Comparison of Satellite Driven Surface Energy Balance Models in Estimating Crop Evapotranspiration in Semi-Arid to Arid Inter-Mountain Region

The regional-scale estimation of crop evapotranspiration (ETc) over a heterogeneous surface is an important tool for the decision-makers in managing and allocating water resources. This is especially critical in the arid to semi-arid regions that require supplemental water due to insufficient precipitation, soil moisture, or groundwater. Over the years, various remote sensing-based surface energy balance (SEB) models have been developed to accurately estimate ETc over a regional scale. However, it is important to carry out the SEB model assessment for a particular geographical setting to ensure the suitability of a model. Thus, in this study, four commonly used and contrasting remote sensing models viz. METRIC (mapping evapotranspiration at high resolution with internalized calibration), SEBAL (surface energy balance algorithm for land), S-SEBI (simplified surface energy balance index), and SEBS (surface energy balance system) were compared and used to quantify and map the spatio-temporal variation of ETc in the semi-arid to arid inter-mountain region of Big Horn Basin, Wyoming (Landsat Path/Row: 37/29). Model estimates from 19 cloud-free Landsat 7 and 8 images were compared with the Bowen ratio energy balance system (BREBS) flux stationed in a center pivot irrigated field during 2017 (sugar beet), 2018 (dry bean), and 2019 (barley) growing seasons. The results indicated that all SEB models are effective in capturing the variation of ETc with R2 ranging in between 0.06 to 0.95 and RMSD between 0.07 to 0.15 mm h−1. Pooled data over three vegetative surfaces for three years under irrigated conditions revealed that METRIC (NSE = 0.9) performed better across all land cover types, followed by SEBS (NSE = 0.76), S-SEBI (NSE = 0.73), and SEBAL (NSE = 0.65). In general, all SEB models substantially overestimated ETc and underestimated sensible heat (H) fluxes under dry conditions when only crop residue was available at the surface. A mid-season density plot and absolute difference maps at image scale between the models showed that models involving METRIC, SEBAL, and S-SEBI are close in their estimates of daily crop evapotranspiration (ET24) with pixel-wise RMSD ranged from 0.54 to 0.76 mm d−1 and an average absolute difference across the study area ranged from 0.47 to 0.56 mm d−1. Likewise, all the SEB models underestimated the seasonal ETc, except SEBS.

[1]  M. Tasumi,et al.  Application of GCOM-C SGLI for agricultural water management via field evapotranspiration , 2019, Paddy and Water Environment.

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

[3]  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 .

[4]  William A. Beckman,et al.  Solar Engineering of Thermal Processes, 2nd ed. , 1994 .

[5]  Charles L. Walthall,et al.  Estimation of Shortwave Hemispherical Reflectance (Albedo) from Bidirectionally Reflected Radiance Data , 1991 .

[6]  徳田 迪夫 Oregon State University留学記 , 1982 .

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

[8]  Zhao-Liang Li,et al.  How sensitive is SEBAL to changes in input variables, domain size and satellite sensor? , 2011 .

[9]  John M. Norman,et al.  Estimating Fluxes on Continental Scales Using Remotely Sensed Data in an Atmospheric–Land Exchange Model , 1999 .

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

[11]  J. Gibson Short-term evaporation and water budget comparisons in shallow Arctic lakes using non-steady isotope mass balance , 2002 .

[12]  Ge Sun,et al.  A COMPARISON OF SIX POTENTIAL EVAPOTRANSPIRATION METHODS FOR REGIONAL USE IN THE SOUTHEASTERN UNITED STATES 1 , 2005 .

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

[14]  J. Heitholt,et al.  Dry Bean [Phaseolus vulgaris L.] Growth and Yield Response to Variable Irrigation in the Arid to Semi-Arid Climate , 2020 .

[15]  Minha Choi,et al.  Dual-model approaches for evapotranspiration analyses over homo- and heterogeneous land surface conditions , 2014 .

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

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

[18]  C. Paulson The Mathematical Representation of Wind Speed and Temperature Profiles in the Unstable Atmospheric Surface Layer , 1970 .

[19]  W. Oechel,et al.  Improved global simulations of gross primary product based on a new definition of water stress factor and a separate treatment of C3 and C4 plants , 2015 .

[20]  Y. Kerr,et al.  Disaggregation of MODIS surface temperature over an agricultural area using a time series of Formosat-2 images , 2010 .

[21]  John S. Kimball,et al.  Satellite-Based Evapotranspiration in Hydrological Model Calibration , 2020, Remote. Sens..

