comparative analysis of gpp products estimated … · comparative analysis of gpp products...

Pecora 16 “Global Priorities in Land Remote Sensing” October 23 – 27, 2005 * Sioux Falls, South Dakota

COMPARATIVE ANALYSIS OF GPP PRODUCTS ESTIMATED FROM AN EMPIRICAL MODEL AND MODIS

Li Zhang1

Bruce Wylie1 Thomas Loveland2

Lei Ji3 1SAIC, USGS Earth Resources Observation and Science Center,

Sioux Falls, SD 57198, United States 2 USGS Earth Resources Observation and Science Center,

Sioux Falls, SD 57198, United States 3Cooperative Institute for Research in the Atmosphere,

Colorado State University, Fort Collins, CO 80523, United States [email protected] [email protected]

[email protected] [email protected]

ABSTRACT

Carbon-cycle models have uncertainties associated with data inputs, parameters, and model algorithms. The prerequisite for an applicable model is that it should perform at an acceptable level of accuracy and uncertainties should be documented. In this study, we validated the gross primary productivity (GPP) data from a piecewise regression (PWR) model and the MODIS GPP model at five grassland flux towers in the Northern Great Plains. The results showed a good agreement of GPP values (agreement coefficient d = 0.88–0.98) among PWR, MODIS, and tower measurements at Fort Peck, Mandan, and Cheyenne sites; but MODIS GPP did not agree well (d = 0.62–0.79) with tower measurements at Miles City and Lethbridge sites. Additionally, we compared PWR GPP and MODIS GPP for grasslands in the entire study area. We found that the PWR GPP was lower than or similar to the MODIS GPP in the east and higher in the west and south. We explored possible factors that may cause the GPP difference in spatial distribution between the two models.

INTRODUCTION

The mean global surface temperature has increased by 0.3 to 0.6°C in the last 100 years (IPCC, 1992), due

largely to human-induced emissions of greenhouse gases. The atmospheric carbon dioxide (CO2) concentration has risen by 31% since 1750, which likely affected climate through changes in temperature and precipitation (IPCC, 2001). Concern has grown that global warming and associated increasing CO2 concentrations may influence human, biological, geochemical, and atmospheric processes. This concern has led to international negotiations on carbon emissions, which require a better understanding of the carbon fluxes and environmental factors that determine the magnitude of fluxes and the mutual feedback of terrestrial ecosystems and climate (Gilmanov et al., 2005).

Numerous techniques and studies have investigated the CO2 exchange between biosphere and atmosphere at regional, continental, and global scales. In the mid-1990s, the International Geosphere-Biosphere Programme (IGBP) proposed a global network of flux towers to monitor long-term and continuous CO2 fluxes from diverse biomes and climate regions (Baldocchi et al., 2001). At present, more than 300 flux towers are registered on the FLUXNET (http://www.fluxnet.ornl.gov/fluxnet/index.cfm). Two types of micrometeorological flux towers, eddy covariance and Bowen ratio-energy balance (BREB), provide detailed and localized quantification of carbon fluxes. Current flux towers, however, do not sufficiently represent the world’s diverse terrestrial ecosystems. In addition to the gaps in space, temporal gaps in measurement records are inevitable due to system failures or data rejection. Simulation models are needed to fill the measurement gaps.

Since the early 1980s, vegetation index data sets have been produced from the Advanced Very High Resolution Radiometer (AVHRR) aboard the National Oceanic and Atmospheric Administration (NOAA) satellites. The


vegetation index data have provided a new opportunity for globally studying and monitoring vegetation conditions in terrestrial ecosystems at 1-km resolution (Tucker, 1996). Models for predicting vegetation productivity have been developed that combine CO2 flux measurements with remote sensing observations (Potter et al., 1993; Wylie et al., 2004; Turner et al., 2004a; Xiao et al., 2004 and 2005). These models range in complexity from “data driven” empirical models to “process-based” biogeochemical models. Empirical models have been developed to relate GPP, NPP, and net ecosystem exchange (NEE) to variables that influence plant productivity. They are simply constructed based on ground-based observations, and easily used to produce spatial and temporal data sets at regional scale. Although these models imply no direct cause-and-effect relationship, they reveal valuable insights into the conditions under which plant productivity is strongly influenced. The biogeochemical model simulates the dynamics of carbon, nutrient, and water cycles in terrestrial ecosystems, based on vegetation structure, soil, and climate characteristics and considers how these cycles are influenced by environmental conditions.

