Soil Moisture Atmosphere Coupling Experiment Research Articles

A One-Source Approach for Estimating Land Surface Heat Fluxes Using Remotely Sensed Land Surface Temperature

The partitioning of available energy between sensible heat and latent heat is important for precise water resources planning and management in the context of global climate change. Land surface temperature (LST) is a key variable in energy balance process and remotely sensed LST is widely used for estimating surface heat fluxes at regional scale. However, the inequality between LST and aerodynamic surface temperature (Taero) poses a great challenge for regional heat fluxes estimation in one-source energy balance models. To address this issue, we proposed a One-Source Model for Land (OSML) to estimate regional surface heat fluxes without requirements for empirical extra resistance, roughness parameterization and wind velocity. The proposed OSML employs both conceptual VFC/LST trapezoid model and the electrical analog formula of sensible heat flux (H) to analytically estimate the radiometric-convective resistance (rae) via a quartic equation. To evaluate the performance of OSML, the model was applied to the Soil Moisture-Atmosphere Coupling Experiment (SMACEX) in United States and the Multi-Scale Observation Experiment on Evapotranspiration (MUSOEXE) in China, using remotely sensed retrievals as auxiliary data sets at regional scale. Validated against tower-based surface fluxes observations, the root mean square deviation (RMSD) of H and latent heat flux (LE) from OSML are 34.5 W/m2 and 46.5 W/m2 at SMACEX site and 50.1 W/m2 and 67.0 W/m2 at MUSOEXE site. The performance of OSML is very comparable to other published studies. In addition, the proposed OSML model demonstrates similar skills of predicting surface heat fluxes in comparison to SEBS (Surface Energy Balance System). Since OSML does not require specification of aerodynamic surface characteristics, roughness parameterization and meteorological conditions with high spatial variation such as wind speed, this proposed method shows high potential for routinely acquisition of latent heat flux estimation over heterogeneous areas.

An enhanced two-source evapotranspiration model for land (ETEML): Algorithm and evaluation

Satellite remote sensing provides a promising way to estimate regional evapotranspiration (ET) in a spatially distributed manner. In this study, an enhanced two-source evapotranspiration model for land (ETEML) is proposed based on a trapezoid framework of the vegetation fractional cover and land surface temperature (VFC/LST) space. In ETEML, a VFC/LST trapezoid space is theoretically defined for each pixel, and a pixel-wise mixed surface temperature decomposition method is proposed. ETEML is based on a two-source scheme, and the crop water stress index (CWSI) concept is applied to parameterize the soil evaporation and the vegetation transpiration separately. The proposed model was applied to the Soil Moisture–Atmosphere Coupling Experiment (SMACEX) site in central Iowa, USA. Evaluation with a remotely sensed dataset from Landsat was carried out to assess the performance of ETEML. Compared with the tower observations, the mean absolute deviation (MAD) and the root mean square deviation (RMSD) for the ETEML estimated latent heat flux (LE) are, respectively, 49W/m2 and 59W/m2, comparable to retrieval accuracies published in other studies. Comparison between ETEML and variations on a simpler trapezoid interpolation model (TIM1 and TIM2) indicates that ETEML reduces the subjectivity and uncertainties involved in TIM1 and TIM2. Overall, the results suggest that ETEML is promising and can expand the application of the trapezoid framework-based ET modeling approaches to heterogeneous surfaces.

Validation of a practical normalized soil moisture model with in situ measurements in humid and semi-arid regions

