CoChat
Sign Up
Cited by 68Plant Water Relations and Carbon DynamicsSoil Moisture and Remote SensingHydrology and Drought Analysis

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

The role of aerodynamic resistance in thermal remote sensing-based evapotranspiration models

Open access
Trebs, Ivonne, Mallick, Kaniska, Bhattarai, Nishan, Sulis, Mauro, Cleverly, Jamie, Woodgate, William, Silberstein, Richard, Hinko-Najera, Nina, Beringer, Jason, Meyer, Wayne S., Su, Zhongbo, Boulet, Gilles·2021·Remote Sensing of Environment
DOI 10.1016/j.rse.2021.112602
Open this paper in CoChat — free account

Abstract

Aerodynamic resistance (hereafter ra) is a preeminent variable in evapotranspiration (ET) modelling. The accurate quantification of ra plays a pivotal role in determining the performance and consistency of thermal remote sensing-based surface energy balance (SEB) models for estimating ET at local to regional scales. Atmospheric stability links ra with land surface temperature (LST) and the representation of their interactions in the SEB models determines the accuracy of ET estimates. The present study investigates the influence of ra and its relation to LST uncertainties on the performance of three structurally different SEB models. It used data from nine Australian OzFlux eddy covariance sites of contrasting aridity in conjunction with MODIS Terra and Aqua LST and leaf area index (LAI) products. Simulations of the sensible heat flux (H) and the latent heat flux (LE, the energy equivalent of ET in W/m2) from the SPARSE (Soil Plant Atmosphere and Remote Sensing Evapotranspiration), SEBS (Surface Energy Balance System) and STIC (Surface Temperature Initiated Closure) models forced with MODIS LST, LAI, and in-situ meteorological datasets were evaluated against flux observations in water-limited (arid and semi-arid) and energy-limited (mesic) ecosystems from 2011 to 2019. Our results revealed an overestimation tendency of instantaneous LE by all three models in the water-limited shrubland, woodland and grassland ecosystems by up to 50% on average, which was caused by an underesti…

