[Show abstract][Hide abstract] ABSTRACT: There is a continued need for models to improve consistency and agreement with observations [Friedlingstein et al., 2006], both overall and under more frequent extreme climatic events related to global environmental change such as drought [Trenberth et al., 2007]. Past validation studies of terrestrial biosphere models have focused only on few models and sites, typically in close proximity and primarily in forested biomes [e.g., Amthor et al., 2001; Delpierre et al., 2009; Grant et al., 2005; Hanson et al., 2004; Granier et al., 2007; Ichii et al., 2009; Ito, 2008; Siqueira et al., 2006; Zhou et al., 2008]. Furthermore, assessing model‐data agreement relative to drought requires, in addition to high‐quality observedCO2 exchange data, a reliable drought metric as well as a natural experiment across sites and drought conditions.
Journal of Geophysical Research 02/2013; 115. · 3.17 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Precipitation is one of the most important climate factors that can affect the gross ecosystem production (GEP) of terrestrial ecosystems. Positive impacts of precipitation on annual GEP have been reported for vegetated areas worldwide. However, little is known about the influence of precipitation intensity on GEP, especially at the monthly to seasonal temporal scale. Here we show that monthly GEP is insensitive to the sum of monthly total precipitation (Ps, mm), but positively correlated to precipitation intensity (Pa, mm), defined as the average precipitation per event from half-hourly measurements over a month. Different plant functional types (PFTs) exhibit substantial differences in the sensitivity of monthly GEP to Pa. PFTs of water-limited regions responded more intensely than those in mesic environments, as demonstrated by a negative correlation between the slope of the GEP-Pa regression line and average Pa. Furthermore, this slope increases with latitude, indicating higher sensitivity of GEP to Pa for boreal ecosystems than for temperate regions. Therefore, we anticipate increased intensity of storms, as projected by some climate models, may impart a previously overlooked positive impact on precipitation intensity on GEP.
Global and Planetary Change 01/2013; 100:204–214. · 3.16 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: In this study, an inexpensive camera-observation system called the Crop Phenology Recording System (CPRS), which consists
of a standard digital color camera (RGB cam) and a modified near-infrared (NIR) digital camera (NIR cam), was applied to estimate
green leaf area index (LAI), total LAI, green leaf biomass and total dry biomass of stalks and leaves of maize. The CPRS was
installed for the 2009 growing season over a rainfed maize field at the University of Nebraska-Lincoln Agricultural Research
and Development Center near Mead, NE, USA. The vegetation indices called Visible Atmospherically Resistant Index (VARI) and
two green–red–blue (2g–r–b) were calculated from day-time RGB images taken by the standard commercially-available camera.
The other vegetation index called Night-time Relative Brightness Index in NIR (NRBINIR) was calculated from night-time flash NIR images taken by the modified digital camera on which a NIR band-pass filter was
attached. Sampling inspections were conducted to measure bio-physical parameters of maize in the same experimental field.
The vegetation indices were compared with the biophysical parameters for a whole growing season. The VARI was found to accurately
estimate green LAI (R2=0.99) and green leaf biomass (R2=0.98), as well as track seasonal changes in maize green vegetation fraction. The 2g–r–b was able to accurately estimate
total LAI (R2=0.97). The NRBINIR showed the highest accuracy in estimation of the total dry biomass weight of the stalks and leaves (R2=0.99). The results show that the camera-observation system has potential for the remote assessment of maize biophysical
parameters at low cost.
