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 (SIVIACEX) 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, 49 W/m(2) and 59 W/m(2), 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. (C) 2015 Elsevier Inc. All rights reserved.

[1]  William P. Kustas,et al.  A reexamination of the crop water stress index , 1988, Irrigation Science.

[2]  S. Idso,et al.  Canopy temperature as a crop water stress indicator , 1981 .

[3]  J. Jacobs.  Ecohydrology: Darwinian Expression of Vegetation Form and Function , 2003 .

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

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

[6]  V. Singh,et al.  A Two-source Trapezoid Model for Evapotranspiration (TTME) from satellite imagery , 2012 .

[7]  H. Mooney,et al.  Modeling the Exchanges of Energy, Water, and Carbon Between Continents and the Atmosphere , 1997, Science.

[8]  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 .

[9]  G. Campbell,et al.  An Introduction to Environmental Biophysics , 1977 .

[10]  Terry A. Howell,et al.  Comparison of five models to scale daily evapotranspiration from one-time-of-day measurements , 2006 .

[11]  W. Kustas,et al.  A verification of the 'triangle' method for obtaining surface soil water content and energy fluxes from remote measurements of the Normalized Difference Vegetation Index (NDVI) and surface e , 1997 .

[12]  T. Foken.  The energy balance closure problem: an overview. , 2008, Ecological applications : a publication of the Ecological Society of America.

[13]  Martha C. Anderson,et al.  A climatological study of evapotranspiration and moisture stress across the continental United States based on thermal remote sensing: 2. Surface moisture climatology , 2007 .

[14]  T. Jacksona,et al.  Effects of remote sensing pixel resolution on modeled energy flux variability of croplands in Iowa , 2004 .

[15]  A. Holtslag,et al.  A remote sensing surface energy balance algorithm for land (SEBAL)-1. Formulation , 1998 .

[16]  Wilfried Brutsaert,et al.  Application of self‐preservation in the diurnal evolution of the surface energy budget to determine daily evaporation , 1992 .

[17]  Bo-Hui Tang,et al.  An application of the Ts–VI triangle method with enhanced edges determination for evapotranspiration estimation from MODIS data in arid and semi-arid regions: Implementation and validation , 2010 .

[18]  Hongbo Su,et al.  Two Improvements of an Operational Two-Layer Model for Terrestrial Surface Heat Flux Retrieval , 2008, Sensors.

[19]  James L. Wright,et al.  Satellite-Based Energy Balance for Mapping Evapotranspiration with Internalized Calibration (METRIC)—Applications , 2007 .

[20]  Wilfried Brutsaert,et al.  Hydrology: An Introduction , 2005 .

[21]  Zhanqing Li,et al.  Estimation of evaporative fraction from a combination of day and night land surface temperatures and NDVI: A new method to determine the Priestley-Taylor parameter , 2006 .

[22]  S. Seneviratne,et al.  Global intercomparison of 12 land surface heat flux estimates , 2011 .

[23]  S. Shang,et al.  A hybrid dual‐source scheme and trapezoid framework–based evapotranspiration model (HTEM) using satellite images: Algorithm and model test , 2013 .

[24]  Inge Sandholt,et al.  Estimation of regional evapotranspiration over the North China Plain using geostationary satellite data , 2011, Int. J. Appl. Earth Obs. Geoinformation.

[25]  William P. Kustas,et al.  Daily evapotranspiration estimates from extrapolating instantaneous airborne remote sensing ET values , 2008, Irrigation Science.

[26]  W. J. Shuttleworth,et al.  Putting the "vap" into evaporation , 2007 .

[27]  Chad W. Higgins,et al.  Evapotranspiration: A process driving mass transport and energy exchange in the soil‐plant‐atmosphere‐climate system , 2012 .

[28]  S. Idso,et al.  Analysis of an empirical model for soil heat flux under a growing wheat crop for estimating evaporation by an infrared-temperature based energy balance equation , 1987 .

