Intra-Seasonal Mapping of CO2 Flux in Rangelands of ... - Springer Link

DOI: 10.1007/s00267-003-9156-8

Intra-Seasonal Mapping of CO2 Flux in Rangelands of Northern Kazakhstan at One-Kilometer Resolution BRUCE K. WYLIE* SAIC, Science and Application USGS EROS Data Center Sioux Falls, South Dakota 57198, USA

EMILIO LACA Department of Agronomy and Range Science University of California Davis, California, 95616-8515, USA

TAGIR G. GILMANOV Department of Biology and Microbiology South Dakota State University Brookings, South Dakota 57007-0595, USA

ABSTRACT / Algorithms that establish relationships between variables obtained through remote sensing and geographic information system (GIS) technologies are needed to allow the scaling up of site-specific CO2 flux measurements to regional levels. We obtained Bowen ratio– energy balance (BREB) flux tower measurements during the growing seasons of 1998 –2000 above a grassland steppe in Kazakhstan. These BREB data were analyzed using ecosystem light– curve equations to quantify 10-day CO2 fluxes associated with gross primary production (GPP) and total respiration (R). Remotely sensed, temporally smoothed normalized difference vegetation index (NDVIsm) and environmental variables were used to develop multiple regression models for the mapping of 10-day CO2 fluxes for the Kazakh steppe. Tenday GPP was estimated (R2 ⫽ 0.72) by day of year (DOY) and NDVIsm, and 10-day R was estimated (R2 ⫽ 0.48) with the estimated GPP and estimated 10-day photosynthetically active radiation (PAR). Regression tree analysis estimated 10-day PAR from latitude, NDVIsm, DOY, and precipitation (R2 ⫽ 0.81). Fivefold cross-validation indicated that these algorithms were reasonably robust. GPP, R, and resulting net ecosystem exchange (NEE) were mapped for the Kazakh steppe grassland every 10 days and summed to produce regional growing season estimates of GPP, R, and NEE. Estimates of 10-day NEE agreed well with BREB observations in 2000, showing a slight underestimation in the late summer. Growing season (May to October) mean NEE for Kazakh steppe grasslands was 1.27 Mg C/ha in 2000. Winter flux data were collected during the winter of 2001–2002 and are being analyzed to close the annual carbon budget for the Kazakh steppe.

DOUGLAS A. JOHNSON US Department of Agriculture Agricultural Research Service Forage and Range Research Lab Utah State University, Logan, Utah 84322-6300, USA NICANOR Z. SALIENDRA Forest, Range & Wildlife Sciences Utah State University Logan, Utah 84322-6300, USA KANAT AKSHALOV Barayev Kazakh Research Institute for Grain Farming Shortandy 474070 Kazakhstan LARRY L. TIESZEN US Geological Survey, Science and Application EROS Data Center Sioux Falls, South Dakota 57198, USA BRADLEY C. REED SAIC, Science and Application, USGS EROS Data Center Sioux Falls, South Dakota 57198, USA

Rangelands are not conducive to intensive agriculture and have lower primary production and potential

KEY WORDS: Carbon; Respiration; Gross primary production; Remote sensing; NDVI; Scaling up Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the US Government. Published online March 4, 2004. *Author to whom correspondence should be addressed, email: [email protected]

for CO2 sequestration than forests or cropping areas with improved agricultural practices. However, rangelands cover extensive areas, where high proportions of biomass occur below ground, and stored belowground carbon is relatively immune from losses due to fire. Given the expansiveness of rangeland systems and the fact that they often occur in arid and semiarid environments, scaling up of site-specific point measurements to regional or global rangeland CO2 fluxes can result in biased estimates (Biondini and others 1991, Rahman and others 2001). Algorithms that establish relation-

Environmental Management Vol. 33, Supplement 1, pp. S482–S491

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2004 Springer-Verlag New York, LLC

