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William P Kustas - One of the best experts on this subject based on the ideXlab platform.
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application of Remote Sensing based two source energy balance model for mapping field surface fluxes with composite and component surface temperatures
Agricultural and Forest Meteorology, 2016Co-Authors: William P Kustas, Ji Zhou, Lisheng Song, Shaomin Liu, Ting XiaAbstract:Abstract Operational application of a Remote Sensing-based two source energy balance model (TSEB) to estimate evaportranspiration ( ET ) and the components evaporation ( E ), transpiration ( T ) at a range of space and time scales is very useful for managing water resources in arid and semiarid watersheds. The TSEB model uses composite land surface temperature as input and applies a simplified Priestley–Taylor formulation to partition this temperature into soil and vegetation component temperatures and then computes subsequent component energy fluxes. The Remote Sensing-based TSEB model using component temperatures of the soil and canopy has not been adequately evaluated due to a dearth of reliable observations. In this study, soil and vegetation component temperatures partitioned from visible and near infrared and Thermal Remote Sensing data supplied by advanced scanning Thermal emission and reflection radiometer (ASTER) are applied as model inputs (TSEB CT ) to assess and refine the subsequent component energy fluxes estimation in TSEB scheme under heterogeneous land surface conditions in an advective environment. The model outputs including sensible heat flux ( H ), latent heat flux ( LE ), component LE from soil and canopy from the TSEB CT and original model (TSEB PT ) are compared with ground measurements from eddy covariance (EC) and larger aperture scintillometers (LAS) technique, and stable isotopic method. Both model versions yield errors of about 10% with LE observations. However, the TSEB CT model output of H and LE are in closer agreement with the observations and is found to be generally more robust in component flux estimation compared to the TSEB PT using the ASTER data in this heterogeneous advective environment. Thus given accurate soil and canopy temperatures, TSEB CT may provide more reliable estimates of plant water use and values of water use efficiency at large scales for water resource management in arid and semiarid landscapes.
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application of Remote Sensing based two source energy balance model for mapping field surface fluxes with composite and component surface temperatures
Agricultural and Forest Meteorology, 2016Co-Authors: William P Kustas, Ji Zhou, Lisheng Song, Shaomin Liu, Ziwei Xu, Ting Xia, Mingsong LiAbstract:Abstract Operational application of a Remote Sensing-based two source energy balance model (TSEB) to estimate evaportranspiration ( ET ) and the components evaporation ( E ), transpiration ( T ) at a range of space and time scales is very useful for managing water resources in arid and semiarid watersheds. The TSEB model uses composite land surface temperature as input and applies a simplified Priestley–Taylor formulation to partition this temperature into soil and vegetation component temperatures and then computes subsequent component energy fluxes. The Remote Sensing-based TSEB model using component temperatures of the soil and canopy has not been adequately evaluated due to a dearth of reliable observations. In this study, soil and vegetation component temperatures partitioned from visible and near infrared and Thermal Remote Sensing data supplied by advanced scanning Thermal emission and reflection radiometer (ASTER) are applied as model inputs (TSEB CT ) to assess and refine the subsequent component energy fluxes estimation in TSEB scheme under heterogeneous land surface conditions in an advective environment. The model outputs including sensible heat flux ( H ), latent heat flux ( LE ), component LE from soil and canopy from the TSEB CT and original model (TSEB PT ) are compared with ground measurements from eddy covariance (EC) and larger aperture scintillometers (LAS) technique, and stable isotopic method. Both model versions yield errors of about 10% with LE observations. However, the TSEB CT model output of H and LE are in closer agreement with the observations and is found to be generally more robust in component flux estimation compared to the TSEB PT using the ASTER data in this heterogeneous advective environment. Thus given accurate soil and canopy temperatures, TSEB CT may provide more reliable estimates of plant water use and values of water use efficiency at large scales for water resource management in arid and semiarid landscapes.