–Night-time flash image–Dry biomass–Crop phenology
[Show abstract][Hide abstract] ABSTRACT: Crop physiological and phenological status is an important factor that characterizes crop yield as well as carbon exchange between the atmosphere and the terrestrial biosphere in agroecosystems. It is difficult to establish high frequency observations of crop status in multiple locations using conventional approaches such as agronomical sampling and also remote sensing techniques that use spectral radiometers because of the labor intensive work required for field surveys and the high cost of radiometers designed for scientific use. This study explored the potential utility of an inexpensive camera observation system called crop phenology recording system (CPRS) as an alternative approach for the observation of seasonal change in crop growth. The CPRS consisting of two compact digital cameras was used to capture visible and near infrared (NIR) images of maize in 2009 and soybean in 2010 for every hour both day and night continuously. In addition, a four channel sensor SKYE measured crop reflectance and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images were acquired over crop fields. The six different camera- radiometer- and MODIS-derived vegetation indices (VIs) were calculated and compared with the ground-measured crop biophysical parameters. In addition to VIs that use digital numbers, we proposed to use daytime exposure value-adjusted VIs. The camera-derived VIs were compared with the VIs calculated from spectral reflectance observations taken by SKYE and MODIS. It was found that new camera-derived VIs using daytime exposure values are closely related to VIs calculated using SKYE and MODIS reflectance and good proxies of crop biophysical parameters. Camera-derived green chlorophyll index, simple ratio and NDVI were found to be able to estimate the total leaf area index (LAI) of maize and soybean with high accuracy and were better than the widely used 2g-r-b. However, camera-derived 2g-r-b showed the best accuracy in estimating daily fAPAR in vegetative and reproductive stages of both crops. Visible atmospherically resistant vegetation index showed the highest accuracy in the estimation of the green LAI of maize. A unique VI, calculated from nighttime flash NIR images called the nighttime relative brightness index of NIR, showed a strong relationship with total aboveground biomass for both crops. The study concludes that the CPRS is a practical and cost-effective approach for monitoring temporal changes in crop growth, and it also provides an alternative source of ground truth data to validate time-series VIs derived from MODIS and other satellite systems.
Agricultural and Forest Meteorology. 01/2012; 154-155:113–126.
[Show abstract][Hide abstract] ABSTRACT: Estimating seasonal evapotranspiration (ET) has many applications in water resources planning and management, including hydrological and ecological mod-eling. Availability of satellite remote sensing images is limited due to repeat cycle of satellite or cloud cover. This study was conducted to determine the suitability of different methods namely cubic spline, fixed, and linear for esti-mating seasonal ET from temporal remotely sensed images. Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC) model in conjunction with the wet METRIC (wMETRIC), a modified version of the METRIC model, was used to estimate ET on the days of satellite overpass using eight Landsat images during the 2001 crop growing season in Midwest USA. The model-estimated daily ET was in good agreement (R 2 = 0.91) with the eddy covariance tower-measured daily ET. The standard error of daily ET was 0.6 mm (20%) at three validation sites in Nebraska, USA. There was no statistically significant difference (P [ 0.05) among the cubic spline, fixed, and linear methods for computing seasonal (July–December) ET from temporal ET estimates. Overall, the cubic spline resulted in the lowest standard error of 6 mm (1.67%) for seasonal ET. However, further testing of this method for multiple years is necessary to determine its suitability.
[Show abstract][Hide abstract] ABSTRACT: Ecosystem models are important tools for diagnosing the carbon cycle and projecting its behavior across space and time. Despite the fact that ecosystems respond to drivers at multiple time scales, most assessments of model performance do not discriminate different time scales. Spectral methods, such as wavelet analyses, present an alternative approach that enables the identification of the dominant time scales contributing to model performance in the frequency domain. In this study we used wavelet analyses to synthesize the performance of 21 ecosystem models at 9 eddy covariance towers as part of the North American Carbon Program's site-level intercomparison. This study expands upon previous single-site and single-model analyses to determine what patterns of model error are consistent across a diverse range of models and sites. To assess the significance of model error at different time scales, a novel Monte Carlo approach was developed to incorporate flux observation error. Failing to account for observation error leads to a misidentification of the time scales that dominate model error. These analyses show that model error (1) is largest at the annual and 20-120 day scales, (2) has a clear peak at the diurnal scale, and (3) shows large variability among models in the 2-20 day scales. Errors at the annual scale were consistent across time, diurnal errors were predominantly during the growing season, and intermediate-scale errors were largely event driven. Breaking spectra into discrete temporal bands revealed a significant model-by-band effect but also a nonsignificant model-by-site effect, which together suggest that individual models show consistency in their error patterns. Differences among models were related to model time step, soil hydrology, and the representation of photosynthesis and phenology but not the soil carbon or nitrogen cycles. These factors had the greatest impact on diurnal errors, were less important at annual scales, and had the least impact at intermediate time scales.
