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Remotely sensed reflectance parameters from corn and soybean surfaces can be correlated to crop production. Surface reflectance of a typical Upper Midwest corn /soybean region in central Iowa across multiple years reveal subtle dynamics in vegetative surface response to a contin ually varying climate. From 2006 through 2014 remotely sensed data have been acquired over production fields of corn and soybeans in central IA, U.S.A. with the fields alternating between corn and soybeans. The data have been acquired using ground-based radiometers with 16 wavebands covering the visible, near-infrared, shortwave infrared wavebands and combined into a se ries of vegetative indices. These data were collected on clear days with the goal of collecting data at a minimum of once per week from prior to planting until after fall tillage operations. Within each field, five sites were established and sampled during the year to reduce spatial variation and allow for an assessment of changes in the vegetative indices throughout the growing season. Ancillary data collected for each crop included the phenological stage at each sampling date along with biomass sampled at the onset of the reproductive stage and at physiological maturity. Evaluation of the vegetativ e indices for the different years revealed that patterns were related to weather effects on corn and soybean growth. Remote sensing provides a method to evaluate changes within and among growing seasons to assess crop growth and developm ent as affected by differences in weather variability. 1. INTRODUCTION

A simple relationship, referred to as a mass conversion factor (MCF), is presented to convert optically based particle measurements to mass concentration. It is calculated from filter-based samples and optical particle counter (OPC) data on a daily or sample period basis. The MCF allows for greater temporal and spatial mass concentration information than typical filter-based measurements. Results of MCF calculations from several field studies are summarized. Pairwise comparisons from a collocated study with multiple OPCs and mass samplers suggest the minimum variability of the MCF is 5–10%. The variability of the MCF within a sample period during a field study with distributed samplers averaged 17–21%. In addition, the precision of the Airmetrics MiniVol Portable Air Sampler for particulate matter (PM) was typically

Particulate-matter (PM) emissions from a typical spring agricultural tillage sequence and a strip–till conservation tillage sequence in California’s San Joaquin Valley were estimated to calculate the emissions control efficiency (η) of the strip–till conservation management practice (CMP). Filter-based PM samplers, PM-calibrated optical particle counters (OPCs), and a PM-calibrated light detection and ranging (LIDAR) system were used to monitored upwind and downwind PM concentrations during May and June 2008. Emission rates were estimated through inverse modeling coupled with the filter and OPC measurements and through applying a mass balance to the PM concentrations derived from LIDAR data. Sampling irregularities and errors prevented the estimation of emissions from 42% of the sample periods based on filter samples. OPC and LIDAR datasets were sufficiently complete to estimate emissions and the strip–till CMP η, which were ∼90% for all size fractions in both datasets. Tillage time was also reduced by 84%. Calculated emissions for some operations were within the range of values found in published studies, while other estimates were significantly higher than literature values. The results demonstrate that both PM emissions and tillage time may be reduced by an order of magnitude through the use of a strip–till conservation tillage CMP when compared to spring tillage activities.

Management of plant litter or crop residues in agricultural fields is an important consideration for reducing soil erosion and increasing soil organic C. Current methods of quantifying crop residue cover are inadequate for characterizing the spatial variability of residue cover within fields or across large regions. Our objectives were to evaluate several spectral indices for measuring crop residue cover using satellite multispectral and hyperspectral data and to categorize soil tillage intensity in agricultural fields. Landsat Thematic Mapper (TM) and EO-1 Hyperion imaging spectrometer data were acquired over agricultural fields in central Iowa in May and June 2004. Crop residue cover was measured in corn (Zea mays L.) and soybean (Glycine max Merr.) fields using line-point transects. Spectral residue indices using Landsat TM bands were weakly related to crop residue cover. With the Hyperion data, crop residue cover was linearly related to the cellulose absorption index (CAI), which measures the relative intensity of cellulose and lignin absorption features near 2100 nm. Coefficients of determination (r2) for crop residue cover as a function of CAI were 0.85 for the May and 0.77 for the June Hyperion data. Three tillage intensity classes, corresponding to intensive ( 30% cover) tillage, were correctly identified in 66-68% of fields. Classification accuracy increased to 80-82% for two classes, corresponding to conventional (intensive + reduced) and conservation tillage. By combining information on previous season's (2003) crop classification with crop residue cover after planting in 2004, an inventory of soil tillage intensity by previous crop type was generated for the whole Hyperion scene. Regional surveys of soil management practices that affect soil conservation and soil C dynamics are possible using advanced multispectral or hyperspectral imaging systems.

A two-source (soil + vegetation) energy balance model using microwave-derived near-surface soil moisture as a key boundary condition (TSM SM ) and another scheme using thermal-infrared (radiometric) surface temperature (TSM TH ) were applied to remote sensing data collected over a corn and soybean production region in central Iowa during the Soil Moisture Atmosphere Coupling Experiment (SMACEX)/Soil Moisture Experiment of 2002 (SMEX02). The TSM SM was run using fields of near-surface soil moisture from microwave imagery collected by aircraft on six days during the experiment, yielding a root mean square difference (RMSD) between model estimates and tower measurements of net radiation (Rn) and soil heat flux ( G ) of approximately 20 W m − 2 , and 45 W m − 2 for sensible ( H ) and latent heating (LE). Similar results for H and LE were obtained at landscape/regional scales when comparing model output with transect-average aircraft flux measurements. Flux predictions from the TSM SM and TSM TH models were compared for two days when both airborne microwave-derived soil moisture and radiometric surface temperature ( T R ) data from Landsat were available. These two days represented contrasting conditions of moderate crop cover/dry soil surface and dense crop cover/moist soil surface. Surface temperature diagnosed by the TSM SM was also compared directly to the remotely sensed T R fields as an additional means of model validation. The TSM SM performed well under moderate crop cover/dry soil surface conditions, but yielded larger discrepancies with observed heat fluxes and T R under the high crop cover/moist soil surface conditions. Flux predictions from the thermal-based two-source model typically showed biases of opposite sign, suggesting that an average of the flux output from both modeling schemes may improve overall accuracy in flux predictions, in effect incorporating multiple remote-sensing constraints on canopy and soil fluxes.

The Los Alamos Raman lidar has been used to make high resolution (25 m) estimates of the evapotranspiration rate over adjacent corn and soybean canopies. The lidar makes three-dimensional measurements of the water vapor content of the atmosphere directly above the canopy that are inverted using Monin–Obukhov similarity theory. This may be used to examine the relationship between evapotranspiration and surface moisture/soil type. Lidar estimates of evapotranspiration reveal a high degree of spatial variability over corn and soybean fields that may be associated with small elevation changes in the area. The spatial structure of the variability is characterized using a structure function and correlation function approach. The power law relationship found by other investigators for soil moisture is not clear in the data for evapotranspiration, nor is the data a straight line over the measured lags. The magnitude of the structure function and the slope changes with time of day, with a probable connection to the amount of evapotranspiration and the spatial variability of the water vapor source. The data used was taken during the soil moisture–atmosphere coupling experiment (SMACEX) conducted in the Walnut Creek Watershed near Ames, Iowa in June and July 2002.

