@article{PAR00011388,
  title = {{A}n approach to constructing a homogeneous time series of soil moisture using {SMOS}},
  author = {{L}eroux, {D}. {J}. and {K}err, {Y}ann and {W}ood, {E}. {F}. and {S}ahoo, {A}. {K}. and {B}indlish, {R}. and {J}ackson, {T}. {J}.},
  editor = {},
  language = {{ENG}},
  abstract = {{O}verlapping soil moisture time series derived from two satellite microwave radiometers (the {S}oil {M}oisture and {O}cean {S}alinity ({SMOS}) and the {A}dvanced {M}icrowave {S}canning {R}adiometer-{E}arth {O}bserving {S}ystem) are used to generate a soil moisture time series from 2003 to 2010. {T}wo statistical methodologies for generating long homogeneous time series of soil moisture are considered. {G}enerated soil moisture time series using only morning satellite overpasses are compared to ground measurements from four watersheds in the {U}. {S}. with different climatologies. {T}he two methods, cumulative density function ({CDF}) matching and copulas, are based on the same statistical theory, but the first makes the assumption that the two data sets are ordered the same way, which is not needed by the second. {B}oth methods are calibrated in 2010, and the calibrated parameters are applied to the soil moisture data from 2003 to 2009. {R}esults from these two methods compare well with ground measurements. {H}owever, {CDF} matching improves the correlation, whereas copulas improve the root-mean-square error.},
  keywords = {{A}dvanced {M}icrowave {S}canning {R}adiometer-{E}arth {O}bserving {S}ystem ({AMSR}-{E}) ; cumulative density function ({CDF}) matching ; copulas ; {S}oil {M}oisture and {O}cean {S}alinity ({SMOS}) ; soil moisture ; time series ; {ETATS} {UNIS}},
  booktitle = {},
  journal = {{IEEE} {T}ransactions on {G}eoscience and {R}emote {S}ensing},
  volume = {52},
  numero = {1},
  pages = {393--405},
  ISSN = {0196-2892},
  year = {2014},
  DOI = {10.1109/tgrs.2013.2240691},
  URL = {https://www.documentation.ird.fr/hor/{PAR}00011388},
}