Estimating evapotranspiration from terrestrial groundwater-dependent ecosystems using landsat images

Xihua Yang*, Peter L. Smith, Tao Yu, Hailiang Gao

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)


Understanding and mitigating against the impact of groundwater extraction on groundwater-dependent ecosystems (GDE) requires information of evapotranspiration(ET) of these ecosystems. In this pilot study, we tested two remote sensing methods, Surface Energy Balance Algorithms for Land (SEBAL) and Vegetation Index/Temperature Trapezoid (VITT), for ET estimation from terrestrial GDEs. Multi-temporal Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) images were used to derive vegetation indices and land surface temperatures for ET estimation. Radiative transfer model was used for atmospheric correction of the Landsat images. Field measurements were used to validate the remote sensing estimation of VI and surface temperature. Both methods have been implemented in a geographic information system (GIS) using automated scripts and ancillary GIS data for quality control process. Comparison of predicted ET by SEBAL to VITT model indicates relatively good agreement(R2>0.90) and promise for use in groundwater management. The average ET from woodland GDEs within the zone of influence of the pumping stations is in general lower than similar woodlands outside of the pumping area, particularly in summer seasons which demonstrates that the pumping regime has an impact on those GDEs. The study also demonstrates that even a simple physical ET model can provide useful information for groundwater management, and more broadly other applications in hydrologic modelling and digital earth studies.

Original languageEnglish
Pages (from-to)154-170
Number of pages17
JournalInternational Journal of Digital Earth
Issue number2
Publication statusPublished - Mar 2011
Externally publishedYes


  • Digital earth
  • Evapotranspiration
  • GIS
  • Groundwater-dependent ecosystems
  • Landsat
  • Remote sensing
  • Surface energy balance

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