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

Hua Zhang,Deepthi Rajsekhar,Nicolas Avisse,Jim Yoon,Steven Gorelick,Amaury Tilmant

doi:10.3390/rs8090735

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.

Highlights

Evapotranspiration is the largest component of water loss from the Earth’s land surface, accounting for the consumption of more than 80% of the annual available water in semi-arid environments [1,2]
As most of the pixels are included in two elevation zones and are included twice in the total pixel count, the four triangles of Triangle Algorithm with Variable Edges (TAVE) show a total of 8729 pixels, compared to the 4689 pixels shown in the triangle of triangle method (TA)
We propose a new Triangle Algorithm with Variable Edges (TAVE) to improve satellite-based regional actual evapotranspiration estimates in semi-arid regions

Summary

Introduction

Evapotranspiration is the largest component of water loss from the Earth’s land surface, accounting for the consumption of more than 80% of the annual available water in semi-arid environments [1,2]. A variety of remote sensing methods with varying complexity have been developed to generate regional AET estimates based on surface energy balance or vegetation status Among these approaches, the temperature-vegetation triangle method and its variants [6,7,8,9,10] have been widely used in different regions and across different sensors [11,12,13,14,15]. They are efficient methods that use the triangular geometry of the pixel distribution in surface temperature-vegetation fraction space to establish boundary conditions for the solution of equations for surface energy budget models [16] These methods provide an efficient means to estimate a combined-effect parameter φ, which is derived from the Priestley-Taylor equation and accounts for aerodynamic and surface resistance, vapor pressure gradient, and soil moisture availability, based on satellite images [10,14,17]. The triangle methods only require satellite images of vegetation indices (e.g., normalized difference vegetation index or NDVI) and land surface temperature (Ts ), and yet can yield accuracies comparable to more complex methods [18]

Methods

Results

Discussion

Conclusion