Overview

Integrity signals

Registry

Crossref · 10.1016/j.rse.2021.112602

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Show all 1 references

Research works printed but not linked: 76

  1. Anderson, 2011, Mapping daily evapotranspiration at field to continental scales using geostationary and polar orbiting satellite imagery, Hydrol. Earth Syst. Sci., 15, 223, 10.5194/hess-15-223-2011 · DOI
  2. Bahir, 2017, Evaluation and aggregation properties of thermal infra-red-based evapotranspiration algorithms from 100 m to the km scale over a semi-arid irrigated agricultural area, Remote Sens., 9, 1178, 10.3390/rs9111178 · DOI
  3. Banerjee, 2017, Explaining the convector effect in canopy turbulence by means of large-eddy simulation., Hydrol. Earth Syst. Sci., 21, 2987, 10.5194/hess-21-2987-2017 · DOI
  4. Beringer, 2016, An introduction to the Australian and New Zealand flux tower network – OzFlux, Biogeosciences, 13, 5895, 10.5194/bg-13-5895-2016 · DOI
  5. Bhattarai, 2018, Regional evapotranspiration from an image-based implementation of the surface temperature initiated closure (STIC1.2) model and its validation across an aridity gradient in the conterminous US, Hydrol. Earth Syst. Sci., 22, 2311, 10.5194/hess-22-2311-2018 · DOI
  6. Boegh, 2002, Evaluating evapotranspiration rates and surface conditions using Landsat TM to estimate atmospheric resistance and surface resistance, Remote Sens. Environ., 79, 329, 10.1016/S0034-4257(01)00283-8 · DOI
  7. Boulet, 2012, An empirical expression to relate aerodynamic and surface temperatures for use within single-source energy balance models, Agric. For. Meteorol., 161, 148, 10.1016/j.agrformet.2012.03.008 · DOI
  8. Boulet, 2015, The SPARSE model for the prediction of water stress and evapotranspiration components from thermal infra-red data and its evaluation over irrigated and rainfed wheat, Hydrol. Earth Syst. Sci., 19, 4653, 10.5194/hess-19-4653-2015 · DOI
  9. Bowman, 2010, Biogeography of the Australian monsoon tropics, J. Biogeogr., 37, 201, 10.1111/j.1365-2699.2009.02210.x · DOI
  10. Brutsaert, 1992, Stability correction functions for the mean wind speed and temperature in the unstable surface layer, Geophys. Res. Lett., 19, 469, 10.1029/92GL00084 · DOI
  11. Brutsaert, 1999, Aspects of bulk atmospheric boundary layer similarity under free-convective conditions, Rev. Geophys., 37, 439, 10.1029/1999RG900013 · DOI
  12. Cai, 2011, Influence of global-scale variability on the subtropical ridge over Southeast Australia, J. Clim., 24, 6035, 10.1175/2011JCLI4149.1 · DOI
  13. Cai, 2011, Teleconnection pathways of ENSO and the IOD and the mechanisms for impacts on Australian rainfall, J. Clim., 24, 3910, 10.1175/2011JCLI4129.1 · DOI
  14. Chen, 2020, Evolution of evapotranspiration models using thermal and shortwave remote sensing data, Remote Sens. Environ., 237, 10.1016/j.rse.2019.111594 · DOI
  15. Chen, 2013, An improvement of roughness height parameterization of the surface energy balance system (SEBS) over the Tibetan plateau, J. Appl. Meteorol. Climatol., 52, 607, 10.1175/JAMC-D-12-056.1 · DOI
  16. Chen, 2017, An accurate estimate of monthly mean land surface temperatures from MODIS clear-sky retrievals, J. Hydrometeorol., 18, 2827, 10.1175/JHM-D-17-0009.1 · DOI
  17. Chen, 2019, A column canopy-air turbulent diffusion method for different canopy structures, J. Geophys. Res.-Atmos., 124, 488, 10.1029/2018JD028883 · DOI
  18. Chen, 2019, Optimization of a remote sensing energy balance method over different canopy applied at global scale, Agric. For. Meteorol., 279, 15, 10.1016/j.agrformet.2019.107633 · DOI
  19. Chen, 2020, Biophysical impacts of earth greening largely controlled by aerodynamic resistance, Sci. Adv., 6, 10.1126/sciadv.abb1981 · DOI
  20. Chong, 2005, Performance of some variable selection methods when multicollinearity is present, Chemom. Intell. Lab. Syst., 78, 103, 10.1016/j.chemolab.2004.12.011 · DOI
  21. Choudhury, 1988, A 4-layer model for heat-budget of homogeneous land surfaces, Q. J. R. Meteorol. Soc., 114, 373
  22. Choudhury, 1987, Analysis of an empirical model for soil heat flux under a growing wheat crop for estimating evaporation by an infrared-temperature based energy balance equation, Agric. For. Meteorol., 39, 283, 10.1016/0168-1923(87)90021-9 · DOI
  23. Cleverly, 2013, Aerodynamic resistance and Penman-Monteith evapotranspiration over a seasonally two-layered canopy in semiarid Central Australia, J. Hydrometeorol., 14, 1562, 10.1175/JHM-D-13-080.1 · DOI
  24. Cleverly, 2016, Productivity and evapotranspiration of two contrasting semiarid ecosystems following the 2011 global carbon land sink anomaly, Agric. For. Meteorol., 220, 151, 10.1016/j.agrformet.2016.01.086 · DOI
  25. Foken, 2006, 50 years of the Monin-Obukhov similarity theory, Bound.-Layer Meteorol., 119, 431, 10.1007/s10546-006-9048-6 · DOI