[Show abstract][Hide abstract] ABSTRACT: Information on gross primary production (GPP) of maize croplands is needed for assessing and monitoring maize crop conditions and the carbon cycle. A number of studies have used the eddy covariance technique to measure net ecosystem exchange (NEE) of CO2 between maize cropland fields and the atmosphere and partitioned NEE data to estimate seasonal dynamics and interannual variation of GPP in maize fields having various crop rotation systems and different water management practices. How to scale up in situ observations from flux tower sites to regional and global scales is a challenging task. In this study, the Vegetation Photosynthesis Model (VPM) and satellite images from the Moderate Resolution Imaging Spectroradiometer (MODIS) are used to estimate seasonal dynamics and interannual variation of GPP during 2001–2005 at five maize cropland sites located in Nebraska and Minnesota of the U.S.A. These sites have different crop rotation systems (continuously maize vs. maize and soybean rotated annually) and different water management practices (irrigation vs. rain-fed). The VPM is based on the concept of light absorption by chlorophyll and is driven by the Enhanced Vegetation Index (EVI) and the Land Surface Water Index (LSWI), photosynthetically active radiation (PAR), and air temperature. The seasonal dynamics of GPP predicted by the VPM agreed well with GPP estimates from eddy covariance flux tower data over the period of 2001–2005. These simulation results clearly demonstrate the potential of the VPM to scale-up GPP estimation of maize cropland, which is relevant to food, biofuel, and feedstock production, as well as food and energy security.
Agricultural and Forest Meteorology 12/2011; 151(12):1514–1528. · 3.89 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: More accurate projections of future carbon dioxide concentrations in the atmosphere and associated climate change depend on improved scientific understanding of the terrestrial carbon cycle. Despite the consensus that U.S. terrestrial ecosystems provide a carbon sink, the size, distribution, and interannual variability of this sink remain uncertain. Here we report a terrestrial carbon sink in the conterminous U.S. at 0.63 pg C yr−1 with the majority of the sink in regions dominated by evergreen and deciduous forests and savannas. This estimate is based on our continuous estimates of net ecosystem carbon exchange (NEE) with high spatial (1 km) and temporal (8-day) resolutions derived from NEE measurements from eddy covariance flux towers and wall-to-wall satellite observations from Moderate Resolution Imaging Spectroradiometer (MODIS). We find that the U.S. terrestrial ecosystems could offset a maximum of 40% of the fossil-fuel carbon emissions. Our results show that the U.S. terrestrial carbon sink varied between 0.51 and 0.70 pg C yr−1 over the period 2001–2006. The dominant sources of interannual variation of the carbon sink included extreme climate events and disturbances. Droughts in 2002 and 2006 reduced the U.S. carbon sink by ∼20% relative to a normal year. Disturbances including wildfires and hurricanes reduced carbon uptake or resulted in carbon release at regional scales. Our results provide an alternative, independent, and novel constraint to the U.S. terrestrial carbon sink.Research highlights▶ We report a terrestrial carbon sink in the conterminous U.S. at 0.63 pg C yr−1. ▶ U.S. carbon sink varied between 0.51 and 0.70 pg C yr−1 over the period 2001–2006. ▶ The severe droughts in 2002 and 2006 substantially reduced the U.S. carbon sink. ▶ Disturbances reduced carbon uptake or resulted in carbon release at regional scales. ▶ Our results provide an alternative and novel constraint to the U.S. carbon sink.