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Upscaling of evapotranspiration fluxes from instantaneous to daytime scales for Thermal Remote Sensing applications
Hydrology and Earth System Sciences, 2014Co-Authors: C. Cammalleri, M. C. Anderson, William P KustasAbstract:Four upscaling methods for estimating daytime actual evapotranspiration (ET) from single time-of-day snapshots, as commonly retrieved using Remote Sensing, were compared. These methods assume self-preservation of the ratio between ET and a given reference variable over the daytime hours. The analysis was performed using eddy covariance data collected at 12 AmeriFlux towers, sampling a fairly wide range in climatic and land cover conditions. The choice of energy budget closure method significantly impacted performance using different scaling methodologies. Therefore, a statistical evaluation approach was adopted to better account for the inherent uncertainty in ET fluxes using eddy covariance technique. Overall, this approach suggested that at-surface solar radiation was the most robust reference variable amongst those tested, due to high accuracy of upscaled fluxes and absence of systematic biases. Top-of-atmosphere irradiance was also tested and proved to be reliable under near clear-sky conditions, but tended to overestimate the observed daytime ET during cloudy days. Use of reference ET as a scaling flux yielded higher bias than the solar radiation method, although resulting errors showed similar lack of seasonal dependence. Finally, the commonly used evaporative fraction method yielded satisfactory results only in summer months, July and August, and tended to underestimate the observations in the fall/winter seasons from November to January at the flux sites studied. In general, the proposed methodology clearly showed the added value of an intercomparison of different upscaling methods under scenarios that account for the uncertainty in eddy covariance flux measurements due to closure errors.
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Evaluating the two-source energy balance model using local Thermal and surface flux observations in a strongly advective irrigated agricultural area
Advances in Water Resources, 2012Co-Authors: William P Kustas, A. N. French, Steven R Evett, Paul D Colaizzi, Joseph G Alfieri, Christopher M U Neale, John H Prueger, Martha C Anderson, Lawrence E. Hipps, José L. ChávezAbstract:Application and validation of many Thermal Remote Sensing-based energy balance models involve the use of local meteorological inputs of incoming solar radiation, wind speed and air temperature as well as accurate land surface temperature (LST), vegetation cover and surface flux measurements. For operational applications at large scales, such local information is not routinely available. In addition, the uncertainty in LST estimates can be several degrees due to sensor calibration issues, atmospheric effects and spatial variations in surface emissivity. Time differencing techniques using multi-temporal Thermal Remote Sensing observations have been developed to reduce errors associated with deriving the surface-air temperature gradient, particularly in complex landscapes. The Dual-Temperature-Difference (DTD) method addresses these issues by utilizing the Two-Source Energy Balance (TSEB) model of Norman et al. (1995) [1], and is a relatively simple scheme requiring meteorological input from standard synoptic weather station networks or mesoscale modeling. A comparison of the TSEB and DTD schemes is performed using LST and flux observations from eddy covariance (EC) flux towers and large weighing lysimeters (LYs) in irrigated cotton fields collected during BEAREX08, a large-scale field experiment conducted in the semi-arid climate of the Texas High Plains as described by Evett et al. (2012) [2]. Model output of the energy fluxes (i.e., net radiation, soil heat flux, sensible and latent heat flux) generated with DTD and TSEB using local and Remote meteorological observations are compared with EC and LY observations. The DTD method is found to be significantly more robust in flux estimation compared to the TSEB using the Remote meteorological observations. However, discrepancies between model and measured fluxes are also found to be significantly affected by the local inputs of LST and vegetation cover and the representativeness of the Remote Sensing observations with the local flux measurement footprint. © 2012.