CO2 Fluxes in Kazakhstan

ships between variables obtained through remote sensing and geographic information system (GIS) technologies would allow scaling up of site-specific measurements to the regional level (Rahman and others 2001). Because of high costs and technical support requirements, not enough flux towers are available to adequately sample important ecosystems. As a result, it is necessary to develop simple, general, and robust algorithms to scale up CO2 fluxes from flux towers to the regional level. Algorithms developed from multipleyear flux tower observations are likely to be more robust and adapted to a wider range of environmental conditions than those derived from a single site or single years (Wylie and others 2003). Net ecosystem exchange (NEE) of CO2 is the difference between gross primary production (GPP) and total respiration (R). Spatial mapping of these flux components may not only improve mapping estimates by facilitating more functional and robust prediction algorithms, but also improve our understanding of carbon sources and sinks (Choudhury 2001, Valentini and others 2000). Remotely sensed vegetation indices and GPP are functionally related as stated by the theory of radiation use efficiency (RUE) (Moncrieff and others 1997). Changes in values of RUE depend on moisture stress, growth form, and phenological stage (Wilson and others 2001). The refinement of normalized difference vegetation index (NDVI)–GPP relationships should lead to the identification of important spatial and temporal environmental variables associated with variations in RUE. Respiration is an important determinant of carbon source and sink strengths (Valentini and others 2000). Developing algorithms for R from environmental datasets is difficult because of the various types of respiration (Cannel and Thornley 2000). Most algorithms use temperature to estimate R (Goulden and others 1996), but others include soil water content (Tufekcioglu and others 2001) and photosynthesis (Li and others 2002). The documentation of sequestered carbon in soil stocks requires detailed soil sampling capable of quantifying relatively large changes in soil carbon that occur across time spans of five years or more. Micrometeorological flux towers, however, allow the quantification of relatively small CO2 fluxes between terrestrial ecosystems and the atmosphere for both short- and long-term periods. Eddy covariance and Bowen ratio– energy balance (BREB) are two micrometeorological approaches for quantifying energy, water vapor, and CO2 fluxes. Eddy covariance directly measures fluxes, but it is susceptible to the lack of energy balance closure and requires sophisticated instrumentation and data processing. In contrast, BREB indirectly measures fluxes

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and uses relatively simple instrumentation and data processing techniques. Networks of flux towers have been used to document CO2 fluxes for various ecosystems and land use in Europe (Aubinet and others 2000) and the United States (Desjardins and others 1992, Svejcar and others 1997). Flux towers continuously measure net CO2 fluxes, which can be temporally partitioned into day and nighttime fluxes that are used as surrogates for GPP and R (Choudhury 2001, Wylie and others 2003). Some investigators have used nighttime respiration fluxes to estimate daytime respiration (Rahman and others 2001, Vourlitis and others 2000, Williams and others 2000), and others have used ecosystem light-curve equations to derive daytime respiration estimates from daytime observations (Gilmanov and others 2002, and this issue). These light– curve respiration algorithms were derived from 20-min BREB measurements and agreed well with nighttime observations. Ecosystem light– curve equations also provide a more process-based approach than the data gap filling procedures used by Falge and others (2001).

Study Area The field site was located 40 km north of Astana, Kazakhstan, and 20 km south of the Barayev Kazakh Research Institute for Grain Farming (BKRIGF) near the town of Shortandy. The study site is within a 200-ha pristine grassland steppe that was excluded from wheat cultivation during the Kruschev era. The site was inside the boundaries of the field experiment station of the BKRIGF, which has been the main research center for wheat breeding and production in Kazakhstan and the former Soviet Union. The coordinates for the site are 51°34'N. latitude, 71°16'E. longitude, and the elevation of the site is 430 m. The study site was representative of the vast area of the Kazakh steppe grasslands extending from the lowlands of the northern Black Sea through southern parts of the Russian Plain to the steppes of northern Kazakhstan (Figure 1). The annual average air temperature at this site is 16°C, and precipitation is 324 mm. The climate at the study site is characterized by a summer of maximum precipitation, with generally favorable growth conditions during the growing season. Precipitation is characterized by high inter-annual variability, with up to 50% of the years exhibiting drought in June and sometimes in July. Vegetation at the site is classified as a semiarid, grass-forb steppe dominated by Stipa capillata, Stipa lessingiana, Agropyron cristatum, Kochia prostrata, Medicago falcata, Festuca valesiaca, Salvia stepposa, Artemisia mar-

