Impact of the Spatial Domain Size on the Performance of the Ts-VI Triangle Method in Terrestrial Evapotranspiration Estimation

This study aims to investigate the impact of the spatial size of the study domain on the performance of the triangle method using progressively smaller domains and Moderate Resolution Imaging Spectroradiometer (MODIS) observations in the Heihe River basin located in the arid region of northwestern China. Data from 10 clear-sky days during the growing season from April to September 2009 were used. Results show that different dry/wet edges in the surface temperature-vegetation index space directly led to the deviation of evapotranspiration (ET) estimates due to the variation of the spatial domain size. The slope and the intercept of the limiting edges are dependent on the range and the maximum of surface temperature over the spatial domain. The difference of the limiting edges between different domain sizes has little impact on the spatial pattern of ET estimates, with the Pearson correlation coefficient ranging from 0.94 to 1.0 for the 10 pairs of ET estimates at different domain scales. However, it has a larger impact on the degree of discrepancies in ET estimates between different domain sizes, with the maximum of 66 W∙m−2. The largest deviation of ET estimates between different domain sizes was found at the beginning of the growing season.

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

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

[3]  Jing Tian,et al.  Estimation of regional evapotranspiration based on remote sensing: case study in the Heihe River Basin , 2012 .

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

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

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

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

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

[9]  Ping Wang,et al.  Vegetation responses to integrated water management in the Ejina basin, northwest China , 2011 .

[10]  J. H. Shaw,et al.  Thermal Radiation from the Atmosphere , 1956 .

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

[12]  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 .

[13]  Jin Chen,et al.  Scale Effect of Vegetation-Index-Based Spatial Sharpening for Thermal Imagery: A Simulation Study by ASTER Data , 2012, IEEE Geoscience and Remote Sensing Letters.

[14]  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 .

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

[16]  Stephen J. Lord,et al.  The New Global Operational Analysis System at the National Meteorological Center , 1991 .

[17]  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 .

[18]  Gautam Bisht,et al.  Comparison of evaporative fractions estimated from AVHRR and MODIS sensors over South Florida , 2004 .

[19]  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 .

[20]  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 .

[21]  Le Jiang,et al.  A methodology for estimation of surface evapotranspiration over large areas using remote sensing observations , 1999 .

[22]  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 .

[23]  Philippe Richaume,et al.  Disaggregation of SMOS Soil Moisture in Southeastern Australia , 2012, IEEE Transactions on Geoscience and Remote Sensing.

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

[25]  Renhua Zhang,et al.  A new algorithm to automatically determine the boundary of the scatter plot in the triangle method for evapotranspiration retrieval , 2011, 2011 IEEE International Geoscience and Remote Sensing Symposium.

[26]  V. Singh,et al.  Deriving theoretical boundaries to address scale dependencies of triangle models for evapotranspiration estimation , 2012 .