using normalized difference vegetation index to estimate carbon fluxes from small rotationally...

Crop E

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972 Agronomy Journa l • Volume 103 , I s sue 4 • 2011

Using Normalized Difference Vegetation Index to Estimate Carbon Fluxes from Small Rotationally Grazed Pastures

R. H. Skinner,* B. K. Wylie, and T. G. Gilmanov

Published in Agron. J. 103:972–979 (2011)Posted online 18 Apr 2011doi:10.2134/agronj2010.0495Copyright © 2011 by the American Society of Agronomy, 5585 Guilford Road, Madison, WI 53711. All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher.

Properly managed agricultural systems have the ability to greatly reduce net greenhouse gas emissions from

farms, primarily by sequestering C in the soil profi le (Rotz et al., 2009). Decades of plowing have depleted organic C stocks in many agricultural soils and it has been estimated that most agri-cultural soils in the Midwest, for example, have lost 30 to 50% of their original C pool (Lal, 2002). Conversion of plowed fi elds to pasture has the potential to reverse this process, recapturing organic matter that was lost under more intensive cropping sys-tems (Schnabel et al., 2001; Soussana et al., 2004). To properly assess the C sequestration potential of perennial pastures robust measurement techniques need to be developed and employed.

Th e use of eddy covariance to estimate C fl uxes has become increasingly common over the last decade. Currently, nearly 500 eddy covariance towers are monitoring C fl uxes worldwide (FLUXNET, 2010) which contribute important information on not only the magnitude and variation of fl uxes into and out of global ecosystems, but also on the environmental and bio-logical factors controlling those fl uxes (e.g., see Flanagan et al., 2002; Jacobs et al., 2007; Ma et al., 2007; Reichstein et al., 2007; Svejcar et al., 2008). In general, grazing increases C sequestration compared with cutting and moderate N fertilizer use increases

sequestration compared with highly fertilized or nutrient poor grasslands (Soussana et al., 2004). Aft er several years following conversion from cropland, pasture soil C eventually reaches a new equilibrium so that mature pastures no longer serve as C sinks (Skinner, 2008). In addition, almost any site can be either a sink or source depending on yearly weather patterns (Svejcar et al., 2008).

An important priority for soil C research in relation to climate change is the development of techniques for measuring C pools and fl uxes at regional scales (Lal and Follett, 2009). Field-based measurements of pasture C sequestration potential must be combined with modeling, remote sensing, or similar procedures to apply results from research plots to whole-farm, watershed, or regional scales. Th e combination of remote sensing and modeling has the potential to predict these fl uxes and map them spatially and temporally (Wylie et al., 2003). Th rough the use of models, direct measurements of net ecosys-tem exchange by eddy covariance towers have been partitioned between GPP and ecosystem respiration. Th is information has then been combined with remotely sensed NDVI data from satellite images to create regional C fl ux estimates for the sagebrush-steppe ecosystem of northeastern Idaho (Wylie et al., 2003), the northern Great Plains (Gilmanov et al., 2005; Zhang et al., 2010), and for the conterminous United States (Xiao et al., 2008). Th ese estimates have used MODIS images with spatial resolutions from 250 to 1 km. While appropriate for large land areas, use of MODIS data could become prob-lematic when applied to northeastern U.S. pastures which are oft en smaller than the MODIS resolution limits.

Ground-based spectral imaging devises have also been used to calculate vegetation indices to assess forage biomass and its’ spatial variability within grazed pastures (Flynn et al., 2008).