  26. Glenn, 2011, Actual evapotranspiration estimation by ground and remote sensing methods: the Australian experience, Hydrol. Process., 25, 4103, 10.1002/hyp.8391 · DOI
  27. Gokmen, 2012, Integration of soil moisture in SEBS for improving evapotranspiration estimation under water stress conditions, Remote Sens. Environ., 121, 261, 10.1016/j.rse.2012.02.003 · DOI
  28. Gokool, 2020, Evaluating the accuracy of satellite-derived evapotranspiration estimates acquired during conditions of water stress, Int. J. Remote Sens., 41, 704, 10.1080/01431161.2019.1646940 · DOI
  29. González-Dugo, 2020, Long-term water stress and drought monitoring of Mediterranean oak savanna vegetation using thermal remote sensing, Hydrol. Earth Syst. Sci. Discuss., 2020, 1
  30. Griebel, 2016, Effects of inhomogeneities within the flux footprint on the interpretation of seasonal, annual, and interannual ecosystem carbon exchange, Agric. For. Meteorol., 221, 50, 10.1016/j.agrformet.2016.02.002 · DOI
  31. Haghighi, 2017, Near-surface turbulence as a missing link in modeling evapotranspiration-soil moisture relationships, Water Resour. Res., 53, 5320, 10.1002/2016WR020111 · DOI
  32. Haghighi, 2015, Interactions of bluff-body obstacles with turbulent airflows affecting evaporative fluxes from porous surfaces, J. Hydrol., 530, 103, 10.1016/j.jhydrol.2015.09.048 · DOI
  33. Holwerda, 2012, Wet canopy evaporation from a Puerto Rican lower montane rain forest: the importance of realistically estimated aerodynamic conductance, J. Hydrol., 414, 1, 10.1016/j.jhydrol.2011.07.033 · DOI
  34. Isaac, 2017, OzFlux data: network integration from collection to curation, Biogeosciences, 14, 2903, 10.5194/bg-14-2903-2017 · DOI
  35. Jarvis, 1986, Stomatal control of transpiration - scaling up from leaf to region, Adv. Ecol. Res., 15, 1, 10.1016/S0065-2504(08)60119-1 · DOI
  36. Jimenez-Munoz, 2006, Improved land surface emissivities over agricultural areas using ASTER NDVI, Remote Sens. Environ., 103, 474, 10.1016/j.rse.2006.04.012 · DOI
  37. Jin, 2006, An improved land surface emissivity parameter for land surface models using global remote sensing observations, J. Clim., 19, 2867, 10.1175/JCLI3720.1 · DOI
  38. Knoben, 2019, Technical note: inherent benchmark or not? Comparing Nash-Sutcliffe and Kling-Gupta efficiency scores, Hydrol. Earth Syst. Sci., 23, 4323, 10.5194/hess-23-4323-2019 · DOI
  39. Kustas, 2009, Advances in thermal infrared remote sensing for land surface modeling, Agric. For. Meteorol., 149, 2071, 10.1016/j.agrformet.2009.05.016 · DOI
  40. Kustas, 2007, Utility of radiometric-aerodynamic temperature relations for heat flux estimation, Bound.-Layer Meteorol., 122, 167, 10.1007/s10546-006-9093-1 · DOI
  41. Kustas, 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, 10.1016/j.rse.2016.07.024 · DOI
  42. Lee, 2020, Evaluation of Monin–Obukhov and Bulk Richardson parameterizations for surface–atmosphere exchange, J. Appl. Meteorol. Climatol., 59, 1091, 10.1175/JAMC-D-19-0057.1 · DOI
  43. Lhomme, 2012, Evaporation from heterogeneous and sparse canopies: on the formulations related to multi-source representations, Bound.-Layer Meteorol., 144, 243, 10.1007/s10546-012-9713-x · DOI
  44. Li, 2019, Evaluating soil resistance formulations in thermal-based two-source energy balance (TSEB) model: implications for heterogeneous semiarid and arid regions, Water Resour. Res., 55, 1059, 10.1029/2018WR022981 · DOI
  45. Liao, 2018, Attribution of local temperature response to deforestation, J. Geophys. Res. Biogeosci., 123, 1572, 10.1029/2018JG004401 · DOI
  46. Liu, 2007, Evaluating parameterizations of aerodynamic resistance to heat transfer using field measurements, Hydrol. Earth Syst. Sci., 11, 769, 10.5194/hess-11-769-2007 · DOI
  47. Mallick, 2014, A surface temperature initiated closure (STIC) for surface energy balance fluxes, Remote Sens. Environ., 141, 243, 10.1016/j.rse.2013.10.022 · DOI
  48. Mallick, 2015, Reintroducing radiometric surface temperature into the Penman-Monteith formulation, Water Resour. Res., 51, 6214, 10.1002/2014WR016106 · DOI
  49. Mallick, 2016, Canopy-scale biophysical controls of transpiration and evaporation in the Amazon Basin, Hydrol. Earth Syst. Sci., 20, 4237, 10.5194/hess-20-4237-2016 · DOI
  50. Mallick, 2018, Bridging thermal infrared sensing and physically-based evapotranspiration modeling: from theoretical implementation to validation across an aridity gradient in Australian ecosystems, Water Resour. Res., 54, 3409, 10.1029/2017WR021357 · DOI
  51. Mallick, 2018, A critical evaluation on the role of aerodynamic and canopy-surface conductance parameterization in SEB and SVAT models for simulating evapotranspiration: a case study in the upper Biebrza National Park Wetland in Poland, Water, 10, 10.3390/w10121753 · DOI