Soil moisture is an important parameter that influences the exchange of water and energy fluxes between the land surface and the atmosphere. Through the simulation by a Soil–Vegetation–Atmosphere Transfer model, Carlson proposed the universal spatial information-based method to determine soil moisture that is insensitive to the initial atmospheric and surface conditions, net radiation, and atmospheric correction. In this study, a practical normalized soil moisture model is established to describe the relationship among the normalized soil moisture (M), the normalized land surface temperature (T*), and the fractional vegetation cover. The dry and wet points are determined using the surface energy balance principle, which has a robust physical basis. This method is applied to retrieve soil moisture for the Soil Moisture-Atmosphere Coupling Experiment campaign in the Walnut Creek watershed, which has a humid climate, and at the Linzestation, which has a semi-arid climate. The validation data are obtained on days of year (DOYs) 182 and 189 in 2002 in the humid region and on DOYs 148 and 180 in 2008 for the semi-arid region; these data collection days are coincident with the overpass of the Landsat Thematic Mapper/Enhanced Thematic Mapper Plus. When the estimates are compared with the in situ measurements of soil water content, the root mean square error is approximately 0.10 m3 m−3 with a bias of 0.05 m3 m−3 for the humid region and 0.08 m3 m−3 with a bias of 0.03 m3 m−3 for the semi-arid region. These results demonstrate that the practical normalized soil moisture model is applicable in both humid and semi-arid regions.

A hybrid dual‐source scheme and trapezoid framework–based evapotranspiration model (HTEM) using satellite images: Algorithm and model test

Satellite remote sensing techniques are widely considered as the most promising way to estimate evapotranspiration (ET) over large geographic extents. In this study, a hybrid dual‐source scheme and trapezoid framework–based evapotranspiration model (HTEM) is developed to map evapotranspiration from satellite imagery. It adopts a theoretically determined vegetation index/land surface temperature trapezoidal space to decompose bulk radiative surface temperature into component temperatures (soil and canopy) and uses a hybrid dual‐source scheme of the layer approach and patch approach to partition net radiation and estimate sensible and latent fluxes separately from the soil and canopy. The proposed model was tested at the Soil Moisture‐Atmosphere Coupling Experiment (SMACEX) site in central Iowa, USA, for 3 days during the campaign in 2002 using Landsat Thematic Mapper/Enhanced Thematic Mapper Plus (TM/ETM+) data, and at the Weishan flux site in the North China Plain during the main growing season of 2007 with Moderate Resolution Imaging Spectroradiometer Terra images. Results indicate that HTEM is capable of estimating latent heat flux (LE) with mean absolute percentage errors of 6.4% and 11.2% for the SMACEX and the Weishan sites, respectively. In addition, the model was found to be able to give reasonable evaporation and transpiration partitioning at both sites. Compared with other models, HTEM generally produced better sensible and latent flux estimates at the two sites and had comparable abilities in estimating net radiation and ground heat flux. Sensitivity analysis suggests that HTEM is most sensitive to temperature variables and less sensitive to other meteorological observations and parameters.

A Two-source Trapezoid Model for Evapotranspiration (TTME) from satellite imagery

This study develops a Two-source Trapezoid Model for Evapotranspiration (TTME) from satellite imagery by interpreting the remotely sensed fractional vegetation cover (fc)–radiative surface temperature (Trad) space and the concept of soil surface moisture availability isopleths superimposed on the space. The theoretical upper boundary condition of TTME is determined by solving for temperatures of the driest bare surface (Ts,max) and the driest fully vegetated surface (Tc,max) both implicit in radiation budget and energy balance equations. Air temperature (Ta) constitutes the lower boundary of TTME. Trad of a pixel within the fc–Trad space is decomposed into temperature components (Tc and Ts) by interpolating the slope of the theoretical boundaries and interpreting variation in Trad with fc for each isopiestic line going across the pixel. Vegetation transpiration and soil surface evaporation are then separately parameterized. TTME was applied to the Soil Moisture-Atmosphere Coupling Experiment (SMACEX) site in central Iowa, U.S., on three days in 2002 during the period of rapid growth in corn and soybean when three scenes of Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper Plus (ETM+) images and one scene of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) image were acquired. Results indicate that TTME is capable of reproducing latent heat flux (LE) with a mean absolute percentage difference (MAPD) of ~10%, and a root mean square difference (RMSD) of 45.6Wm−2 and 63.1Wm−2 for Landsat TM/ETM+ and ASTER images, respectively. Comparison of TTME with other one-source and two-source models using the same data set suggests that TTME shows comparable accuracy as the Two-Source Energy Balance (TSEB), but requires relatively fewer inputs and obviates the computation of resistance networks in the modeling domain and the overestimation of vegetation transpiration incurred by using the Priestley–Taylor equation. Sensitivity analysis suggests that TTME is most sensitive to Trad and Ta, but not sensitive to a range of meteorological observations and variables and parameters derived/specified.