ABSTRACTSatellite-based normalized diff erence vegetation index (NDVI) data have been extensively used for estimating gross primary produc-tivity (GPP) and yield of grazing lands throughout the world. However, the usefulness of satellite-based images for monitoring rota-tionally-grazed pastures in the northeastern United States might be limited because paddock size is oft en smaller than the resolution limits of the satellite image. Th is research compared NDVI data from satellites with data obtained using a ground-based system capa-ble of fi ne-scale (submeter) NDVI measurements. Gross primary productivity was measured by eddy covariance on two pastures in central Pennsylvania from 2003 to 2008. Weekly 250-m resolution satellite NDVI estimates were also obtained for each pasture from the moderate resolution imaging spectroradiometer (MODIS) sensor. Ground-based NDVI data were periodically collected in 2006, 2007, and 2008 from one of the two pastures. Multiple-regression and regression-tree estimates of GPP, based primarily on MODIS 7-d NDVI and on-site measurements of photosynthetically active radiation (PAR), were generally able to predict growing-season GPP to within an average of 3% of measured values. Th e exception was drought years when estimated and measured GPP diff ered from each other by 11 to 13%. Ground-based measurements improved the ability of vegetation indices to capture short-term grazing manage-ment eff ects on GPP. However, the eMODIS product appeared to be adequate for regional GPP estimates where total growing-season GPP across a wide area would be of greater interest than short-term management-induced changes in GPP at individual sites.

R.H. Skinner, USDA-ARS, Pasture Systems and Watershed Management Research Unit, University Park, PA 16802; B.K. Wylie, USGS EROS, Sioux Falls, SD 57198; T.G. Gilmanov, Dep. Biology & Microbiology, South Dakota State University, Brookings, SD, 57007. Received 2 Dec. 2010. *Corresponding author ([email protected]).

Abbreviations: GPP, gross primary productivity; MODIS, moderate resolution imaging spectroradiometer; NDVI, normalized diff erence vegetation index; NEE, net ecosystem exchange; PAR, photosynthetically active radiation.

Agronomy Journa l • Volume 103, Issue 4 • 2011 973

Vescovo and Gianelle (2006) used ground-based, aerial, and satellite-derived spectral refl ectance to estimate the green herb-age ratio in meadows on the Monte Bondone plateau in Italy. Th ey found that NDVI calculated from ground-based and aerial imagery was signifi cantly correlated with green herbage ratio whereas the satellite-based NDVI was not and concluded that satellite images with 25-m resolution were not precise enough to investigate spatial variability in their alpine grasslands.

Because MODIS NDVI spatial resolution is in many cases larger than typical pastures in the northeastern United States, there is reason to question its’ usefulness for estimating vegeta-tive properties such as biomass or C fl ux. Th e purposes of this research were to: (i) compare MODIS 250-m NDVI data with GPP determined by eddy covariance systems on two pastures that were frequently cut or grazed during the growing season; (ii) determine what additional environmental data might be needed to improve NDVI estimates of GPP, and (iii) determine if ground-based NDVI can provide insights into discrepancies between NDVI-estimated and measured GPP.

MATERIALS AND METHODSTh e study was conducted on two pastures at the Pennsylva-

nia State University Haller Research Farm located about 10 km northeast of State College, PA (40°53’60’’ N, 77°47’60’’ W). Soil type was a Hublersburg silt loam (Typic Hapludault) with 3 to 8% slopes. Carbon dioxide fl uxes were measured continuously from January 2003 through December 2008 from a grass-dominated permanent pasture that had been traditionally cut once in the spring for hay then rotationally grazed three to four times per year by beef cattle (Bos taurus), and from an adjacent, alfalfa (Medicago sativa L.)–dominated pasture typically managed for hay production during spring and summer then grazed in the fall.

Th e grass-based pasture was dominated by a mixture of cool-season grasses including, orchardgrass (Dactylis glomerata L.), tall fescue (Lolium arundinaceum Schreb.), and Kentucky bluegrass (Poa pratensis L.). Other common species included smooth bromegrass (Bromus inermis Leyss.), dandelion (Tarax-acum offi cinale L.), and alfalfa. Th e alfalfa-based pasture was planted as an alfalfa monoculture in 1995. Intermixed with the alfalfa during the period of fl ux monitoring were patches of orchardgrass, smooth bromegrass, dandelion, Kentucky bluegrass, reed canarygrass (Phalaris arundinacea L.), and tall fescue. Pastures were harvested three to four times per year between mid-May and mid-November. Th e number and timing of harvests varied from year to year based on pasture growth, weather constraints, and equipment availability. Pastures were subdivided into approximately 0.5 ha paddocks and each pad-dock was either cut for hay or grazed for 3 to 4 d.

