Evaluating the two-source energy balance model using local thermal and surface flux observations in a strongly advective irrigated agricultural area

Abstract 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.

[1]  William P. Kustas,et al.  Time Difference Methods for Monitoring Regional Scale Heat Fluxes with Remote Sensing , 2013 .

[2]  J. Norman,et al.  Source approach for estimating soil and vegetation energy fluxes in observations of directional radiometric surface temperature [Agric. For. Meteorol., 77 (1995) 263–293]☆ , 1996 .

[3]  W. Kustas,et al.  On the opposing roles of air temperature and wind speed variability in flux estimation from remotely sensed land surface states , 2007 .

[4]  Craig S. T. Daughtry,et al.  Estimation of the soil heat flux/net radiation ratio from spectral data , 1990 .

[5]  William P. Kustas,et al.  An intercomparison of the Surface Energy Balance Algorithm for Land (SEBAL) and the Two-Source Energy Balance (TSEB) modeling schemes , 2007 .

[6]  W. Kustas,et al.  Estimating Spatial Variability in Atmospheric Properties over Remotely Sensed Land Surface Conditions , 2008 .

[7]  Paul D. Colaizzi,et al.  Two-source energy balance model estimates of evapotranspiration using component and composite surface temperatures☆ , 2012 .

[8]  Thomas J. Jackson,et al.  Utility of Remote Sensing–Based Two-Source Energy Balance Model under Low- and High-Vegetation Cover Conditions , 2005 .

[9]  M. Mccabe,et al.  Estimating Land Surface Evaporation: A Review of Methods Using Remotely Sensed Surface Temperature Data , 2008 .

[10]  L. McKee,et al.  Surface soil water content spatial organization within irrigated and non-irrigated agricultural fields , 2012 .

[11]  Martha C. Anderson,et al.  Advances in thermal infrared remote sensing for land surface modeling , 2009 .

[12]  J. Norman,et al.  Terminology in thermal infrared remote sensing of natural surfaces , 1995 .

[13]  C. B. Tanner,et al.  Estimating Evaporation and Transpiration from a Row Crop during Incomplete Cover1 , 1976 .

[14]  William P. Kustas,et al.  Reply to comments about the basic equations of dual-source vegetation–atmosphere transfer models , 1999 .

[15]  Paul D. Colaizzi,et al.  On the discrepancy between eddy covariance and lysimetry-based surface flux measurements under strongly advective conditions ☆ , 2012 .

[16]  William P. Kustas,et al.  An intercomparison study on models of sensible heat flux over partial canopy surfaces with remotely sensed surface temperature , 1996 .

[17]  Martha C. Anderson,et al.  Overview of the Bushland Evapotranspiration and Agricultural Remote sensing EXperiment 2008 (BEAREX08): A field experiment evaluating methods for quantifying ET at multiple scales ☆ , 2012 .

[18]  Paul D. Colaizzi,et al.  Application of the Priestley–Taylor Approach in a Two-Source Surface Energy Balance Model , 2010 .

[19]  William P. Kustas,et al.  Evaluating the effects of subpixel heterogeneity on pixel average fluxes. , 2000 .

[20]  M. Friedl,et al.  Diurnal Covariation in Soil Heat Flux and Net Radiation , 2003 .

[21]  J. Norman,et al.  Evaluation of soil and vegetation heat flux predictions using a simple two-source model with radiometric temperatures for partial canopy cover , 1999 .

[22]  C. Willmott,et al.  Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance , 2005 .

[23]  J. Norman,et al.  A Two-Source Energy Balance Approach Using Directional Radiometric Temperature Observations for Sparse Canopy Covered Surfaces , 2000 .

[24]  Martha C. Anderson,et al.  A thermal-based remote sensing technique for routine mapping of land-surface carbon, water and energy fluxes from field to regional scales , 2008 .

[25]  C. Stöckle,et al.  Comparison of methods for applying the Priestley–Taylor equation at a regional scale , 2001 .

[26]  J. Norman,et al.  Surface flux estimation using radiometric temperature: A dual‐temperature‐difference method to minimize measurement errors , 2000 .

[27]  William P. Kustas,et al.  Effects of Vegetation Clumping on Two–Source Model Estimates of Surface Energy Fluxes from an Agricultural Landscape during SMACEX , 2005 .

[28]  Martha C. Anderson,et al.  A Two-Source Time-Integrated Model for Estimating Surface Fluxes Using Thermal Infrared Remote Sensing , 1997 .

[29]  K. Mallick,et al.  Efficiency based wheat yield prediction in a semi-arid climate using surface energy budgeting with satellite observations , 2011 .

[30]  C. Priestley,et al.  On the Assessment of Surface Heat Flux and Evaporation Using Large-Scale Parameters , 1972 .

[31]  William P. Kustas,et al.  Utility of Radiometric–aerodynamic Temperature Relations for Heat Flux Estimation , 2007 .

[32]  K. S. Copeland,et al.  Can weighing lysimeter ET represent surrounding field ET well enough to test flux station measurements of daily and sub-daily ET? , 2012 .

[33]  William P. Kustas,et al.  A two‐source approach for estimating turbulent fluxes using multiple angle thermal infrared observations , 1997 .

[34]  Terry A. Howell,et al.  Design and Construction of Large Weighing Monolithic Lysimeters , 1988 .

[35]  Martha C. Anderson,et al.  Use of Landsat thermal imagery in monitoring evapotranspiration and managing water resources , 2012 .

[36]  Martha C. Anderson,et al.  Mapping daily evapotranspiration at Landsat spatial scales during the BEAREX’08 field campaign , 2012 .

[37]  Paul D. Colaizzi,et al.  Radiometer Footprint Model to Estimate Sunlit and Shaded Components for Row Crops , 2010 .

[38]  Ronglin Tang,et al.  An intercomparison of three remote sensing-based energy balance models using Large Aperture Scintillometer measurements over a wheat–corn production region , 2011 .

[39]  J. Norman,et al.  Source approach for estimating soil and vegetation energy fluxes in observations of directional radiometric surface temperature , 1995 .

[40]  William P. Kustas,et al.  Tower and Aircraft Eddy Covariance Measurements of Water Vapor, Energy, and Carbon Dioxide Fluxes during SMACEX , 2005 .

[41]  Paul D. Colaizzi,et al.  Using radiation thermography and thermometry to evaluate crop water stress in soybean and cotton , 2011 .