A modified surface energy balance algorithm for land (M‐SEBAL) based on a trapezoidal framework

The surface energy balance algorithm for land (SEBAL) has been designed and widely used (and misused) worldwide to estimate evapotranspiration across varying spatial and temporal scales using satellite remote sensing over the past 15 yr. It is, however, beset by visual identification of a hot and cold pixel to determine the temperature difference (dT) between the surface and the lower atmosphere, which is assumed to be linearly correlated with surface radiative temperature (Trad) throughout a scene. To reduce ambiguity in flux estimation by SEBAL due to the subjectivity in extreme pixel selection, this study first demonstrates that SEBAL is of a rectangular framework of the contextual relationship between vegetation fraction (fc) and Trad, which can distort the spatial distribution of heat flux retrievals to varying degrees. End members of SEBAL were replaced by a trapezoidal framework of the fc‐Trad space in the modified surface energy balance algorithm for land (M‐SEBAL). The warm edge of the trapezoidal framework is determined by analytically deriving temperatures of the bare surface with the largest water stress and the fully vegetated surface with the largest water stress implicit in both energy balance and radiation budget equations. Areally averaged air temperature (Ta) across a study site is taken to be the cold edge of the trapezoidal framework. Coefficients of the linear relationship between dT and Trad can vary with fc but are assumed essentially invariant for the same fc or within the same fc class in M‐SEBAL. SEBAL and M‐SEBAL are applied to the soil moisture‐atmosphere coupling experiment (SMACEX) site in central Iowa, U.S. Results show that M‐SEBAL is capable of reproducing latent heat flux in terms of an overall root‐mean‐square difference of 41.1 W m−2 and mean absolute percentage difference of 8.9% with reference to eddy covariance tower‐based measurements for three landsat thematic mapper/enhanced thematic mapper plus imagery acquisition dates in 2002. The retrieval accuracy of SEBAL is generally lower than M‐SEBAL, depending largely on the selected extremes. Spatial distributions of heat flux retrievals from SEBAL are distorted to a certain degree due to its intrinsic rectangular framework.

Reliable estimation of evapotranspiration on agricultural fields predicted by the Priestley–Taylor model using soil moisture data from ground and remote sensing observations compared with the Common Land Model

Evapotranspiration (ET) is a crucial factor in understanding the hydrological cycle and is essential to many applications in hydrology, ecology and water resources management. However, reliable ET measurements and predictions for a range of temporal and spatial scales are difficult. This study focused on the comparison of ET estimates using a relatively simple model, the Priestley–Taylor (P-T) approach, and the physically based Common Land Model (CLM) using ground and remotely sensed soil moisture data as input. The results from both models were compared directly with hourly eddy covariance measurements at two agricultural field sites during the Soil Moisture–Atmosphere Coupling Experiment (SMACEX) in the corn soybean production region in the Upper Midwest, USA. The P-T model showed a significant overestimation of the potential ET compared to the measurements, with a root mean square error (RMSE) between 115 and 130 W m–2. Actual ET was better predicted by the CLM, with the RMSE ranging between 50 and 75 W m–2. However, actual ET from the P-T model constrained with a soil moisture dependency parameterization showed improved results when compared to the measurements, with a significantly reduced bias and RMSE values between 60 and 65 W m–2. This study suggests that even with a simple semi-empirical ET model, similar performance in estimating actual ET for agricultural crops compared to more complex land surface–atmosphere models (i.e. the CLM) can be achieved when constrained with the soil moisture function. This suggests that remote sensing soil moisture estimates from the Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E) and others such as the Soil Moisture and Ocean Salinity (SMOS) mission may be effective alternatives under certain environmental conditions for estimating actual ET of agricultural crops using a fairly simple algorithm.