Pasture-scale CO2 fl uxes were quantifi ed using a Campbell Scientifi c1 eddy covariance CO2 fl ux system featuring a LI-7500 open path CO2/H2O analyzer and CSAT3 3-D sonic anemom-eter (Campbell Scientifi c Inc., Logan, UT). Th is system uses micrometeorological techniques to monitor biosphere–atmo-sphere exchanges of CO2 by correlating fl uctuations in vertical wind velocity with CO2 density (Dugas et al., 1991). Data were collected continuously at 10 Hz and averaged over 20-min time intervals. Th e open-path CO2/H2O analyzer and CSAT3 3-D sonic anemometer were placed at a height of 1.75 m above the

soil surface in the center of 7-ha (grass) and 9-ha (alfalfa) pas-tures, providing >200 m fetch in the direction of the prevailing winds. Neighboring pastures also contained cool-season grasses so that confounding features such as roads, tree lines, and farm buildings were all more than 200 m from the eddy covari-ance systems in all directions. Coordinate rotation, frequency response corrections (Moore, 1986), corrections for density eff ects due to heat and water vapor transfer (Webb et al., 1980), and corrections for internal and external heating of the LI-7500 (Burba et al., 2008) were applied to the raw CO2 fl ux data. Th e ecological sign convention is used throughout the manuscript, where positive fl uxes indicate CO2 uptake by plants.

Frequent gaps in eddy covariance data resulted whenever rainwater or dew coated the LI-7500 and CSAT3 sensors. Flux data also became unreliable when winds were calm and friction velocity (u*) decreased below about 0.12 m s−1. Low u* generally occurred at night and during early morning hours. Approxi-mately 50% of the data typically needed to be discarded because of the frequent rainfall (about 1000 mm yr−1) and low wind velocity characteristic of this study site. Gap fi lling procedures were used to replace spurious and missing values. In a few cases, when only one or two consecutive 20-min values were miss-ing, gaps were fi lled by interpolating between valid data points. Most nighttime missing data, however, were estimated by regressing nighttime net ecosystem exchange (NEE) against soil temperature (Xu and Baldocchi, 2004). Daytime missing values were estimated from light response curves derived from daytime NEE regressed against PAR. Daytime ecosystem respiration was calculated using the fl ux-temperature relationship developed from nighttime NEE and GPP was calculated by subtracting respiration from NEE for each 20-min daytime period.

Ancillary data were collected at 20-min intervals at each site and included, air temperature and relative humidity (HMP45C temperature and RH probe, Vaisala, Woburn, MA), net solar radiation (Q7.1 net radiometer, REBS, Seattle, WA), PAR (190SZ quantum sensor, Li-Cor, Lincoln, NE), soil temperature at a depth of 3 cm (Campbell Scientifi c model 107 soil temperature probe), soil moisture at 5- and 30-cm depths (Campbell Scientifi c CS616 water content refl ectometer), and rainfall (TE525 tipping bucket rain gauge, Texas Electronics, Dallas, TX). Data were collected continuously, except for brief interruptions, since January 2003, with the exception that soil moisture data are missing from the alfalfa-based site before DOY 117 in 2003. Weekly estimates of standing biomass were made using a rising plate meter that had been calibrated against clipped samples (Sanderson et al., 2001). Plate meter readings from the weeks immediately before and aft er harvests were used to estimate biomass removal.

In theory, CO2 exchange by grazing animals could be directly monitored by the eddy covariance system. In practice, however, the short-duration, high stocking rate, and variable proximity of cattle to the eddy covariance towers resulted in extremely erratic and variable fl ux data. Because of a lack of confi dence in the eddy covariance data during grazing, data were discarded from times when the presence of cattle could be detected in the eddy covari-ance data, and NEE was determined with gap-fi lling procedures.

1 Mention of trade names or commercial products in this publication is solely for the purpose of providing specifi c information and does not imply recommendation or endorsement by the U.S. Department of Agriculture. USDA is an equal opportunity provider and employer.