All models have uncertainties associated with errors in input data, model parameters, and model algorithms (Reich et al., 1999; Turner et al., 2004b; Xiao et al., 2004). The prerequisite for an applicable model is that it should perform at an acceptable level of accuracy and uncertainties should be documented. A global monitoring network integrating the flux tower measurements with ecosystem modeling and NASA Earth Observing System (EOS) satellite data could improve monitoring and understanding of global carbon fluxes and provides consistent checks and validation to each others (Running et al., 1999). Predictive models require evaluation of the performance with data sets from ground-based measurements (Amthor et al., 2001; Kramer et al., 2002; Veroustraete et al., 2002) and model intercomparison (VEMAP Members, 1995). The flux tower measurements are the most accurate variables for model evaluation. Goulden et al. (1996) stated that the long-term eddy covariance is well suited for quantifying the carbon exchange and for developing and testing mechanistic models. But the validation of the estimated NPP and GPP is limited by the lack of extensive observations over large areas. Model intercomparison has been used as an alternative method for the direct validation where ground observations are lacking. Model intercomparison could identify the weaknesses and inconsistencies of models. However, it cannot absolutely validate whether a model is an accurate representation of the real world (Cramer et al., 1999).

Grassland covers nearly one-fifth of the global terrestrial surface (Eswaran et al., 1993). Temperate grasslands are important to the global carbon cycle (Frank, 2002). The carbon and water flux data for the grassland ecosystems are lacking, and grassland contributions to local and regional water and carbon fluxes remain uncertain (Novick et al., 2004). In this study, we validated a piecewise regression (PWR) model that estimates grassland GPP over the growing season (April–October). We compared the GPP from the regional PWR model and MODIS GPP with ground observations at five grassland flux tower sites in the Northern Great Plains. The PWR GPP was also compared with MODIS GPP (Collection 4.5) during 2000 and 2001 in the grasslands of two ecoregions (Omernik, 1987) in the Northern Great Plains. We explored factors that contribute to the difference between the PWR GPP and MODIS GPP.

MATERIALS AND METHODS

Study Area Grassland constitutes the major land cover in the Northern Great Plains. The five grassland flux towers are

distributed throughout the Northern Great Plains (Figure 1). They are part of the Ameriflux network (Lethbridge, Canada, 2000–2001; Fort Peck, MT, 2000) and United States Department of Agriculture (USDA), Agricultural Research Service, Agriflux network (Mandan, ND, 2000–2001; Miles City, MT, 2000–2001; and Cheyenne, WY, 1998) (Table 1).

Model Descriptions The empirical piecewise regression (PWR) model. The PWR model was developed to estimate grassland and shrubland GPP in the Northern Great Plains and Northern Kazakhstan (Wylie et al., 2003 and 2004). This model uses a piecewise regression technique to spatially scale up the localized flux tower measurements across a landscape or region with similar land-cover types at 1-km resolution. The aim for scaling GPP is to maximize the use of available site-specific flux tower data, minimize the number of input spatial variables, and produce accurate grassland fluxes estimates.