Agricultural and Forest Meteorology 01/2011; 151(1):60-69. · 3.89 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Continuous measurements of CO2 and water vapor exchanges made in three cropping systems (irrigated continuous maize, irrigated maize–soybean rotation, and rainfed maize–soybean rotation) in eastern Nebraska, USA during 6 years are discussed. Close coupling between seasonal distributions of gross primary production (GPP) and evapotranspiration (ET) were observed in each growing season. Mean growing season totals of GPP in irrigated maize and soybean were 1738 ± 114 and 996 ± 69 g C m−2, respectively (±standard deviation). Corresponding mean values of growing season ET totals were 545 ± 27 and 454 ± 23 mm, respectively. Irrigation affected GPP and ET similarly, both growing season totals were about 10% higher than those of corresponding rainfed crops. Maize, under both irrigated and rainfed conditions, fixed 74% more carbon than soybean while using only 12–20% more water. The green leaf area index (LAI) explained substantial portions (91% for maize and 90% for soybean) of the variability in GPPPAR (GPP over a narrow range of incident photosynthetically active radiation) and in ET/ETo (71% for maize and 75% for soybean, ETo is the reference evapotranspiration). Water productivity (WP or water use efficiency) is defined here as the ratio of cumulative GPP or above-ground biomass and ET (photosynthetic water productivity = ∑GPP/∑ET and biomass water productivity = above-ground biomass/∑ET). When normalized by ETo, the photosynthetic water productivity (WPETo) was 18.4 ± 1.5 g C m−2 for maize and 12.0 ± 1.0 g C m−2 for soybean. When normalized by ETo, the biomass water productivity (WPETo) was 27.5 ± 2.3 g DM m−2 for maize and 14.1 ± 3.1 g DM m−2 for soybean. Comparisons of these results, among different years of measurement and management practices (continuous vs rotation cropping, irrigated vs rainfed) in this study and those from other locations, indicated the conservative nature of normalized water productivity, as also pointed out by previous investigators.
[Show abstract][Hide abstract] ABSTRACT: The simulation of gross primary production (GPP) at various spatial and temporal scales remains a major challenge for quantifying the global carbon cycle. We developed a light use efficiency model, called EC-LUE, driven by only four variables: normalized difference vegetation index (NDVI), photosynthetically active radiation (PAR), air temperature, and the Bowen ratio of sensible to latent heat flux. The EC-LUE model may have the most potential to adequately address the spatial and temporal dynamics of GPP because its parameters (i.e., the potential light use efficiency and optimal plant growth temperature) are invariant across the various land cover types. However, the application of the previous EC-LUE model was hampered by poor prediction of Bowen ratio at the large spatial scale. In this study, we substituted the Bowen ratio with the ratio of evapotranspiration (ET) to net radiation, and revised the RS-PM (Remote Sensing-Penman Monteith) model for quantifying ET. Fifty-four eddy covariance towers, including various ecosystem types, were selected to calibrate and validate the revised RS-PM and EC-LUE models. The revised RS-PM model explained 82% and 68% of the observed variations of ET for all the calibration and validation sites, respectively. Using estimated ET as input, the EC-LUE model performed well in calibration and validation sites, explaining 75% and 61% of the observed GPP variation for calibration and validation sites respectively.Global patterns of ET and GPP at a spatial resolution of 0.5° latitude by 0.6° longitude during the years 2000–2003 were determined using the global MERRA dataset (Modern Era Retrospective-Analysis for Research and Applications) and MODIS (Moderate Resolution Imaging Spectroradiometer). The global estimates of ET and GPP agreed well with the other global models from the literature, with the highest ET and GPP over tropical forests and the lowest values in dry and high latitude areas. However, comparisons with observed GPP at eddy flux towers showed significant underestimation of ET and GPP due to lower net radiation of MERRA dataset. Applying a procedure to correct the systematic errors of global meteorological data would improve global estimates of GPP and ET. The revised RS-PM and EC-LUE models will provide the alternative approaches making it possible to map ET and GPP over large areas because (1) the model parameters are invariant across various land cover types and (2) all driving forces of the models may be derived from remote sensing data or existing climate observation networks.
