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Modeling Landscape Evapotranspiration by Integrating Land Surface Phenology and a Water Balance Algorithm

G. Senay

Published 2008 in Algorithms

ABSTRACT

Abstract: The main objective of this study is to present an improved modeling technique called Vegetation ET (VegET) that integrates commonly used water balance algorithms with remotely sensed Land Surface Phenology (LSP) parameter to conduct operational vegetation water balance modeling of rainfed systems at the LSP’s spatial scale using readily available global data sets. Evaluation of the VegET model was conducted using Flux Tower data and two-year simulation for the conterminous US. The VegET model is capable of estimating actual evapotranspiration (ETa) of rainfed crops and other vegetation types at the spatial resolution of the LSP on a daily basis, replacing the need to estimate crop- and region-specific crop coefficients. Keywords: Evapotranspiration, modeling, phenology, land surface, VegET, remote sensing 1. Introduction Evapotranspiration (ET) is an important component of the hydrologic budget because it expresses the exchange of mass and energy between the soil-water-vegetation system and the atmosphere. The prevailing weather conditions influence the potential, or reference, ET through variables such as radiation, temperature, wind, and relativity humidity. In addition to these weather variables, actual ET (ETa) is also affected by land cover type, land cover condition, and soil moisture. ETa’s dependence on land cover and soil moisture, along with its direct relationship with carbon dioxide assimilation in plants, makes it an important variable to monitor and estimate crop yield and biomass for decision makers interested in food security, grain markets, water allocation, and carbon sequestration. Although the estimation of ETa is the ultimate goal of many researchers for hydrological and agronomical applications, it is often difficult to quantify and requires expensive instrumentation.

PUBLICATION RECORD

  • Publication year

    2008

  • Venue

    Algorithms

  • Publication date

    2008-10-30

  • Fields of study

    Computer Science, Environmental Science

  • Identifiers
    DOI 10.3390/a1020052
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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