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B. K. Wylie and others

Figure 1. Location of Bowen ratio-energy balance (BREB) flux towers in Central Asia and the extent of the Kazakh steppe ecoregion.

shalliana, and Artemisia glauca. Soils at the site are characterized as a southern chernozem with a soil organic carbon reserve of 18.8 kg cm2 (Titlyanova 1988).

Methods The BREB system (model 023/CO2 Bowen Ratio, Campbell Scientific Inc., Logan, Utah, USA) was used to obtain continuous measurements of CO2 NEE at the study site during the 1998 –2000 growing seasons. The theory and operation of the BREB system have been described in detail by Dugas (1993), Dugas and others (1999), and Wylie and others (2003). The BREB technique is an indirect method for quantifying the turbulent exchange on the basis of the flux– gradient approach to vertical transport of an entity from or to a surface, assuming steady diffusion along a concentration gradient. It samples the air at two or more points along the vertical axis, uses simpler sensors than the eddy-covariance technique, and assumes that the eddy diffusivity for heat or energy is equivalent to the water vapor and CO2 eddy diffusivities (Moncrieff and others 1997). Some meteorologists pre-

fer to use the eddy-covariance technique now that it is commercially available because it directly measures the eddy diffusivity, vertical wind speed, and gas concentration as the air passes through a sampling point. The quality of eddy-covariance data, however, is influenced by its ability to close the surface energy balance. For example, recent flux measurements above a grassland by Twine and others (2000) indicated that CO2 fluxes measured by the eddy-covariance method were underestimated by an amount similar to water vapor fluxes when energy balance closure was not achieved. The BREB technique, for which the energy balance closure is forced, should have reliable measurements of CO2 fluxes, particularly in short-statured rangeland ecosystems such as the pristine grasslands of northern Kazakhstan. Ecosystem light-curves were used to quantify CO2 fluxes associated with GPP and R from 20-min flux tower data. Using these techniques, we obtained coefficients for estimating daytime respiration from daytime flux tower data. Good agreement was observed between respiration estimates derived from daytime coefficients applied to nighttime observations and actual nighttime respiration CO2 fluxes (Gilmanov and


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Figure 2. Determination of temporally smoothed NDVI (NDVIsm) with subtraction of start-of-season NDVI (NDVIsos).

Table 1. Input variables used to predict 10-day gross primary production (GPP), total respiration (R), and photosynthetically active radiation (PAR) Data set

Description

NDVIsm NDVIsos ppt TAIR DOY lat estGPP estPAR PAR_NDsm PAR_NDsos

SPOT VEGETATION NDVI temporally smoothed (Swets and others 1999) SPOT VEGETATION NDVI with start-of-season NDVI removed (Reed and others 1994) Mapped precipitation from NOAA Climate Prediction Center (Xie and Arkin 1997) Mapped air temperature from NOAA Climate Prediction Center (Xie and Arkin 1997) Day of year Latitude Mapped gross primary production from regression analysis Mapped photosynthetically active radiation from regression tree analysis PAR * NDVIsm PAR * NDVIsos

others this issue). These methods have also been used to fill gaps in flux tower data and estimate GPP and R in North American sagebrush steppe (Gilmanov 2002b, Gilmanov and others 2002), grasslands, and wheat fields (Gilmanov 2002a). Estimates of ecosystem GPP and R were then averaged across the same 10-day period that corresponded to the NDVI sampling. Maximum value composite NDVI data were obtained from SPOT VEGETATION. These 1-km data sets were temporally smoothed to remove NDVI anomalies associated with clouds and haze (Swets and others 1999). Phenological metrics (Reed and others 1994) were derived from the temporally smoothed NDVI (NDVIsm). The derived estimates for NDVI at the start of the growing season were subtracted from NDVIsm to remove or minimize background soil and dormant vegetation effects (NDVIsos; Figure 2).

