A new temperature-vegetation triangle algorithm with variable edges (TAVE) for satellite-based actual evapotranspiration estimation

dc.contributor.authorZhang, Hua
dc.contributor.authorGorelick, Steven M.
dc.contributor.authorAvisse, Nicolas
dc.contributor.authorTilmant, Amaury
dc.contributor.authorRajsekhar, Deepthi
dc.contributor.authorYoon, Jim
dc.creator.orcidhttps://orcid.org/0000-0001-6996-7269en_US
dc.creator.orcidhttps://orcid.org/0000-0001-6996-7269
dc.creator.orcidhttps://orcid.org/0000-0001-6996-7269https://orcid.org/0000-0001-6996-7269
dc.creator.orcidhttps://orcid.org/0000-0001-6996-7269
dc.creator.orcidhttps://orcid.org/0000-0001-6996-7269
dc.date.accessioned2021-10-28T19:15:21Z
dc.date.available2021-10-28T19:15:21Z
dc.date.issued2016-09-07
dc.description.abstractThe 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.en_US
dc.description.abstractThe 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.
dc.identifier.citationZhang, H., Gorelick, S.M., Avisse, N., Tilmant, A., Rajsekhar, D. and Yoon, J., 2016. A new temperature-vegetation triangle algorithm with variable edges (TAVE) for satellite-based actual evapotranspiration estimation. Remote Sensing, 8(9), p.735.en_US
dc.identifier.citationZhang, H., Gorelick, S.M., Avisse, N., Tilmant, A., Rajsekhar, D. and Yoon, J., 2016. A new temperature-vegetation triangle algorithm with variable edges (TAVE) for satellite-based actual evapotranspiration estimation. Remote Sensing, 8(9), p.735.
dc.identifier.doihttps://doi.org/10.3390/rs8090735
dc.identifier.urihttps://hdl.handle.net/1969.6/89919
dc.language.isoen_USen_US
dc.language.isoen_US
dc.publisherMDPIen_US
dc.publisherMDPI
dc.rightsAttribution 4.0 International*
dc.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectevapotranspirationen_US
dc.subjectremote sensingen_US
dc.subjecttriangle methoden_US
dc.subjectwater stressen_US
dc.subjectwater resourcesen_US
dc.subjectevapotranspiration
dc.subjectremote sensing
dc.subjecttriangle method
dc.subjectwater stress
dc.subjectwater resources
dc.titleA new temperature-vegetation triangle algorithm with variable edges (TAVE) for satellite-based actual evapotranspiration estimationen_US
dc.titleA new temperature-vegetation triangle algorithm with variable edges (TAVE) for satellite-based actual evapotranspiration estimation
dc.typeArticleen_US
dc.typeArticle

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