  52. Marcolla, 2018, Geometry of the hemispherical radiometric footprint over plant canopies, Theor. Appl. Climatol., 134, 981, 10.1007/s00704-017-2326-z · DOI
  53. Massman, 1999, An analytical one-dimensional second-order closure model of turbulence statistics and the Lagrangian time scale within and above plant canopies of arbitrary structure, Bound.-Layer Meteorol., 91, 81, 10.1023/A:1001810204560 · DOI
  54. Mauder, 2020, Surface-energy-balance closure over land: a review, Bound.-Layer Meteorol., 177, 395, 10.1007/s10546-020-00529-6 · DOI
  55. Morillas, 2013, Using radiometric surface temperature for surface energy flux estimation in Mediterranean drylands from a two-source perspective, Remote Sens. Environ., 136, 234, 10.1016/j.rse.2013.05.010 · DOI
  56. Norman, 1995, Source approach for estimating soil and vegetation energy fluxes in observations of directional radiometric surface temperature, Agric. For. Meteorol., 77, 263, 10.1016/0168-1923(95)02265-Y · DOI
  57. Novick, 2020, The duality of reforestation impacts on surface and air temperature, J. Geophys. Res. Biogeosci., 125, 10.1029/2019JG005543 · DOI
  58. Panwar, 2020, Imprints of evaporative conditions and vegetation type in diurnal temperature variations, Hydrol. Earth Syst. Sci., 24, 4923, 10.5194/hess-24-4923-2020 · DOI
  59. Pardo, 2014, SEBS validation in a Spanish rotating crop, Agric. For. Meteorol., 195, 132, 10.1016/j.agrformet.2014.05.007 · DOI
  60. Paul, 2014, Investigating the influence of roughness length for heat transport (z(oh)) on the performance of SEBAL in semi-arid irrigated and dryland agricultural systems, J. Hydrol., 509, 231, 10.1016/j.jhydrol.2013.11.040 · DOI
  61. Payero, 2006, Effect of stability correction on remote sensing estimates of near-noon sensible heat flux of alfalfa and tall fescue grass, Int. J. Remote Sens., 27, 307, 10.1080/01431160500222657 · DOI
  62. Pereira, 1999, Evapotranspiration: concepts and future trends, J. Irrig. Drain. Eng., 125, 45, 10.1061/(ASCE)0733-9437(1999)125:2(45) · DOI
  63. Raupach, 2001, Combination theory and equilibrium evaporation, Q. J. R. Meteorol. Soc., 127, 1149, 10.1002/qj.49712757402 · DOI
  64. Saadi, 2018, Assessment of actual evapotranspiration over a semiarid heterogeneous land surface by means of coupled low-resolution remote sensing data with an energy balance model: comparison to extra-large aperture scintillometer measurements, Hydrol. Earth Syst. Sci., 22, 2187, 10.5194/hess-22-2187-2018 · DOI
  65. Seneviratne, 2010, Investigating soil moisture-climate interactions in a changing climate: a review, Earth Sci. Rev., 99, 125, 10.1016/j.earscirev.2010.02.004 · DOI
  66. Su, 2002, The surface energy balance system (SEBS) for estimation of turbulent heat fluxes, Hydrol. Earth Syst. Sci., 6, 85, 10.5194/hess-6-85-2002 · DOI
  67. Su, 2001, An evaluation of two models for estimation of the roughness height for heat transfer between the land surface and the atmosphere, J. Appl. Meteorol., 40, 1933, 10.1175/1520-0450(2001)040<1933:AEOTMF>2.0.CO;2 · DOI
  68. Sun, 2020, Understanding physical processes represented by the Monin-Obukhov bulk formula for momentum transfer, Bound.-Layer Meteorol., 177, 69, 10.1007/s10546-020-00546-5 · DOI
  69. Troufleau, 1997, Sensible heat flux and radiometric surface temperature over sparse Sahelian vegetation .1. An experimental analysis of the kB-1 parameter, J. Hydrol., 188, 815, 10.1016/S0022-1694(96)03172-1 · DOI
  70. Twine, 2000, Correcting eddy-covariance flux underestimates over a grassland, Agric. For. Meteorol., 103, 279, 10.1016/S0168-1923(00)00123-4 · DOI
  71. van der Kwast, 2009, Evaluation of the surface energy balance system (SEBS) applied to ASTER imagery with flux-measurements at the SPARC 2004 site (Barrax, Spain), Hydrol. Earth Syst. Sci., 13, 1337, 10.5194/hess-13-1337-2009 · DOI
  72. van Dijk, 2015, Rainfall interception and the coupled surface water and energy balance, Agric. For. Meteorol., 214, 402, 10.1016/j.agrformet.2015.09.006 · DOI
  73. van Gorsel, 2016, Carbon uptake and water use in woodlands and forests in southern Australia during an extreme heat wave event in the “angry summer” of 2012/2013, Biogeosciences, 13, 5947, 10.5194/bg-13-5947-2016 · DOI
  74. Vivoni, 2010, Improved land-atmosphere relations through distributed footprint sampling in a subtropical scrubland during the North American monsoon, J. Arid Environ., 74, 579, 10.1016/j.jaridenv.2009.09.031 · DOI
  75. Winckler, 2019, Importance of surface roughness for the local biogeophysical effects of deforestation, J. Geophys. Res.-Atmos., 124, 8605, 10.1029/2018JD030127 · DOI
  76. Zhou, 2012, Significant decrease of uncertainties in sensible heat flux simulation using temporally variable aerodynamic roughness in two typical Forest ecosystems of China, J. Appl. Meteorol. Climatol., 51, 1099, 10.1175/JAMC-D-11-0243.1 · DOI

