Advances in a two-source energy balance model: Partitioning of evaporation and transpiration for cotton

P. D. Colaizzi, N. Agam, J. A. Tolk, S. R. Evett, T. A. Howell, S. A. O'Shaughnessy, P. H. Gowda, W. P. Kustas, M. C. Anderson

Research output: Contribution to journalReview articlepeer-review

16 Scopus citations


Accurate partitioning of the evaporation (E) and transpiration (T) components of evapotranspiration (ET) in remote sensing models is important for evaluating strategies aimed at increasing crop water productivity. A two-source energy balance (TSEB) model designed for row crops solves the energy balance of the soil-canopy-atmosphere continuum using surface brightness temperature. By solving the energy balance of the soil and plant canopy separately, the TSEB model can calculate E and T, which cannot be done with single-source models. However, few studies have tested the TSEB model where E or T measurements were available, which until recently has impeded its advance. This article reviews recent physically based advances of the TSEB model. The advances were tested using measurements of E, T, and ET by microlysimeters, sap flow gauges, and weighing lysimeters, respectively, at Bushland, Texas, for irrigated cotton having a wide range of canopy cover. Root mean square error (RMSE) and mean bias error (MBE) were 0.54 and -0.19 mm d-1, respectively, between measured and calculated E. RMSE and MBE were 0.87 and 0.31 mm d-1, respectively, between measured and calculated T. This was deemed an improvement over previous TSEB model versions, which overestimated E and underestimate T, resulting in RMSE and MBE of up to 3.8 and -3.5 mm d-1, respectively. Ongoing research includes testing the TSEB model using different remote sensing platforms, from ground-based to satellite scales.

Original languageEnglish
Pages (from-to)181-197
Number of pages17
JournalTransactions of the ASABE
Issue number1
StatePublished - 1 Jan 2016


  • Evapotranspiration
  • Irrigation
  • Remote sensing
  • Texas

ASJC Scopus subject areas

  • Forestry
  • Food Science
  • Biomedical Engineering
  • Agronomy and Crop Science
  • Soil Science


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