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

Net ecosystem exchange (NEE) of CO₂ between the atmosphere and forest ecosystems is determined by gross primary production (GPP) of vegetation and ecosystem respiration. CO₂ flux measurements at individual CO₂ 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 CO₂ 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.     

Estimating daily gross primary production of maize based only on MODIS WDRVI and shortwave radiation data

Accurate assessment of temporal changes in gross primary production (GPP) is important for carbon budget assessments and evaluating the impact of climate change on crop productivity. The objective of this study was to devise a simple remote sensing-based GPP model to quantify daily GPP of maize. In the model, (1) daily shortwave radiation (SW), derived from the reanalysis data (North American Land Data Assimilation System; NLDAS-2) and (2) smoothed Wide Dynamic Range Vegetation Index (WDRVI) data, derived from Moderate Resolution Imaging Spectroradiometer (MODIS) 250-m observations were used as proxy variables of the incident photosynthetically active radiation (PAR) and the total canopy chlorophyll content, respectively. The model was calibrated and validated by using tower-based CO₂ flux observations over an 8-year period (2001 to 2008) for one rainfed and two irrigated sites planted to maize as part of the Carbon Sequestration Program at the University of Nebraska-Lincoln. The results showed the temporal features of the product SW*WDRVI closely related to the temporal GPP variations in terms of both daily variations and seasonal patterns. The simple GPP model was able to predict the daily GPP values and accumulated GPP values of maize with high accuracy.     

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

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.     

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 CO₂, between the atmosphere and terrestrial ecosystems, ecosystem respiration, and gross primary production (GPP) at a variety of CO₂ 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 CO₂ flux data from a CO₂ 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 CO₂ flux tower sites, a key component for the study of the carbon cycle at regional and global scales.     

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.     

A method for estimating the gross primary production of alpine meadows using MODIS and climate data in China

The use of remotely sensed data to estimate and monitor the gross primary production (GPP) of an ecosystem on regional scales is an important method in climate change research. Under the unremitting efforts of scientists, many successful remote-sensing-based GPP models have been developed for various vegetation types and regions. However, in practice, some models have been applied to a wide variety of ecosystems, and the suitability of a particular model for the environment under consideration has seldom been taken into account. Due to ecosystem diversity and climatic and environmental variation, it is often difficult to find a model that is suitable for a specific vegetation region. In this article, a new method is proposed for estimating the GPP of alpine vegetation, known as the alpine vegetation model (AVM). The accuracy of the AVM in estimating the GPP was compared to that of four other models: the vegetation photosynthesis model (VPM), eddy covariance–light use efficiency (EC–LUE) model, temperature and greenness (TG) model, and vegetation index (VI) model. The results demonstrated that the AVM displays superior accuracy in estimating the GPP of alpine vegetation. We also found that there is information redundancy in the input variables of these four models, which may account for their lower accuracy in estimating the GPP. In addition, the GPP estimates using the enhanced vegetation index are affected more in the case of low rather than high GPP by the influence of senesced grass during the early and late grassland growing season.     

Parallel adjustments in vegetation greenness and ecosystem CO2 exchange in response to drought in a Southern California chaparral ecosystem

Some form of the light use efficiency (LUE) model is used in most models of ecosystem carbon exchange based on remote sensing. The strong relationship between the normalized difference vegetation index (NDVI) and light absorbed by green vegetation make models based on LUE attractive in the remote sensing context. However, estimation of LUE has proven problematic since it varies with vegetation type and environmental conditions. Here we propose that LUE may in fact be correlated with vegetation greenness (measured either as NDVI at constant solar elevation angle, or a red edge chlorophyll index), making separate estimates of LUE unnecessary, at least for some vegetation types. To test this, we installed an automated tram system for measurement of spectral reflectance in the footprint of an eddy covariance flux system in the Southern California chaparral. This allowed us to match the spatial and temporal scales of the reflectance and flux measurements and thus to make direct comparisons over time scales ranging from minutes to years. The 3-year period of this study included both “normal” precipitation years and an extreme drought in 2002. In this sparse chaparral vegetation, diurnal and seasonal changes in solar angle resulted in large variation in NDVI independent of the actual quantity of green vegetation. In fact, one would come to entirely different conclusions about seasonal changes in vegetation greenness depending on whether NDVI at noon or NDVI at constant solar elevation angle were used. Although chaparral vegetation is generally considered “evergreen”, we found that the majority of the shrubs were actually semi-deciduous, leading to large seasonal changes in NDVI at constant solar elevation angle. LUE was correlated with both greenness indices at the seasonal timescale across all years. In contrast, the relationship between LUE and PRI was inconsistent. PRI was well correlated with LUE during the “normal” years but this relationship changed dramatically during the extreme drought. Contrary to expectations, none of the spectral reflectance indices showed consistent relationships with CO₂ flux or LUE over the diurnal time-course, possibly because of confounding effects of sun angle and stand structure on reflectance. These results suggest that greenness indices can be used to directly estimate CO₂ exchange at weekly timescales in this chaparral ecosystem, even in the face of changes in LUE. Greenness indices are unlikely to be as good predictors of CO₂ exchange in dense evergreen vegetation as they were in the sparse, semi-deciduous chaparral. However, since relatively few ecosystems are entirely evergreen at large spatial scales or over long time spans due to disturbance, these relationships need to be examined across a wider range of vegetation types.     