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evaluation of drought indices based on Thermal Remote Sensing of evapotranspiration over the continental united states
Journal of Climate, 2011Co-Authors: Martha C Anderson, Christopher Hain, Brian D Wardlow, Agustin Pimstein, John R Mecikalski, William P KustasAbstract:AbstractThe reliability of standard meteorological drought indices based on measurements of precipitation is limited by the spatial distribution and quality of currently available rainfall data. Furthermore, they reflect only one component of the surface hydrologic cycle, and they cannot readily capture nonprecipitation-based moisture inputs to the land surface system (e.g., irrigation) that may temper drought impacts or variable rates of water consumption across a landscape. This study assesses the value of a new drought index based on Remote Sensing of evapotranspiration (ET). The evaporative stress index (ESI) quantifies anomalies in the ratio of actual to potential ET (PET), mapped using Thermal band imagery from geostationary satellites. The study investigates the behavior and response time scales of the ESI through a retrospective comparison with the standardized precipitation indices and Palmer drought index suite, and with drought classifications recorded in the U.S. Drought Monitor for the 2000–0...
Martha C Anderson - One of the best experts on this subject based on the ideXlab platform.
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an intercomparison of drought indicators based on Thermal Remote Sensing and nldas 2 simulations with u s drought monitor classifications
Journal of Hydrometeorology, 2013Co-Authors: Martha C Anderson, Christopher Hain, Jason A Otkin, Xiwu Zhan, Mark Svoboda, Brian D Wardlow, Agustin PimsteinAbstract:AbstractComparison of multiple hydrologic indicators, derived from independent data sources and modeling approaches, may improve confidence in signals of emerging drought, particularly during periods of rapid onset. This paper compares the evaporative stress index (ESI)—a diagnostic fast-response indicator describing evapotranspiration (ET) deficits derived within a Thermal Remote Sensing energy balance framework—with prognostic estimates of soil moisture (SM), ET, and runoff anomalies generated with the North American Land Data Assimilation System (NLDAS). Widely used empirical indices based on Thermal Remote Sensing [vegetation health index (VHI)] and precipitation percentiles [standardized precipitation index (SPI)] were also included to assess relative performance. Spatial and temporal correlations computed between indices over the contiguous United States were compared with historical drought classifications recorded in the U.S. Drought Monitor (USDM). Based on correlation results, improved forms for...
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Evaluating the two-source energy balance model using local Thermal and surface flux observations in a strongly advective irrigated agricultural area
Advances in Water Resources, 2012Co-Authors: William P Kustas, A. N. French, Steven R Evett, Paul D Colaizzi, Joseph G Alfieri, Christopher M U Neale, John H Prueger, Martha C Anderson, Lawrence E. Hipps, José L. ChávezAbstract:Application and validation of many Thermal Remote Sensing-based energy balance models involve the use of local meteorological inputs of incoming solar radiation, wind speed and air temperature as well as accurate land surface temperature (LST), vegetation cover and surface flux measurements. For operational applications at large scales, such local information is not routinely available. In addition, the uncertainty in LST estimates can be several degrees due to sensor calibration issues, atmospheric effects and spatial variations in surface emissivity. Time differencing techniques using multi-temporal Thermal Remote Sensing observations have been developed to reduce errors associated with deriving the surface-air temperature gradient, particularly in complex landscapes. The Dual-Temperature-Difference (DTD) method addresses these issues by utilizing the Two-Source Energy Balance (TSEB) model of Norman et al. (1995) [1], and is a relatively simple scheme requiring meteorological input from standard synoptic weather station networks or mesoscale modeling. A comparison of the TSEB and DTD schemes is performed using LST and flux observations from eddy covariance (EC) flux towers and large weighing lysimeters (LYs) in irrigated cotton fields collected during BEAREX08, a large-scale field experiment conducted in the semi-arid climate of the Texas High Plains as described by Evett et al. (2012) [2]. Model output of the energy fluxes (i.e., net radiation, soil heat flux, sensible and latent heat flux) generated with DTD and TSEB using local and Remote meteorological observations are compared with EC and LY observations. The DTD method is found to be significantly more robust in flux estimation compared to the TSEB using the Remote meteorological observations. However, discrepancies between model and measured fluxes are also found to be significantly affected by the local inputs of LST and vegetation cover and the representativeness of the Remote Sensing observations with the local flux measurement footprint. © 2012.
