Advertisement

Utility of the two-source energy balance (TSEB) model in vine and interrow flux partitioning over the growing season

  • W. P. Kustas
  • J. G. Alfieri
  • H. Nieto
  • T. G. Wilson
  • F. Gao
  • M. C. Anderson
Original Paper

Abstract

For monitoring water use in vineyards, it becomes important to evaluate the evapotranspiration (ET) contributions from the two distinct management zones: the vines and the interrow. Often the interrow is not completely bare soil but contains a cover crop that is senescent during the main growing season (nominally May–August), which in Central California is also the dry season. Drip irrigation systems running during the growing season supply water to the vine plant and re-wet some of the surrounding bare soil. However, most of the interrow cover crop is dry stubble by the end of May. This paper analyzes the utility of the thermal-based two-source energy balance (TSEB) model for estimating daytime ET using tower-based land surface temperature (LST) estimates over two Pinot Noir (Vitis vinifera) vineyards at different levels of maturity in the Central Valley of California near Lodi, CA. The data were collected as part of the Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX). Local eddy covariance (EC) flux tower measurements are used to evaluate the performance of the TSEB model output of the fluxes and the capability of partitioning the vine and cover crop transpiration (T) from the total ET or T/ET ratio. The results for the 2014–2016 growing seasons indicate that TSEB output of the energy balance components and ET, particularly, over the daytime period yield relative differences with flux tower measurements of less than 15%. However, the TSEB model in comparison with the correlation-based flux partitioning method overestimates T/ET during the winter and spring through bud break, but then underestimates during the growing season. A major factor that appears to affect this temporal behavior in T/ET is the daily LAI used as input to TSEB derived from a remote sensing product. An additional source of uncertainty is the use of local tower-based LST measurements, which are not representative of the flux tower measurement source area footprint.

Notes

Acknowledgements

Funding provided by E.&J. Gallo Winery contributed towards the acquisition and processing of the ground truth data collected during GRAPEX IOPs. In addition, we would like to thank the staff of Viticulture, Chemistry and Enology Division of E.&J. Gallo Winery for the assistance in 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 vineyard staff, for logistical support of GRAPEX field and research activities. The senior author would like to acknowledge financial support for this research from NASA Applied Sciences-Water Resources Program [Announcement number NNH16ZDA001N-WATER]. Proposal no. 16-WATER16_2–0005, Request number: NNH17AE39I. USDA is an equal opportunity provider and employer.