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/m²/day and an R² 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/m²/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.     

Validation of collection of 6 MODIS/Terra and MODIS/Aqua gross primary production in an alpine meadow of the Northern Tibetan Plateau

Moderate Resolution Imaging Spectroradiometer (MODIS) continuously monitors gross primary production (GPP), which is an extremely important component of carbon cycling, at the global scale. Uncertainties about MODIS GPP limit our ability to accurately quantify GPP at the regional scales. The Collection 6 MODIS/Terra and MODIS/Aqua GPP products (i.e. MOD17A2H and MYD17A2H) were compared with the estimated GPP (GPP_EC) by eddy covariance measurements in an alpine meadow in the Northern Tibetan Plateau during three consecutive growing seasons of 2005–2007. The Collection 6 MODIS/Terra and MODIS/Aqua fractional photosynthetically active radiation (FPAR) products (i.e. MOD15A2H and MYD15A2H) were also validated. The MOD17A2H and MYD17A2H products tended to overestimate GPP_EC by 2.17% and 7.35% in 2005–2007, respectively, although these differences were not significant. The MOD15A2H and MYD15A2H products also tended to overestimate ground-based FPAR (FPAR_G) by 20.31% and 24.73% in 2005–2007, respectively. The overestimation of FPAR resulted in about 17.51–23.97% overestimation of GPP_EC. The default maximum light-use efficiency (ε_max) of 0.86 g C MJ^?1 only underestimated the ground-based ε_max (0.88 g C MJ^?1) by 2.27%, which in turn resulted in about 2.13–2.72% underestimation of GPP_EC. The meteorology data errors only caused about 0.48–1.06% underestimation of GPP_EC. Therefore, although MODIS Collection 6 GPP had a very high accuracy, the input parameters had relative greater errors in the alpine meadow of the Northern Tibetan Plateau. The differences between MODIS GPP and GPP_EC mainly resulted from FPAR, followed by ε_max and meteorological data.     

Comparison of multiple models for estimating gross primary production using MODIS and eddy covariance data in Harvard Forest

Gross primary production (GPP) defined as the overall rate of fixation of carbon through the process of vegetation photosynthesis is important for carbon cycle and climate change research. Three models, the Vegetation Photosynthesis Model (VPM), the Temperature and Greenness (TG) model and the Vegetation Index (VI) model have been compared for the estimation of GPP in Harvard Forest from 2003 to 2006 using climate variables acquired by eddy covariance (EC) measurements and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images. All these models provide more reliable estimates of GPP than that of MODIS GPP product. High Pearsons correlation coefficients r equal to 0.94, 0.92 and 0.90 are observed for the VPM, the TG and the VI model, respectively. Relationships between GPP and land surface temperature (LST, R² = 0.72), and vapor pressure deficit (VPD, R² = 0.45) indicate that climate variables are important for GPP estimation. Due to proper characterization of temperature, water stress and leaf age by three scalars, VPM best follows the seasonal variations of GPP. By incorporation of the MODIS surface reflectance and LST product, the TG model is the most suitable choice for areas without prior knowledge as it is based entirely on remote sensing observations. Results from the VI model demonstrate the possibility of using a single vegetation index for light use efficiency (LUE) estimation in deciduous forest that is of high spatial heterogeneity. The validation and comparison of models will be helpful in development of future GPP models using combinations of climate variables and/or remote sensing observations.     