[Show abstract][Hide abstract] ABSTRACT: The crop developmental stage represents essential information for irrigation scheduling/fertilizer management, understanding seasonal ecosystem carbon dioxide (CO2) exchange, and evaluating crop productivity. In this study, we devised an approach called the Two-Step Filtering (TSF) for detecting the phenological stages of maize and soybean from time-series Wide Dynamic Range Vegetation Index (WDRVI) data derived from Moderate Resolution Imaging Spectroradiometer (MODIS) 250-m observations. The TSF method consists of a Two-Step Filtering scheme that includes: (i) smoothing the temporal WDRVI data with a wavelet-based filter and (ii) deriving the optimum scaling parameters from shape-model fitting procedure. The date of key crop development stages are then estimated by using the optimum scaling parameters and an initial value of the specific phenological date on the shape model, which are preliminary defined in reference to ground-based crop growth stage observations. The shape model is a crop-specific WDRVI curve with typical seasonal features, which were defined by averaging smoothed, multi-year WDRVI profiles from MODIS 250-m data collected over irrigated maize and soybean study sites.In this study, the TSF method was applied to MODIS-derived WDRVI data over a 6-year period (2003 to 2008) for two irrigated sites and one rainfed site planted to either maize or soybean as part of the Carbon Sequestration Program (CSP) at the University of Nebraska-Lincoln. A comparison of satellite-based retrievals with ground-based crop growth stage observations collected by the CSP over the six growing seasons for these three sites showed that the TSF method can accurately estimate the date of four key phenological stages of maize (V2.5: early vegetative stage, R1: silking stage, R5: dent stage and R6: maturity) and soybean (V1: early vegetative stage, R5: beginning seed, R6: full seed and R7: beginning maturity). The root mean square error (RMSE) of phenological-stage estimation for maize ranged from 2.9 [R1] to 7.0 [R5] days and from 3.2 [R6] to 6.9 [R7] days for soybean, respectively. In addition, the TSF method was also applied for two years (2001 and 2002) over eastern Nebraska to test its ability to characterize the spatio-temporal patterns of these key phenological stages over a larger geographic area. The MODIS-derived crop phenological stage dates agreed well with the statistical crop progress data reported by the United State Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) for eastern Nebraska's three crop agricultural statistic districts (ASDs). At the ASD-level, the RMSE of phenological-stage estimation ranged from 1.6 [R1] to 5.6 [R5] days for maize and from 2.5 [R7] to 5.3 [R5] days for soybean.
[Show abstract][Hide abstract] ABSTRACT: Over the last two and half decades, strong evidence showed that the terrestrial ecosystems are acting as a net sink for atmospheric carbon. However the spatial and temporal patterns of variation in the sink are not well known. In this study, we examined latitudinal patterns of interannual variability (IAV) in net ecosystem exchange (NEE) of CO2 based on 163 site-years of eddy covariance data, from 39 northern-hemisphere research sites located at latitudes ranging from ∼29°N to ∼64°N. We computed the standard deviation of annual NEE integrals at individual sites to represent absolute interannual variability (AIAV), and the corresponding coefficient of variation as a measure of relative interannual variability (RIAV). Our results showed decreased trends of annual NEE with increasing latitude for both deciduous broadleaf forests and evergreen needleleaf forests. Gross primary production (GPP) explained a significant proportion of the spatial variation of NEE across evergreen needleleaf forests, whereas, across deciduous broadleaf forests, it is ecosystem respiration (Re). In addition, AIAV in GPP and Re increased significantly with latitude in deciduous broadleaf forests, but AIAV in GPP decreased significantly with latitude in evergreen needleleaf forests. Furthermore, RIAV in NEE, GPP, and Re appeared to increase significantly with latitude in deciduous broadleaf forests, but not in evergreen needleleaf forests. Correlation analyses showed air temperature was the primary environmental factor that determined RIAV of NEE in deciduous broadleaf forest across the North American sites, and none of the chosen climatic factors could explain RIAV of NEE in evergreen needleleaf forests. Mean annual NEE significantly increased with latitude in grasslands. Precipitation was dominant environmental factor for the spatial variation of magnitude and IAV in GPP and Re in grasslands.