Compliance with ethical standards

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

References

  1. Alfieri JG, Kustas WP, Prueger JH, McKee LG, Hipps LE, Gao F (this issue) A multi-year intercomparison of micrometeorological observations at adjacent vineyards in California’s central valley during GRAPEX. Irrig SciGoogle Scholar
  2. Anderson MC, Norman JM, Kustas WP, Li F, Prueger JH, Mecikalski JR (2005) Effects of vegetation clumping on two–source model estimates of surface energy fluxes from an agricultural landscape during SMACEX. J Hydromet 6(6):892–909CrossRefGoogle Scholar
  3. Anderson MC, Norman JM, Kustas WP, Houborg JM, Starks PJ, Agam N (2008) thermal-based remote sensing technique for routine mapping of land-surface carbon, water and energy fluxes from field to regional scales. Remote Sens Environ 112:4227–4241CrossRefGoogle Scholar
  4. Bellvert J, Zarco-Tejada P, Marsal J, Girona J, Gonzalez-Dugo V, Fereres E (2016) Vineyard irrigation scheduling based on airborne thermal imagery and water potential thresholds. Aust J Grape Wine Res 22(2):307–315.  https://doi.org/10.1111/ajgw.12173 CrossRefGoogle Scholar
  5. Brutsaert W (1999) Aspects of bulk atmospheric boundary layer similarity under free-convective conditions. Rev Geophys 37(4):439–451CrossRefGoogle Scholar
  6. Brutsaert W (2005) Hydrology. An introduction. Cambridge University Press, Cambridge (ISBN-13 978-0-521-82479-8) CrossRefGoogle Scholar
  7. Cammalleri C, Anderson MC, Ciraolo G, D’Urso G, Kustas WP, La Loggia G, Minacapilli M (2010) The impact of in-canopy wind profile formulations on heat flux estimation in an open orchard using the remote sensing-based two-source model. Hydrol Earth Sys Sci 14(12):2643–2659CrossRefGoogle Scholar
  8. Campbell GS, Norman JM (1998) An introduction to environmental biophysics, 2nd edn. Springer, New YorkCrossRefGoogle Scholar
  9. Colaizzi PD, Evett SR, Howell TA, Li F, Kustas WP, Anderson MC (2012a) Radiation model for row crops: I. Geometric view factors and parameter optimization. Agron J 104:225–240CrossRefGoogle Scholar
  10. Colaizzi PD, Kustas WP, Anderson MC, Agam N, Tolk JA, Evett SR, Howell TA, Gowda PH, O’Shaughnessy SA (2012b) Two-source energy balance model estimates of evapotranspiration using component and composite surface temperatures. Adv Water Resour 50:134–151CrossRefGoogle Scholar
  11. Colaizzi PD, Agam N, Tolk JA, Evett SR, Howell TA, Gowda PH, O’Shaughnessy SA, Kustas WP, Anderson MC (2014) Two-source energy balance model to calculate E, T, and ET: comparison of Priestley–Taylor and Penman–Monteith formulations and two time scaling methods. Trans ASABE 57(2):479–498Google Scholar
  12. Colaizzi PD, Evett SR, Agam N, Schwartz RC, Kustas WP (2016a) Soil heat flux calculation for sunlit and shaded surfaces under row crops: 1. Model development and sensitivity analysis. Agric For Meteorol 216:115–128CrossRefGoogle Scholar
  13. Colaizzi PD, Evett SR, Agam N, Schwartz RC, Kustas WP, Cosh MH, McKee LG (2016b) Soil heat flux calculation for sunlit and shaded surfaces under row crops: 2. Model test. Agric For Meteorol 216:129–140CrossRefGoogle Scholar
  14. Colaizzi PD, Agam N, Tolk JA, Evett SR, Howell TA, O’Shaughnessy SA, Gowda PH, Kustas WP, Anderson MC (2016c) Advances in a two-source energy balance model: partitioning of evaporation and transpiration for cotton using component and composite surface temperatures. Trans ASABE 59(1):181–197.  https://doi.org/10.13031/trans.59.11215 CrossRefGoogle Scholar
  15. Gao F, Anderson MC, Kustas WP, Wang Y (2012) A simple method for retrieving leaf area index from landsat using MODIS LAI products as reference. J Appl Remote Sens.  https://doi.org/10.1117/.JRS.1116.063554 CrossRefGoogle Scholar
  16. Goudriaan J (1977) Crop micrometeorology: a simulation stud. Tech. rep. Center for Agricultural Publications and Documentation, WageningenGoogle Scholar
  17. Hillel D (1998) Environmental soil physics. Academic Press, New YorkGoogle Scholar
  18. Holland S, Heitman JL, Howard A, Sauer TJ, Giese W, Ben-Gal A, Agam N, Kool D, Havlin J (2013) Micro-Bowen ratio system for measuring evapotranspiration in a vineyard interrow. Agric For Meteorol 177:93–100CrossRefGoogle Scholar
  19. Knipper KR, Kustas WP, Anderson MC, Aleri JG, Prueger JH, Hain CR, Gao F, Yang Y, McKee LG, Nieto H, Hipps LE, Alsina MM, Sanchez L (this issue) Evapotranspiration estimates derived using thermal-based satellite remote sensing and data fusion for irrigation management in California vineyards. Irrig SciGoogle Scholar
  20. Kondo J, Ishida S (1997) Sensible heat flux from the earth’s surface under natural convective conditions. J Atmos Sci 54(4):498–509CrossRefGoogle Scholar
  21. Kool D, Agam N, Lazarovitch N, Heitman JL, Sauer TJ, Ben-Gal A (2014) A review of approaches for evapotranspiration partitioning. Agric For Meteorol 184:56–70CrossRefGoogle Scholar
  22. Kool D, Kustas WP, Ben-Gal A, Lazarovitch N, Heitman JL, Sauer TJ, Agam N (2016) Energy and evapotranspiration partitioning in a desert vineyard. Agric For Meteorol 218–219:277–287CrossRefGoogle Scholar
  23. Kustas WP, Norman JM (1999) Evaluation of soil and vegetation heat flux predictions using a simple two-source model with radiometric temperatures for partial canopy cover. Agric For Meteorol 94:13–29CrossRefGoogle Scholar
