@article{fdi:010082730,
  title = {{T}he role of aerodynamic resistance in thermal remote sensing-based evapotranspiration models},
  author = {{T}rebs, {I}. and {M}allick, {K}. and {B}hattarai, {N}. and {S}ulis, {M}. and {C}leverly, {J}. and {W}oodgate, {W}. and {S}ilberstein, {R}. and {H}inko-{N}ajera, {N}. and {B}eringer, {J}. and {M}eyer, {W}. {S}. and {S}u, {Z}. {B}. and {B}oulet, {G}illes},
  editor = {},
  language = {{ENG}},
  abstract = {{A}erodynamic resistance (hereafter r(a)) is a preeminent variable in evapotranspiration ({ET}) modelling. {T}he accurate quantification of r(a) 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. {A}tmospheric stability links r(a) with land surface temperature ({LST}) and the representation of their interactions in the {SEB} models determines the accuracy of {ET} estimates. {T}he present study investigates the influence of r(a) and its relation to {LST} uncertainties on the performance of three structurally different {SEB} models. {I}t used data from nine {A}ustralian {O}z{F}lux eddy covariance sites of contrasting aridity in conjunction with {MODIS} {T}erra and {A}qua {LST} and leaf area index ({LAI}) products. {S}imulations of the sensible heat flux ({H}) and the latent heat flux ({LE}, the energy equivalent of {ET} in {W}/m(2)) from the {SPARSE} ({S}oil {P}lant {A}tmosphere and {R}emote {S}ensing {E}vapotranspiration), {SEBS} ({S}urface {E}nergy {B}alance {S}ystem) and {STIC} ({S}urface {T}emperature {I}nitiated {C}losure) 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. {O}ur 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 underestimation of {H}. {O}verestimation of {LE} was associated with discrepancies in r(a) retrievals under conditions of high atmospheric instability, during which uncertainties in {LST} (expressed as the difference between {MODIS} {LST} and in-situ {LST}) apparently played a minor role. {O}n the other hand, a positive difference in {LST} coincided with low r(a) (high wind speeds) and caused a slight underestimation of {LE} at the water-limited sites. {T}he impact of r(a) on the {LE} residual error was found to be of the same magnitude as the influence of {LST} uncertainties in the semi-arid ecosystems as indicated by variable importance in projection ({VIP}) coefficients from partial least squares regression above unity. {I}n contrast, our results for the mesic forest ecosystems indicated minor dependency on r(a) for modelling {LE} ({VIP} < 0.4), which was due to a higher roughness length and lower {LST} resulting in the dominance of mechanically generated turbulence, thereby diminishing the importance of buoyancy production for the determination of r(a).},
  keywords = {{T}hermal remote sensing ; {A}erodynamic resistance ; {L}and surface temperature ; {E}vapotranspiration ; {S}urface energy balance model ; {A}ridity},
  booktitle = {},
  journal = {{R}emote {S}ensing of {E}nvironment},
  volume = {264},
  numero = {},
  pages = {112602 [29 p.]},
  ISSN = {0034-4257},
  year = {2021},
  DOI = {10.1016/j.rse.2021.112602},
  URL = {https://www.documentation.ird.fr/hor/fdi:010082730},
}