The independent inputs consisted of 10-day SPOT VEGETATION Normalized Difference Vegetation Index (NDVI), 10-day climatological data, phenological metrics (Reed et al., 1994), and proportions of C4 grasses. The phenological metrics were calculated from time-series SPOT VEGETATION NDVI data, which are important surrogates of ecosystem function. The metrics included the day of onset of season (sost), NDVI on the date of onset of season (sosn), and time integrated NDVI (TIN). Precipitation and air temperature were acquired from the NOAA Climate Prediction Center, and Photosynthetically Active Radiation (PAR) was obtained from NOAA National Environmental Satellite, Data and Information

Service (NESDIS) (http://www.atmos.umd.edu/~srb/gcip/). The PWR model derived the empirical relationships between the inputs and tower-measured GPP from the five flux towers and multiple years at the 10-day time interval and produced a series of stratified piecewise regression equations between the inputs and GPP. Based on the stratification criteria, the appropriate multiple regression models were applied through time and space to map 10-day GPP across the study area.

Table 1. Descriptions of the flux tower sites (Gilmanov et al., 2005)

Sites Years

Latitude, Longitude

Elevation (m)

Annual Precipitation

(mm)

Mean temperature January/July (°C) Sensor Principal

Investigator

Lethbridge 2000 – 2001

49°42′N 112°56′W 960 378 –8.6 / 18.0 Eddy-

covariance Flanagan, L. B.

Fort Peck 2000

48°18′N 105°06′W 634 310 –11.9 / 18.0 Eddy-

covariance Meyers, T. P.

Mandan 2000 – 2001

46°46′N 100°55′W 518 404 –8.7 / 23.5 BREB Frank, A. B.

Miles City 2000 – 2001

46°18′N 105°58′W 719 343 –12.2 / 21.2 BREB Haferkamp, M. R.

Cheyenne 1998

41°11′N 104°54′W

1910 397 –2.5 / 17.5 BREB Morgan, J. A.

MODIS GPP model. MODIS, which provides a quantitative and dynamic measurement of spatial and temporal vegetable productivity, has estimated 8-day summation of GPP and annual NPP at 1-km spatial resolution since March 2000 (Justice et al., 1998). The MODIS GPP product (Collection 4.5) is freely available from Numerical Terradynamic Simulation Group, School of Forestry, University of Montana (http://www.ntsg.umt.edu/). The MODIS GPP algorithm employs a light use efficiency (LUE) approach. The major inputs to the MODIS GPP algorithm include MODIS land cover products (MOD12Q1), MODIS Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) (MOD15A2), meteorological data from Data Assimilation Office (DAO), and parameterization of the Biome Parameter Look-up Table (BPLUT) (Heinsch et al., 2003; Turner et al., 2003; Running et al., 2004).

Figure 1. Grassland flux towers in the Northern Great Plains


Data Comparison The 10-day averaged GPP from the PWR model was acquired in Albers Equal Area projection. MODIS GPP data from tiles H10V04 and H11V04 were reprojected from their Sinusoidal (SIN) projection to Albers Equal Area projection, and the GPP_1km data layer for the Northern Great Plains was extracted using the MODIS Reprojection Tool (MRT) (http://lpdaac.usgs.gov/landdaac/tools/modis/index.asp). Both GPP outputs were converted to average daily GPP in g C m -2 day-1. GPP comparison at flux tower sites. At each tower, we extracted GPP values from the 1-km PWR GPP for each 10-day interval and MODIS GPP for each 8-day interval during the growing season. We compared PWR GPP and MODIS GPP with tower-measured GPP, based on the statistical analysis and the ability of the models to capture the seasonal dynamics of vegetation productivity. Seasonal total GPP comparison for entire study area. This analysis was restricted to grassland in the Northern Great Plains using MODIS land cover (MOD12Q1) Type 2 data as a mask layer. The PWR GPP and MODIS GPP were temporally integrated to total GPP for the growing season in 2000 and 2001. We generated a difference GPP map between the two GPP products.

