Evaluating five remote sensing based single-source surface energy balance models for estimating daily evapotranspiration in a humid subtropical climate

Abstract In the last two decades, a number of single-source surface energy balance (SEB) models have been proposed for mapping evapotranspiration (ET); however, there is no clear guidance on which models are preferable under different conditions. In this paper, we tested five models-Surface Energy Balance Algorithm for Land (SEBAL), Mapping ET at high Resolution with Internalized Calibration (METRIC), Simplified Surface Energy Balance Index (S-SEBI), Surface Energy Balance System (SEBS), and operational Simplified Surface Energy Balance (SSEBop)—to identify the single-source SEB models most appropriate for use in the humid southeastern United States. ET predictions from these models were compared with measured ET at four sites (marsh, grass, and citrus surfaces) for 149 cloud-free Landsat image acquisition days between 2000 and 2010. The overall model evaluation statistics showed that SEBS generally outperformed the other models in terms of estimating daily ET from different land covers (e.g., the root mean squared error (RMSE) was 0.74 mm day−1). SSEBop was consistently the worst performing model and overestimated ET at all sites (RMSE = 1.67 mm day−1), while the other models typically fell in between SSEBop and SEBS. However, for short grass conditions, SEBAL, METRIC, and S-SEBI appear to work much better than SEBS. Overall, our study suggests that SEBS may be the best SEB model in humid regions, although it may require modifications to work better over short vegetation.

[1]  I. A. Walter,et al.  The ASCE standardized reference evapotranspiration equation , 2005 .

[2]  C. Woodcock,et al.  Improvement and expansion of the Fmask algorithm: cloud, cloud shadow, and snow detection for Landsats 4–7, 8, and Sentinel 2 images , 2015 .

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

[4]  G. Senay,et al.  A comprehensive evaluation of two MODIS evapotranspiration products over the conterminous United States: Using point and gridded FLUXNET and water balance ET , 2013 .

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

[6]  Yoram J. Kaufman,et al.  Water vapor retrievals using Moderate Resolution Imaging Spectroradiometer (MODIS) near‐infrared channels , 2003 .

[7]  R. Thompson.  A Note on Restricted Maximum Likelihood Estimation with an Alternative Outlier Model , 1985 .

[8]  A. S. Thom,et al.  Momentum, mass and heat exchange of vegetation , 1972 .

[9]  Albert Olioso,et al.  Evaluation of the Surface Energy Balance System (SEBS) applied to ASTER imagery with flux-measurements at the SPARC 2004 site (Barrax, Spain) , 2009 .

[10]  W. Bastiaanssen.  SEBAL-based sensible and latent heat fluxes in the irrigated Gediz Basin, Turkey , 2000 .

[11]  E. Noordman,et al.  SEBAL model with remotely sensed data to improve water-resources management under actual field conditions , 2005 .

[12]  Philippe Lagacherie,et al.  Comparison of two temperature differencing methods to estimate daily evapotranspiration over a Mediterranean vineyard watershed from ASTER data , 2011 .

[13]  Jeffrey G. Arnold,et al.  Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations , 2007 .

[14]  Yuan Zeng,et al.  Estimation and Validation of Land Surface Evaporation Using Remote Sensing and Meteorological Data in North China , 2010, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[15]  Wout Verhoef,et al.  Integration of soil moisture in SEBS for improving evapotranspiration estimation under water stress conditions , 2012 .

[16]  J. Monteith.  Evaporation and environment. , 1965, Symposia of the Society for Experimental Biology.

[17]  Randy Kautz,et al.  FLORIDA VEGETATION 2003 AND LAND USE CHANGE BETWEEN 1985–89 AND 2003 , 2007 .

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

[19]  Tsegaye Tadesse,et al.  Evaluating a satellite-based seasonal evapotranspiration product and identifying its relationship with other satellite-derived products and crop yield: A case study for Ethiopia , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[20]  Michael D. Dukes,et al.  Comparison of Evapotranspiration Rates for Flatwoods and Ridge Citrus , 2007 .

[21]  Albert Olioso,et al.  Evaluation of the Surface Energy Balance System (SEBS) applied to ASTER imagery with flux-measurements at the SPARC 2004 site (Barrax, Spain) , 2009 .

[22]  Sophia Kluge,et al.  Introduction To Ocean Remote Sensing , 2016 .

[23]  J. Nash,et al.  River flow forecasting through conceptual models part I — A discussion of principles☆ , 1970 .

[24]  Z. Su.  The Surface Energy Balance System (SEBS) for estimation of turbulent heat fluxes , 2002 .