The Soil Moisture–Atmosphere Coupling Experiment (SMACEX) was conducted in the Walnut Creek watershed near Ames, Iowa, over the period from 15 June to 11 July 2002. A main focus of SMACEX is the investigation of the interactions between the atmospheric boundary layer, surface moisture, and canopy. A vertically staring elastic lidar was used to provide a high-time-resolution continuous record of the boundary layer height at the edge between a soybean and cornfield. The height and thickness of the entrainment zone are used to estimate the surface sensible heat flux using the Batchvarova–Gryning boundary layer model. Flux estimates made over 6 days are compared to conventional eddy correlation measurements. The calculated values of the sensible heat flux were found to be well correlated (R2 = 0.79, with a slope of 0.95) when compared to eddy correlation measurements in the area. The standard error of the flux estimates was 21.4 W m−2 (31% rms difference between this method and surface measurements), which is somewhat higher than a predicted uncertainty of 16%. The major sources of error were from the estimates of the vertical potential temperature gradient and an assumption that the entrainment parameter A was equal to the ratio of the entrainment flux and the surface heat flux.

The Soil Moisture–Atmosphere Coupling Experiment (SMACEX) was conducted in conjunction with the Soil Moisture Experiment 2002 (SMEX02) during June and July 2002 near Ames, Iowa—a corn and soybean production region. The primary objective of SMEX02 was the validation of microwave soil moisture retrieval algorithms for existing and new prototype satellite microwave sensor systems under rapidly changing crop biomass conditions. The SMACEX study was designed to provide direct measurement/remote sensing/modeling approaches for understanding the impact of spatial and temporal variability in vegetation cover, soil moisture, and other land surface states on turbulent flux exchange with the atmosphere. The unique dataset consisting of in situ and aircraft measurements of atmospheric, vegetation, and soil properties and fluxes allows for a detailed and rigorous analysis, and the validation of surface states and fluxes being diagnosed using remote sensing methods at various scales. Research results presented in this special issue have illuminated the potential of satellite remote sensing algorithms for soil moisture retrieval, land surface flux estimation, and the assimilation of surface states and diagnostically modeled fluxes into prognostic land surface models. Ground- and aircraft-based remote sensing of the land surface and atmospheric boundary layer properties are used to quantify heat fluxes at the tower footprint and regional scales. Tower- and aircraft-based heat and momentum fluxes are used to evaluate local and regional roughness. The spatial and temporal variations in water, energy, and carbon fluxes from the tower network and aircraft under changing vegetation cover and soil moisture conditions are evaluated. An overview of the experimental site, design, data, hydrometeorological conditions, and results is presented in this introduction, and serves as a preface to this special issue highlighting the SMACEX results.

Two resistance network formulations that are used in a two-source model for parameterizing soil and canopy energy exchanges are evaluated for a wide range of soybean and corn crop cover and soil moisture conditions during the Soil Moisture–Atmosphere Coupling Experiment (SMACEX). The parallel resistance formulation does not consider interaction between the soil and canopy fluxes, whereas the series resistance algorithms provide interaction via the computation of a within-air canopy temperature. Land surface temperatures were derived from high-resolution Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper (ETM) scenes and aircraft imagery. These data, along with tower-based meteorological data, provided inputs for the two-source energy balance model. Comparison of the local model output with tower-based flux observations indicated that both the parallel and series resistance formulations produced basically similar estimates with root-mean-square difference (RMSD) values ranging from approximately 20 to 50 W m−2 for net radiation and latent heat fluxes, respectively. The largest relative difference in percentage [mean absolute percent difference (MAPD)] was for sensible heat flux, which was ≈35%, followed by a MAPD ≈ 25% for soil heat flux, ≈10% for latent heat flux, and a MAPD < 5% for net radiation. Although both series and parallel versions gave similar results, the parallel resistance formulation was found to be more sensitive to model parameter specification, particularly in accounting for the effects of vegetation clumping resulting from row crop planting on flux partitioning. A sensitivity and model stability analysis for a key model input variable, that is, fractional vegetation cover, also show that the parallel resistance network is more sensitive to the errors vegetation cover estimates. Furthermore, it is shown that for a much narrower range in vegetation cover fraction, compared to the series resistance network, the parallel resistance scheme is able to achieve a balance in both the radiative temperature and convective heat fluxes between the soil and canopy components. This result appears to be related to the moderating effects of the air temperature in the canopy air space computed in the series resistance scheme, which represents the effective source height for turbulent energy exchange across the soil–canopy–atmosphere system.

Eddy covariance measurements of wind speed u and shear velocity u* from tower- and aircraft-based systems collected over rapidly developing corn- (Zea mays L.) and soybean [Glycine max (L.) Merr.] fields were used in determining the local and regional (effective) surface roughness length zo and 〈zo〉, respectively. For corn, canopy height increased from ∼1 to 2 m and the leaf area index changed from ∼1 to 4 during the study period, while for soybean, canopy height increased from ∼0.1 to 0.5 m and the leaf area index increased from ∼0.5 to 2. A procedure for the aggregation of local roughness values from the different land cover types based on blending-height concepts yielded effective surface roughness values that were from ∼1/2 to 1/4 of the magnitude estimated with the aircraft data. This indicated additional kinematic stress caused by form drag from isolated obstacles (i.e., trees, houses, and farm buildings), and the interaction of adjacent corn- and soybean fields were probably important factors influencing the effective surface roughness length for this landscape. The comparison of u* measurements from the towers versus the aircraft indicated that u* from aircraft was 20%–30% higher, on average, and that u* over corn was 10%–30% higher than over soybean, depending on stability. These results provide further evidence for the likely sources of additional kinematic stress. Although there was an increase in zo and 〈zo〉 over time as the crops rapidly developed, particularly for corn, there was a more significant trend of increasing roughness length with decreasing wind speed at wind speed thresholds of around 5 m s−1 for the aircraft and 3 m s−1 for the tower measurements. Other studies have recently reported such a trend. The impact on computed sensible heat flux H using 〈zo〉 derived from the aggregation of zo from the different land cover types, using the blending-height scheme, and that estimated from the aircraft observations, was evaluated using a calibrated single-source/bulk resistance approach with surface–air temperature differences from the aircraft observations. An underestimate of 〈zo〉 by 50% and 75% resulted in a bias in the H estimates of approximately 10% and 15%, respectively. This is a relatively minor error when considering that the root-mean-square error (rmse) value between single-source estimates and the aircraft observations of H was 15 W m−2 using the aircraft-derived 〈zo〉, and only increased to approximately 20 and 25 W m−2 using the 1/2 and 1/4 〈zo〉 values, as estimated from the blending-height scheme. The magnitude of the excess resistance relative to the aerodynamic resistance to heat transfer was a major contributing factor in minimizing the error in heat flux calculations resulting from these underestimations of 〈zo〉.