974 Agronomy Journa l • Volume 103, Issue 4 • 2011

Satellite-based NDVI was calculated from 250-m resolu-tion eMODIS data (ft p://emodisft p.cr.usgs.gov/eMODIS/). Th e eMODIS product, developed at the USGS EROS Center, was processed using the same level-1B swath data used by standard MODIS as input. Level-2 atmospherically corrected surface refl ectance data were calculated using standard NASA Collection 5 MODIS algorithms. Th e level-2 swath data were then projected directly using nearest neighbor resampling to Lambert Azimuthal Equal Area projection for the contermi-nous United States. Th e 7-d NDVI compositing process used an enhanced maximum-value-compositing algorithm to fi lter through input refl ectance with bad quality, negative values, clouds, snow cover, or low view angles (Jenkerson et al., 2010). Th e sequence of 7-d NDVI images was temporally smoothed to remove residual clouds (Swets et al., 2000).

Canopy GPP can be modeled as a function of intercepted radiation modifi ed by the radiation use effi ciency (RUE) of the plants as expressed in the following equation

GPP = ε × PAR × fPAR [1]

where ε is the RUE (g CO2 mol photons–1), and fPAR is the fraction of PAR intercepted by the plant canopy. For this study, fPAR was assumed to equal NDVI as NDVI has been shown to be closely correlated with fPAR (Law and Waring, 1994). Multiplying PAR by fPAR provides the total amount of radia-tion intercepted by the plant canopy (IPAR). Radiation use effi ciency was calculated by dividing GPP by IPAR.

Ground-based canopy refl ectance data in the visible (590 nm) and near infrared (880 nm) wavelengths was collected using a Crop Circle ACS-210 sensor (Holland Scientifi c, Lincoln, NE) and used to calculate green NDVI (gNDVI). Th e Crop Circle sen-sor is an active sensor, using light emitting diodes to direct light in the green and infrared wavelengths toward the canopy surface. Th e portion of emitted light refl ected back to the sensor is detected by a silicon photodiode array. Additionally, by modulating the light source, the sensor can distinguish its own light signal from that of surrounding ambient light, allowing the sensor to make canopy measurements under all ambient lighting conditions. Th e sensor was held approximately 100 cm above the canopy as the operator walked across the pasture. Th e area sampled in each pasture was approximately 100 by 140 m and was centered on the eddy covari-ance towers. Reported gNDVI values are means of about 5500 readings per pasture per sampling date. Calculation of gNDVI is identical to calculation of NDVI with the exception that diff er-ent wavelengths are used in the visible range. Measurements were

taken on eight dates throughout the growing season from the grass and alfalfa pastures in 2006, and on 12 dates each year from the alfalfa pasture in 2007 and 2008. Sensor readings were collected at all stages of the pasture regrowth cycle.

Multiple linear regression and regression tree analysis (Wylie et al., 2003) were used to examine relationships between GPP and the following candidate independent variables: eMODIS NDVI, PAR, air temperature (Tair), soil moisture content at 5-cm depth (SM5), and day of year (DOY). Data from the grass and alfalfa pastures were pooled for regression analysis. Multiple regression analysis used a forward selection process in which each variable was tested and the one that produced the largest F value was entered fi rst into the regression. Th e process was repeated until all variables had been added. Only those variables explain-ing more than 3% of total variance were included in the fi nal model. Regression tree analysis develops relationships that take nonlinearity into consideration and account for high-order inter-actions (De’ath and Fabricius, 2000). Th is technique hierarchi-cally splits the data then allows development of predictive linear equations for each subset. Equations from multiple regression and regression tree analyses were used to predict annual GPP and results were compared with measured values.

RESULTSTh e fi rst two summers of the study (2003–2004) were

wetter than the last 4 yr (Table 1) and soil moisture in the top 5 cm was near fi eld capacity (30–35%) throughout much of the growing season(Fig. 1). In contrast, 2005 was the warmest and driest year and substantial soil moisture stress occurred. Water defi cits were less severe than in 2005 during the fi nal 3 yr (2006–2008). Mean daily PAR varied little among years, averaging 27.8 ± 0.2 mol m–2 d–1.