Statistical analysis. We used four different measures to evaluate the data agreement: the coefficient of determination (R2), root mean square difference (RMSD), and two indices of agreement. A widely used index in measuring data agreement is R2, which measures proportion of variation in the dependent variable explained by the regression model. But it does not measure the actual difference between different data sets. RMSD measures actual difference between two different data sets. But RMSD is not standardized and not bounded, and is dependent on data units. Two other indices of agreement were devised by Willmott for model validation (Willmott, 1981 and 1982) and by Ji and Gallo (2005) for data comparison.

Willmott’s index of agreement (d) is defined as

( )

( )∑

∑

=

=

−+−

−−= n

iii

n

iii

XYXX

YXd

1

2

1

2

1 (1)

where Xi is the observed value, Yi is the estimated value, and X is the mean of observed values.

Ji and Gallo’s agreement coefficient (AC) is defined as

( )

( )( )YYYXXXYX

YXAC

i

n

ii

n

iii

−+−−+−

−−=

∑

∑

=

=

1

1

2

1 (2)

where Xi and Yi are the estimated (or observed) values of two data sets, and X and Y are the mean values of X and Y, respectively. Both Willmott’s index of agreement (d) and Ji and Gallo’s agreement coefficient (AC) are non-dimensional measures that are bounded below by 0 and above by 1. Therefore, d or AC is 1 when two data sets are in perfect agreement. The difference between the two indices of agreement is that d is asymmetric and AC is symmetric. The asymmetric index measures agreement between estimates and observations (or reference), while the symmetric index measures agreement between estimates or between observations. Willmott’s d is used to compare model estimates with reference data. The reference data, flux tower measurements in this study, are assumed to be error-free. Thus Willmott’s d measures the magnitude of the agreement between the model estimates and the true values. Ji and Gallo’s AC is suitable when the two data sets are both subject to measurement error. That is, neither data set can be treated as the reference data.

Additionally, both Willmott’s d and Ji and Gallo’s AC are capable of measuring unsystematic and systematic differences between the two data sets. Unsystematic difference is associated with the random error between the points and regression line built from the two data sets. Systematic difference can be adjusted by using the regression function. The ratio of unsystematic mean square difference (MSDu) to total mean square difference (MSD) is used to


measure proportion of unsystematic difference; the ratio of systematic mean square difference (MSDs) to MSD is used to measure proportion of systematic difference. In our study, we adopted Willmott’s index of agreement (d) to compare the PWR GPP and MODIS GPP with tower measurements and used Ji and Gallo’s agreement coefficient (AC) to compare the PWR GPP with MODIS GPP.

RESULTS AND DISCUSSION

GPP Comparison at the Flux Tower Sites The PWR model used the flux tower data sets to develop the empirical relationship. But the MODIS GPP

probably did not use the Agriflux tower data sets to calibrate and parameterize the algorithm. Therefore, we expected that the PWR model would better fit the flux tower data. Cross validation analysis indicated that the PWR modeling approach was robust, with Mandan and Cheyenne being influential sites. The 10-day GPP from the PWR model and the 8-day MODIS GPP were compared with the flux tower measured GPP (Figures 2 and 3). The quantitative analysis using Willmott’s index of agreement between the PWR and MODIS GPP estimates are shown in Tables 2 and 3. The seasonal dynamics of the estimated PWR GPP and MODIS GPP were compared with the tower-observed GPP over the growing season (Figure 4).

At Fort Peck and Mandan, there was a good agreement for both the MODIS and PWR GPP predictions, and most of the differences were unsystematic (Tables 2 and 3). Both MODIS and PWR tracked the seasonal dynamics of GPP well. But MODIS underestimated GPP from June to July at Fort Peck (Figure 4).

At Miles City, the PWR model tended to agree better with tower observations than MODIS did. MODIS GPP lagged during the growing season in 2000 but captured the seasonal dynamics well in 2001. MODIS GPP tended to overestimate GPP in 2000 but underestimate GPP in 2001. Only PWR predictions were available at Cheyenne in 1998, because MODIS was not available until 2000. PWR GPP captured the onset and end of growing season well but underestimated GPP during June and July at Cheyenne.

