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Remote Sens. 2016, 8(9), 735; doi:10.3390/rs8090735

A New Temperature-Vegetation Triangle Algorithm with Variable Edges (TAVE) for Satellite-Based Actual Evapotranspiration Estimation

1
Department of Engineering, School of Engineering and Computing Sciences, Texas A&M University-Corpus Christi, Corpus Christi, 78412 TX, USA
2
Department of Earth System Science, Stanford University, Stanford, 94305-2115 CA, USA
3
Department of Civil Engineering and Water Engineering, Université Laval, Québec City, QC G1V 0A6, Canada
*
Author to whom correspondence should be addressed.
Academic Editors: Toby Carlson, Zhaoliang Li, Richard Müller and Prasad S. Thenkabail
Received: 8 May 2016 / Revised: 19 August 2016 / Accepted: 27 August 2016 / Published: 7 September 2016
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Abstract

The estimation of spatially-variable actual evapotranspiration (AET) is a critical challenge to regional water resources management. We propose a new remote sensing method, the Triangle Algorithm with Variable Edges (TAVE), to generate daily AET estimates based on satellite-derived land surface temperature and the vegetation index NDVI. The TAVE captures heterogeneity in AET across elevation zones and permits variability in determining local values of wet and dry end-member classes (known as edges). Compared to traditional triangle methods, TAVE introduces three unique features: (i) the discretization of the domain as overlapping elevation zones; (ii) a variable wet edge that is a function of elevation zone; and (iii) variable values of a combined-effect parameter (that accounts for aerodynamic and surface resistance, vapor pressure gradient, and soil moisture availability) along both wet and dry edges. With these features, TAVE effectively addresses the combined influence of terrain and water stress on semi-arid environment AET estimates. We demonstrate the effectiveness of this method in one of the driest countries in the world—Jordan, and compare it to a traditional triangle method (TA) and a global AET product (MOD16) over different land use types. In irrigated agricultural lands, TAVE matched the results of the single crop coefficient model (−3%), in contrast to substantial overestimation by TA (+234%) and underestimation by MOD16 (−50%). In forested (non-irrigated, water consuming) regions, TA and MOD16 produced AET average deviations 15.5 times and −3.5 times of those based on TAVE. As TAVE has a simple structure and low data requirements, it provides an efficient means to satisfy the increasing need for evapotranspiration estimation in data-scarce semi-arid regions. This study constitutes a much needed step towards the satellite-based quantification of agricultural water consumption in Jordan. View Full-Text
Keywords: evapotranspiration; remote sensing; triangle method; water stress; water resources evapotranspiration; remote sensing; triangle method; water stress; water resources
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Zhang, H.; Gorelick, S.M.; Avisse, N.; Tilmant, A.; Rajsekhar, D.; Yoon, J. A New Temperature-Vegetation Triangle Algorithm with Variable Edges (TAVE) for Satellite-Based Actual Evapotranspiration Estimation. Remote Sens. 2016, 8, 735.

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