Soil respiration is an important component of the carbon dynamics of terrestrial ecosystems. Many factors exert controls on soil respiration, including temperature, soil water content, organic matter, soil texture, and plant root activity. This study was conducted to quantify soil respiration in the Walnut Creek watershed in central Iowa, and to investigate the factors controlling this process. Six agricultural fields were identified for this investigation: three of the fields were cropped with soybean [Glycine max (L.) Merr.] and three were cropped with corn (Zea mays L.). Within each field, soil respiration was measured at nine locations, with each location corresponding to one of three general landscape positions (summit, side slope, and depression). Soil respiration was measured using a portable vented chamber connected to an infrared gas analyzer. Soil samples were collected at each location for the measurement of soil water content, pH, texture, microbial biomass, and respiration potential. Field respiration rates did not show a significant landscape effect. However, there was a significant crop effect, with respiration from cornfields averaging 37.5 g CO2 m−2 day−1 versus an average respiration of 13.1 g CO2 m−2 day−1 in soybean fields. In contrast, laboratory measurements of soil respiration potential, which did not include plant roots, showed a significant landscape effect and an insignificant cropping system effect. Similar relationships were observed for soil organic C and microbial biomass. Additional analyses indicate that corn roots may be more important than soybean roots in their contribution to surface CO2 flux, and that root respiration masked landscape effects on total soil respiration. Also, the failure to account for soil respiration may lead to biased estimates of net primary production measured by eddy covariance.

In an effort to better evaluate distributed airborne remotely sensed sensible and latent heat flux estimates, two heat flux source area (footprint) models were applied to the imagery, and their pixel weighting/integrating functionality was investigated through statistical analysis. Soil heat flux and sensible heat flux models were calibrated. The latent heat flux was determined as a residual from the energy balance equation. The resulting raster images were integrated using the 2D footprints and were compared to eddy covariance energy balance flux measurements. The results show latent heat flux estimates (adjusted for closure) with errors of (mean ± std dev) −9.2 ± 39.4 W m−2, sensible heat flux estimate errors of 9.4 ± 28.3 W m−2, net radiation error of −4.8 ± 20.7 W m−2, and soil heat flux error of −0.5 ± 24.5 W m−2. This good agreement with measured values indicates that the adopted methodology for estimating the energy balance components, using high-resolution airborne multispectral imagery, is appropriate for modeling latent heat fluxes. The method worked well for the unstable atmospheric conditions of the study. The footprint weighting/integration models tested indicate that they perform better than simple pixel averages upwind from the flux stations. In particular the flux source area model (footprint) seemed to better integrate the resulting heat flux image pixels. It is suggested that future studies test the methodology for heterogeneous surfaces under stable atmospheric conditions.

Two eddy covariance instrument comparison studies were conducted before and after the Soil Moisture–Atmosphere Coupling Experiment (SMACEX) field campaign to 1) determine if observations from multiple sensors were equivalent for the measured variables over a uniform surface and to 2) determine a least significant difference (LSD) value for each variable to discriminate between daily and hourly differences in latent and sensible heat and carbon dioxide fluxes, friction velocity, and standard deviation of the vertical wind velocity from eddy covariance instruments placed in different locations within the study area. The studies were conducted in early June over an alfalfa field and in mid-September over a short grass field. Several statistical exploratory, graphical, and multiple-comparison procedures were used to evaluate each daily variable. Daily total or average data were used to estimate a pooled standard error and corresponding LSD values at the P = 0.05 and P = 0.01 levels using univariate procedures. There were no significant sensor differences in any of the daily measurements for either intercomparison period. Hourly averaged data were used to estimate a pooled standard error and corresponding LSD values at the P = 0.05 and P = 0.01 levels using mixed model procedures. Sensor differences for pre- and post-intercomparisons were minimal for hourly and daily values of CO2, water vapor, sensible heat, friction velocity, and standard deviation for vertical wind velocity. Computed LSD values were used to determine significant daily differences and threshold values for the variables monitored during the SMACEX campaign.

A network of eddy covariance (EC) and micrometeorological flux (METFLUX) stations over corn (Zea mays L.) and soybean [Glycine max (L.) Merr.] canopies was established as part of the Soil Moisture–Atmosphere Coupling Experiment (SMACEX) in central Iowa during the summer of 2002 to measure fluxes of heat, water vapor, and carbon dioxide (CO2) during the growing season. Additionally, EC measurements of water vapor and CO2 fluxes from an aircraft platform complemented the tower-based measurements. Sensible heat, water vapor, and CO2 fluxes showed the greatest spatial and temporal variability during the early crop growth stage. Differences in all of the energy balance components were detectable between corn and soybean as well as within similar crops throughout the study period. Tower network–averaged fluxes of sensible heat, water vapor, and CO2 were observed to be in good agreement with area-averaged aircraft flux measurements.

Accurate estimation of surface energy fluxes from space at high spatial resolution has the potential to improve prediction of the impact of land-use changes on the local environment and to provide a means to assess local crop conditions. To achieve this goal, a combination of physically based surface flux models and high-quality remote-sensing data are needed. Data from the ASTER sensor are particularly well-suited to the task, as it collects high spatial resolution (15–90 m) images in visible, near-infrared, and thermal infrared bands. Data in these bands yield surface temperature, vegetation cover density, and land-use types, all critical inputs to surface energy balance models for assessing local environmental conditions. ASTER is currently the only satellite sensor collecting multispectral thermal infrared images, a capability allowing unprecedented surface temperature estimation accuracy for a variety of surface cover types. Availability of ASTER data to study surface energy fluxes allows direct comparisons against ground measurements and facilitates detection of modeling limitations, both possible because of ASTER's higher spatial resolution. Surface energy flux retrieval from ASTER is demonstrated using data collected over an experimental site in central Iowa, USA, in the framework of the Soil Moisture Atmosphere Coupling Experiment (SMACEX). This experiment took place during the summer of 2002 in a study of heterogeneous agricultural croplands. Two different flux estimation approaches, designed to account for the spatial variability, are considered: the Two-Source Energy Balance model (TSEB) and the Surface Energy Balance Algorithm or Land model (SEBAL). ASTER data are shown to have spatial and spectral resolution sufficient to derive surface variables required as inputs for physically based energy balance modeling. Comparison of flux model results against each other and against ground based measurements was promising, with flux values commonly agreeing within ∼50 W m− 2. Both TSEB and SEBAL showed systematic agreement and responded to spatially varying surface temperatures and vegetation densities. Direct comparison against ground Eddy Covariance data suggests that the TSEB approach is helpful over sparsely vegetated terrain.

NOAA AVHRR has been used extensively for monitoring vegetation condition and changes across the United States. Integration of crop growth models with MODIS imagery at 250 m resolution from the Terra Satellite potentially offers an opportunity for operational assessment of the crop condition and yield at both field and regional scales. The primary objective of this research was to evaluate the quality of the MODIS 250 m resolution data for retrieval of crop biophysical parameters that could be integrated in crop yield simulation models. A secondary objective was evaluating the potential use of MODIS 250 m resolution data for crop classification. A field study (24 fields) was conducted during the 2000 crop season in McLean County, Illinois, in the U.S. Midwest to evaluate the applicability of the MODIS 8-day, 250 m resolution composite imagery (version 4) for operational assessment of crop condition and yields. Ground-based canopy and leaf reflectance and leaf area index (LAI) measurements were used to calibrate a radiative transfer model to create a look up table (LUT) that was used to simulate LAI. The seasonal trend of MODIS derived LAI was used to find crop model parameters by adjusting the LAI simulated from the climate-based crop yield model. Other intermediate products such as crop phenological events were adjusted from the LAI seasonal profile. Corn (Zea mays L.) and soybean (Glycine max (L.) Merr.) yield simulations were conducted on a 1.6 × 1.6 km2 spatial resolution grid and the results integrated to the county level. The results were within 10% of county yields reported by the USDA National Agricultural Statistics Service (NASS).