Figure 2. Comparison between observed GPP and PWR GPP

during the growing season at flux towers (10-day interval)

At Lethbridge, PWR GPP was not available due to the lack of mapped meteorological data in Canada. MODIS GPP failed to capture seasonal dynamics, and the agreement was poor, and the major difference was systematic (Table 3). In 2000, MODIS GPP showed later initiation and later cessation of the growing season than the tower


measurement (Figure 4). In 2001, MODIS GPP captured the onset of the growing season, but it failed to capture the peak and the end of the growing season. In both years, tower-measured GPP dropped sharply after mid-June and was not reflected by MODIS GPP until the end of July. MODIS GPP predicted a much lower maximum GPP than was measured at the tower.

Table 2. Agreement analysis of observed GPP and PWR GPP

The deviation of the model estimates from the flux tower measurements may be caused by errors in pixel

registration, flux tower measurements, and model estimates. The GPP difference was probably related to mismatch in scale when comparing localized tower GPP with the 1-km PWR and MODIS GPP. Tower GPP represents a small, unfixed footprint (<1km2), that might change in size and shape due to wind speed, wind direction, surface roughness, and atmospheric stability. The 1-km pixel of PWR GPP and MODIS GPP could not exactly overlay the tower footprint. When we extracted pixel values from the PWR and MODIS GPP maps using the coordinates of the flux towers, the pixel whose center is nearest to the flux tower center was extracted. However, when the flux towers are located in large (greater than 3 km) and relatively uniform area with similar land cover, this effect is small.

Parameters Fort Peck 2000

Mandan 2000

Mandan 2001

Miles City 2000

Miles City 2001

Cheyenne 1998

n 21 21 20 19 21 18 Mean of Tower GPP (T ) 1.853 1.899 2.250 1.414 1.571 2.926 Mean of PWR GPP ( P ) 1.729 2.211 2.414 1.308 1.562 2.812

Difference of Mean ( TP − ) –0.124 0.312 0.164 –0.106 –0.009 –0.114 R2 0.946 0.882 0.869 0.960 0.782 0.713 d 0.984 0.952 0.958 0.975 0.933 0.892

RMSD (g C m-2 d-1) 0.322 0.554 0.481 0.293 0.426 1.143 MPDs/MSD (%) 16 43 26 68 27 49 MPDu/MSD (%) 84 57 74 32 73 51

Figure 3. Comparison between observed GPP and MODIS GPP during the growing season at flux towers (8-day interval)


Tower-measured GPP is another source of error. Flux towers directly measure NEE, while daily GPP is the difference between NEE and ecosystem respiration (Re) during the daylight period (Turner et al., 2003; Gilmanov et al., 2003; Falge et al., 2002). The Re estimate may come with uncertainties (Goulden et al., 1996; Turner et al., 2003) that could propagate to the GPP estimate. Additional errors could originate from model estimates.

Table 3. Agreement analysis of observed GPP and MODIS GPP

Figure 4. Seasonal dynamics of GPP during the growing season at flux towers

Comparison of the Spatial Pattern of the Seasonal GPP The total GPP for the growing season from both the PWR and MODIS models showed consistently higher GPP

in the east and south, but the PWR GPP was lower than MODIS GPP in the east and higher in the west and south (Figure 5). The spatial pattern reflected the inconsistency between the two GPP models, which may be attributed to differences in the underlying model structure, model algorithms, and model inputs. An inspection of the PWR model and MODIS GPP algorithms suggested that several variables might contribute to the GPP difference in spatial distribution.

