@article{fdi:010081006,
  title = {{O}n the utility of high-resolution soil moisture data for better constraining thermal-based energy balance over three semi-arid agricultural areas},
  author = {{H}ssaine, {B}. {A}. and {C}hehbouni, {A}bdelghani and {E}r-{R}aki, {S}. and {K}habba, {S}. and {E}zzahar, {J}. and {O}uaadi, {N}. and {O}jha, {N}. and {R}ivalland, {V}. and {M}erlin, {O}.},
  editor = {},
  language = {{ENG}},
  abstract = {{O}ver semi-arid agricultural areas, the surface energy balance and its components are largely dependent on the soil water availability. {I}n such conditions, the land surface temperature ({LST}) retrieved from the thermal bands has been commonly used to represent the high spatial variability of the surface evaporative fraction and associated fluxes. {I}n contrast, however, the soil moisture ({SM}) retrieved from microwave data has rarely been used thus far due to the unavailability of high-resolution (field scale) {SM} products until recent times. {S}oil evaporation is controlled by the surface {SM}. {M}oreover, the surface {SM} dynamics is temporally related to root zone {SM}, which provides information about the water status of plants. {T}he aim of this work was to assess the gain in terms of flux estimates when integrating microwave-derived {SM} data in a thermal-based energy balance model at the field scale. {I}n this study, {SM} products were derived from three different methodologies: the first approach inverts {SM}, labeled hereafter as '{SMO}20', from the backscattering coefficient and the interferometric coherence derived from {S}entinel-1 products in the water cloud model ({WCM}); the second approach inverts {SM} from {S}entinel-1 and {S}entinel-2 data based on machine learning algorithms trained on a synthetic dataset simulated by the {WCM} noted '{SME}16'; and the third approach disaggregates the soil moisture active and passive {SM} at 100 m resolution using {L}andsat optical/thermal data '{SMO}19'. {T}hese {SM} products, combined with the {L}andsat based vegetation index and {LST}, are integrated simultaneously within an energy balance model ({TSEB}-{SM}) to predict the latent ({LE}) and sensible ({H}) heat fluxes over two irrigated and rainfed wheat crop sites located in the {H}aouz {P}lain in the center of {M}orocco. {H} and {LE} were measured over each site using an eddy covariance system and their values were used to evaluate the potential of {TSEB}-{SM} against the classical two source energy balance ({TSEB}) model solely based on optical/thermal data. {G}lobally, {TSEB} systematically overestimates {LE} (mean bias of 100 {W}/m(2)) and underestimates {H} (mean bias of -110 {W}/m(2)), while {TSEB}-{SM} significantly reduces those biases, regardless of the {SM} product used as input. {T}his is linked to the parameterization of the {P}riestley {T}aylor coefficient, which is set to alpha({PT}) = 1.26 by default in {TSEB} and adjusted across the season in {TSEB}-{SM}. {T}he best performance of {TSEB}-{SM} was obtained over the irrigated field using the three retrieved {SM} products with a mean {R}-2 of 0.72 and 0.92, and a mean {RMSE} of 31 and 36 {W}/m(2) for {LE} and {H}, respectively. {T}his opens up perspectives for applying the {TSEB}-{SM} model over extended irrigated agricultural areas to better predict the crop water needs at the field scale.},
  keywords = {{TSEB} ; {TSEB}-{SM} ; surface soil moisture ; land surface temperature ; vegetation index ; winter wheat ; semi-arid region ; {MAROC} ; {TENSIFT} {BASSIN} ; {HAOUZ} {PLAINE} ; {SIDI} {RAHAL} ; {CHICHAOUA} ; {ZONE} {SEMIARIDE}},
  booktitle = {},
  journal = {{R}emote {S}ensing},
  volume = {13},
  numero = {4},
  pages = {727 [23 p.]},
  year = {2021},
  DOI = {10.3390/rs13040727},
  URL = {https://www.documentation.ird.fr/hor/fdi:010081006},
}