Cited by

Sorted by citations

See all 68

Metadata

Authors
Trebs, I., Mallick, K., Bhattarai, N., Sulis, M., Cleverly, J., Woodgate, W., Silberstein, R., Hinko-Najera, N., Beringer, J., Meyer, W. S., Su, Z., & Boulet, G.
Affiliations
  • Luxembourg Institute of Science and Technology (LU)
  • University of Michigan (US)
  • Commonwealth Scientific and Industrial Research Organisation (AU)
  • Edith Cowan University (AU)
  • The University of Western Australia (AU)
  • The University of Adelaide (AU)
  • International Institute for Geo-Information Science and Earth Observation (NL)
  • Centre National de la Recherche Scientifique (FR)
Corresponding author
  • Trebs, Ivonne
  • Mallick, Kaniska
ORCID
Venue
Remote Sensing of Environment
Publisher
Elsevier BV
Published
2021-10
Volume
264
Pages
112602
ISSN
0034-4257 (ISSN-L)
DOI
10.1016/j.rse.2021.112602
OpenAlex
W3193585998
Cited by
68
References
1
Citations by year
2026: 16 · 2025: 17 · 2024: 13 · 2023: 12 · 2022: 9 · 2021: 1
FWCI
3.46
Type
journal article
Language
en
Open access
Licence
Locations
  • Remote Sensing of Environment (publishedVersion, cc-by-nc-nd)
  • UWA Profiles and Research Repository (UWA) (submittedVersion, cc-by-nc-nd)
  • Australasian Journal of Paramedicine (submittedVersion, cc-by-nc-nd)
  • HAL (submittedVersion, cc-by-nc-nd)
  • Adelaide Research & Scholarship (AR&S) (University of Adelaide) (submittedVersion, cc-by-nc-nd)
  • University of Twente Research Information (submittedVersion, cc-by-nc-nd)
Funders
  • Australian Research Council (00002308)
  • Luxembourg Institute of Science and Technology (00002308)
Topics
  • Physical Sciences › Environmental Science › Global and Planetary Change › Plant Water Relations and Carbon Dynamics
  • Physical Sciences › Environmental Science › Environmental Engineering › Soil Moisture and Remote Sensing
  • Physical Sciences › Environmental Science › Global and Planetary Change › Hydrology and Drought Analysis
Keywords
aerodynamic resistance, evapotranspiration modeling, surface energy balance model, sensible heat flux, latent heat flux, atmospheric stability, MODIS land surface temperature, sparse models, SEBS model, arid and semi-arid ecosystems, roughness length, buoyancy-driven turbulence

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: The role of aerodynamic resistance in thermal remote sensing-based evapotranspiration models. https://app.cochat.ai/papers/10.1016/j.rse.2021.112602
The paper
Trebs, I., Mallick, K., Bhattarai, N., Sulis, M., Cleverly, J., Woodgate, W., Silberstein, R., Hinko-Najera, N., Beringer, J., Meyer, W. S., Su, Z., & Boulet, G. (2021). The role of aerodynamic resistance in thermal remote sensing-based evapotranspiration models. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2021.112602