Gross primary productivity at both sites began to increase each year in mid-March and C uptake continued at relatively high rates until the end of November (Fig. 2). Gross primary pro-ductivity during the remainder of the year (December through early March) was low, but nevertheless greater than zero, averag-ing 2.3 g CO2 m−2 d−1 (data not shown). For the purpose of this paper we examined GPP from the time it began to increase above the wintertime rate (DOY 80) to when it returned to that rate in the fall (DOY 330). During the fi rst 49 d of the growing season, GPP increased rapidly and steadily to a maximum uptake rate of about 40 g CO2 m–2 d–1. Th ere was little diff erence among years during this initial period of increasing uptake. Aft er about DOY 129, the overall trend was for decreasing GPP throughout the remainder of the year. Uptake rates also became highly variable aft er DOY 129 as harvests and environmental stress (mainly drought) began to aff ect GPP. Th ere was little diff erence between the alfalfa and grass pastures in terms of the overall magnitude or seasonal patterns of GPP. Averaged over all 6 yr, growing season GPP in the grass pasture was 4736 compared with 4424 g CO2 m−2 yr−1 for the alfalfa pasture.

Th e NDVI calculated from satellite observations followed a very similar pattern to GPP during the fi rst 49 d of the grow-ing season, increasing rapidly to a maximum of about 0.80 to 0.85 by DOY 129 (Fig. 3). In contrast with GPP, NDVI then oscillated but remained relatively high until about the end of October when it then began to decrease and become highly vari-able until the end of the growing season. Th ere was much less

Table 1. Daily mean photosynthetically active radiation (PAR), air temperature, volumetric soil moisture at 5-cm depth, and total rainfall during the growing season (DOY 80–330) at University Park, PA.

Year PARAir

temperature RainfallVolumetric soil

moisture

mol m–2 d–1 °C mm %2003 28.1 14.2 884 33.12004 27.7 14.6 979 35.02005 28.1 15.2 516 17.22006 27.4 14.1 691 22.72007 28.2 15.1 557 20.82008 27.3 13.9 599 18.2


variability within the growing season and less diff erence among years for NDVI than there was for GPP. August and September (DOY 213–273) NDVI was lower in 2005 and 2008 than in other years. Th ese were also the 2 yr with the lowest soil water content (Table 1). As with GPP, little diff erence in NDVI existed between pastures with an overall mean for the grass pasture of 0.71 compared with 0.70 for the alfalfa pasture.

Both multiple regression and regression tree analyses identifi ed NDVI and PAR as the two most important predictors of GPP. In the multiple regression analysis, NDVI explained 38% of the variability in GPP and PAR explained 20%. In addition, DOY was signifi cant and explained another 4% of total variability (see Table 2 for regression equation). No other variable explained more than 1% of observed variability in GPP. Regression tree analysis explained 82% of variability in GPP, and identifi ed three uniquely diff erent periods throughout the growing season. Separate predic-tion equations were developed for each period (Table 2). Th e fi rst period corresponded to the time before DOY 129 when GPP was rapidly increasing and identifi ed PAR, NDVI, DOY, and Tair as the most important variables for predicting GPP. Th e second period, defi ned as DOY > 129 and NDVI > 0.55, essentially included the rest of the growing season, occasionally excluding the last 2 wk of November. During that period, PAR, NDVI, DOY, and Tair were again identifi ed as the most important variables for

predicting GPP. Th e third period included measurements between DOY 320 and 330 when NDVI decreased below 0.55. On those occasions, Tair dropped out of the predictive equation leaving PAR, NDVI and a small eff ect from DOY.