[Show abstract][Hide abstract] ABSTRACT: The quantification of carbon fluxes between the terrestrial biosphere and the atmosphere is of scientific importance and also relevant to climate-policy making. Eddy covariance flux towers provide continuous measurements of ecosystem-level exchange of carbon dioxide spanning diurnal, synoptic, seasonal, and interannual time scales. However, these measurements only represent the fluxes at the scale of the tower footprint. Here we used remotely sensed data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to upscale gross primary productivity (GPP) data from eddy covariance flux towers to the continental scale. We first combined GPP and MODIS data for 42 AmeriFlux towers encompassing a wide range of ecosystem and climate types to develop a predictive GPP model using a regression tree approach. The predictive model was trained using observed GPP over the period 2000–2004, and was validated using observed GPP over the period 2005–2006 and leave-one-out cross-validation. Our model predicted GPP fairly well at the site level. We then used the model to estimate GPP for each 1 km × 1 km cell across the U.S. for each 8-day interval over the period from February 2000 to December 2006 using MODIS data. Our GPP estimates provide a spatially and temporally continuous measure of gross primary production for the U.S. that is a highly constrained by eddy covariance flux data. Our study demonstrated that our empirical approach is effective for upscaling eddy flux GPP data to the continental scale and producing continuous GPP estimates across multiple biomes. With these estimates, we then examined the patterns, magnitude, and interannual variability of GPP. We estimated a gross carbon uptake between 6.91 and 7.33 Pg C yr− 1 for the conterminous U.S. Drought, fires, and hurricanes reduced annual GPP at regional scales and could have a significant impact on the U.S. net ecosystem carbon exchange. The sources of the interannual variability of U.S. GPP were dominated by these extreme climate events and disturbances.
Remote Sensing of Environment 01/2009; · 5.10 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Carbon dioxide fluxes are being measured in three maize-based agroecosystems in eastern Nebraska in an effort to better understand the potential for these systems to sequester carbon in the soil. Landscape-level fluxes of carbon, water and energy were measured using tower eddy covariance systems. In order to better understand the landscape-level results, measurements at smaller scales, using techniques promoted by John Norman, were made and scaled up to the landscape-level. Single leaf gas exchange properties (CO2 assimilation rate and stomatal conductance) and optical properties, direct and diffuse radiation incident on the canopy, and photosynthetically active radiation (PAR) reflected and transmitted by the canopy were measured at regular intervals throughout the growing season. In addition, soil surface CO2 fluxes were measured using chamber techniques. From leaf measurements, the responses of net CO2 assimilation rate to relevant biophysical controlling factors were quantified. Single leaf gas exchange data were scaled up to the canopy level using a simple radiative model that considers direct beam and diffuse PAR penetration into the canopy. Canopy level photosynthesis was estimated, coupled with the soil surface CO2 fluxes, and compared to measured net ecosystem CO2 exchange (NEE) values from the eddy covariance approach. Estimated values of canopy level absorbed PAR was also compared to measured values. The agreement between estimated and observed values increases our confidence in the measured carbon pools and fluxes in these agroecosystems and enhances our understanding of biophysical controls on carbon sequestration.