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evaluation of drought indices based on Thermal Remote Sensing of evapotranspiration over the continental united states
Journal of Climate, 2011Co-Authors: Martha C Anderson, Christopher Hain, Brian D Wardlow, Agustin Pimstein, John R Mecikalski, William P KustasAbstract:AbstractThe reliability of standard meteorological drought indices based on measurements of precipitation is limited by the spatial distribution and quality of currently available rainfall data. Furthermore, they reflect only one component of the surface hydrologic cycle, and they cannot readily capture nonprecipitation-based moisture inputs to the land surface system (e.g., irrigation) that may temper drought impacts or variable rates of water consumption across a landscape. This study assesses the value of a new drought index based on Remote Sensing of evapotranspiration (ET). The evaporative stress index (ESI) quantifies anomalies in the ratio of actual to potential ET (PET), mapped using Thermal band imagery from geostationary satellites. The study investigates the behavior and response time scales of the ESI through a retrospective comparison with the standardized precipitation indices and Palmer drought index suite, and with drought classifications recorded in the U.S. Drought Monitor for the 2000–0...
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a Thermal based Remote Sensing technique for routine mapping of land surface carbon water and energy fluxes from field to regional scales
Remote Sensing of Environment, 2008Co-Authors: Martha C Anderson, William P Kustas, John M Norman, Rasmus Houborg, Patrick J Starks, Nurit AgamAbstract:Abstract Robust yet simple Remote Sensing methodologies for mapping instantaneous land-surface fluxes of water, energy and CO2 exchange within a coupled framework add significant value to large-scale monitoring networks like FLUXNET, facilitating upscaling of tower flux observations to address questions of regional carbon cycling and water availability. This study investigates the implementation of an analytical, light-use efficiency (LUE) based model of canopy resistance within a Two-Source Energy Balance (TSEB) scheme driven primarily by Thermal Remote Sensing inputs. The LUE model computes coupled canopy-scale carbon assimilation and transpiration fluxes, and replaces a Priestley–Taylor (PT) based transpiration estimate used in the original form of the TSEB model. In turn, the Thermal Remote Sensing data provide valuable diagnostic information about the sub-surface moisture status, obviating the need for precipitation input data and prognostic modeling of the soil water balance. Both the LUE and PT forms of the model are compared with eddy covariance tower measurements acquired in rangeland near El Reno, OK. The LUE method resulted in improved partitioning of the surface energy budget, capturing effects of midday stomatal closure in response to increased vapor pressure deficit and reducing errors in half-hourly flux predictions from 16 to 12%. The spatial distribution of CO2 flux was mapped over the El Reno study area using data from an airborne Thermal imaging system and compared to fluxes measured by an aircraft flying a transect over rangeland, riparian areas, and harvested winter wheat. Soil respiration contributions to the net carbon flux were modeled spatially using Remotely sensed estimates of soil surface temperature, soil moisture, and leaf area index. Modeled carbon and water fluxes from this heterogeneous landscape compared well in magnitude and spatial pattern to the aircraft fluxes. The Thermal inputs proved to be valuable in modifying the effective LUE from a nominal species-dependent value. The model associates cooler canopy temperatures with enhanced transpiration, indicating higher canopy conductance and carbon assimilation rates. The surface energy balance constraint in this modeling approach provides a useful and physically intuitive mechanism for incorporating subtle signatures of soil moisture deficiencies and reduced stomatal aperture, manifest in the Thermal band signal, into the coupled carbon and water flux estimates.
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Thermal Remote Sensing of drought and evapotranspiration
Eos Transactions American Geophysical Union, 2008Co-Authors: Martha C Anderson, William P KustasAbstract:Water lost to the atmosphere through evapotranspiration (ET; soil evaporation + canopy transpiration) serves to cool the Earth's surface. Just as a thermometer is used to diagnose stress in the human body, land surface temperature (LST) derived from Remote Sensing data in the Thermalinfrared (TIR) band (8–14 microns) is a valuable diagnostic of biospheric stress resulting from soil moisture deficiencies. Soil surface temperature increases with decreasing water content, while moisture depletion in the plant root zone leads to stomatal closure, reduced transpiration, and elevated canopy temperatures that can be effectively detected from space. In land surface modeling, TIR imagery can serve as an effective substitute for precipitation data, providing much needed water information in data-poor regions of the world.
