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William P. Kustas, John H. Prueger, and Lawrence E. Hipps

Abstract

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 micrometeorological input data used in estimating the resistance to heat transfer. The results indicate that, with short time-averaged radiometric surface temperatures (essentially “instantaneous” from a satellite), variations in sensible heat flux strongly correlate to fluctuations in net radiation conditions. Under near-constant net radiation input, natural perturbations in surface temperature also contribute to variations in sensible heat flux but are typically an order of magnitude smaller. The resulting implications for computed heat flux estimates using data from remotely sensing platforms and validation with flux tower measurements along riparian corridors are discussed.

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William P. Kustas, Jerry L. Hatfield, and John H. Prueger

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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.

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JoséL. Chávez, Christopher M. U. Neale, Lawrence E. Hipps, John H. Prueger, and William P. Kustas

Abstract

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.

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William P. Kustas, John H. Prueger, Karen S. Humes, and Patrick J. Starks

Abstract

Radiometric surface temperature observations T R(ϕ), near-surface meteorological/surface energy flux (METFLUX), and atmospheric boundary layer (ABL) data were collected during the Washita ’94 Experiment conducted in the Little Washita Experimental Watershed near Chickasha, Oklahoma. The T R(ϕ) measurements were made from ground and aircraft platforms near the METFLUX stations located over vegetated surfaces of varying amounts of cover and over bare soil. Continuous, half-hourly averaged ground-based T R(ϕ) measurements essentially at the point scale were calibrated with periodic ground transect and aircraft-based T R(ϕ) observations at coarser resolutions so that the continuous T R(ϕ) measurements would be representative of surface temperatures at the field scale (i.e., on the order of 104 m2). The METFLUX data were collected nominally at 2 m above the surface, while ABL measurements were made in the lower 8–10 km of the atmosphere. The “local” wind speed, u, and air temperature, T A, from the METFLUX stations, as well as the mixed-layer wind speed, U M, and potential temperature, ΘM, were used in a two-source energy balance model for computing fluxes with continuous T R(ϕ) measurements from the various surfaces. Standard Monin–Obukhov surface layer similarity was used with the “local” u and T A data from the METFLUX stations. Bulk similarity approaches were used with the U M and ΘM data referenced either to ABL height or the top of the surface layer. This latter approach of using mixed-layer data to drive model computations for the different sites is similar to the so-called flux-aggregation schemes or methods proposed to account for subgrid variability in atmospheric models, such as the“tile” or “mosaic” approach. There was less agreement between modeled and measured fluxes when using mixed-layer versus local meteorological variables data for driving the model, and the type of bulk formulation used (i.e., whether local or regional surface roughness was used) also had a significant impact on the results. Differences between the flux observations and model predictions using surface layer similarity with local u and T A data were about 25% on average, while using the bulk formulations with U M and ΘM differences averaged about 30%. This larger difference was caused by an increase in biases and scatter between modeled and measured fluxes for some sites. Therefore, computing spatially distributed local-scale fluxes with ABL observations of mixed-layer properties will probably yield less reliable flux predictions than using local meteorological data, if available. Given the uncertainty in flux observations is about 20%, these estimates are still considered reasonable and moreover permit the mapping of spatially distributed surface fluxes at regional scales using a single observation of U M and ΘM with high resolution T R(ϕ) data. Such T R(ϕ) observations with a 90-m pixel resolution will be available from the Advanced Spaceborne Thermal Emission and Reflection Radiometer to be launched on NASA’s Earth Observing System.

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William P. Kustas, John H. Prueger, J. Ian MacPherson, Mengistu Wolde, and Fuqin Li

Abstract

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〉.

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Fuqin Li, William P. Kustas, John H. Prueger, Christopher M. U. Neale, and Thomas J. Jackson

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 < 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.