[Show abstract][Hide abstract] ABSTRACT: We have been making year-round measurements of mass and energy exchange in three cropping systems: (a) irrigated continuous maize, (b) irrigated maize–soybean rotation, and (c) rainfed maize–soybean rotation in eastern Nebraska since 2001. In this paper, we present results on evapotranspiration (ET) of these crops for the first 5 years of our study. Growing season ET in the irrigated and rainfed maize averaged 548 and 482 mm, respectively. In irrigated and rainfed soybean, the average growing season ET was 452 and 431 mm, respectively. On average, the maize ET was higher than the soybean ET by 18% for irrigated crops and by 11% for rainfed crops. The mid-season crop coefficient Kc (=ET/ET0 and ET0 is the reference ET) for irrigated maize was 1.03 ± 0.07. For rainfed maize, significant dry-down conditions prevailed and mid-season Kc was 0.84 ± 0.20. For irrigated soybean, the mid-season Kc was 0.98 ± 0.02. The mid-season dry down in rainfed soybean years was not severe and the Kc (0.90 ± 0.13) was only slightly lower than the values for the irrigated fields. Non-growing season evaporation ranged from 100 to 172 mm and contributed about 16–28% of the annual ET in irrigated/rainfed maize and 24–26% in irrigated/rainfed soybean. The amount of surface mulch biomass explained 71% of the variability in non-growing season evaporation totals. Water use efficiency (or biomass transpiration efficiency), defined as the ratio of total plant biomass (YDM) to growing season transpiration (T) was 5.20 ± 0.34 and 5.22 ± 0.36 g kg−1, respectively for irrigated and rainfed maize crops. Similarly, the biomass transpiration efficiency for irrigated and rainfed soybean crops was 3.21 ± 0.35 and 2.96 ± 0.30 g kg−1. Thus, the respective biomass transpiration efficiency of these crops was nearly constant regardless of rainfall and irrigation.
[Show abstract][Hide abstract] ABSTRACT: A surface energy balance model based on the Shuttleworth and Wallace (Q J R Meteorol Soc 111:839–855, 1985) and Choudhury and Monteith (Q J R Meteorol Soc 114:373–398, 1988) methods was developed to estimate evaporation from soil and crop residue, and transpiration from crop canopies. The model
describes the energy balance and flux resistances for vegetated and residue-covered surfaces. The model estimates latent,
sensible and soil heat fluxes to provide a method to partition evapotranspiration (ET) into soil/residue evaporation and plant
transpiration. This facilitates estimates of the effect of residue on ET and consequently on water balance studies, and allows
for simulation of ET during periods of crop dormancy. ET estimated with the model agreed favorably with eddy covariance flux
measurements from an irrigated maize field and accurately simulated diurnal variations and hourly amounts of ET during periods
with a range of crop canopy covers. For hourly estimations, the root mean square error was 41.4Wm−2, the mean absolute error was 29.9Wm−2, the Nash–Sutcliffe coefficient was 0.92 and the index of agreement was 0.97.