Lisheng Song - One of the best experts on this subject based on the ideXlab platform.
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application of Remote Sensing based two source energy balance model for mapping field surface fluxes with composite and component surface temperatures
Agricultural and Forest Meteorology, 2016Co-Authors: William P Kustas, Ji Zhou, Lisheng Song, Shaomin Liu, Ting XiaAbstract:Abstract Operational application of a Remote Sensing-based two source energy balance model (TSEB) to estimate evaportranspiration ( ET ) and the components evaporation ( E ), transpiration ( T ) at a range of space and time scales is very useful for managing water resources in arid and semiarid watersheds. The TSEB model uses composite land surface temperature as input and applies a simplified Priestley–Taylor formulation to partition this temperature into soil and vegetation component temperatures and then computes subsequent component energy fluxes. The Remote Sensing-based TSEB model using component temperatures of the soil and canopy has not been adequately evaluated due to a dearth of reliable observations. In this study, soil and vegetation component temperatures partitioned from visible and near infrared and Thermal Remote Sensing data supplied by advanced scanning Thermal emission and reflection radiometer (ASTER) are applied as model inputs (TSEB CT ) to assess and refine the subsequent component energy fluxes estimation in TSEB scheme under heterogeneous land surface conditions in an advective environment. The model outputs including sensible heat flux ( H ), latent heat flux ( LE ), component LE from soil and canopy from the TSEB CT and original model (TSEB PT ) are compared with ground measurements from eddy covariance (EC) and larger aperture scintillometers (LAS) technique, and stable isotopic method. Both model versions yield errors of about 10% with LE observations. However, the TSEB CT model output of H and LE are in closer agreement with the observations and is found to be generally more robust in component flux estimation compared to the TSEB PT using the ASTER data in this heterogeneous advective environment. Thus given accurate soil and canopy temperatures, TSEB CT may provide more reliable estimates of plant water use and values of water use efficiency at large scales for water resource management in arid and semiarid landscapes.
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application of Remote Sensing based two source energy balance model for mapping field surface fluxes with composite and component surface temperatures
Agricultural and Forest Meteorology, 2016Co-Authors: William P Kustas, Ji Zhou, Lisheng Song, Shaomin Liu, Ziwei Xu, Ting Xia, Mingsong LiAbstract:Abstract Operational application of a Remote Sensing-based two source energy balance model (TSEB) to estimate evaportranspiration ( ET ) and the components evaporation ( E ), transpiration ( T ) at a range of space and time scales is very useful for managing water resources in arid and semiarid watersheds. The TSEB model uses composite land surface temperature as input and applies a simplified Priestley–Taylor formulation to partition this temperature into soil and vegetation component temperatures and then computes subsequent component energy fluxes. The Remote Sensing-based TSEB model using component temperatures of the soil and canopy has not been adequately evaluated due to a dearth of reliable observations. In this study, soil and vegetation component temperatures partitioned from visible and near infrared and Thermal Remote Sensing data supplied by advanced scanning Thermal emission and reflection radiometer (ASTER) are applied as model inputs (TSEB CT ) to assess and refine the subsequent component energy fluxes estimation in TSEB scheme under heterogeneous land surface conditions in an advective environment. The model outputs including sensible heat flux ( H ), latent heat flux ( LE ), component LE from soil and canopy from the TSEB CT and original model (TSEB PT ) are compared with ground measurements from eddy covariance (EC) and larger aperture scintillometers (LAS) technique, and stable isotopic method. Both model versions yield errors of about 10% with LE observations. However, the TSEB CT model output of H and LE are in closer agreement with the observations and is found to be generally more robust in component flux estimation compared to the TSEB PT using the ASTER data in this heterogeneous advective environment. Thus given accurate soil and canopy temperatures, TSEB CT may provide more reliable estimates of plant water use and values of water use efficiency at large scales for water resource management in arid and semiarid landscapes.