Evaluation of a fully coupled large‐eddy simulation–land surface model and its diagnosis of land‐atmosphere feedbacks

Daytime land‐atmosphere interactions are the result of two‐way coupled processes where the land states strongly affect the overlying atmospheric properties and vice versa. This study presents a numerical framework integrating a radiative parameterization, a large‐eddy simulation of the atmospheric boundary layer (ABL), and a force‐restore land surface model to investigate these coupled processes and the impact of local‐scale atmospheric feedbacks on surface fluxes and states. Using field measurements collected during the Soil Moisture–Atmosphere Coupling Experiment 2002, a 12 h daytime diurnal cycle simulation is performed to evaluate the coupled model performance. The estimates of surface fluxes, near‐surface micrometeorological air properties, surface temperature, and vertical profiles of boundary layer characteristics are compared to the measurements collected by meteorological towers, satellites, and radiosondes. Good agreement is obtained in comparisons between model outputs and observations at both footprint‐ and domain‐averaged scales indicating that such a coupled model can reasonably represent the coupled land‐ABL system. Using the verified model baseline, simulated results for cases with different degrees of coupling show that the estimates of surface fluxes and near‐surface states are mainly dominated by the feedback of air temperature and wind speed, respectively. The feedback of each air property on surface fluxes and states for different vegetation cover can be either positive or negative, with the feedbacks via air temperature and wind speed playing opposite roles in the estimation of sensible heat flux. Thus, the total feedback of multiple air properties increases if all individual impacts are of the same sign; otherwise, the impacts partially cancel each other. Results indicate that the impact of ignoring atmospheric feedbacks (i.e., the local‐scale spatial variability of near‐surface air properties due to coupled interactions) results in a clear error in the estimation of sensible heat flux (up to 18%) and near‐surface soil moisture (up to 10%). The direct impacts of feedbacks on latent heat flux and surface temperature are relatively unimportant for the case examined.

An intercomparison of three remote sensing-based surface energy balance algorithms over a corn and soybean production region (Iowa, U.S.) during SMACEX

Reliable estimation of the surface energy balance from local to regional scales is crucial for many applications including weather forecasting, hydrologic modeling, irrigation scheduling, water resource management, and climate change research. Numerous models have been developed using remote sensing, which permits spatially distributed mapping of the surface energy balance over large areas. This study compares flux maps over a relatively simple agricultural landscape in central Iowa, comprised of soybean and corn fields, generated with three different remote sensing-based surface energy balance models: the Two-Source Energy Balance (TSEB) model, Mapping EvapoTranspiration at high Resolution using Internalized Calibration (METRIC), and the Trapezoid Interpolation Model (TIM). The three models have different levels of complexity and input requirements, but all have operational capabilities. METRIC and TIM make use of the remotely sensed surface temperature–vegetation cover relation to define key model variables linked to wet and dry hydrologic extremes, while TSEB uses these remotely sensed inputs to define component soil and canopy temperatures, aerodynamic resistances, and fluxes. The models were run using Landsat imagery collected during the Soil Moisture Atmosphere Coupling Experiment (SMACEX) in 2002 and model results were compared with observations from a network of flux towers deployed within the study area. While TSEB and METRIC yielded similar and reasonable agreement with measured heat fluxes, with root-mean-square errors (RMSE) of ∼50–75 W/m 2, errors for TIM exceeded 100 W/m 2. Despite the good agreement between TSEB and METRIC at discrete locations sampled by the flux towers, a spatial intercomparison of gridded model output (i.e., comparing output on a pixel-by-pixel basis) revealed significant discrepancies in modeled turbulent heat flux patterns that were largely correlated with vegetation density. Generally, the largest discrepancies, primarily a bias in H, between these two models occurred in areas with partial vegetation cover and a leaf area index (LAI) < 2.0. Adjustment of the minimum LE assumed for the hot/dry hydrologic extreme condition in METRIC reduced the bias in H between METRIC and TSEB, but caused a significant increase in bias in LE between the models. Spatial intercomparison of modeled flux patterns over a variety of landscapes will be required to better assess uncertainties in remote sensing surface energy balance models, and to work toward an improved hybrid modeling system.