Table 2. Regression tree for estimating 10-day photosynthetically active radiation (PAR) from temporally smoothed NDVI (NDVIsm), precipitation (ppt), and day of year (DOY) for growing seasons of 1998 –2000 if DoY ⬎ 239 then: PAR ⫽ f{DoY, lat, ppt, and NDVIsm} if NDVIsma ⬎ 116 and DoY ⱕ 239 then: PAR ⫽ f{NDVIsm, lat, DoY. And ppt} if NDVIsma ⱕ 116 and DoY ⱕ 127.5 then: PAR ⫽ 497.639 if NDVIsma ⱕ 116 and DoY ⬎ 127.5 and DoY ⱕ 239 then: PAR ⫽ f{lat, NDVIsm, DoY, ppt} a

NDVIsm values were scaled to 8-bit range using (NDVI ⫹ 1) * 100.

Table 1 shows the data layers for the growing seasons of 1998 –2000 that were used to predict 10-day photo-

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Table 3. Algorithms for estimating 10-day values of photosynthetically active radiation (PAR), gross primary production (GPP), and total respiration (R) Dependent variable

Independent variable(s)

Method

R2 training

R2 cross-validation

Mean abs. err. cross-validation

Units

PAR GPP R

lat, NDVIsm, DOY, ppt NDVI, DOY estGPP, estPAR

Regression tree Regression Regression

0.88 0.72 0.48

0.81 0.71 0.42

51.7 2.9 2.5

␮mol/m2/s g CO2/m2/day g CO2/m2/day

Figure 3. Comparison of observed and predicted 10-day photosynthetically active radiation (PAR; ␮mol/m2/sec), Shortandy, Kazakhstan, 1998 –2000, R2 ⫽ 0.78.

synthetically active radiation (PAR) and CO2 fluxes associated with GPP and R. All data layers represented time periods that corresponded to the 10-day NDVI composite periods except latitude (lat). The 10-day growing season data values of these variables were used for flux tower sites in Turkmenistan, Uzbekistan, and Kazakhstan to model PAR. The 10-day data values were pooled across the growing seasons of 1998 –2001 for the flux tower site at Shortandy, Kazakhstan, only for GPP and R models, because prediction of these was restricted to the Kazakh steppe. Compound variables (PAR_NDsm and PAR_NDsos) were used to approximate the light-use efficiency theory, with NDVI serving as a surrogate for the fraction of photosynthetically active radiation absorbed by vegetation (fPAR). Modeling approaches included multiple regression analysis and regression tree analysis. Regression tree analysis produces a series of stratified regression models that can deal with nonlinear relationships and highorder interaction (De’ath and Fabricius 2000). Fivefold cross-validation was used to estimate model performances on withheld data, which allowed the assessment of model robustness.

Results and Discussion The estimation of 10-day PAR was accomplished with four stratified regressions using regression tree analysis (Table 2). This series of regressions had a combined R2 of 0.81 when all observations were used to train the model (Table 3). The similar R2 of 0.81 from the crossvalidation indicated quite a robust prediction. Day of year (DOY) and NDVIsm were used to stratify the four regressions. The independent variables used to predict PAR were NDVIsm, precipitation (ppt), lat, and DOY. DOY and lat are important variables needed to model length of day, an important driver of PAR. Reductions of PAR associated with cloud cover appear to be crudely approximated with ppt. The phenological temporal trend of NDVIsm also loosely tracked the seasonal trends in day length. These model predictions of 10-day PAR agreed closely with actual tower observations (Figure 3). The slope and intercept of the regression were not significantly different than the 1:1 line, indicating an unbiased prediction of 10-day PAR. This regression tree was used to map 10-day PAR for the study area in 2000. The prediction of PAR could possibly be im-


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Figure 4. Comparison of observed and predicted 10-day gross primary production (GPP; g CO2/m2/day), Kazakhstan 2000 growing season, R2 ⫽ 0.87.