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Evan J. Coopersmith, Michael H. Cosh, Walt A. Petersen, John Prueger, and James J. Niemeier

Abstract

Soil moisture monitoring with in situ technology is a time-consuming and costly endeavor for which a method of increasing the resolution of spatial estimates across in situ networks is necessary. Using a simple hydrologic model, the estimation capacity of an in situ watershed network can be increased beyond the station distribution by using available precipitation, soil, and topographic information. A study site was selected on the Iowa River, characterized by homogeneous soil and topographic features, reducing the variables to precipitation only. Using 10-km precipitation estimates from the North American Land Data Assimilation System (NLDAS) for 2013, high-resolution estimates of surface soil moisture were generated in coordination with an in situ network, which was deployed as part of the Iowa Flood Studies (IFloodS). A simple, bucket model for soil moisture at each in situ sensor was calibrated using four precipitation products and subsequently validated at both the sensor for which it was calibrated and other proximal sensors, the latter after a bias correction step. Average RMSE values of 0.031 and 0.045 m3 m−3 were obtained for models validated at the sensor for which they were calibrated and at other nearby sensors, respectively.

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Martha C. Anderson, J. M. Norman, William P. Kustas, Fuqin Li, John H. Prueger, and John R. Mecikalski

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.

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Christopher J. Watts, Russell L. Scott, Jaime Garatuza-Payan, Julio C. Rodriguez, John H. Prueger, William P. Kustas, and Michael Douglas

Abstract

The vegetation in the core region of the North American monsoon (NAM) system changes dramatically after the onset of the summer rains so that large changes may be expected in the surface fluxes of radiation, heat, and moisture. Most of this region lies in the rugged terrain of western Mexico and very few measurements of these fluxes have been made in the past. Surface energy balance measurements were made at seven sites in Sonora, Mexico, and Arizona during the intensive observation period (IOP) of the North American Monsoon Experiment (NAME) in summer 2004 to better understand how land surface vegetation change alters energy flux partitioning. Satellite data were used to obtain time series for vegetation indices and land surface temperature for these sites. The results were analyzed to contrast conditions before the onset of the monsoon with those afterward. As expected, precipitation during the 2004 monsoon was highly variable from site to site, but it fell in greater quantities at the more southern sites. Likewise, large changes in the vegetation index were observed, especially for the subtropical sites in Sonora. However, the changes in the broadband albedo were very small, which was rather surprising. The surface net radiation was consistent with the previous observations, being largest for surfaces that are transpiring and cool, and smallest for surfaces that are dry and hot. The largest evaporation rates were observed for the subtropical forest and riparian vegetation sites. The evaporative fraction for the forest site was highly correlated with its vegetation index, except during the dry spell in August. This period was clearly detected in the land surface temperature data, which rose steadily in this period to a maximum at its end.

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Nurit Agam, William P. Kustas, Martha C. Anderson, John M. Norman, Paul D. Colaizzi, Terry A. Howell, John H. Prueger, Tilden P. Meyers, and Tim B. Wilson

Abstract

The Priestley–Taylor (PT) approximation for computing evapotranspiration was initially developed for conditions of a horizontally uniform saturated surface sufficiently extended to obviate any significant advection of energy. Nevertheless, the PT approach has been effectively implemented within the framework of a thermal-based two-source model (TSM) of the surface energy balance, yielding reasonable latent heat flux estimates over a range in vegetative cover and climate conditions. In the TSM, however, the PT approach is applied only to the canopy component of the latent heat flux, which may behave more conservatively than the bulk (soil + canopy) system. The objective of this research is to investigate the response of the canopy and bulk PT parameters to varying leaf area index (LAI) and vapor pressure deficit (VPD) in both natural and agricultural vegetated systems, to better understand the utility and limitations of this approximation within the context of the TSM. Micrometeorological flux measurements collected at multiple sites under a wide range of atmospheric conditions were used to implement an optimization scheme, assessing the value of the PT parameter for best performance of the TSM. Overall, the findings suggest that within the context of the TSM, the optimal canopy PT coefficient for agricultural crops appears to have a fairly conservative value of ∼1.2 except when under very high vapor pressure deficit (VPD) conditions, when its value increases. For natural vegetation (primarily grasslands), the optimal canopy PT coefficient assumed lower values on average (∼0.9) and dropped even further at high values of VPD. This analysis provides some insight as to why the PT approach, initially developed for regional estimates of potential evapotranspiration, can be used successfully in the TSM scheme to yield reliable heat flux estimates over a variety of land cover types.

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