Pesticide volatilization is a significant loss pathway that may have unintended consequences in nontarget environments. Field-scale pesticide volatilization involves the interaction of a number of complex variables. There is a need to acquire pesticide volatilization fluxes from a location where several of these variables can be held constant. Accordingly, soil properties, tillage practices, surface residue management, and pesticide formulations were held constant while fundamental information regarding metolachlor volatilization (a pre-emergent pesticide) was monitored over a five-year period as influenced by meteorological variables and soil water content. Metolachlor vapor concentrations were measured continuously for 120 h after each application using polyurethane foam plugs in a logarithmic profile above the soil surface. A flux gradient technique was used to compute volatilization fluxes from metolachlor concentration profiles and turbulent fluxes of heat and water vapor (as determined from eddy covariance measurements). Differences in meteorological conditions and surface soil water contents resulted in variability of the volatilization losses over the years studied. The peak volatilization losses for each year occurred during the first 24 h after application with a maximum flux rate in 2001 (1500 ng m(-2) s(-1)) associated with wet surface soil conditions combined with warm temperatures. The cumulative volatilization losses for the 120-hour period following metolachlor application varied over the years from 5 to 25% of the applied active ingredient, with approximately 87% of the losses occurring during the first 72 h. In all of the years studied, volatilization occurred diurnally and accounted for between 43 and 86% during the day and 14 and 57% during the night of the total measured loss. The results suggest that metolachlor volatilization is influenced by multiple factors involving meteorological, surface soil, and chemical factors.

With increased availability of satellite data products used in mapping surface energy balance and evapotranspiration (ET), routine ET monitoring at large scales is becoming more feasible. Daily satellite coverage is available, but an essential model input, surface temperature, is at 1 km or greater pixel resolution. At such coarse spatial resolutions, the capability to monitor the impact of land cover change and disturbances on ET or to evaluate ET from different crop covers is severely hampered. The effect of sensor resolution on model output for an agricultural region in central Iowa is examined using Landsat data collected during the Soil Moisture Atmosphere Coupling Experiment (SMACEX). This study was conducted in concert with the Soil Moisture Experiment 2002 (SMEX02). Two images collected during a rapid growth period in soybean and corn crops are used with a two-source (soil+vegetation) energy balance model, which explicitly evaluates soil and vegetation contributions to the radiative temperature and to the net turbulent exchange/surface energy balance. The pixel resolution of the remote sensing inputs are varied from 60 m to 120, 240, and 960 m. Model output at high resolution are first validated with tower and aircraft-based flux measurements to assure reliability of model computations. Histograms of the flux distributions and resulting statistics at the different pixel resolutions are compared and contrasted. Results indicate that when the input resolution is on the order of 1000 m, variation in fluxes, particularly ET, between corn and soybean fields is not feasible. However, results also suggest that thermal sharpening techniques for estimating surface temperature at higher resolutions (∼250 m) using the visible/near infrared waveband resolutions could provide enough spatial detail for discriminating ET from individual corn and soybean fields. Additional support for this nominal resolution requirement is deduced from a geostatistical analysis of the vegetation index and surface temperature images.

Monitoring crop condition and yields at regional scales using imagery from operational satellites remains a challenge because of the problem in scaling local yield simulations to the regional scales. NOAA AVHRR satellite imagery has been traditionally used to monitor vegetation changes that are used indirectly to assess crop condition and yields. Additionally, the 1-km spatial resolution of NOAA AVHRR is not adequate for monitoring crops at the field level. Imagery from the new MODIS sensor onboard the NASA Terra satellite offers an excellent opportunity for daily coverage at 250-m resolution, which is adequate to monitor field sizes are larger than 25 ha. A field study was conducted in the predominantly corn and soybean area of Iowa to evaluate the applicability of the 8-day MODIS composite imagery in operational assessment of crop condition and yields. Ground-based canopy reflectance and leaf area index (LAI) measurements were used to calibrate the models. The MODIS data was used in a radiative transfer model to estimate LAI through the season. LAI was integrated into a climate-based crop simulation model to scale from local simulation of crop development and responses to a regional scale. Simulations of corn and soybean yields at a 1.6×1.6-km2 grid scale were comparable to county yields reported by the USDA–National Agricultural Statistics Service (NASS). Weekly changes in soil moisture for the top 1-m profile were also simulated as part of the crop model as one of the critical parameters influencing crop condition and yields.

Watershed scale soil moisture estimates are necessary to validate current remote sensing products, such as those from the Advanced Microwave Scanning Radiometer (AMSR). Unfortunately, remote sensing technology does not currently resolve the land surface at a scale that is easily observed with ground measurements. One approach to validation is to use existing soil moisture measurement networks and scale these point observations up to the resolution of remote sensing footprints. As part of the Soil Moisture Experiment 2002 (SMEX02), one such soil moisture gaging system in the Walnut Creek Watershed, Iowa, provided robust estimates of the soil moisture average for a watershed throughout the summer of 2002. Twelve in situ soil moisture probes were installed across the watershed. These probes recorded soil moisture at a depth of 5 cm from June 29, 2002 to August 19, 2002. The sampling sites were analyzed for temporal and spatial stability by several measures including mean relative difference, Spearman rank, and correlation coefficient analysis. Representative point measurements were used to estimate the watershed scale (∼25 km) soil moisture average and shown to be accurate indicators with low variance and bias of the watershed scale soil moisture distribution. This work establishes the validity of this approach to provide watershed scale soil moisture estimates in this study region for the purposes of satellite validation with estimation errors as small as 3%. Also, the potential sources of error in this type of analysis are explored. This study is a first step in the implementation of large-scale soil moisture validation using existing networks such as the Soil Climate Analysis Network (SCAN) and several Agricultural Research Service watersheds as a basis for calibrating satellite soil moisture products, for networks design, and designing field experiments.

Aerodynamic parameters of roughness length ( z 0 ) and displacement height ( d 0 ) for semi-arid grassland, mixed grass–mesquite and mesquite rangeland were computed from eddy covariance measurements of sensible heat flux ( H ), standard deviations of vertical velocity and air temperature ( σ w and σ T ), and mean wind speed ( u ) within the framework of Monin–Obukhov similarity theory. Reasonable estimates of z 0 were obtained for all sites but unrealistic d 0 values resulted for most sites except mesquite. Independent estimates of z 0 and d 0 were also determined at the mesquite site with a traditional wind speed profile method and compared with results computed from an approach using laser altimeter data. Results showed good agreement among approaches. Also, within the framework of flux–variance using, σ T , and derived z 0 , d 0 with u , computed H fluxes were evaluated and compared to eddy covariance measurements.