Figure 5. Comparison of observed and predicted 10-day total respiration (R; g CO2/ m2/day), Shortandy, Kazakhstan, 2000 growing season, R2 ⫽ 0.70.

proved by the addition of a length-of-day variable that can be calculated from DOY and latitude. Multiple regression analysis using NDVIsm and DOY performed better for the 10-day data for the 1998 –2000 growing seasons than the regression tree for the prediction of GPP. Consistent with the observation of De’ath and Fabricius (2000), if a strong overall linear relationship existed in the data, then linear regression outperformed regression trees. The R2 from all the data (0.72) was very similar to the R2 from the cross-validation (0.71), indicating a robust prediction from these variables (Table 3). The cross-validation mean absolute error was 2.9 g CO2/m2/day. The multiple regression, which used NDVIsos and DOY, had a higher R2 value

from all the data (0.74) than the NDVIsm and DOY regression, but it had a similar cross validation R2 (0.71). Because the additional processing involved with the calculation of NDVIsos did not significantly improve the R2 value, the simpler NDVIsm and DOY regression model was used to map GPP for the study area every 10 days. Annual regressions of GPP and NDVI indicated a significant hysteresis effect (Gilmanov and others this issue), probably associated with plant phenological development (Wilson and others 2001) or residual standing dead vegetation (Frank and Aase 1994). This effect was reduced by including DOY in the model developed from the multiple-year dataset (1998 –2000). Observed and predicted GPP showed good

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Figure 6. Comparison of observed and predicted 10-day net ecosystem exchange (NEE; g CO2/m2/day), Shortandy, Kazakhstan, 2000 growing season, R2 ⫽ 0.62.

Figure 7. Comparison of observed and predicted 10-day cumulative net ecosystem exchange (NEE) during the 2000 growing season in Kazakhstan.

agreement in the 2000 growing season (Figure 4). The 95% confidence intervals for both the slope and the intercept coefficients of the 2000 relationship overlapped those of the 1:1 line, indicating no bias in prediction. We estimated R with a multiple regression using the outputs of the GPP (estGPP) and PAR (estPAR) models (Table 3). Respiration is often modeled with air temperature or soil moisture; however, Li and others (2002) reported similar results with R in Mongolian grasslands being linked to live-canopy biomass. In the present study, we found low R2 values, but these included errors associated with the estimation of GPP and

PAR. The mean absolute error was comparable to that from the GPP model, indicating that a lower percentage of the variability was explained in the R model than in the GPP model. Similarity between the R2 value for the training data and the cross-validation indicates that the model is robust. The 95% confidence intervals for the estimated versus observed regression coefficients overlapped those of the 1:1 line for the 2000 growing season (Figure 5). R models may be improved by the addition of spatial data layers. The approximation of soil moisture from a spatially mapped water balance model also might improve predictions of R.


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Figure 8. Carbon fluxes (Mg C/ha) for growing season gross primary production (GPP), total respiration (R), and net ecosystem exchange (NEE) in the Kazakh steppe, May–October 2000.

Estimates of 10-day NEE were obtained by subtracting estimated 10-day R from estimated 10-day GPP (estGPP). Comparisons of the 10-day estimated NEE with observed 10-day NEE had an R2 value of 0.62 for the 2000 growing season (Figure 6). The 95% confidence intervals for the regression coefficients overlapped those of the 1:1 line. The modeling of GPP and R produced useful estimates of the temporal dynamics of GPP, R, and NEE. The accuracy of the NEE prediction integrated across a growing season or ultimately across an entire year is important. Estimates of cumulative NEE for the 2000 growing season tracked most of the seasonal variation for tower 10-day cumulative NEE (Figure 7). Cumulative NEE was slightly underestimated beginning in July. Regional maps derived from these algorithms should have similar or slightly lower prediction accuracies. Regional maps of 10-day GPP, R, and NEE were produced for the study area from May to October 2000.