[29]  R. Crago,et al.  Conservation and variability of the evaporative fraction during the daytime , 1996 .

[30]  Pamela L. Nagler,et al.  Integrating Remote Sensing and Ground Methods to Estimate Evapotranspiration , 2007 .

[31]  J. Norman,et al.  Correcting eddy-covariance flux underestimates over a grassland , 2000 .

[32]  Xiaomin Sun,et al.  Impact of the Spatial Domain Size on the Performance of the Ts-VI Triangle Method in Terrestrial Evapotranspiration Estimation , 2013, Remote. Sens..

[33]  Matthew F. McCabe,et al.  Surface energy fluxes with the Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER) at the Iowa 2002 SMACEX site (USA) , 2005 .

[34]  T. Schmugge,et al.  Deriving land surface temperature from Landsat 5 and 7 during SMEX02/SMACEX , 2004 .

[35]  D. García-Castellanos,et al.  Decoupled crust-mantle accommodation of Africa-Eurasia convergence in the NW Moroccan margin , 2011 .

[36]  Thomas J. Jackson,et al.  Comparing the utility of microwave and thermal remote-sensing constraints in two-source energy balance modeling over an agricultural landscape , 2006 .

[37]  Chenghu Zhou,et al.  A Review of Current Methodologies for Regional Evapotranspiration Estimation from Remotely Sensed Data , 2009, Sensors.

[38]  Y. Masumoto,et al.  The reversal of the multi‐decadal trends of the equatorial Pacific easterly winds, and the Indonesian Throughflow and Leeuwin Current transports , 2011 .

[39]  William P. Kustas,et al.  Modelling surface energy fluxes over maize using a two-source patch model and radiometric soil and canopy temperature observations , 2008 .

[40]  W. Bastiaanssen,et al.  A remote sensing surface energy balance algorithm for land (SEBAL). , 1998 .

[41]  Maosheng Zhao,et al.  Improvements to a MODIS global terrestrial evapotranspiration algorithm , 2011 .

[42]  M. S. Moran,et al.  Determination of sensible heat flux over sparse canopy using thermal infrared data , 1989 .

[43]  H. E. Jobson.  Evaporation Into the Atmosphere: Theory, History, and Applications , 1982 .

[44]  Tim R. McVicar,et al.  Upscaling latent heat flux for thermal remote sensing studies: Comparison of alternative approaches and correction of bias , 2012 .

[45]  Derek M. Cunnold,et al.  Observations of 1,1‐difluoroethane (HFC‐152a) at AGAGE and SOGE monitoring stations in 1994–2004 and derived global and regional emission estimates , 2007 .

[46]  R. Allen,et al.  At-Surface Reflectance and Albedo from Satellite for Operational Calculation of Land Surface Energy Balance , 2008 .

[47]  F. Wimmer,et al.  Some aspects of the energy balance closure problem , 2006 .

[48]  L. Jiang,et al.  An intercomparison of regional latent heat flux estimation using remote sensing data , 2003 .

[49]  Martha C. Anderson,et al.  A data fusion approach for mapping daily evapotranspiration at field scale , 2013 .

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

[51]  Maosheng Zhao,et al.  Development of a global evapotranspiration algorithm based on MODIS and global meteorology data , 2007 .

[52]  W. Kustas,et al.  The Soil Moisture–Atmosphere Coupling Experiment (SMACEX): Background, Hydrometeorological Conditions, and Preliminary Findings , 2005 .

[53]  William P. Kustas,et al.  An intercomparison of three remote sensing-based surface energy balance algorithms over a corn and soybean production region (Iowa, U.S.) during SMACEX , 2009 .

[54]  Matthew F. McCabe,et al.  Modeling Evapotranspiration during SMACEX: Comparing Two Approaches for Local- and Regional-Scale Prediction , 2005 .