Recent wind-farm studies have revealed elevated nighttime surface temperatures but have not validated physical mechanisms that create the observed effects. We report measurements of concurrent differences in surface wind speed, temperature, fluxes, and turbulence upwind and downwind of two turbine lines at the windward edge of a utility-scale wind farm. On the basis of these measurements, we offer a conceptual model based on physical mechanisms of how wind farms affect their own microclimate. Periods of documented curtailment and zero power production of the wind farm offer useful opportunities to rigorously evaluate the microclimate impact of both stationary and operating turbines. During an 80-min nighttime wind farm curtailment, we measured abrupt and large changes in turbulent fluxes of momentum and heat leeward of the turbines. At night, wind speed decreases in the near wake when turbines are off but abruptly increases when turbine operation is resumed. Our measurements are compared with MODIS Terra and Aqua satellite measurements reporting wind farms to have higher nighttime surface temperatures. We demonstrate that turbine wakes modify surface fluxes continuously through the night, with similar magnitudes during the Terra and Aqua transit periods. Cooling occurs in the near wake and warming in the far wake when turbines are on, but cooling is negligible when turbines are off.. Wind speed and surface stratification have a regulating effect of enhancing or decreasing the impact on surface microclimate due to turbine wake effects.

The Los Alamos National Laboratory scanning Raman lidar was used to measure the three-dimensional moisture field over a salt cedar canopy. A critical question concerning these measurements is; what are the spatial properties of the source region that contributes to the observed three-dimensional moisture field? Traditional methods used to address footprint properties rely on point sensor time-series data and the assumption of Taylor’s hypothesis to transform temporal data into the spatial domain. In this paper, the analysis of horizontal source-area size is addressed from direct lidar-based spatial analysis of the moisture field, eddy covariance co-spectra, and a dedicated footprint model. The results of these analysis techniques converged on the microscale average source region of between 25 and 75 m under ideal conditions. This work supports the concept that the scanning lidar can be used to map small scale boundary layer processes, including riparian zone moisture fields and fluxes.

Aerodynamic roughness length (z(0)) and displacement height (d(0)) are important surface parameters for estimating surface fluxes in numerical models. These parameters are generally determined from wind flow characteristics using logarithmic wind profiles measured at a meteorological tower or by balloon release. It would be an advantage to use measurements of land surface characteristics instead of wind flow characteristics to estimate the z(0), and d(0) for large areas. Important land surface characteristics are the size and distribution of roughness elements (obstacles). This research evaluates the use of high resolution laser altimeter data to obtain these land surface characteristics. Data were collected at the US Department of Agriculture, Agricultural Research Service (USDA-ARS), Jornada Experimental Range in southern New Mexico, USA over a coppice dune dominated area. These dunes are covered by honey mesquite (Prosopis glandulosa Torr.) with flat and mostly bare interdunal areas. For this analysis, three 450 m laser transects with a 2 cm measurement interval were used. The distribution and size of dunes were calculated from these laser transects and used to compute z(0). Analysis gave an average z(0) = 4.3 cm and d(0) = 70 cm for the three laser transects, which compares to z(0) = 7 +/- 4 cm and d(0) = 98 +/- 48 cm calculated from wind profile data measured at a 10 m tower near the laser transects. These results show that the estimation of z(0) and d(0) for a complex terrain is possible using simple land surface features computed from high resolution laser altimeter data.

Unacceptable levels of NO 3 leaching to ground water and drainage systems can occur under corn (Zea mays L.)-soybean [Glycine max (L.) Merr.] rotations. Cover crops have the potential to reduce NO 3 leaching, but this process has not been well documented. Lysimeters utilizing large soil monoliths are an excellent approach for studying NO 3 leaching because inputs can be controlled and outputs accurately measured. The objective of this study was to see if fall cover crops could reduce NO 3 leaching from large soil monoliths. We used three (1 by 1 by 1.5 m deep) monoliths in each of two controlled climate chambers with oat (Avena sativa L.) or rye (Secale cereale L.) fall cover crop interplanted into soybean in mid-August. The study was continued for two cover crop cycles in each chamber. In Chamber 1, drainage was significantly reduced due to oat or rye cover crops for the fall through summer of Years 1 and 2 (first cover crop cycle), and NO 3 loss was reduced for most of the same time period. In Chamber 2, NO 3 loss was reduced for the spring-summer season of the second year (first cover crop cycle). Although drainage was less under cover crops for Chamber 1, the soil water content was not consistently lower because of replenishment by watering. The soil monoliths were useful for showing that oat and rye cover crops in a corn-soybean rotation can reduce NO 3 leaching from lysimeters and suggest that the same trend would be true in the field.

Measurements of pesticide emission from a bare soil were used to calculate the turbulent Schmidt number (Sc): the ratio of eddy diffusivity for momentum (eddy viscosity) to the diffusivity for tracer mass. The value of Sc has implications for the measurement of trace gas emissions, and there is a broad range of reported values for the atmospheric surface layer. During our experiment Sc averaged 0.6, with large variability between observation periods. The standard deviation in Sc was 0.31, with no obvious correlation to atmospheric conditions. Some of this variability is due to measurement uncertainty, but we believe it also reflects true variability in Sc. We show that flux-gradient formula, which assume higher values of Sc, underestimate the true tracer emission rate Q. We also show that a dispersion model with Sc = 0.6, does better at inferring Q than a model with Sc = 0.45. Published by Elsevier Science B.V.

Analysis of field water balance components provides information necessary to minimize the risk of offsite movement of contaminants from crop production practices or animal manure applications. The objective of this study was to determine the timing and amount of surface runoff and drainage from the root zone for a hillslope in the Ozark Highlands of US. A 0.4 ha watershed with slopes of 8–20% having tall fescue ( Festuca arundinacea Schreb.) cover was established in northwestern Arkansas (35°56′W, 93°51′N). Continuous measurements of water balance parameters were made from June 1997 to August 1998. Soil water drainage was estimated as the residual of weekly water balance calculations. Runoff occurred in response to three precipitation events in the winter of 1998 and totaled 30.6 mm of water or 2.6% of the 1185 mm of precipitation that fell at the site during the study period. Storms of comparable or greater intensity during other seasons failed to produce runoff, a result that was likely due to dry soil conditions and taller grass canopy. Drainage through the root zone totaled 117 mm and occurred primarily during an 83-day interval in the winter of 1998. The water balance was dominated by evaporation, which accounted for 91% (1080 mm) of the precipitation. Tall fescue was capable of sustaining relatively high evaporation rates between infrequent summer rains thereby dewatering the soil profile, which was not replenished until winter. Delaying spring animal manure applications in the Ozarks until evaporation has increased and the soil profile has begun to dry would decrease the risk of offsite transport of potential contaminants contained in the manure.

A riparian corridor along the Rio Grande dominated by the Eurasian tamarisk or salt cedar (Tamarix spp.) is being studied to determine water and energy exchange rates using eddy covariance instrumentation mounted on a 12-m tower. The potential of using remotely sensed data to extrapolate these local estimates of the heat fluxes to large sections of the Rio Grande basin is under investigation. In particular, remotely sensed (radiometric) surface temperature can be used to estimate partitioning of net radiation energy into sensible and latent heat fluxes from vegetated landscapes. An important issue that has not been addressed adequately in the application of radiometric surface temperature data is the effect of using different time-averaged quantities in heat transfer formulations. This study evaluates the impact on sensible heat flux estimation of using relatively short time-averaged (1 min) canopy temperatures measured from a fixed-head infrared radiometer with 1-, 10-, and 30-min time-averaged ...