[25]  Christopher A. Barnes,et al.  Completion of the 2006 National Land Cover Database for the conterminous United States. , 2011 .

[26]  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 .

[27]  Jennifer M. Jacobs,et al.  A comparison of models for estimating potential evapotranspiration for Florida land cover types , 2009 .

[28]  B. Markham,et al.  Summary of Current Radiometric Calibration Coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI Sensors , 2009 .

[29]  Alfred Stein,et al.  Validation of ETWatch using field measurements at diverse landscapes: A case study in Hai Basin of China , 2012 .

[30]  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 .

[31]  William P. Kustas,et al.  An Evaluation of Two Models for Estimation of the Roughness Height for Heat Transfer between the Land Surface and the Atmosphere , 2001 .

[32]  L. S. Pereira,et al.  Using remote sensing energy balance and evapotranspiration to characterize montane landscape vegetation with focus on grass and pasture lands , 2013, Int. J. Appl. Earth Obs. Geoinformation.

[33]  Henk A. R. DeBruin.  TIME TO THINK Reflections of a Pre-Pensioned Scintillometer Researcher , 2009 .

[34]  C. Tucker.  Red and photographic infrared linear combinations for monitoring vegetation , 1979 .

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

[36]  Di Long,et al.  A modified surface energy balance algorithm for land (M‐SEBAL) based on a trapezoidal framework , 2012 .

[37]  Olivier Merlin,et al.  Intercomparison of four remote-sensing-based energy balance methods to retrieve surface evapotranspiration and water stress of irrigated fields in semi-arid climate , 2013 .

[38]  Lindi J. Quackenbush,et al.  A simple Landsat–MODIS fusion approach for monitoring seasonal evapotranspiration at 30 m spatial resolution , 2015 .

[39]  Ayse Irmak,et al.  Satellite‐based ET estimation in agriculture using SEBAL and METRIC , 2011 .

[40]  Luke J. Marzen,et al.  Validation of evaporation estimates from a modified surface energy balance algorithm for land (SEBAL) model in the south-eastern United States , 2012 .

[41]  Prasanna H. Gowda,et al.  Operational Evapotranspiration Mapping Using Remote Sensing and Weather Datasets: A New Parameterization for the SSEB Approach , 2013 .

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

[43]  J. Wickham,et al.  Completion of the 2001 National Land Cover Database for the conterminous United States , 2007 .

[44]  Yuei-An Liou,et al.  Evapotranspiration Estimation with Remote Sensing and Various Surface Energy Balance Algorithms—A Review , 2014 .

[45]  Gabriel B. Senay,et al.  Comparison of Four Different Energy Balance Models for Estimating Evapotranspiration in the Midwestern United States , 2015 .

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

[47]  Manfred Owe,et al.  On the relationship between thermal emissivity and the normalized difference vegetation index for natural surfaces , 1993 .

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

[49]  Luiz Eduardo Oliveira E. Cruz de Aragão,et al.  A MODIS-Based Energy Balance to Estimate Evapotranspiration for Clear-Sky Days in Brazilian Tropical Savannas , 2012, Remote. Sens..

[50]  Kelly R. Thorp,et al.  Remote sensing of evapotranspiration over cotton using the TSEB and METRIC energy balance models , 2015 .

[51]  Eric F. Wood,et al.  Global estimates of evapotranspiration for climate studies using multi-sensor remote sensing data: Evaluation of three process-based approaches , 2011 .

[52]  David I. Stannard,et al.  Comparison of Penman‐Monteith, Shuttleworth‐Wallace, and Modified Priestley‐Taylor Evapotranspiration Models for wildland vegetation in semiarid rangeland , 1993 .

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

[54]  Cristian Mattar,et al.  stimation of real evapotranspiration and its variation in editerranean landscapes of central-southern Chile , 2013 .

[55]  J. Jacobs,et al.  Utility of Penman–Monteith, Priestley–Taylor, reference evapotranspiration, and pan evaporation methods to estimate pasture evapotranspiration , 2005 .

[56]  Wossenu Abtew,et al.  EVAPOTRANSPIRATION MEASUREMENTS AND MODELING FOR THREE WETLAND SYSTEMS IN SOUTH FLORIDA 1 , 1996 .

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

[58]  Massimo Menenti,et al.  S-SEBI: A simple remote sensing algorithm to estimate the surface energy balance , 2000 .

[59]  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 .

[60]  Minha Choi,et al.  Surface energy fluxes in the Northeast Asia ecosystem: SEBS and METRIC models using Landsat satellite images , 2015 .