[55]  T. Carlson.  An Overview of the “Triangle Method” for Estimating Surface Evapotranspiration and Soil Moisture from Satellite Imagery , 2007, Sensors (Basel, Switzerland).

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

[57]  G. Petropoulos,et al.  A review of Ts/VI remote sensing based methods for the retrieval of land surface energy fluxes and soil surface moisture , 2009 .

[58]  L. S. Pereira,et al.  Crop evapotranspiration : guidelines for computing crop water requirements , 1998 .

[59]  Matthew F. McCabe,et al.  Scale influences on the remote estimation of evapotranspiration using multiple satellite sensors , 2006 .

[60]  Jeffrey P. Walker,et al.  THE GLOBAL LAND DATA ASSIMILATION SYSTEM , 2004 .

[61]  R. Dickinson,et al.  A review of global terrestrial evapotranspiration: Observation, modeling, climatology, and climatic variability , 2011 .

[62]  Martha C. Anderson,et al.  A comparison of operational remote sensing-based models for estimating crop evapotranspiration , 2009 .

[63]  Tim R. McVicar,et al.  Correcting for systematic error in satellite-derived latent heat flux due to assumptions in temporal scaling: Assessment from flux tower observations , 2011 .

[64]  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 .

[65]  S. Schubert,et al.  MERRA: NASA’s Modern-Era Retrospective Analysis for Research and Applications , 2011 .

[66]  Martha C. Anderson,et al.  A climatological study of evapotranspiration and moisture stress across the continental United States based on thermal remote sensing: 1. Model formulation , 2007 .

[67]  William J. Massman,et al.  Reflections on the surface energy imbalance problem , 2012 .

[68]  Enli Wang,et al.  Decadal Trends in Evaporation from Global Energy and Water Balances , 2012 .

[69]  Richard G. Allen,et al.  Satellite-Based Energy Balance for Mapping Evapotranspiration with Internalized Calibration (METRIC)—Model , 2007 .

[70]  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 .

[71]  Rasmus Fensholt,et al.  Combining the triangle method with thermal inertia to estimate regional evapotranspiration — Applied to MSG-SEVIRI data in the Senegal River basin , 2008 .

[72]  Sun Xiaomin,et al.  An operational two-layer remote sensing model to estimate surface flux in regional scale: Physical background , 2005 .

[73]  M. S. Moran,et al.  Combining the Penman-Monteith equation with measurements of surface temperature and reflectance to estimate evaporation rates of semiarid grassland , 1996 .

[74]  Daoyi Chen,et al.  Diurnal Variation of Surface Fluxes During Thorough Drying (or Severe Drought) of Natural Prairie , 1996 .

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

[76]  Matthew F. McCabe,et al.  Evaluation of Remotely Sensed Evapotranspiration Over the CEOP EOP-1 Reference Sites , 2007 .

[77]  S. Seneviratne,et al.  Evaluation of global observations‐based evapotranspiration datasets and IPCC AR4 simulations , 2011 .

[78]  Martha C. Anderson,et al.  Upscaling ground observations of vegetation water content, canopy height, and leaf area index during SMEX02 using aircraft and Landsat imagery , 2004 .

[79]  M. S. Moran,et al.  Estimating crop water deficit using the relation between surface-air temperature and spectral vegetation index , 1994 .

[80]  I. Sandholt,et al.  A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status , 2002 .

[81]  John Moncrieff.  Vegetation, Water, Humans and the Climate: A New Perspective on an Interactive System , 2004 .

[82]  Markus Reichstein,et al.  Benchmark products for land evapotranspiration: LandFlux-EVAL multi-data set synthesis , 2013 .

[83]  S. Islam,et al.  Estimation of surface evaporation map over Southern Great Plains using remote sensing data , 2001 .

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

[85]  William P. Kustas,et al.  Comparing Aircraft-Based Remotely Sensed Energy Balance Fluxes with Eddy Covariance Tower Data Using Heat Flux Source Area Functions , 2005 .