These estimates were summed and converted from CO2 to C for growing season totals and masked to exclude areas of nonrangeland land cover and areas outside the Kazakh steppe ecoregion (Figure 8). Our analysis indicated that rangelands in this ecoregion had an average of 1.27 Mg C/ha sequestration of CO2 from the atmosphere during the 2000 growing season. These estimates are similar to other estimates of growing season C fluxes for rangelands (Gilmanov and others this issue). A better understanding of the drivers for carbon sources and sinks across time and space is important so that additional causal factors can be investigated. Anomalies in C flux may occur because of unanticipated conditions, such as a killing frost or a locust infestation. Our prediction methods allow the investigation of the temporal dynamics of any selected location within the Kazakh steppe (Figure 9). These growing season C estimates need to be combined with winter fluxes to close the annual carbon budget. Winter flux

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Figure 9. Growing season 2000 carbon flux temporal dynamics of gross primary production (GPP), total respiration (R), and cumulative net ecosystem exchange (NEE) for Shortandy in the eastern Kazakh steppe.

estimates obtained during the winter of 2001–2002 are currently being analyzed. The robustness of these predictions could be improved with additional flux towers to ensure that the C flux measurements are spatially representative. This would be useful for both algorithm development and validation. The USGS, in a collaborative effort with the US Department of Agriculture’s Agricultural Research Service (USDA-ARS) and the Global Livestock Collaborative Research Support Program, is applying these same data sets and approaches to CO2 flux data from other flux towers in the USDA-ARS Rangeland CO2 Flux Network (Svejcar and others 1997). This will allow model validation using withheld sites and years, and also will facilitate comparisons of CO2 fluxes among rangeland ecosystems.

bon sinks and sources. The inclusion of NDVI in our models allowed the spatial estimation of rangeland growing season C fluxes for the northern Kazakh steppe. Additional flux towers on different sites would improve these mapping efforts.

Acknowledgements This work was performed in part by the Raytheon Corporation under US Geological Survey Contract 1434-CR-97CN-40274. Additional support for this research came from the project, Livestock Development and Rangeland Conservation Tools for Central Asia; funded by USAID Global Livestock Collaborative Research Support Program, Office of Agriculture and Food Security, Global Bureau, US Agency for International Development grant 98-00036-00.

Conclusions Variations in PAR, R, GPP, and NEE for 10-day periods in the 1998 –2000 growing seasons in the Kazakh steppe were estimated using regression and regression tree analysis from BREB tower measurements. Fivefold cross-validation indicated that the prediction algorithms were relatively robust. Prediction accuracy may be improved with additional environmental variables. This approach allowed us to map the temporal dynamics of NEE and its components (GPP and R) for the Kazakh steppe. The temporal mapping of NEE, GPP, and R can improve our understanding of the underlying factors that influence the strengths of car-

References Aubinet, M., A. Grelle, A. Ibrom, U. Rannik, J. Moncrieff, T. Foken, A. S. Kowalski, P. H. Martin, P. Berbigier, C. Bernhofer, R. Clement, J. Elbers, A. Granier, T. Grunwald, K. Morgenstern, K. Pilegaard, C. Rebmann, W. Snijders, R. Valentini, and T. Vesala. 2000. Estimates of the annual net carbon and water exchange of forest: The EUROFLUX methodology. Advances in Ecological Research 30:113–175. Biondini, M. E., W. K. Lauenroth, and O. E. Sala. 1991. Correcting estimates of net primary production: are we overestimating plant production in rangelands?. Journal of Range Management 44:194 –198. Cannel, M. G. R., and J. H. M. Thornley. 2000. Modelling the


S491

components of plant respiration: some guiding principles. Annals of Botany 85:45–54.

boreal forest using hyperspectral indices from AVIRIS imagery. Journal of Geophysical Research 106:33579 –33591.

