GPP, and might be an effective tool for scaling-up carbon fluxes. © Science in China Press and Springer-Verlag GmbH 2008.

Modeling gross primary production of alpine ecosystems in the Tibetan Plateau using MODIS images and climate data

The eddy covariance technique provides measurements of net ecosystem exchange (NEE) of CO 2 between the atmosphere and terrestrial ecosystems, which is widely used to estimate ecosystem respiration and gross primary production (GPP) at a number of CO 2 eddy flux tower sites. In this paper, canopy-level maximum light use efficiency, a key parameter in the satellite-based Vegetation Photosynthesis Model (VPM), was estimated by using the observed CO 2 flux data and photosynthetically active radiation (PAR) data from eddy flux tower sites in an alpine swamp ecosystem, an alpine shrub ecosystem and an alpine meadow ecosystem in Qinghai–Tibetan Plateau, China. The VPM model uses two improved vegetation indices (Enhanced Vegetation Index (EVI), Land Surface Water Index (LSWI)) derived from the Moderate Resolution Imaging Spectral radiometer (MODIS) data and climate data at the flux tower sites, and estimated the seasonal dynamics of GPP of the three alpine grassland ecosystems in Qinghai–Tibetan Plateau. The seasonal dynamics of GPP predicted by the VPM model agreed well with estimated GPP from eddy flux towers. These results demonstrated the potential of the satellite-driven VPM model for scaling-up GPP of alpine grassland ecosystems, a key component for the study of the carbon cycle at regional and global scales.

MODELING GROSS PRIMARY PRODUCTION OF AN EVERGREEN NEEDLELEAF FOREST USING MODIS AND CLIMATE DATA

Forest canopies are composed of photosynthetically active vegetation (PAV, chloroplasts) and nonphotosynthetic vegetation (NPV, e.g., cell wall, vein, branch). The fraction of photosynthetically active radiation (PAR) absorbed by the canopy (FAPAR) should be partitioned into FAPARPAV and FAPARNPV. Gross primary production (GPP) of forests is affected by FAPARPAV. In this study we developed and validated a satellite‐based vegetation photosynthesis model (VPM; GPP = εg × FAPAPPAV × PAR) that incorporates improved vegetation indices derived from the moderate resolution imaging spectroradimeter (MODIS) sensor. Site‐specific data from the CO2 flux tower site (evergreen needleleaf forest) at Howland, Maine, USA, were used. The enhanced vegetation index (EVI) better correlated with the seasonal dynamics of GPP than did the normalized difference vegetation index (NDVI). Simulations of the VPM model were conducted, using both daily and eight‐day composites of MODIS images (500‐m spatial resolution) and climate data (air temperature and PAR), respectively. Predicted GPP values in 2001 agree reasonably well with estimated GPP from the CO2 flux tower site. There were no significant differences in VPM‐predicted GPP (from eight‐day MODIS composites) among one pixel (∼500‐m resolution), 3 × 3 pixel block (∼1.5‐km resolution), and 5 × 5 pixel block (∼2.5‐km resolution). The differences between VPM‐predicted and observed GPP were smaller for simulations using eight‐day MODIS composites than for simulations using daily MODIS images. The results of this study have shown the potential of MODIS data (both daily and eight‐day composites) and the VPM model for quantifying seasonal and interannual variations of GPP of evergreen needleleaf forests.

Modeling gross primary production of temperate deciduous broadleaf forest using satellite images and climate data

Net ecosystem exchange (NEE) of CO 2 between the atmosphere and forest ecosystems is determined by gross primary production (GPP) of vegetation and ecosystem respiration. CO 2 flux measurements at individual CO 2 eddy flux sites provide valuable information on the seasonal dynamics of GPP. In this paper, we developed and validated the satellite-based Vegetation Photosynthesis Model (VPM), using site-specific CO 2 flux and climate data from a temperate deciduous broadleaf forest at Harvard Forest, Massachusetts, USA. The VPM model is built upon the conceptual partitioning of photosynthetically active vegetation and non-photosynthetic vegetation (NPV) within the leaf and canopy. It estimates GPP, using satellite-derived Enhanced Vegetation Index (EVI), Land Surface Water Index (LSWI), air temperature and photosynthetically active radiation (PAR). Multi-year (1998–2001) data analyses have shown that EVI had a stronger linear relationship with GPP than did the Normalized Difference Vegetation Index (NDVI). Two simulations of the VPM model were conducted, using vegetation indices from the VEGETATION (VGT) sensor onboard the SPOT-4 satellite and the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the Terra satellite. The predicted GPP values agreed reasonably well with observed GPP of the deciduous broadleaf forest at Harvard Forest, Massachusetts. This study highlighted the biophysical performance of improved vegetation indices in relation to GPP and demonstrated the potential of the VPM model for scaling-up of GPP of deciduous broadleaf forests.