We classified the GPP difference map into three categories: 1) PWR GPP was much higher than MODIS GPP; 2) PWR GPP was close to MODIS GPP; and 3) PWR GPP was much lower than MODIS GPP. The factors considered included the inputs of the PWR model: SPOT VEGETATION NDVI, total precipitation (April–June, July–October, and April–October), PAR, temperature, TIN, sost, and sosn. Other factors included percentage of C4 grasses, clay

Fort Peck 2000

Mandan 2000

Mandan 2001

Miles City 2000

Miles City 2001

Lethbridge 2000

Lethbridge 2001

n 27 26 23 23 26 27 25 Mean of Tower GPP (T ) 1.844 1.956 2.494 1.35 1.612 1.268 1.506

Mean of MODIS GPP ( M ) 1.486 2.397 2.765 1.88 0.978 0.990 1.547 Difference of Mean ( TM − ) –0.358 0.441 0.271 0.53 –0.634 –0.278 0.041

R2 0.8 0.8 0.653 0.508 0.612 0.225 0.563 d 0.913 0.92 0.875 0.785 0.706 0.622 0.763

RMSD (g C m-2 d-1) 0.68 0.78 0.82 0.918 0.90 0.942 1.178 MPDs/MSD (%) 52 33 11 48 88 67 79 MPDu/MSD (%) 48 67 89 52 12 33 21


content in the surface layer derived from the State Soil Geographic (STATSGO) Data Base (http://www.ncgc.nrcs.usda.gov/products/datasets/statsgo/), and percentage of crop derived from the 30m National Land Cover Dataset (NLCD, 1992). Using the decision tree analysis package named C5 (http://www.rulequest.com/ see5-info.html), we identified the variables that would best explain the distribution of the GPP difference map, based on the frequency and percentage of utilization of each spatial variable. Multivariate analysis using the decision tree revealed that the factors from the PWR model that accounted for the GPP difference were: 1) total precipitation from April to June; 2) temperature; 3) NDVI; and 4) sost and sosn. The sources of the PWR model inputs are different from those of MODIS GPP. The difference between those inputs of the two models could propagate to GPP difference between the two models. Our result showed that the precipitation from April to June was critical to grassland GPP in the Northern Great Plains, which agrees with Smart et al. (2005).

Figure 5. Spatial pattern of the seasonal total GPP in the Northern Great Plains Soil is the primary storage for water used by plants. Soil moisture and soil texture control vegetation growth

(Churkina et al., 1999). Williams et al. (1997) observed that soil water availability is a stronger constraint on GPP than vapor pressure deficit (VPD). Surface clay content provides a measure of water holding capacity. Neither the PWR model nor the MODIS model takes into account soil water availability, which is especially important in dry years. Summer water stress is particularly severe during the late summer in the Northern Great Plains grasslands. The MODIS GPP algorithm is dependent on VPD and the LUE model does not simulate the water balance, which limits its ability to detect drought stress (Turner et al., 2005). The DAO meteorology, used by the MODIS GPP model, may underestimate local VPD under dry conditions, resulting in the local overestimation of MODIS GPP (Heinsch et al., 2005).

At Miles City, precipitation prior to July 2000 was lower than the average precipitation but abundant in June and July 2001 (Heitschmidt, 2005).The intense drought in the spring and early summer of 2000 reduced soil water content and subsequently reduced total production by 20% to 40% (Heitschmidt, 2005). In the dry year 2000 at Miles City, MODIS GPP was probably unable to reflect the influence of low water content in soil, which led to MODIS GPP higher than tower GPP. Also, MODIS GPP had a later cessation of the end of growing season than tower observations. In 2001, the reduced water limitation on plant growth resulted in higher productivity and GPP. Yet this was not reflected by MODIS GPP, leading to a seasonal underestimate of GPP at Miles City; although MODIS captured the growing season dynamics of GPP well. At Lethbridge, the drought in 2000 and 2001 (Flanagan


et al., 2002 and 2005) caused an early end of the growing season. Soil water was largely depleted by early July. The global MODIS algorithm did not reflect these changes in soil moisture and resulted in MODIS GPP having an artificially prolonged growing season at Lethbridge.