Water use efficiency (WUE) represents a given level of biomass or grain yield per unit of water used by the crop. With increasing concern about the availability of water resources in both irrigated and rainfed agriculture, there is renewed interest in trying to develop an understanding of how WUE can be improved and how farming systems can be modified to be more efficient in water use. This review and synthesis of the literature is directed toward understanding the role of soil management practices for WUE. Soil management practices affect the processes of evapotranspiration by modifying the available energy, the available water in the soil profile, or the exchange rate between the soil and the atmosphere. Plant management practices, e.g., the addition of N and P, have an indirect effect on water use through the physiological efficiency of the plant. A survey of the literature reveals a large variation in measured WUE across a range of climates, crops, and soil management practices. It is possible to increase WUE by 25 to 40% through soil management practices that involve tillage. Overall, precipitation use efficiency can be enhanced through adoption of more intensive cropping systems in semiarid environments and increased plant populations in more temperate and humid environments. Modifying nutrient management practices can increase WUE by 15 to 25%. Water use efficiency can be increased through proper management, and field-scale experiences show that these changes positively affect crop yield.

Nitrogen (N) loss from agricultural systems raises concerns about the potential impact of farming practices on environmental quality. N is a critical input to agricultural production. However, there is little understanding of the interactions among crop water use, N application rates, and soil types. This study was designed to quantify these interactions in corn (Zea mays L.) grown in production-size fields in central Iowa on the Clarion-Nicollet-Webster soil association. Seasonal water use varied by soil type and N application rate. Yield varied with N application rate, with the highest average yield obtained at 100 kg ha-1. N use efficiency (NUE) decreased with increasing N application rates, having values around 50%. Water use efficiency (WUE) decreased as N fertilizer rates increased. Analysis of plant growth patterns showed that in the low organic matter soils (lower water-holding capacities), potential yield was not achieved because of water deficits during the grain-filling period. Using precipitation data coupled with daily water use throughout the season, lower organic matter soils showed these soils began to drain earlier in the spring and continued to drain more water throughout the season. The low NUE in these soils together with increased drainage lead to greater N loss from these soils. Improved management decisions have shown that it is possible to couple water use patterns with N application to increase both WUE and NUE.

A method is presented in which estimates of evaporation may be made over an area approaching three quarters of a square kilometer, with relatively fine (25 m) spatial resolution, using three-dimensional measurements of water vapor concentration from a scanning Raman lidar. The method is based upon Monin‐Obukhov similarity theory applied to spatially and temporally averaged data. Data from the lidar is used to sense the location and orientation of the surface and the location of the water vapor measurements with respect to that surface. Maps of the spatial distribution of evaporation have been produced showing the evaporation rates at regular intervals throughout the day. The method was applied to the SALSA experimental site during the 1997 summer field campaign. The estimates of evaporation rates made during the campaign compare favorably with estimates made using sap flux methods with RMS differences of 18 W/m 2 . While the method has certain limitations, the three-dimensional character of the data allows for the detection of anomalous situations so that analysts may alter the analysis technique or reject the estimates from the affected regions. This information can be used in a wide variety of ways to study the spatial variations in evaporation caused by changes in soil type and moisture content, canopy type and topography. © 2000 Elsevier Science B.V. All rights reserved.

Surface temperature serves as a key boundary condition that defines the partitioning of surface radiation into sensible and latent heat fluxes. Surface brightness temperature measurements from satellites offer the unique possibility of mapping surface heat fluxes at regional scales. Because uncertainties in satellite measurements of surface radiometric temperature arise from atmospheric corrections, surface emissivity, and instrument calibrations, a number of studies have found significant discrepancies between modeled and measured heat fluxes when using radiometric temperature. Recent research efforts have overcome these uncertainties and in addition have accounted for the difference between radiometric and aerodynamic temperature by considering soil and vegetative-canopy aerodynamic resistances. The major remaining obstacle to using satellite data for regional heat flux estimation is inadequate density of near-surface air temperature observations. In this paper we describe a simple, operational, double-difference approach for relating surface sensible heat flux to remote observations of surface brightness temperature, vegetative cover and type, and measurements of near-surface wind speed and air temperature from the synoptic weather network. A double difference of the time rate of change in radiometric and air temperature observations is related to heat flux. This double-difference approach reduces both the errors associated with deriving a radiometric temperature and with defining meteorological quantities at large scales. The scheme is simpler than other recent approaches because it requires minimal ground-based data and does not require modeling boundary layer development. The utility of this scheme is tested with ground-based radiometric temperature observations from several arid and subhumid climates with a wide range of vegetative cover and meteorological conditions.

Independent measurements of the major energy balance flux components are not often consistent with the principle of conservation of energy. This is referred to as a lack of closure of the surface energy balance. Most results in the literature have shown the sum of sensible and latent heat fluxes measured by eddy covariance to be less than the difference between net radiation and soil heat fluxes. This under-measurement of sensible and latent heat fluxes by eddy-covariance instruments has occurred in numerous field experiments and among many different manufacturers of instruments. Four eddy-covariance systems consisting of the same models of instruments were set up side-by-side during the Southern Great Plains 1997 Hydrology Experiment and all systems under-measured fluxes by similar amounts. One of these eddy-covariance systems was collocated with three other types of eddy-covariance systems at different sites; all of these systems under-measured the sensible and latent-heat fluxes. The net radiometers and soil heat flux plates used in conjunction with the eddy-covariance systems were calibrated independently and measurements of net radiation and soil heat flux showed little scatter for various sites. The 10% absolute uncertainty in available energy measurements was considerably smaller than the systematic closure problem in the surface energy budget, which varied from 10 to 30%. When available-energy measurement errors are known and modest, eddy-covariance measurements of sensible and latent heat fluxes should be adjusted for closure. Although the preferred method of energy balance closure is to maintain the Bowen‐ratio, the method for obtaining closure appears to be less important than assuring that eddy-covariance measurements are consistent with conservation of energy. Based on numerous measurements over a sorghum canopy, carbon dioxide fluxes, which are measured by eddy covariance, are underestimated by the same factor as eddy covariance evaporation measurements when energy balance closure is not achieved. Published by Elsevier Science B.V.