Modeling gross primary production of temperate deciduous broadleaf forest using satellite images and climate data

Net ecosystem exchange (NEE) of CO 2 between the atmosphere and forest ecosystems is determined by gross primary production (GPP) of vegetation and ecosystem respiration. CO 2 flux measurements at individual CO 2 eddy flux sites provide valuable information on the seasonal dynamics of GPP. In this paper, we developed and validated the satellite-based Vegetation Photosynthesis Model (VPM), using site-specific CO 2 flux and climate data from a temperate deciduous broadleaf forest at Harvard Forest, Massachusetts, USA. The VPM model is built upon the conceptual partitioning of photosynthetically active vegetation and non-photosynthetic vegetation (NPV) within the leaf and canopy. It estimates GPP, using satellite-derived Enhanced Vegetation Index (EVI), Land Surface Water Index (LSWI), air temperature and photosynthetically active radiation (PAR). Multi-year (1998–2001) data analyses have shown that EVI had a stronger linear relationship with GPP than did the Normalized Difference Vegetation Index (NDVI). Two simulations of the VPM model were conducted, using vegetation indices from the VEGETATION (VGT) sensor onboard the SPOT-4 satellite and the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the Terra satellite. The predicted GPP values agreed reasonably well with observed GPP of the deciduous broadleaf forest at Harvard Forest, Massachusetts. This study highlighted the biophysical performance of improved vegetation indices in relation to GPP and demonstrated the potential of the VPM model for scaling-up of GPP of deciduous broadleaf forests.

Satellite-based modeling of gross primary production in an evergreen needleleaf forest

The eddy covariance technique provides valuable information on net ecosystem exchange (NEE) of CO 2, between the atmosphere and terrestrial ecosystems, ecosystem respiration, and gross primary production (GPP) at a variety of CO 2 eddy flux tower sites. In this paper, we develop a new, satellite-based Vegetation Photosynthesis Model (VPM) to estimate the seasonal dynamics and interannual variation of GPP of evergreen needleleaf forests. The VPM model uses two improved vegetation indices (Enhanced Vegetation Index (EVI), Land Surface Water Index (LSWI)). We used multi-year (1998–2001) images from the VEGETATION sensor onboard the SPOT-4 satellite and CO 2 flux data from a CO 2 eddy flux tower site in Howland, Maine, USA. The seasonal dynamics of GPP predicted by the VPM model agreed well with observed GPP in 1998–2001 at the Howland Forest. These results demonstrate the potential of the satellite-driven VPM model for scaling-up GPP of forests at the CO 2 flux tower sites, a key component for the study of the carbon cycle at regional and global scales.

Carbon dioxide exchange in Norway spruce at the shoot, tree and ecosystem scale.

Net CO2 exchange in a 35-year-old boreal Norway spruce (Picea abies (L.) Karst.) forest in northern Sweden was measured at the shoot (NSE), tree (NTE) and ecosystem levels (NEE) by means of shoot cuvettes, whole-tree chambers and the eddy covariance technique, respectively. We compared the dynamics of gross primary production (GPP) at the three levels during the course of a single week. The diurnal dynamics of GPP at each level were estimated by subtracting half-hourly or hourly model-estimated values of total respiration (excluding light-dependent respiration) from net CO(2) exchange. The relationship between temperature and total respiration at each level was derived from nighttime measurements of NSE, NTE and NEE over the course of 1 month. There was a strong linear relationship (r2 = 0.93) between the hourly estimates of GPP at the shoot and tree levels, but the correlation between shoot- and ecosystem-level GPP was weaker (r2 = 0.69). However, the correlation between shoot- and ecosystem-level GPP was improved (r2 = 0.88) if eddy covariance measurements were restricted to periods when friction velocity was > or = 0.5 m s(-1). Daily means were less dependent on friction velocity, giving an r2 value of 0.94 between shoot- and ecosystem-level GPP. The correlation between shoot and tree levels also increased when daily means were compared (r2 = 0.98). Most of the measured variation in carbon exchange rate among the shoot, tree and ecosystem levels was the result of periodic low coupling between vegetation and the atmosphere at the ecosystem level. The results validate the use of measurements at the shoot and tree level for analyzing the contribution of different compartments to net ecosystem CO2 exchange.