Both multiple regression and regression tree analysis provide excellent predictions of GPP before DOY 129 (Fig. 4). Th ey also did an adequate job of capturing the tendency for GPP to decline during the remainder of the growing season. However, both were incapable of capturing weekly extremes in GPP, either high or low, that occasionally occurred throughout the growing season. Th e greatest diff erences between predicted and observed GPP occurred during the period between about DOY 130 and 200 when most of the extreme values were observed. Multiple regres-sion analysis also frequently predicted negative GPP near the end of the growing season, a situation that was avoided with regression tree analysis. However, when paired points are compared there were generally only small diff erences between multiple regression and regression tree estimates of GPP. Predicted total growing season GPP diff ered by an average of 2 to 3% from observed GPP regardless of analysis method (Table 3). Exceptions were the two driest years, 2005 and 2008, when GPP was overestimated by 11% in 2005 and underestimated by 13% in 2008.

Ground-based gNDVI was generally lower than eMODIS-NDVI for a given level of GPP (Fig. 5A). However, both were

Fig. 1. Volumetric soil moisture content at 5-cm depth during the growing season (DOY 80–330) for a grass-based pasture in Central Pennsylvania.


signifi cant correlated with GPP (P < 0.01) with similar R2 val-ues, and the ground-based measurements did no better at captur-ing reductions in canopy cover following defoliation than did the satellite measurements. However, when gNDVI was substituted for NDVI into multiple regression estimates of GPP (Fig. 5B), the amount of variation explained by the prediction model was considerably greater than for estimates based on NDVI.

DISCUSSIONEven though GPP and NDVI were monitored for two

pastures, they cannot be considered true replicates because of diff erences in species composition, management history, and harvest practices. Th us, variability estimates must be based on general knowledge of variability in eddy covariance and satellite systems. Anthoni et al. (1999) suggested that systematic errors were about ±12% of daytime CO2 fl uxes, and environmental

variability and instruments errors typically restrict the accuracy of individual measurements to between 10 and 20% (Moncrieff et al., 1996). Assuming a value of 15% provides a mean estimate of 4580 ± 687 g CO2 m–2 yr–1 for the 6 yr of the study (Table 3). Variability in MODIS-NDVI is about ± 0.025 (Gao et al., 2003), producing an error of ±293 g CO2 m–2 yr–1 in multiple regression estimates of growing season GPP.

Even though the total amount of land in pasture on north-eastern U.S. farms can be relatively large, rotationally grazed pastures are typically subdivided and managed as paddocks that are considerably smaller than the 250 m resolution size of the eMODIS images. In this experiment, typical paddock size for grazing purposes was about 50 by 140 m, although when cut for hay they could be as large as 100 by 400 m. Of particu-lar concern for this study was the ability of satellite images to

Table 2. Regression equations for predicting gross primary productivity (GPP) at University Park, PA.

Multiple regressionGPP = –14.9 + 46.7 NDVI + 0.26 PAR – 0.034 DOY

Regression treeDOY ≤ 129: GPP =

–38.66 + 20.6 NDVI + 0.128 PAR + 0.335 DOY + 0.45 Tair

DOY > 129: NDVI > 0.55: GPP = –9.47 + 46.7 NDVI + 0.181 PAR + 0.05 Tair – 0.054 DOY

DOY > 129: NDVI ≤ 0.55: GPP = 4.09 + 5.0 NDVI + 0.023 PAR – 0.009 DOY

Fig. 2. Growing season (DOY 80–330) mean gross primary productivity (GPP) for two pasture sites in Central Pennsylvania. The growing season was defined as the period when daily GPP, averaged over 7 d, was >2.0 g CO2 m–2 d–1.

Fig. 3. Growing season (DOY 80–330) normalized difference vegetation index (NDVI) as measured from the moderate resolution imaging spectroradiometer (MODIS) satellite for two pasture sites in Central Pennsylvania.

Table 3. Measured and predicted growing season (DOY 80–330) gross primary productivity (GPP). Measured data from eddy covariance are compared with estimates calculated from multiple regression and regression tree analyses.

Year Measured Multiple regression Regression treeg CO2 m

–2

2003 5208 5004 48322004 4786 4912 48212005 3699 4121 40992006 4552 4737 45122007 4535 4610 45252008 4700 4083 4068Mean 4580 4578 4476


capture reductions in leaf area and, thus, of IPAR when only a portion of any particular pixel was grazed at a given time. Despite the potential problems with pixel size, growing season GPP estimated from satellite images was reasonably representa-tive of measured GPP (Table 3), with the exception of the two driest years (2005 and 2008). In general, the 250-m resolution images were adequate for estimating total growing season fl ux and should be appropriate for regional estimates of GPP.