[Show abstract][Hide abstract] ABSTRACT: The seasonal cycle of plant community photosynthesis is one of the most important biotic oscillations to mankind. This study
built upon previous efforts to develop a comprehensive framework to studying this cycle systematically with eddy covariance
flux measurements. We proposed a new function to represent the cycle and generalized a set of phenological indices to quantify
its dynamic characteristics. We suggest that the seasonal variation of plant community photosynthesis generally consists of
five distinctive phases in sequence each of which results from the interaction between the inherent biological and ecological
processes and the progression of climatic conditions and reflects the unique functioning of plant community at different stages
of the growing season. We applied the improved methodology to seven vegetation sites ranging from evergreen and deciduous
forests to crop to grasslands and covering both cool-season (vegetation active during cool months, e.g. Mediterranean climate
grasslands) and warm-season (vegetation active during warm months, e.g. temperate and boreal forests) vegetation types. Our
application revealed interesting phenomena that had not been reported before and pointed to new research directions. We found
that for the warm-season vegetation type, the recovery of plant community photosynthesis at the beginning of the growing season
was faster than the senescence at the end of the growing season while for the cool-season vegetation type, the opposite was
true. Furthermore, for the warm-season vegetation type, the recovery was closely correlated with the senescence such that
a faster photosynthetic recovery implied a speedier photosynthetic senescence and vice versa. There was evidence that a similar
close correlation could also exist for the cool-season vegetation type, and furthermore, the recovery-senescence relationship
may be invariant between the warm-season and cool-season vegetation types up to an offset in the intercept. We also found
that while the growing season length affected how much carbon dioxide could be potentially assimilated by a plant community
over the course of a growing season, other factors that affect canopy photosynthetic capacity (e.g. nutrients, water) could
be more important at this time scale. These results and insights demonstrate that the proposed method of analysis and system
of terminology can serve as a foundation for studying the dynamics of plant community photosynthesis and such studies can
[Show abstract][Hide abstract] ABSTRACT: Eddy covariance flux towers provide continuous measurements of net ecosystem carbon exchange (NEE) for a wide range of climate and biome types. However, these measurements only represent the carbon fluxes at the scale of the tower footprint. To quantify the net exchange of carbon dioxide between the terrestrial biosphere and the atmosphere for regions or continents, flux tower measurements need to be extrapolated to these large areas. Here we used remotely sensed data from the Moderate Resolution Imaging Spectrometer (MODIS) instrument on board the National Aeronautics and Space Administration's (NASA) Terra satellite to scale up AmeriFlux NEE measurements to the continental scale. We first combined MODIS and AmeriFlux data for representative U.S. ecosystems to develop a predictive NEE model using a modified regression tree approach. The predictive model was trained and validated using eddy flux NEE data over the periods 2000–2004 and 2005–2006, respectively. We found that the model predicted NEE well (r = 0.73, p < 0.001). We then applied the model to the continental scale and estimated NEE for each 1 km × 1 km cell across the conterminous U.S. for each 8-day interval in 2005 using spatially explicit MODIS data. The model generally captured the expected spatial and seasonal patterns of NEE as determined from measurements and the literature. Our study demonstrated that our empirical approach is effective for scaling up eddy flux NEE measurements to the continental scale and producing wall-to-wall NEE estimates across multiple biomes. Our estimates may provide an independent dataset from simulations with biogeochemical models and inverse modeling approaches for examining the spatiotemporal patterns of NEE and constraining terrestrial carbon budgets over large areas.
[Show abstract][Hide abstract] ABSTRACT: In this paper, we present results from 4 years (May 2001–May 2005) of water and energy flux measurements made in a no-till, irrigated maize–soybean rotation system in eastern Nebraska, USA. The peak green leaf area index (LAI) reached 6.0 and 5.5 in maize (2001 and 2003, respectively) and 5.7 and 4.4 in soybean (2002 and 2004, respectively). The dependence of evapotranspiration (ET) on leaf area was consistent with previous studies. There was a nearly linear relationship between the daily ET/ETo (where ETo is the reference evapotranspiration over a grass reference crop) and LAI until a threshold LAI (between 3 and 4). Above this threshold LAI, the ET/ETo was virtually independent of LAI. The cumulative growing season (planting to harvest) evapotranspiration was 544 and 578mm for maize, and 474 and 430mm for soybean. The interannual variability in the growing season ET totals correlated very well with the number of days when the LAI was greater than 3. The non-growing season period (harvest to subsequent planting) contributed between 20 and 25% of the annual ET totals for both crops. The maximum canopy surface conductance (Gsmax) was 29mms−1 for maize in both years, 41mms−1 for soybean in 2002 (peak LAI=5.7) and 36mms−1 for soybean in 2004 (peak LAI=4.4). The variability in Gsmax was largely explained by the leaf nitrogen concentration, consistent with the literature.
Agricultural and Forest Meteorology - AGR FOREST METEOROL. 01/2008; 148(3):417-427.