A new model of gross primary productivity for North American ecosystems based solely on the enhanced vegetation index and land surface temperature from MODIS

Many current models of ecosystem carbon exchange based on remote sensing, such as the MODIS product termed MOD17, still require considerable input from ground based meteorological measurements and look up tables based on vegetation type. Since these data are often not available at the same spatial scale as the remote sensing imagery, they can introduce substantial errors into the carbon exchange estimates. Here we present further development of a gross primary production (GPP) model based entirely on remote sensing data. In contrast to an earlier model based only on the enhanced vegetation index (EVI), this model, termed the Temperature and Greenness (TG) model, also includes the land surface temperature (LST) product from MODIS. In addition to its obvious relationship to vegetation temperature, LST was correlated with vapor pressure deficit and photosynthetically active radiation. Combination of EVI and LST in the model substantially improved the correlation between predicted and measured GPP at 11 eddy correlation flux towers in a wide range of vegetation types across North America. In many cases, the TG model provided substantially better predictions of GPP than did the MODIS GPP product. However, both models resulted in poor predictions for sparse shrub habitats where solar angle effects on remote sensing indices were large. Although it may be possible to improve the MODIS GPP product through improved parameterization, our results suggest that simpler models based entirely on remote sensing can provide equally good predictions of GPP.     

Deriving maximal light use efficiency from coordinated flux measurements and satellite data for regional gross primary production modeling

Remote sensing models based on light use efficiency (LUE) provide promising tools for monitoring spatial and temporal variation of gross primary production (GPP) at regional scale. In most of current LUE-based models, maximal LUE (ε_max) heavily relies on land cover types and is considered as a constant, rather than a variable for a certain vegetation type or even entire eco-region. However, species composition and plant functional types are often highly heterogeneous in a given land cover class; therefore, spatial heterogeneity of ε_max must be fully considered in GPP modeling, so that a single cover type does not equate to a single ε_max value. A spatial dataset of ε_max accurately represents the spatial heterogeneity of maximal light use would be of significant beneficial to regional GPP models. Here, we developed a spatial dataset of ε_max by integrating eddy covariance flux measurements from 14 field sites in a network of coordinated observation across northern China and satellite derived indices such as enhanced vegetation index (EVI) and visible albedo to simulate regional distribution of GPP. This dynamic modeling method recognizes the spatial heterogeneity of ε_max and reduces the uncertainties in mixed pixels. Further, we simulated GPP with the spatial dataset of ε_max generated above. Both ε_max and growing season GPP show complex patterns over northern China that reflect influences of humidity, green vegetation fractions, and land use intensity. “Green spots” such as oasis meadow and alpine forests in dryland and “brown spots” such as build-up and heavily degraded vegetation in the east are clearly captured by the simulation. The correlation between simulated GPP and EC measured GPP indicate that the simulated GPP from this new approach is well matched with flux-measured GPP. Those results have demonstrated the importance of considering ε_max as both a spatially and temporally variable values in GPP modeling.     

Normalized difference spectral indices for estimating photosynthetic efficiency and capacity at a canopy scale derived from hyperspectral and CO2 flux measurements in rice

We explored simple and useful spectral indices for estimating photosynthetic variables (radiation use efficiency and photosynthetic capacity) at a canopy scale based on seasonal measurements of hyperspectral reflectance, ecosystem CO₂ flux, and plant and micrometeorological variables. An experimental study was conducted over the simple and homogenous ecosystem of an irrigated rice field. Photosynthetically active radiation absorbed by the canopy (APAR), the canopy absorptivity of APAR (fAPAR), net ecosystem exchange of CO₂ (NEE_CO2) gross primary productivity (GPP), photosynthetic capacity at the saturating APAR (P_max), and three parameters of radiation use efficiency (ε_N: NEE_CO2/APAR; ε_G: GPP/APAR; φ: quantum efficiency) were derived from the data set. Based on the statistical analysis of relationships between these ecophysiological variables and reflectance indicators such as normalized difference spectral indices (NDSI[i,j]) using all combinations of two wavelengths (i and j nm), we found several new indices that would were more effective than conventional spectral indices such as photochemical reflectance index (PRI) and normalized difference vegetation index (NDVI = NDSI[near-infrared, red]). ε_G was correlated well with NDSI[710, 410], NDSI[710, 520], and NDSI[530, 550] derived from nadir measurements. φ was best correlated with NDSI[450, 1330]. NDSI[550, 410] and NDSI[720, 420] had a consistent linear relationships with fAPAR throughout the growing season, whereas conventional indices such as NDVI showed very different relationships before and after heading. Off-nadir measurements were more closely related to the efficiency parameters than nadir measurements. Our results provide useful insights for assessing plant productivity and ecosystem CO₂ exchange, using a wide range of available spectral data as well as useful information for designing future sensors for ecosystem observations.     