For many natural and agricultural landscapes, vegetation partially covers the ground surface, resulting in significant variations in soil heat flux between interspace areas and underneath vegetation. This is particularly apparent in arid and semiarid regions where vegetation cover is low and clustered or ‘clumped’ with large areas of exposed soil. Surface heterogeneity presents significant challenges to the use of standard micro-meteorological measurement techniques for estimating surface energy balance components. The objective of this study was to use an array of 20 soil heat flux plates and soil temperature sensors to characterize the spatial and temporal variability in soil heat flux as affected by vegetation and micro-topographic effects of mesquite dunes in the Jornada Experimental Range in southern New Mexico. Maximum differences in soil heat flux among sensors were nearly 300 W m 2 . Maximum differences among individual sensors under similar cover conditions (i.e. no cover or interdune, partial or open canopy cover and full canopy cover) were significant, reaching values of 200‐250 W m 2 . The ‘area-average’ soil heat flux from the array was compared with an estimate using three sensors from a nearby micro-meteorological station. These sensors were positioned to obtain soil heat flux estimates representative of the three main cover conditions: namely, no cover or interdune, partial or open canopy cover, and full canopy cover. Comparisons between the array-average soil heat flux and the three-sensor system indicate that maximum differences on the order of 50 to nearly 100 W m 2 are obtained in the early morning and mid-afternoon periods, respectively. These discrepancies are caused by shading from the vegetation and micro-topography. The array-derived soil heat flux also produced a significantly higher temporal varying soil heat flux/net radiation ratio than what has been observed in other studies under more uniform cover conditions. Results from this study suggest that, to determine the number and location of sensors needed for estimating area-average soil heat flux in this type of landscape, one needs to account not only for the clustering of the vegetation cover but also micro-topography. Published by Elsevier Science B.V.

Rainfall amounts and the distribution across the landscape are critical to decision-making and evaluation of hydrological models. Spatial variation in rainfall has been observed through anecdotal evidence and limited studies; however, there is little quantitative evidence that can be used to assess rainfall variation within a watershed on a daily, monthly, or yearly temporal scale. This study was conducted to quantify the spatial variation within a watershed in central Iowa and to determine if there were consistent differences among rain gages for the period from 1991 through 1998. The study was conducted within Walnut Creek watershed located south of Ames, Iowa on the Des Moines Lobe Landform region. The topography of this 5130 ha watershed is characterized by gently rolling fields with a narrow area of steeper land along the stream in the lower part of the watershed. Twenty-two tipping bucket rain gages were placed throughout the watershed and rainfall was recorded as 5 minute totals and then aggregated into daily totals. Accumulation of errors of the 5 minute values into the daily totals were considered to be random. There was a large coefficient of variation in the average daily totals; however, there was no consistent pattern of variation among rain gages, and coefficient of variation decreased with amount of rain. Each rain gage had an equal chance of receiving the lowest or highest rainfall total for any given storm event. When the daily average was computed over the year, there were no differences among rain gages. Monthly and yearly totals showed a decreased coefficient of variation compared to daily totals. There was no consistent pattern of spottiness within the watershed and if daily rainfall amounts are required for a decision, then direct measurements may be required.

The presence of autocorrelation in the analysis of a variable sampled sequentially at regular time intervals appears to be unknown to many agricultural meteorologists despite abundant documentation found in the traditional meteorological and statistical literature. It follows that the statistical consequences as well as methodological alternatives are also unknown. Through an example using paired radiometer observations, this note discusses recognition of autocorrelation as well as the importance of testing ordinary least squares regression parameters in the presence of autocorrelated residuals. An autoregression example is presented as one alternative way to analyze the given dataset.

Volatilization of pesticides has been considered to be a large part of the loss from fields after application; however, there have been relatively few studies conducted to quantify the amount lost to the atmosphere. This study was designed to evaluate volatilization rates of a preemergent herbicide Dual [a.i. metolachlor 2.24 kg ha -1 (2-chloro-N-(2-ethyl-6-methylphenyl)-N-(2-methoxy-1-methylethyl)-acetamide)] from two fields (175 and 45 ha) in central Iowa under contrasting tillage operations and pesticide application scenarios. Metolachlor vapor was trapped using polyurethane foam plugs at six heights on a profile mast. Average concentrations were measured at each height every 2 h for the first 4 d after application, then every 4 h thereafter. Volatilization rates were calculated with a flux-gradient technique using data from Bowen ratio and eddy correlation measurements. Flux profile concentrations of metolachlor were large during the first 24 h and quickly declined for the duration of the study. There was approximately a threefold greater cumulative volatilization loss of applied metolachlor from the broadcast (22%) treatment than from the banded treatment (6%) for the 10-d study period. Metolachlor volatilization was affected by residue cover, spatial concentration on the surface, and timing of precipitation.

Nonpoint-source pollution has been linked to agricultural practices; however, there is a need for quantitative information describing the effect of specific farming practices on ground and surface water quality. Lack of information at the watershed scale limits our ability to make decisions about the effect of potential changes in either farming practices or landscape management that would enhance water quality. A multidisciplinary study was designed to evaluate the effect of farming practices on subsurface drainage, surface runoff, stream discharge, groundwater, volatilization, and soil processes that influence water quality. Walnut Creek watershed is a 5130-ha intensively cropped area in central Iowa on the Des Moines Lobe landform region. Soils within the watershed are in the Clarion-Nicollet-Webster (Typic Hapludoll-Aquic Hapludoll-Typic Haplaquoll) soil association, and the underlying surficial material is glacial till. Land use is predominantly corn (Zea mays L.)-soybean [Glycine max (L.) Merr.] rotation. Fertilizer use, herbicide application, tillage practices, and crop selection were obtained through surveys of each field operator. Atrazine [6-chloro-N-ethyl-N'-(1-methylethyl)-1,3,5-triazine-2,4-diamine], cyanazine [2-[ 4-chloro-6-(ethylamino)-1,3,5-triazin-2-yl amino]-2-methylpropanenitrile], EPTC [S-ethyl dipropyl carbamothioate], and metolachlor [2-chloro-N-(2-ethyl-6-methylphenyl)-N-(2-methoxy-1-methylethyl)acetamide] are the primary herbicides used within the watershed at rates similar to those for the state. Nitrogen fertilizer was applied as anhydrous ammonia on 60% of the corn fields at an average rate of 153 kg ha -1 for the 1991-1994 period, but the frequency of corn fields receiving

Transport of agricultural chemicals in runoff and recharge waters from snowmelt and soil thawing may represent a significant event in terms of annual contaminant loadings in temperate regions. Improved understanding of the melt dynamics of shallow snowpacks is necessary to fully assess the implications for water quality. The objective of this study was to measure the energy balance components of a corn (Zea mays L.) stubble field during the melting of its snowcover. Net radiation (Rn), soil (G), sensible (H), and latent (Q) heat fluxes were measured in a field near Ames, Iowa, during the winter of 1994–1995. Energy consumed by melting including change in energy storage of the snowpack was determined as the residual of the measured energy balance. There was continuous snowcover at the field site for 71 days (maximum depth = 222 mm) followed by an open period of 11 days before additional snowfall and a second melt period. The net radiation and snow melt/energy storage change (5) terms dominated the energy balance during both measurement intervals. Peak daily sensible and latent heat fluxes were below 100 W m−2 on all days except the last day of the second melt period. There was good agreement between predicted and measured values of H and Q during the melting of an aged snow layer but poorer agreement during the melt of fresh snow. Both snowpacks melted rapidly and coincident changes in soil moisture storage were observed. Improved estimates of Q and H, especially for partially open surfaces, will require better characterization of the surface aerodynamic properties and spatially-representative surface temperature measurements.