Choudhury, B. J. 2001. Estimating gross photosynthesis using satellite and ancillary data. Remote Sensing of the Environment 75:1–21.

Reed, B. C., J. F. Brown, D. VanderZee, T. L. Loveland, J. W. Merchant, and D. O. Ohlen. 1994. Measuring phenological variability from satellite imagery. Journal of Vegetation Science 5:703–714.

De’ath, G., and K. E. Fabricius. 2000. Classification and regression trees: a powerful yet simple technique for ecological data analysis. Ecology 81:3178 –3192. Desjardins, R. L., R. L. Hart, J. L. Macpherson, P. H. Schuepp, and S. B. Verma. 1992. Aircraft-based and tower-based fluxes of carbon-dioxide, latent, and sensible heat. Journal of Geophysical Research–Atmospheres 97:18477. Dugas, W. A. 1993. Micrometeorological and chamber measurements of CO2 flux from bare soil. Agricultural and Forest Meteorology 67:115–128. Dugas, W. A., M. L. Heuer, and H. S. Mayeux. 1999. Carbon dioxide fluxes over Bermuda grass, native prairie, and sorghum. Agricultural and Forest Meteorology 93:121–139. Falge, E., D. Baldocchi, R. Olsen, P. Anthoni, M. Aubinet, C. Bernhofer, G. Burba, R. Ceulemans, R. Clement, H. Dolman, A. Franier, P. Gross, T. Grunwald, D. Hollinger, N. Jensen, G. Katul, P. Keronen, A. Kowalski, C. T. Lai, B. E. Law, T. Meyers, J. Moncrieff, E. Moors, J. W. Munger, K. Pilegaard, U. Rannik, C. Rebmann, A. Suyker, J. Tenhunen, K. Tu, S. Verma, T. Vesala, K. Wilson, and S. Wofsy. 2001. Gap filling strategies for defensible annual sums of net ecosystem exchange. Agricultural and Forest Meteorology 107:43– 69. Frank, A. B., and J. K. Aase. 1994. Residue effects on radiometric reflectance measurements of Northern Great Plains rangelands. Remote Sensing of Environment 49:195–199. Gilmanov, T. G. 2002a. Gross primary production of the Schidler site, 1997 (Oklahoma) and Woodward site, 1997 (Oklahoma), and Shortandy site 2001 (Kazakhstan), in relation to remotely sensed vegetation indices. A report to the Raytheon Company, EROS Data Center, Sioux Falls, 20 May. Gilmanov, T. G. 2002b. Gross primary production of the Dubois site, 2000 (Idaho), and gross primary production and winter flux at the Shortandy site, 2001/2002 (Kazakhstan). A report to the Raytheon Company, EROS Data Center, Sioux Falls, 17 July. Gilmanov, T. G., D. A. Johnson, and N. Z. Saliendra. 2002. Growing season CO2 fluxes in a sagebrush-steppe ecosystem in Idaho: Bowen ratio/energy balance measurements and modeling. Basic and Applied Ecology 4:167–183. Goulden, M. L., J. W. Munger, S. M. Fan, B. C. Daube, and S. C. Wofsy. 1996. Measurements of carbon sequestration by long-term eddy covariance: Methods and a critical evaluation of accuracy. Global Change Biology 2:169 –182. Li, L. H., Y. Zhang, J. Yang, Z. D. Yan, X. Li, W. M. Bai, S. H. Song, X. G. Han, Q. B. Wang, and Q. S. Wang Chen. 2002. Correlations between plant biomass and soil respiration in a Leymus chinensis community in the Xilin River basin of Inner Mongolia. Acta Botanica Sinica 44:593–597. Moncrieff, J., R. Valentini, S. Greco, G. Seufert, and P. Ciccioli. 1997. Trace gas exchange over terrestrial ecosystems: Methods and perspectives in micrometeorology. Journal of Experimental Botany 48:1133–1142. Rahman, A. E., J. A. Gamon, D. A. Fuentes, D. A. Roberts, and D. Prentiss. 2001. Modeling spatially distributed ecosystem flux of