Remote sensing of tundra gross ecosystem productivity and light use efficiency under varying temperature and moisture conditions

Satellite observations have shown greening trends in tundra in response to climate change, suggesting increases in productivity. To better understand the ability of remote sensing to detect climate impacts on tundra vegetation productivity, we applied a photosynthetic light use efficiency model to simulated climate change treatments of tundra vegetation. We examined changes in the Normalized Difference Vegetation Index (NDVI) and photosynthetic light use efficiency (ε) in experimental warming and moisture treatments designed to simulate climate change in northern Alaska. Plots were warmed either passively, using Open Top Chambers, or actively using electric heaters in the soil. In one set of plots water table depth was actively altered, while other plots were established in locations that were naturally wet or dry. Over two growing seasons, plot-level carbon flux and spectral reflectance measurements were collected, and the results were used to derive a light use efficiency model that could explore the effects of moisture and temperature treatments using remote sensing.Warming increased values of canopy greenness (NDVI) relative to control plots, this effect being more pronounced in wet plots than in dry plots. Light use efficiency (LUE), the relationship between absorbed photosynthetically active radiation (PAR) and gross ecosystem production (GEP), was consistent across warming treatments, growing season, subsequent years, and sites. However, LUE was affected by vegetation type, which varied with moisture; plots in naturally dry locations showed reduced light use efficiency relative to moist plots. Additionally moss exhibited reduced LUE relative to vascular plants. Understory moss production, not accounted for by the usual definition of the fraction of absorbed PAR (f_APAR), was found to be a significant part of total GEP, particularly in areas with low vascular plant cover. These results support the use of light use efficiency models driven by spectral reflectance for estimating GEP in tundra vegetation, provided effects of vegetation functional type (e.g. mosses versus vascular plants) and microtopography are considered.     

Improvements of the MODIS terrestrial gross and net primary production global data set

MODIS primary production products (MOD17) are the first regular, near-real-time data sets for repeated monitoring of vegetation primary production on vegetated land at 1-km resolution at an 8-day interval. But both the inconsistent spatial resolution between the gridded meteorological data and MODIS pixels, and the cloud-contaminated MODIS FPAR/LAI (MOD15A2) retrievals can introduce considerable errors to Collection4 primary production (denoted as C4 MOD17) results. Here, we aim to rectify these problems through reprocessing key inputs to MODIS primary vegetation productivity algorithm, resulting in improved Collection5 MOD17 (here denoted as C5 MOD17) estimates. This was accomplished by spatial interpolation of the coarse resolution meteorological data input and with temporal filling of cloud-contaminated MOD15A2 data. Furthermore, we modified the Biome Parameter Look-Up Table (BPLUT) based on recent synthesized NPP data and some observed GPP derived from some flux tower measurements to keep up with the improvements in upstream inputs. Because MOD17 is one of the down-stream MODIS land products, the performance of the algorithm can be largely influenced by the uncertainties from upstream inputs, such as land cover, FPAR/LAI, the meteorological data, and algorithm itself. MODIS GPP fits well with GPP derived from 12 flux towers over North America. Globally, the 3-year MOD17 NPP is comparable to the Ecosystem Model-Data Intercomparison (EMDI) NPP data set, and global total MODIS GPP and NPP are inversely related to the observed atmospheric CO₂ growth rates, and MEI index, indicating MOD17 are reliable products. From 2001 to 2003, mean global total GPP and NPP estimated by MODIS are 109.29 Pg C/year and 56.02 Pg C/year, respectively. Based on this research, the improved global MODIS primary production data set is now ready for monitoring ecological conditions, natural resources and environmental changes.     

Scaling Gross Primary Production (GPP) over boreal and deciduous forest landscapes in support of MODIS GPP product validation

