Evaluation of TSEB turbulent fluxes using different methods for the retrieval of soil and canopy component temperatures from UAV thermal and multispectral imagery

  • Héctor NietoEmail author
  • William P. Kustas
  • Alfonso Torres-Rúa
  • Joseph G. Alfieri
  • Feng Gao
  • Martha C. Anderson
  • W. Alex White
  • Lisheng Song
  • María del Mar Alsina
  • John H. Prueger
  • Mac McKee
  • Manal Elarab
  • Lynn G. McKee
Original Paper


The thermal-based Two-Source Energy Balance (TSEB) model partitions the evapotranspiration (ET) and energy fluxes from vegetation and soil components providing the capability for estimating soil evaporation (E) and canopy transpiration (T). However, it is crucial for ET partitioning to retrieve reliable estimates of canopy and soil temperatures and net radiation, as the latter determines the available energy for water and heat exchange from soil and canopy sources. These two factors become especially relevant in row crops with wide spacing and strongly clumped vegetation such as vineyards and orchards. To better understand these effects, very high spatial resolution remote-sensing data from an unmanned aerial vehicle were collected over vineyards in California, as part of the Grape Remote sensing and Atmospheric Profile and Evapotranspiration eXperiment and used in four different TSEB approaches to estimate the component soil and canopy temperatures, and ET partitioning between soil and canopy. Two approaches rely on the use of composite \(T_\mathrm{rad}\), and assume initially that the canopy transpires at the Priestley–Taylor potential rate. The other two algorithms are based on the contextual relationship between optical and thermal imagery partition \(T_\mathrm{rad}\) into soil and canopy component temperatures, which are then used to drive the TSEB without requiring a priori assumptions regarding initial canopy transpiration rate. The results showed that a simple contextual algorithm based on the inverse relationship of a vegetation index and \(T_\mathrm{rad}\) to derive soil and canopy temperatures yielded the closest agreement with flux tower measurements. The utility in very high-resolution remote-sensing data for estimating ET and E and T partitioning at the canopy level is also discussed.



Partial funding provided by E.&J. Gallo Winery made possible the acquisition and processing of the high-resolution manned aircraft and UAV imagery collected during GRAPEX IOPs. In addition, thanks are given to the Utah Water Research Laboratory for the use of the AggieAir UAV platform, support personnel and partial funding. In addition, we would like to thank the staff of Viticulture, Chemistry and Enology Division of E.&J. Gallo Winery for the collection and processing of field data during GRAPEX IOPs. Finally, this project would not have been possible without the cooperation of Mr. Ernie Dosio of Pacific Agri Lands Management, along with the Borden/ McMannis vineyard staff, for logistical support of GRAPEX field and research activities. USDA is an equal opportunity provider and employer.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Héctor Nieto
    • 1
    Email author
  • William P. Kustas
    • 2
  • Alfonso Torres-Rúa
    • 3
  • Joseph G. Alfieri
    • 2
  • Feng Gao
    • 2
  • Martha C. Anderson
    • 2
  • W. Alex White
    • 2
  • Lisheng Song
    • 5
  • María del Mar Alsina
    • 6
  • John H. Prueger
    • 7
  • Mac McKee
    • 4
  • Manal Elarab
    • 8
  • Lynn G. McKee
    • 2
  1. 1.IRTA, Institute of Agriculture and Food Research and TechnologyLleidaSpain
  2. 2.Hydrology and Remote Sensing Lab, USDA-Agricultural Research ServiceBeltsvilleUSA
  3. 3.Department of Civil and Environmental EngineeringUtah State UniversityLoganUSA
  4. 4.Utah Water Research LaboratoryUtah State UniversityLoganUSA
  5. 5.Chongqing Engineering Research Center for Remote Sensing Big Data Application, School of Geographical SciencesSouthwest UniversityChongqingChina
  6. 6.E&J Gallo WineryModestoUSA
  7. 7.National Laboratory for Agriculture and the Environment, USDA-Agricultural Research ServiceAmesUSA
  8. 8.Manal ElarabMicasenseUSA

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