In the Great Plains, grassland productivity is higher in the south than in the north, partially due to the high proportion of C4 species in the south (Tieszen et al., 1997). The PWR model used the percentage of C4 species as the independent variable in the regression training algorithm. The MODIS GPP algorithm perhaps does not account for differences between C3 and C4 grasses in the Northern Great Plains. The MODIS sensor was designed for global land surface monitoring and may have limitations at the regional scale. MODIS land cover data was consistent with the site-specific vegetation information. However, for the entire ecoregion, MODIS GPP estimates relied upon land cover data whose classification scheme could not differentiate sub-pixel crop components in each 1-km2 grassland pixel. Bradford et al. (2005) reported a positive relationship between crop intensity and productivity. The percentage of crop derived from the NLCD database was compared with MODIS land cover. The density of cropland is unevenly distributed in the Northern Great Plains grasslands, which may be a reason for the disparity between PWR GPP and MODIS GPP. All of the potential factors cannot independently account for the spatial pattern of the GPP difference map. They may interact with each other. Further research will be needed to investigate how those factors work together. Statistical Analysis of Seasonal PWR GPP and MODIS GPP

We randomly sampled 3000 points from the Northern Great Plains grasslands in the PWR GPP map and MODIS GPP map for the quantitatively statistical analysis. Over the growing season, the total PWR GPP agreed well with the total MODIS GPP ( R2 = 0.50, AC = 0.55 in 2000, and R2 = 0.59, AC = 0.60 in 2001). The total MODIS GPP of the growing season was 353 and 375 g C m-2 in 2000 and 2001, respectively. The total PWR GPP of the growing season was 402 and 431 g C m-2 for the two years, respectively. MODIS GPP was 49 g C m-2 lower than PWR GPP on average with an RMSD of 88 g C m-2 in 2000, and 56 g C m-2 lower than PWR GPP on average with an RMSD of 88 g C m-2 in 2001.

CONCLUSIONS

The results showed a good agreement of GPP among PWR, MODIS, and tower measurements at Fort Peck, Mandan, and Cheyenne sites; but MODIS GPP does not agree well with the tower measurements at Miles City and Lethbridge sites (d = 0.62–0.79). Differences between MODIS GPP and tower measurements at the Miles City and Lethbridge implied that the MODIS GPP failed to capture the seasonal dynamics of the growing season and locally overestimated or underestimated the tower-observed GPP at the two sites. Those discrepancies may be attributed to three errors: 1) pixel misregistration, 2) flux tower measurements, and 3) model estimates. Moreover, in the Northern Great Plains, the regional PWR GPP was higher than global MODIS GPP in the west and south and lower than or similar with MODIS GPP in the east. The global MODIS GPP may have three limitations for grassland ecosystems in the Northern Great Plains: 1) failure to capture the soil water content in dry years, 2) spectral interference from the sub-pixel crop components in MODIS grassland pixels because of the 1-km resolution, and 3) inability to differentiate C3 and C4 grasses.

The results also showed that the PWR GPP captured the spatial patterns and seasonal dynamics of GPP in the grassland ecosystem. It demonstrates that the PWR model has the potential to produce regional GPP maps. The PWR model, however, is trained with sampled data that can only extrapolate GPP to regions with similar ecological and climatic characteristics. To improve the PWR model for GPP mapping and to enhance its applicability to other

Figure 6. Statistical comparison of PWR GPP and MODIS GPP


vegetation types and ecosystems, we recommend: 1) using MODIS 250m and daily surface reflectance data, and 2) using soil texture and available water capacity data as part of the independent inputs of PWR model.

ACKNOWLEDGEMENTS This research was supported by the U.S. Geological Survey (USGS) Earth Surface Dynamics, Land Remote

Sensing, and Geographic Analysis and Monitoring Programs. We would like to thank Eugene Fosnight, Larry Tieszen, Zhengxi Tan, and Norman Bliss for valuable comments and suggestions on the manuscript.

.

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