Ting Xia - One of the best experts on this subject based on the ideXlab platform.
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application of Remote Sensing based two source energy balance model for mapping field surface fluxes with composite and component surface temperatures
Agricultural and Forest Meteorology, 2016Co-Authors: William P Kustas, Ji Zhou, Lisheng Song, Shaomin Liu, Ting XiaAbstract:Abstract Operational application of a Remote Sensing-based two source energy balance model (TSEB) to estimate evaportranspiration ( ET ) and the components evaporation ( E ), transpiration ( T ) at a range of space and time scales is very useful for managing water resources in arid and semiarid watersheds. The TSEB model uses composite land surface temperature as input and applies a simplified Priestley–Taylor formulation to partition this temperature into soil and vegetation component temperatures and then computes subsequent component energy fluxes. The Remote Sensing-based TSEB model using component temperatures of the soil and canopy has not been adequately evaluated due to a dearth of reliable observations. In this study, soil and vegetation component temperatures partitioned from visible and near infrared and Thermal Remote Sensing data supplied by advanced scanning Thermal emission and reflection radiometer (ASTER) are applied as model inputs (TSEB CT ) to assess and refine the subsequent component energy fluxes estimation in TSEB scheme under heterogeneous land surface conditions in an advective environment. The model outputs including sensible heat flux ( H ), latent heat flux ( LE ), component LE from soil and canopy from the TSEB CT and original model (TSEB PT ) are compared with ground measurements from eddy covariance (EC) and larger aperture scintillometers (LAS) technique, and stable isotopic method. Both model versions yield errors of about 10% with LE observations. However, the TSEB CT model output of H and LE are in closer agreement with the observations and is found to be generally more robust in component flux estimation compared to the TSEB PT using the ASTER data in this heterogeneous advective environment. Thus given accurate soil and canopy temperatures, TSEB CT may provide more reliable estimates of plant water use and values of water use efficiency at large scales for water resource management in arid and semiarid landscapes.
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application of Remote Sensing based two source energy balance model for mapping field surface fluxes with composite and component surface temperatures
Agricultural and Forest Meteorology, 2016Co-Authors: William P Kustas, Ji Zhou, Lisheng Song, Shaomin Liu, Ziwei Xu, Ting Xia, Mingsong LiAbstract:Abstract Operational application of a Remote Sensing-based two source energy balance model (TSEB) to estimate evaportranspiration ( ET ) and the components evaporation ( E ), transpiration ( T ) at a range of space and time scales is very useful for managing water resources in arid and semiarid watersheds. The TSEB model uses composite land surface temperature as input and applies a simplified Priestley–Taylor formulation to partition this temperature into soil and vegetation component temperatures and then computes subsequent component energy fluxes. The Remote Sensing-based TSEB model using component temperatures of the soil and canopy has not been adequately evaluated due to a dearth of reliable observations. In this study, soil and vegetation component temperatures partitioned from visible and near infrared and Thermal Remote Sensing data supplied by advanced scanning Thermal emission and reflection radiometer (ASTER) are applied as model inputs (TSEB CT ) to assess and refine the subsequent component energy fluxes estimation in TSEB scheme under heterogeneous land surface conditions in an advective environment. The model outputs including sensible heat flux ( H ), latent heat flux ( LE ), component LE from soil and canopy from the TSEB CT and original model (TSEB PT ) are compared with ground measurements from eddy covariance (EC) and larger aperture scintillometers (LAS) technique, and stable isotopic method. Both model versions yield errors of about 10% with LE observations. However, the TSEB CT model output of H and LE are in closer agreement with the observations and is found to be generally more robust in component flux estimation compared to the TSEB PT using the ASTER data in this heterogeneous advective environment. Thus given accurate soil and canopy temperatures, TSEB CT may provide more reliable estimates of plant water use and values of water use efficiency at large scales for water resource management in arid and semiarid landscapes.
John H Prueger - One of the best experts on this subject based on the ideXlab platform.