[22]  Suat Irmak,et al.  Application of Remote Sensing for Quantifying and Mapping Surface Energy Fluxes in South Central Nebraska: Analyses with Respect to Field Measurements , 2015 .

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

[24]  Michael A. Palecki,et al.  Land Surface Temperature product validation using NOAA's surface climate observation networks—Scaling methodology for the Visible Infrared Imager Radiometer Suite (VIIRS) , 2012 .

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

[26]  Suat Irmak,et al.  Nebraska Water and Energy Flux Measurement, Modeling, and Research Network (NEBFLUX) , 2010 .

[27]  Bibek Acharya,et al.  Quantification and Mapping of Satellite Driven Surface Energy Balance Fluxes in Semi-Arid to Arid Inter-Mountain Region , 2020, Remote. Sens..

[28]  R. Jackson,et al.  Spectral response of a plant canopy with different soil backgrounds , 1985 .

[29]  Massimiliano Zappa,et al.  Does model performance improve with complexity? : A case study with three hydrological models , 2015 .

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

[31]  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 .

[32]  Jungho Im,et al.  Evaluating five remote sensing based single-source surface energy balance models for estimating daily evapotranspiration in a humid subtropical climate , 2016, Int. J. Appl. Earth Obs. Geoinformation.

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

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

[35]  J. Dietrich,et al.  Evaluation of SWAT simulated soil moisture at catchment scale by field measurements and Landsat derived indices , 2017 .

[36]  Albert Olioso,et al.  A simple algorithm to estimate evapotranspiration from DAIS data: Application to the DAISEX campaigns , 2005 .

[37]  S. Allen,et al.  Measurement of sap flow in plant stems , 1996 .

[38]  P. Gowda,et al.  Evaluation of Evapotranspiration from Eddy Covariance Using Large Weighing Lysimeters , 2019, Agronomy.

[39]  H. Neumann,et al.  A comparison of bowen ratio and eddy correlation sensible and latent heat flux measurements above deciduous forest , 1994 .

[40]  Role of active–passive scalar relationships in evaporation from vegetated surfaces , 1992 .

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

[42]  M. Tasumi Progress in operational estimation of regional evapotranspiration using satellite imagery , 2003 .

[43]  Angelika Bayer,et al.  Solar Engineering Of Thermal Processes , 2016 .

[44]  K. Trenberth,et al.  Estimates of the Global Water Budget and Its Annual Cycle Using Observational and Model Data , 2007 .

[45]  T. Sauer,et al.  Crop residue effects on surface radiation and energy balance — review , 1996 .

[46]  Minha Choi,et al.  Spatio‐temporal distribution of actual evapotranspiration in the Indus Basin Irrigation System , 2015 .

[47]  Vijaya Gopal Kakani,et al.  Evapotranspiration partitioning and water use efficiency of switchgrass and biomass sorghum managed for biofuel , 2015 .

[48]  Wim G.M. Bastiaanssen,et al.  Discussion of “Application of SEBAL Model for Mapping Evapotranspiration and Estimating Surface Energy Fluxes in South-Central Nebraska” by Ramesh K. Singh, Ayse Irmak, Suat Irmak, and Derrel L. Martin , 2010 .

[49]  W. Brutsaert Evaporation into the atmosphere , 1982 .

[50]  Samuel Ortega-Farías,et al.  water: Tools and Functions to Estimate Actual Evapotranspiration Using Land Surface Energy Balance Models in R , 2016, R J..

[51]  M. Rahimzadegan,et al.  Evaluation of SEBS, SEBAL, and METRIC models in estimation of the evaporation from the freshwater lakes (Case study: Amirkabir dam, Iran) , 2018, Journal of Hydrology.

[52]  S. Irmak,et al.  Temporal Trend Analysis of Meteorological Variables and Reference Evapotranspiration in the Inter-mountain Region of Wyoming , 2020, Water.

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

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

[55]  A. Huete A soil-adjusted vegetation index (SAVI) , 1988 .

[56]  J. Monteith Evaporation and surface temperature , 2007 .

[57]  P. Gowda,et al.  Performance of five surface energy balance models for estimating daily evapotranspiration in high biomass sorghum , 2017 .

[58]  Y. Ouyang,et al.  Evaluation of Using Remote Sensing Evapotranspiration Data in SWAT , 2018, Water Resources Management.

[59]  E. K. Webb Profile relationships: The log‐linear range, and extension to strong stability , 1970 .

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

[61]  Anthony Morse,et al.  A Landsat-based energy balance and evapotranspiration model in Western US water rights regulation and planning , 2005 .