A comparison of operational remote sensing-based models for estimating crop evapotranspiration

The integration of remotely sensed data into models of evapotranspiration (ET) facilitates the estimation of water consumption across agricultural regions. To estimate regional ET, two basic types of remote sensing approaches have been successfully applied. The first approach computes a surface energy balance using the radiometric surface temperature for estimating the sensible heat flux ( H), and obtaining ET as a residual of the energy balance. This paper compares the performance of three different surface energy balance algorithms: an empirical one-source energy balance model; a one-source model calibrated using inverse modeling of ET extremes (namely ET = 0 and ET at potential) which are assumed to exist within the satellite scene; and a two-source (soil + vegetation) energy balance model. The second approach uses vegetation indices derived from canopy reflectance data to estimate basal crop coefficients that can be used to convert reference ET to actual crop ET. This approach requires local meteorological and soil data to maintain a water balance in the root zone of the crop. Output from these models was compared to sensible and latent heat fluxes measured during the soil moisture–atmosphere coupling experiment (SMACEX) conducted over rain-fed corn and soybean crops in central Iowa. The root mean square differences (RMSD) of the estimation of instantaneous latent and heat fluxes were less than 50 W m −2 for the three energy balance models. The two-source energy balance model gave the lowest RMSD (30 W m −2) and highest r 2 values in comparison with measured fluxes. In addition, three schemes were applied for upscaling instantaneous flux estimates from the energy balance models (at the time of satellite overpass) to daily integrated ET, including conservation of evaporative fraction and fraction of reference ET. For all energy balance models, an adjusted evaporative fraction approach produced the lowest RMSDs in daily ET of 0.4–0.6 mm d −1. The reflectance-based crop coefficient model yielded RMSD values of 0.4 mm d −1, but tended to significantly overestimate ET from corn during a prolonged drydown period. Crop stress can be directly detected using radiometric surface temperature, but ET modeling approaches-based solely on vegetation indices will not be sensitive to stress until there is actual reduction in biomass or changes in canopy geometry.

Estimates of the potential temperature profile from lidar measurements of boundary layer evolution

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 mixed layer height at the edge between a soybean and a corn field. The height and thickness of the entrainment zone are used to estimate the vertical potential temperature profile in the boundary layer using surface energy measurements in the Batchvarova‐Gryning mixed layer model. Calculated values of potential temperature compared well to radiosonde measurements taken simultaneously with the lidar measurements. The root‐mean‐square difference between the lidar‐derived values and the balloon‐based values is 1.20°C.

Comparing the utility of microwave and thermal remote-sensing constraints in two-source energy balance modeling over an agricultural landscape

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 Soil Moisture–Atmosphere Coupling Experiment (SMACEX): Background, Hydrometeorological Conditions, and Preliminary Findings

Abstract 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.

Intercomparison of Spatially Distributed Models for Predicting Surface Energy Flux Patterns during SMACEX

Abstract The treatment of aerodynamic surface temperature in soil–vegetation–atmosphere transfer (SVAT) models can be used to classify approaches into two broad categories. The first category contains models utilizing remote sensing (RS) observations of surface radiometric temperature to estimate aerodynamic surface temperature and solve the terrestrial energy balance. The second category contains combined water and energy balance (WEB) approaches that simultaneously solve for surface temperature and energy fluxes based on observations of incoming radiation, precipitation, and micrometeorological variables. To date, few studies have focused on cross comparing model predictions from each category. Land surface and remote sensing datasets collected during the 2002 Soil Moisture–Atmosphere Coupling Experiment (SMACEX) provide an opportunity to evaluate and intercompare spatially distributed surface energy balance models. Intercomparison results presented here focus on the ability of a WEB-SVAT approach [the TOPmodel-based Land–Atmosphere Transfer Scheme (TOPLATS)] and an RS-SVAT approach [the Two-Source Energy Balance (TSEB) model] to accurately predict patterns of turbulent energy fluxes observed during SMACEX. During the experiment, TOPLATS and TSEB latent heat flux predictions match flux tower observations with root-mean-square (rms) accuracies of 67 and 63 W m−2, respectively. TSEB predictions of sensible heat flux are significantly more accurate with an rms accuracy of 22 versus 46 W m−2 for TOPLATS. The intercomparison of flux predictions from each model suggests that modeling errors for each approach are sufficiently independent and that opportunities exist for improving the performance of both models via data assimilation and model calibration techniques that integrate RS- and WEB-SVAT energy flux predictions.

