@article{fdi:010074765,
  title = {{E}valuation of the {SPARSE} dual-source model for predicting water stress and evapotranspiration from thermal infrared data over multiple crops and climates},
  author = {{D}elogu, {E}. and {B}oulet, {G}illes and {O}lioso, {A}. and {G}arrigues, {S}. and {B}rut, {A}. and {T}allec, {T}. and {D}emarty, {J}{\'e}rome and {S}oudani, {K}. and {L}agouarde, {J}. {P}.},
  editor = {},
  language = {{ENG}},
  abstract = {{U}sing surface temperature as a signature of the surface energy balance is a way to quantify the spatial distribution of evapotranspiration and water stress. {I}n this work, we used the new dual-source model named {S}oil {P}lant {A}tmosphere and {R}emote {S}ensing {E}vapotranspiration ({SPARSE}) based on the {T}wo {S}ources {E}nergy {B}alance ({TSEB}) model rationale which solves the surface energy balance equations for the soil and the canopy. {SPARSE} can be used (i) to retrieve soil and vegetation stress levels from known surface temperature and (ii) to predict transpiration, soil evaporation, and surface temperature for given stress levels. {T}he main innovative feature of {SPARSE} is that it allows to bound each retrieved individual flux component (evaporation and transpiration) by its corresponding potential level deduced from running the model in prescribed potential conditions, i.e., a maximum limit if the surface water availability is not limiting. {T}he main objective of the paper is to assess the {SPARSE} model predictions of water stress and evapotranspiration components for its two proposed versions (the "patch" and "layer" resistances network) over 20 in situ data sets encompassing distinct vegetation and climate. {O}ver a large range of leaf area index values and for contrasting vegetation stress levels, {SPARSE} showed good retrieval performances of evapotranspiration and sensible heat fluxes. {F}or cereals, the layer version provided better latent heat flux estimates than the patch version while both models showed similar performances for sparse crops and forest ecosystems. {T}he bounded layer version of {SPARSE} provided the best estimates of latent heat flux over different sites and climates. {B}road tendencies of observed and retrieved stress intensities were well reproduced with a reasonable difference obtained for most of the points located within a confidence interval of 0.2. {T}he synchronous dynamics of observed and retrieved estimates underlined that the {SPARSE} retrieved water stress estimates from {T}hermal {I}nfra-{R}ed data were relevant tools for stress detection.},
  keywords = {evapotranspiration ; water stress ; model ; partition ; remote-sensing},
  booktitle = {},
  journal = {{R}emote {S}ensing},
  volume = {10},
  numero = {11},
  pages = {art. 1806 [20 p.]},
  ISSN = {2072-4292},
  year = {2018},
  DOI = {10.3390/rs10111806},
  URL = {https://www.documentation.ird.fr/hor/fdi:010074765},
}