  24. Kustas W, Norman JM (2000) A two-source energy balance approach using directional radiometric temperature observations for sparse canopy covered surfaces. Agron J 92(5):847–854CrossRefGoogle Scholar
  25. Kustas WP, Alfieri JG, Agam N, Evett SR (2015) Reliable estimation of water use at field scale in an irrigated agricultural region with strong advection. Irrig Sci 33:325–338CrossRefGoogle Scholar
  26. Kustas WP, Nieto H, Morillas L, Anderson MC, Alfieri JG, Hipps LE, Villagarcía L, Domingo F, García M (2016) Revisiting the paper “using radiometric surface temperature for surface energy flux estimation in mediterranean drylands from a two-source perspective. Remote Sens Environ 184:645–653CrossRefGoogle Scholar
  27. Kustas WP, Agam N, Alfieri AJ, McKee LG, Preuger JH, Hipps LE, Howard AM, Heitman JL (this issue) Below canopy radiation divergence in a vineyard—implications on inter-row surface energy balance. Irrig SciGoogle Scholar
  28. Massman WJ, Lee X (2002) Eddy covariance flux corrections and uncertainties in long term studies of carbon and energy exchanges. Agric For Meteorol 113:121–144CrossRefGoogle Scholar
  29. Massman W, Forthofer J, Finney M (2017) An improved canopy wind model for predicting wind adjustment factors and wildland fire behavior. Can J For Res 47(5):594–603CrossRefGoogle Scholar
  30. Nieto H, Kustas W, Gao F, Alfieri J, Torres A, Hipps L (this issue a) Impact of different within-canopy wind attenuation formulations on modelling evapotranspiration using TSEB. Irrig SciGoogle Scholar
  31. Nieto H, Kustas WP, Torres-Rúa A, Alfieri JG, Gao F, Anderson MC, White WA, Song L, del Mar Alsina M, Prueger JH, McKee M, Elarab M, McKee LG (this issue b) Evaluation of TSEB turbulent fluxes using different methods for the retrieval of soil and canopy component temperatures from UAV thermal and multispectral imagery. Irrig Sci.  https://doi.org/10.1007/s00271-018-0585-9
  32. Norman JM, Becker F (1995) Terminology in thermal infrared remote sensing of natural surfaces. Remote Sens Rev 12:159 – 173CrossRefGoogle Scholar
  33. Norman JM, Kustas WP, Humes KS (1995) Source approach for estimating soil and vegetation energy fluxes in observations of directional radiometric surface temperature. Agric Fort Meteorol 77(3–4):263–293CrossRefGoogle Scholar
  34. Palatella L, Rana G, Vitale D (2014) Towards a flux-partitioning procedure based on the direct use of high-frequency eddy-covariance data. Bound Layer Meteorol 153:327–337CrossRefGoogle Scholar
  35. Parry CK, Nieto H, Guillevic P, Agam N, Kustas WP, Alfieri J, McKee L, McElrone AJ (this issue) An intercomparison of radiation partitioning models in vineyard row structured canopies. Irrig SciGoogle Scholar
  36. Priestley CHB, Taylor RJ (1972) On the assessment of surface heat flux and evaporation using large-scale parameters. Mon Weather Rev 100(2):81–92CrossRefGoogle Scholar
  37. Santanello J Jr, Friedl M (2003) Diurnal covariation in soil heat flux and net radiation. J Appl Meteorol 42(6):851–862CrossRefGoogle Scholar
  38. Sauer TJ, Norman JM, Tanner CB, Wilson TB (1995) Measurement of heat and vapor transfer coefficients at the soil surface beneath a maize canopy using source plates. Agric Fort Meteorol 75(1–3):161–189CrossRefGoogle Scholar
  39. Scanlon TM, Kustas WP (2010) Partitioning carbon dioxide and water vapor fluxes using correlation analysis. Agric For Meteorol 150:89–99CrossRefGoogle Scholar
  40. Scanlon TM, Kustas WP (2012) Partitioning evapotranspiration using an eddy covariance-based technique: improved assessment of soil moisture and land-atmosphere exchange dynamics. Vadose Zone J.  https://doi.org/10.2136/vzj2012.0025 CrossRefGoogle Scholar
  41. Scanlon TM, Sahu P (2008) On the correlation structure of water vapor and carbon dioxide in the atmospheric surface layer: a basis for flux partitioning. Water Resour Res 44:W10418.  https://doi.org/10.1029/2008WR006932 CrossRefGoogle Scholar
  42. Semmens KA, Anderson MC, Kustas WP, Gao F, Alfieri JG, McKee L, Prueger JH, Hain CR, Cammalleri C, Yang Y, Xia T, Sanchez L, Alsina MM, Vélez M (2016) Monitoring daily evapotranspiration over two California vineyards using Landsat 8 in a multi-sensor data fusion approach. Remote Sens Environ 185:155–170.  https://doi.org/10.1016/j.rse.2015.10.025 CrossRefGoogle Scholar
  43. Sun L, Gao F, Anderson MC, Kustas WP, Alsina M, Sanchez L, Sams B, McKee LG, Dulaney WP, White A, Alfieri JG, Prueger JH, Melton F, Post K (2017) Daily mapping of 30 m LAI, NDVI for grape yield prediction in California vineyard. Remote Sens 9:317.  https://doi.org/10.3390/rs9040317 CrossRefGoogle Scholar
  44. White AW, Alsina M, Nieto H, McKee L, Gao F, Kustas WP (this issue) Indirect measurement of leaf area index in California vineyards: utility for validation of remote sensing-based retrievals. Irrig SciGoogle Scholar

Copyright information

© This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2018

Authors and Affiliations

  1. 1.USDA-ARS, Hydrology and Remote Sensing LaboratoryBeltsvilleUSA
  2. 2.IRTA-Research and Technology Food and AgricultureLleidaSpain

Personalised recommendations