In 2008, mean growing season NDVI was similar to 2005 and consistent with the level of moisture stress observed during that year. However, measured GPP was above average, and much greater than would be expected for the level of stress. Radiation use effi ciency was particularly high in 2008, perhaps due to the low air temperature (Table 1) which was the lowest of any year in the study. A signifi cant quadratic relationship existed for the entire growing season between RUE and air temperature

(R2 = 0.22, P = 0.02, n = 36), with maximum RUE occurring at 12.5°C. Th us, mean growing season air temperature in 2008 was closer to the optimal temperature for RUE than in any other year. Photosynthetically active radiation was also lowest in 2008. Because GPP saturates at high light intensity, RUE tends to be higher in low light (Sims et al., 2005). Th e increased proportion of diff use light on cloudy days can also lead to increased photo-synthetic uptake and higher RUE (Gu et al., 2003). Th us, the combination of low temperature and relatively low light intensity in 2008 could have allowed greater photosynthetic uptake than would be expected given the prevailing soil moisture conditions.

On a weekly basis, estimated GPP faithfully tracked the cen-tral tendencies in the observed data, but was not able to repro-duce weeks with exceptionally high or low uptake rates (Fig. 4). Th is was particularly the case when low GPP resulted from a harvest event, especially when the pastures were cut for hay. Th e gNDVI calculated from ground-based sensors improved both the relationship between spectral indices and GPP (Fig. 5A) and the relationship between estimated and measured GPP (Fig. 5B). Ground-based measurements have also been shown to improve estimates of grassland herbage production compared with satel-lite measurements when the site of interest is smaller than the spatial resolution of the satellite images (Vescovo and Gianelle, 2006). Th us, it appears that more spatially refi ned data than the

Fig. 4. Multiple-regression and regression-tree estimates of gross primary productivity (GPP) compared with GPP measured by eddy covariance.

Fig. 5. (A) Relationship between satellite-based normalized difference vegetation index (NDVI) or ground-based normalized difference vegetation index (gNDVI) and gross primary productivity (GPP) as measured by eddy covariance. (B) Multiple-regression estimates of GPP using either satellite-based NDVI or ground-based gNDVI.


250-m resolution eMODIS product are necessary to track eff ects of specifi c management events on GPP.

Multiple regression analysis suggested that NDVI and PAR, which together determine the amount of intercepted radiation, were by far the most important variables, explaining 58% of the variability in GPP. Similar results were observed for a Canadian forest where most of the variance in GPP was explained by intercepted PAR (Coops et al., 2007). A study in the northern Great Plains found that a linear function of NDVI explained 53% of variability in GPP (Gilmanov et al., 2005). Adding PAR increased the percent of variability that could be accounted for to 65%. However, in a sagebrush-steppe ecosystem, NDVI was the strongest predictor of daytime fl ux (R2 = 0.79), and inclu-sion of PAR in the predictive equation only slightly increased R2 to 0.81 (Wylie et al., 2003). In this study, NDVI by itself was a rather weak predictor of GPP (R2 = 0.38) and adding PAR to the predictive equation had a greater eff ect than in the previously cited studies. Th is was possibly due to high and variable cloud cover in the northeastern United States. Mean weekly PAR oft en fl uctuated by more than 50% from 1 wk to the next, whereas, NDVI remained relatively constant on a weekly basis, making it diffi cult to predict GPP based on NDVI alone.

Th e decline in GPP during May and June could be explained by the harvest schedule at the sites. In most instances, the fi rst harvest each year occurred between DOY 139 and 162 with the exception of a late harvest (DOY 182) at the alfalfa site in 2003, and early harvests at the grass site in 2005 (DOY 125) and 2006 (DOY 121). Th is period corresponds to the period of decreasing GPP and NDVI. Removal of a large proportion of leaf biomass would be expected to lead to a decrease in both GPP and the pro-portion of incoming PAR intercepted by the plants. Th is would be especially true for the fi rst harvest which was typically a hay cutting that quickly removed most of the standing biomass and, in fact, large decreases in GPP were observed within the same day as harvests when sites were cut for hay (data not shown).