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Evaluating the two-source energy balance model using local Thermal and surface flux observations in a strongly advective irrigated agricultural area
Advances in Water Resources, 2012Co-Authors: William P Kustas, A. N. French, Steven R Evett, Paul D Colaizzi, Joseph G Alfieri, Christopher M U Neale, John H Prueger, Martha C Anderson, Lawrence E. Hipps, José L. ChávezAbstract:Application and validation of many Thermal Remote Sensing-based energy balance models involve the use of local meteorological inputs of incoming solar radiation, wind speed and air temperature as well as accurate land surface temperature (LST), vegetation cover and surface flux measurements. For operational applications at large scales, such local information is not routinely available. In addition, the uncertainty in LST estimates can be several degrees due to sensor calibration issues, atmospheric effects and spatial variations in surface emissivity. Time differencing techniques using multi-temporal Thermal Remote Sensing observations have been developed to reduce errors associated with deriving the surface-air temperature gradient, particularly in complex landscapes. The Dual-Temperature-Difference (DTD) method addresses these issues by utilizing the Two-Source Energy Balance (TSEB) model of Norman et al. (1995) [1], and is a relatively simple scheme requiring meteorological input from standard synoptic weather station networks or mesoscale modeling. A comparison of the TSEB and DTD schemes is performed using LST and flux observations from eddy covariance (EC) flux towers and large weighing lysimeters (LYs) in irrigated cotton fields collected during BEAREX08, a large-scale field experiment conducted in the semi-arid climate of the Texas High Plains as described by Evett et al. (2012) [2]. Model output of the energy fluxes (i.e., net radiation, soil heat flux, sensible and latent heat flux) generated with DTD and TSEB using local and Remote meteorological observations are compared with EC and LY observations. The DTD method is found to be significantly more robust in flux estimation compared to the TSEB using the Remote meteorological observations. However, discrepancies between model and measured fluxes are also found to be significantly affected by the local inputs of LST and vegetation cover and the representativeness of the Remote Sensing observations with the local flux measurement footprint. © 2012.
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monitoring root zone soil moisture through the assimilation of a Thermal Remote Sensing based soil moisture proxy into a water balance model
Remote Sensing of Environment, 2008Co-Authors: Wade T Crow, William P Kustas, John H PruegerAbstract:Two types of Soil Vegetation Atmosphere Transfer (SVAT) modeling approaches can be applied to monitor root-zone soil moisture in agricultural landscapes. Water and Energy Balance (WEB) SVAT modeling is based on forcing a prognostic root-zone water balance model with observed rainfall and predicted evapotranspiration. In contrast, Thermal Remote Sensing (RS) observations of surface radiometric temperature (TR) are integrated into purely diagnostic RS-SVAT models to predict the onset of vegetation water stress. While RS-SVAT models do not explicitly monitor soil moisture, they can be used in the calculation of Thermal-based proxy variables for the availability of soil water in the root zone. Using four growing seasons (2001 to 2004) of profile soil moisture, micro-meteorology, and surface radiometric temperature measurements at the United States Department of Agriculture (USDA) Optimizing Production Inputs for Economic and Environmental Enhancements (OPE3) study site in Beltsville, MD, prospects for improving WEB-SVAT root-zone soil water predictions via the assimilation of diagnostic RS-SVAT soil moisture proxy information are examined. Results illustrate the potential advantages of such an assimilation approach relative to the competing approach of directly assimilating TR measurements. Since TR measurements used in the analysis are tower-based (and not obtained from a Remote platform), a sensitivity analysis demonstrates the potential impact of Remote Sensing limitations on the value of the RS-SVAT proxy. Overall, results support a potential role for RS-SVAT modeling strategies in improving WEB-SVAT model characterization of root-zone soil moisture.
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comparing the utility of microwave and Thermal Remote Sensing constraints in two source energy balance modeling over an agricultural landscape
Remote Sensing of Environment, 2006Co-Authors: William P Kustas, Martha C Anderson, Thomas J Jackson, Rajat Bindlish, John H PruegerAbstract:Abstract 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.
