@article{fdi:010072506,
  title = {{T}oward a surface soil moisture product at high spatiotemporal resolution : temporally interpolated, spatially disaggregated {SMOS} data},
  author = {{M}albeteau, {Y}. and {M}erlin, {O}livier and {B}alsamo, {G}. and {E}r-{R}aki, {S}. and {K}habba, {S}. and {W}alker, {J}. {P}. and {J}arlan, {L}ionel},
  editor = {},
  language = {{ENG}},
  abstract = {{H}igh spatial and temporal resolution surface soil moisture is required for most hydrological and agricultural applications. {T}he recently developed {D}isaggregation based on {P}hysical and {T}heoretical {S}cale {C}hange ({D}is{PATC}h) algorithm provides 1-km-resolution surface soil moisture by downscaling the 40-km {S}oil {M}oisture {O}cean {S}alinity ({SMOS}) soil moisture using {M}oderate {R}esolution {I}maging {S}pectroradiometer ({MODIS}) data. {H}owever, the temporal resolution of {D}is{PATC}h data is constrained by the temporal resolution of {SMOS} (a global coverage every 3 days) and further limited by gaps in {MODIS} images due to cloud cover. {T}his paper proposes an approach to overcome these limitations based on the assimilation of the 1-km-resolution {D}is{PATC}h data into a simple dynamic soil model forced by (inaccurate) precipitation data. {T}he performance of the approach was assessed using ground measurements of surface soil moisture in the {Y}anco area in {A}ustralia and the {T}ensift-{H}aouz region in {M}orocco during 2014. {I}t was found that the analyzed daily 1-km-resolution surface soil moisture compared slightly better to in situ data for all sites than the original disaggregated soil moisture products. {O}ver the entire year, assimilation increased the correlation coefficient between estimated soil moisture and ground measurements from 0.53 to 0.70, whereas the mean unbiased {RMSE} (ub{RMSE}) slightly decreased from 0.07 to 0.06 m(3) m(-3) compared to the open-loop force-restore model. {T}he proposed assimilation scheme has significant potential for large-scale applications over semiarid areas, since the method is based on data available at the global scale together with a parsimonious land surface model.},
  keywords = {{AUSTRALIE} ; {MAROC}},
  booktitle = {},
  journal = {{J}ournal of {H}ydrometeorology},
  volume = {19},
  numero = {1},
  pages = {183--200},
  ISSN = {1525-755{X}},
  year = {2018},
  DOI = {10.1175/jhm-d-16-0280.1},
  URL = {https://www.documentation.ird.fr/hor/fdi:010072506},
}