The Moderate Resolution Imaging Radiometer (MODIS) is the primary instrument in the NASA Earth Observing System for monitoring the seasonality of global terrestrial vegetation. Estimates of 8-day mean daily gross primary production (GPP) at the 1 km spatial resolution are now operationally produced by the MODIS Land Science Team for the global terrestrial surface using a production efficiency approach. In this study, the 2001 MODIS GPP product was compared with scaled GPP estimates (25 km²) based on ground measurements at two forested sites. The ground-based GPP scaling approach relied on a carbon cycle process model run in a spatially distributed mode. Land cover classification and maximum annual leaf area index, as derived from Landsat ETM+ imagery, were used in model initiation. The model was driven by daily meteorological observations from an eddy covariance flux tower situated at the center of each site. Model simulated GPPs were corroborated with daily GPP estimates from the flux tower. At the hardwood forest site, the MODIS GPP phenology started earlier than was indicated by the scaled GPP, and the summertime GPP from MODIS was generally lower than the scaled GPP values. The fall-off in production at the end of the growing season was similar to the validation data. At the boreal forest site, the GPP phenologies generally agreed because both responded to the strong signal associated with minimum temperature. The midsummer MODIS GPP there was generally higher than the ground-based GPP. The differences between the MODIS GPP products and the ground-based GPPs were driven by differences in the timing of FPAR and the magnitude of light use efficiency as well as by differences in other inputs to the MODIS GPP algorithm—daily incident PAR, minimum temperature, and vapor pressure deficit. Ground-based scaling of GPP has the potential to improve the parameterization of light use efficiency in satellite-based GPP monitoring algorithms.     

Modeling of the carbon dioxide capture process system using machine intelligence approaches

Improving the efficiency of the carbon dioxide (CO₂) capture process requires a good understanding of the intricate relationships among parameters involved in the process. The objective of this paper is to study the relationships among the significant parameters impacting CO₂ production. An enhanced understanding of the intricate relationships among the process parameters supports prediction and optimization, thereby improving efficiency of the CO₂ capture process. Our modeling study used the 3-year operational data collected from the amine-based post combustion CO₂ capture process system at the International Test Centre (ITC) of CO₂ Capture located in Regina, Saskatchewan of Canada. This paper describes the data modeling process using the approaches of (1) neural network modeling combined with sensitivity analysis and (2) neuro-fuzzy modeling technique. The results from the two modeling processes were compared from the perspectives of predictive accuracy, inclusion of parameters, and support for explication of problem space. We conclude from the study that the neuro-fuzzy modeling technique was able to achieve higher accuracy in predicting the CO₂ production rate than the combined approach of neural network modeling and sensitivity analysis.     

Evaluating evapotranspiration and water-use efficiency of terrestrial ecosystems in the conterminous United States using MODIS and AmeriFlux data

In this study, we used the remotely-sensed data from the Moderate Resolution Imaging Spectrometer (MODIS), meteorological and eddy flux data and an artificial neural networks (ANNs) technique to develop a daily evapotranspiration (ET) product for the period of 2004-2005 for the conterminous U.S. We then estimated and analyzed the regional water-use efficiency (WUE) based on the developed ET and MODIS gross primary production (GPP) for the region. We first trained the ANNs to predict evapotranspiration fraction (EF) based on the data at 28 AmeriFlux sites between 2003 and 2005. Five remotely-sensed variables including land surface temperature (LST), normalized difference vegetation index (NDVI), normalized difference water index (NDWI), leaf area index (LAI) and photosynthetically active radiation (PAR) and ground-measured air temperature and wind velocity were used. The daily ET was calculated by multiplying net radiation flux derived from remote sensing products with EF. We then evaluated the model performance by comparing modeled ET with the data at 24 AmeriFlux sites in 2006. We found that the ANNs predicted daily ET well (R² = 0.52-0.86). The ANNs were applied to predict the spatial and temporal distributions of daily ET for the conterminous U.S. in 2004 and 2005. The ecosystem WUE for the conterminous U.S. from 2004 to 2005 was calculated using MODIS GPP products (MOD17) and the estimated ET. We found that all ecosystems' WUE-drought relationships showed a two-stage pattern. Specifically, WUE increased when the intensity of drought was moderate; WUE tended to decrease under severe drought. These findings are consistent with the observations that WUE does not monotonously increase in response to water stress. Our study suggests a new water-use efficiency mechanism should be considered in ecosystem modeling. In addition, this study provides a high spatial and temporal resolution ET dataset, an important product for climate change and hydrological cycling studies for the MODIS era.     

Laser marking system for light guide panel using design of experiment and web-based prototyping

A light guide panel (LGP) is an element of the liquid crystal display (LCD) back light unit (BLU), which is used for display devices. In this study, the laser marking process is applied to the fabrication of light guide panels as the new fabrication process. In order to obtain a light guide panel which has high luminance and uniformity, four principal parameters such as power, scanning speed, ratio of line gap, and number of line were selected. A web-based design tool was developed to generate patterns of light guide panel via the network, and the tool may assist the designer to develop various prototype patterns. Topcon-BM7 was used for luminance measurement of each specimen with 100 mm×100 mm area. By Taguchi method, optimized levels of each parameter were found, and luminance of 3523 cd/cm² and uniformity of 92% were achieved using the laser machined BLU.