@article{fdi:010073215,
  title = {{A} phenomenological model of soil evaporative efficiency using surface soil moisture and temperature data},
  author = {{M}erlin, {O}livier and {O}livera-{G}uerra, {L}. and {H}ssaine, {B}. {A}. and {A}mazirh, {A}. and {R}afi, {Z}. and {E}zzahar, {J}. and {G}entine, {P}. and {K}habba, {S}. and {G}ascoin, {S}. and {E}r-{R}aki, {S}.},
  editor = {},
  language = {{ENG}},
  abstract = {{M}odeling soil evaporation has been a notorious challenge due to the complexity of the phenomenon and the lack of data to constrain it. {I}n this context, a parsimonious model is developed to estimate soil evaporative efficiency ({SEE}) defined as the ratio of actual to potential soil evaporation. {I}t uses a soil resistance driven by surface (0-5 cm) soil moisture, meteorological forcing and time (hour) of day, and has the capability to be calibrated using the radiometric surface temperature derived from remotely sensed thermal data. {T}he new approach is tested over a rainfed semi-arid site, which had been under bare soil conditions during a 9-month period in 2016. {T}hree calibration strategies are adopted based on {SEE} time series derived from (1) eddy-covariance measurements, (2) thermal measurements, and (3) eddy-covariance measurements used only over separate drying periods between significant rainfall events. {T}he correlation coefficients (and slopes of the linear regression) between simulated and observed (eddy-covariance-derived) {SEE} are 0.85, 0.86 and 0.87 (and 0.91, 0.87 and 0.91) for calibration strategies 1, 2 and 3, respectively. {M}oreover, the correlation coefficient (and slope of the linear regression) between simulated and observed {SEE} is improved from 0.80 to 0.85 (from 0.86 to 0.91) when including hour of day in the soil resistance. {T}he reason is that, under non-energy-limited conditions, the receding evaporation front during daytime makes {SEE} decrease at the hourly time scale. {T}he soil resistance formulation can be integrated into state-of-the-art dual-source surface models and has calibration capabilities across a range of spatial scales from spacebome microwave and thermal data.},
  keywords = {{S}oil evaporation ; {S}oil resistance ; {S}oil moisture ; {S}urface temperature ; {C}alibration ; {R}emote sensing ; {MAROC}},
  booktitle = {},
  journal = {{A}gricultural and {F}orest {M}eteorology},
  volume = {256},
  numero = {},
  pages = {501--515},
  ISSN = {0168-1923},
  year = {2018},
  DOI = {10.1016/j.agrformet.2018.04.010},
  URL = {https://www.documentation.ird.fr/hor/fdi:010073215},
}