Studies of surface energy and water balance generally require an accurate estimate of net radiation and its spatial distribution. A project quantifying both short term and seasonal water use of shrub and grass vegetation in the Jornada Experimental Range in New Mexico prompted a study to compare net radiation observations using two types of net radiometers currently being used in research. A set of 12 REBS net radiometers were compared with each other and one Swissteco, over wet and dry surfaces in an arid landscape under clear skies. The set of REBS exhibited significant differences in output over both surfaces. However, they could be cross calibrated to yield values within 10 W m −2 , on average. There was also a significant bias between the REBS and Swissteco over a dry surface, but not over a wet one. The two makes of instrument could be made to agree under the dry conditions by using regression or autoregression techniques. However, the resulting equations would induce bias for the wet surface condition. Thus, it is not possible to cross calibrate these two makes of radiometer over the range of environmental conditions observed. This result indicates that determination of spatial distribution of net radiation over a variable surface should be made with identical instruments which have been cross calibrated. The need still exists for development of a radiometer and calibration procedures which will produce accurate and consistent measurements over a range of surface conditions.

Crop residues on the soil surface have the potential to significantly affect the magnitude of individual components of the surface energy balance. Previous research has concentrated on residue effects on soil temperature and moisture early in the growing season. The objective of this study was to measure each of the surface energy balance components of a field during snow-free periods between successive growing seasons. A Bowen ratio system was used to measure surface fluxes within a no-tillage corn ( Zea mays L.) field near Ames, IA, USA. During the fall, large solar zenith angles and short daylengths resulted in −2 d −1 of available energy ( R n − G ). On overcast days with a dry surface, average daytime Bowen ratios ( β ) were β values were >2.3 and β values (1.0 and 1.5 on sunny vs. 0.87 and 1.84 on overcast days) while less available energy was used to evaporate water on sunny days ( 38% on overcast days). More energy was available (up to 12.9 MJ m −2 d −1 ) during the spring measurement interval with daytime Bowen ratios averaging 1.7 and 0.8 on sunny and overcast days, respectively. With overcast conditions and wet soil, evaporation approached potential rates predicted by both the Priestley–Taylor and Penman–Monteith equations. With clear skies and wet soil, Penman–Monteith estimates using a residue resistance term agreed well with measured values.

A study was conducted to evaluate sensible and latent heat fluxes near shelterbelts under low wind conditions using eddy correlation. The study site was located at the Agricultural Meteorological Research Center in Mead, Nebraska during the summer of 1995. A wheat stubble and alfalfa field surrounded by a shelterbelt 12 m in height were instrumented with surface energy balance systems using eddy correlation that were located in the windward and leeward side of the shelterbelts for both fields. Estimates of sensible and latent heat fluxes were compared for the windward and leeward sides of the shelterbelt for both surfaces. Daily sensible heat fluxes tended to be larger in the open location relative to the sheltered while latent heat fluxes tended to be higher behind the shelterbelt. Overall surface energy balance closure during the study averaged 0.85 indicating reliable consistent estimates of turbulence flux using eddy correlation near shelterbelts.

Methane (CH 4 ) is an important greenhouse gas, and it has been estimated that 50% of annual CH 4 comes from terrestrial systems. Better and more accurate methods are needed to quantify CH 4 flux from terrestrial environments. Two general methods commonly applied to measure trace gas fluxes are soil cover (chamber) techniques, and micrometeorology methods. Both of these methods has advantages and disadvantages, yet little information is available concerning the relative performance of the techniques. This study was conducted to compare CH 4 flux measurements obtained by using a dosed-chamber soil cover technique and a micrometeorological method (Bowen-ratio Energy Balance [BREB]). Methane flux rates obtained by both methods were compared using nine time points over 3 d at a peatland site in north central Minnesota. Mean CH 4 fluxes obtained by both methods were of the same magnitude (2.43-5.88 mg CH 4 m -2 h -1 ; however, differences were observed in the magnitudes of temporal variability as well as the detection sensitivities (minimum detectable flux). The minimum detectable flux for the closed-chamber method was 9.32 x 10 -2 mg CH 4 m -2 h -1 , while the minimum detectable flux for the BREB method ranged from 2.16 to 25.5 mg CH 4 m -2 h -1 . Due to analytical uncertainties associated with gas chromatographic determination of CH 4 gradients, the BREB is not recommended.

Water use in agriculture by different cropping systems is of interest in determining crop water use efficiency of different tillage practices that will lead to reduced crop production risk. Lysimeters are considered the standard for evapotranspiration (ET) measurements; however, these units are often not replicated and are few in number at any given location. Our objective was to determine if a simple Bowen-ratio system with nonexchanging psychrometers could provide accurate measurements of ET from lentil (Lens culinaris Medikus) in a semiarid climate. The study was conducted in 1993 and 1994 on two adjacent 180- by 180-m fields with weighing lysimeters (1.68 by 1.68 by 1.83 m) located in the center of each field, on a Williams loam (fine-loamy, mixed Typic Argiboroll) soil near Sidney, MT. A Bowen-ratio system comprised of two nonexchanging psychrometers and anemometers at 0.25 and 1.25 m above the plant canopy surface was placed in the lentil field along with a net radiometer and soil heat flux plate. Precipitation during the growing season from planting to swathing was 367 mm in 1993 and 227 mm in 1994. In 1993, soil water content of the lysimeter was greater than the field after large precipitation events around Day of Year (DOY) 210, even though the lysimeter was drained. After this time, the lysimeter ET exceeded that measured by the Bowen-ratio system. Agreement was closer in 1994, when precipitation was near normal and there was no excess soil water in the lysimeter. Cumulative ET totals from the lysimeter were reflective of the seasonal precipitation patterns. Differences between the lysimeter and Bowen-ratio occurred when there was excess precipitation and inadequate drainage from the lysimeter. Half-hourly ET fluxes from lysimeter and Bowen-ratio values agreed to within 10% throughout the season. Bowen-ratio systems with nonexchanging psychrometers can provide satisfactory estimates of daily and seasonal ET and can be used to estimate ET in semiarid climates.

Crop residues on the soil surface absorb solar radiation and have reflectivity properties that may differ significantly from the underlying soil. The objective of this study was to measure the temporal variation in solar radiation reflectivity and transmissivity of a corn ( Zea mays L.) residue layer under field conditions. Incident and reflected solar and visible radiation were measured in a field with standing corn stubble and com residue prostrate on the soil surface. Transmitted solar and visible radiation were measured with line sensors placed beneath the prostrate residue. Measurements were made during snow-free periods from October 1994 to April 1995. Mean reflectivity decreased from 0.20 ± 0.02, 0.12 ± 0.02, and 0.27 ± 0.03 for the solar, visible, and near-infrared wavebands during the fall, to 0.17 ± 0.01, 0.11 ± 0.01, and 0.22 ± 0.02 during the spring measurement period, respectively. Transmissivity of solar and visible radiation through the com residue layers was directly proportional to residue area index. Extinction coefficients for solar and visible radiation ranged from 0.79 to 0.96 and were higher in the fall for both wavebands. Diurnal patterns of reflectivity and transmissivity showed a sensitivity to the proportion of beam vs. diffuse radiation and wetness of the residue.