Tower and Aircraft Eddy Covariance Measurements of Water Vapor, Energy, and Carbon Dioxide Fluxes during SMACEX

Abstract 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.

Modeling Evapotranspiration during SMACEX: Comparing Two Approaches for Local- and Regional-Scale Prediction

Abstract The Surface Energy Balance System (SEBS) model was developed to estimate land surface fluxes using remotely sensed data and available meteorology. In this study, a dual assessment of SEBS is performed using two independent, high-quality datasets that are collected during the Soil Moisture–Atmosphere Coupling Experiment (SMACEX). The purpose of this comparison is twofold. First, using high-quality local-scale data, model-predicted surface fluxes can be evaluated against in situ observations to determine the accuracy limit at the field scale using SEBS. To accomplish this, SEBS is forced with meteorological data derived from towers distributed throughout the Walnut Creek catchment. Flux measurements from 10 eddy covariance systems positioned on these towers are used to evaluate SEBS over both corn and soybean surfaces. These data allow for an assessment of modeled fluxes during a period of rapid vegetation growth and varied hydrometeorology. Results indicate that SEBS can predict evapotranspiration with accuracies approaching 10%–15% of that of the in situ measurements, effectively capturing the temporal development of surface flux patterns for both corn and soybean, even when the evaporative fraction ranges between 0.50 and 0.90. Second, utilizing high-resolution remote sensing data and operational meteorology, a catchment-scale examination of model performance is undertaken. To extend the field-based assessment of SEBS, information derived from the Landsat Enhanced Thematic Mapper (ETM) and data from the North American Land Data Assimilation System (NLDAS) were combined to determine regional surface energy fluxes for a clear day during the field experiment. Results from this analysis indicate that prediction accuracy was strongly related to crop type, with corn predictions showing improved estimates compared to those of soybean. Although root-mean-square errors were affected by the limited number of samples and one poorly performing soybean site, differences between the mean values of observations and SEBS Landsat-based predictions at the tower sites were approximately 5%. Overall, results from this analysis indicate much potential toward routine prediction of surface heat fluxes using remote sensing data and operational meteorology.

Effects of Vegetation Clumping on Two–Source Model Estimates of Surface Energy Fluxes from an Agricultural Landscape during SMACEX

Abstract The effects of nonrandom leaf area distributions on surface flux predictions from a two-source thermal remote sensing model are investigated. The modeling framework is applied at local and regional scales over the Soil Moisture–Atmosphere Coupling Experiment (SMACEX) study area in central Iowa, an agricultural landscape that exhibits foliage organization at a variety of levels. Row-scale clumping in area corn- and soybean fields is quantified as a function of view zenith and azimuth angles using ground-based measurements of canopy architecture. The derived clumping indices are used to represent subpixel clumping in Landsat cover estimates at 30-m resolution, which are then aggregated to the 5-km scale of the regional model, reflecting field-to-field variations in vegetation amount. Consideration of vegetation clumping within the thermal model, which affects the relationship between surface temperature and leaf area inputs, significantly improves model estimates of sensible heating at both local and watershed scales in comparison with eddy covariance data collected by aircraft and with a ground-based tower network. These results suggest that this economical approach to representing subpixel leaf area hetereogeneity at multiple scales within the two-source modeling framework works well over the agricultural landscape studied here.

Determining Meaningful Differences for SMACEX Eddy Covariance Measurements

Abstract 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.

Utility of Remote Sensing–Based Two-Source Energy Balance Model under Low- and High-Vegetation Cover Conditions

Abstract 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 &amp;lt; 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.

Lidar Measurement of Boundary Layer Evolution to Determine Sensible Heat Fluxes

Abstract 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.