Averaging NDVI from Fig. 3 across years reveals that NDVI reached its maximum in the spring at about DOY 120, remained high for 2 to 3 wk, and then began to decline. On average, peak springtime NDVI occurred about 3 wk before the fi rst harvest, and a curve-linear relationship existed between NDVI and GPP. At the time of peak NDVI, aboveground biomass aver-aged 155 g m–2, which was about 45% of the eventual harvested

biomass. Several studies have found that NDVI tends to plateau with increasing plant height (Payero et al., 2004), leaf area (Han-cock and Dougherty, 2007), or forage yield (Yang et al., 2007), although Flynn et al. (2008) found a linear relationship between NDVI and forage biomass. A strong relationship oft en exists between GPP and leaf area or biomass (Flanagan et al., 2002; Byrne et al., 2005; Aires et al., 2008) so it is not surprising that NDVI would also peak at less than maximum GPP.

Regression tree analysis showed a signifi cant eff ect of DOY on GPP, especially during early-spring growth before DOY 129 (Table 2). In this case, DOY probably acted as a surrogate for RUE (Fig. 6) which was not explicitly included in the model but which steadily increased during this period. Radiation use effi ciency is a measure of a plants ability to convert light energy into biomass (Huemmrich et al., 2010) and can be infl uenced by several physiological and environmental factors, includ-ing leaf age, vegetation type, nutrient availability, disease, soil moisture, temperature and vapor pressure defi cit. In a global analysis of many ecosystems, RUE was mostly constrained by water availability with temperature determining variability in RUE only at the coldest sites (Garbulsky et al., 2010).

During the period of early-spring growth, RUE increased rapidly as mean air temperature increased from 3 to 14°C and soil moisture decreased from 36 to 23%. Th e relationship between RUE and temperature has been described earlier. A quadratic relationship also existed between RUE and soil moisture (R2 = 0.17, P = 0.04, n = 36) with maximum RUE occurring at 27% soil moisture. Th us, soil moisture and air temperature both became more favorable for maximizing RUE as spring green-up progressed.

Th e depression in RUE from mid-May to the end of Septem-ber resulted from a combination of high temperature and low soil moisture, both of which returned to more optimal levels in October when an increase in RUE occurred. Th e maximum RUE of about 1.4 g CO2 mol–1, average growing season RUE of 1.1 g CO2 mol–1 and the season pattern of increasing RUE to a maximum in early spring followed by a summer depression and recovery in the fall were similar to results from a cool-season grassland in Lethbridge, Canada (Sims et al., 2005), and a warm-season grassland in Kansas (Turner et al., 2003), and was similar to average annual RUE for grasslands from several locations worldwide (Garbulsky et al., 2010).

CONCLUSIONSMultiple-regression and regression-tree estimates of GPP, based

primarily on eMODIS NDVI and on-site measurements of PAR, were generally able to predict growing-season GPP to within an average of 3% of measured values. Th e exception was drought years when estimated and measured GPP diff ered by 11 to 13%. Ground-based measurements improved the ability of vegetation indices to capture short-term grazing management eff ects on GPP for these pastures where management units are smaller than the eMODIS pixel size. However, the eMODIS product appears to be adequate for regional GPP estimates where total growing-season GPP across a wide area would be of greater interest than short-term, management-induced changes in GPP at individual sites.

Fig. 6. Radiation use efficiency (RUE) averaged across years and sites. Error bars represent ±1 SE.


ACKNOWLEDGMENTS

We would like to thank Steve LaMar for data collection at the eddy covariance sites, Jennifer Rover (USGS, EROS) for providing the eMODIS data used in this analysis and funding provided by the USGS Program of Geographic Analysis and Monitoring (GAM).

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