Svejcar, T., H. Mayeux, and R. Angell. 1997. The rangeland carbon dioxide flux project. Rangelands 19:16 –18. Swets, D. L., B. C. Reed, J. R. Rowland, and S. E. Marko. 1999. A weighted least-squares approach to temporal smoothing of NDVI. In 1999 ASPRS Annual Conference, From Image to Information, Portland, Oregon, 17–21 May 1999, Proceedings: Bethesda, Maryland, American Society for Photogrametry and Remote Sensing. CD-ROM, 1 disc. Titlyanova, A. A. 1988. Productivity of herbaceous ecosystems [Produktivnost’ travianykh ekosistem]. Pages 109 –127 in B. B. Ilyin Eds, Biologicheskaya produktivnost’ travyanykh ekosistem [Biological productivity of herbaceous ecosystems]. Nauka, Siberian Branch, Novosibirsk (in Russian). Tufekcioglu, A., J. W. Raich, T. M. Isenhart, and R. C. Schultz. 2001. Soil respiration with riparian buffers and adjacent crop fields. Plant and Soil 229:117–124. Twine, T. E., W. P. Kustas, J. M. Norman, D. R. Cook, P. R. Houser, T. P. Meyers, J. H. Prueger, P. J. Starks, and M. L. Wesely. 2000. Correcting eddy-covariance flux underestimates over a grassland. Agricultural and Forest Meteorology 103:279 –300. Valentini, R., G. Matteucci, A. J. Dolman, E. D. Schulze, C. Rebmann, E. J. Moors, A. Granier, P. Gross, N. O. Jensen, K. Pilegaard, A. Lindroth, A. Grelle, C. Bernhofer, T. Grunwald, M. Aubinet, R. Ceulemans, A. S. Kowalski, T. Vesala, U. Rannik, P. Berdigier, D. Loustau, J. Guomundsson, H. Thorgeirsson, A. Ibrom, K. Morgenstern, R. Clement, J. Moncrieff, L. Montagnani, S. Minerbi, and P. G. Jarvis. 2000. Respiration as the main determinant of carbon balance in European forests. Nature 404:861– 865. Vourlitis, G. L., W. C. Oechel, A. Hope, D. Stow, B. Boynton, J. Verfaillie Jr, R. Zulueta, and S. J. Hastings. 2000. Physiological models for scaling plot measurements of CO2 flux across an arctic tundra landscape. Ecological Applications 10:60 –72. Williams, M., W. Eugster, E. B. Rastetter, J. P. McFadden, and F. S. Chapin III. 2000. The controls on net ecosystem productivity along an arctic transect: a model comparison with flux measurements. Global Change Biology 6:116 –126. Wilson, K. B., D. D. Baldocchi, and P. J. Hanson. 2001. Leaf age affects the seasonal pattern of photosynthetic capacity and net ecosystem exchange of carbon in a deciduous forest. Plant Cell Environment 24:571–583. Wylie, B. K., D. A. Johnson, E. A. Laca, N. Z. Saliendra, T. G. Gilmanov, B. C. Reed, L. L. Tieszen, and B. B. Worstell. 2003. Calibration of remotely sensed, coarse-resolution NDVI to CO2 fluxes in a sagebrush-steppe ecosystem. Remote Sensing of Environment 85:243–255. Xie, P., and P. A. Arkin. 1997. A 17-year monthly analysis based on gauge observations, satellite estimations, and numerical model outputs. Bulletin of American Meteorological Society 78:2539 –2558.