@article{fdi:010072032,
  title = {{M}ulti-timescale analysis of the spatial representativeness of in situ soil moisture data within satellite footprints},
  author = {{M}olero, {B}. and {L}eroux, {D}. {J}. and {R}ichaume, {P}. and {K}err, {Y}. {H}. and {M}erlin, {O}livier and {C}osh, {M}. {H}. and {B}indlish, {R}.},
  editor = {},
  language = {{ENG}},
  abstract = {{W}e conduct a novel comprehensive investigation that seeks to prove the connection between spatial scales and timescales in surface soil moisture ({SM}) within the satellite footprint (similar to 50 km). {M}odeled and measured point series at {Y}anco and {L}ittle {W}ashita in situ networks are first decomposed into anomalies at timescales ranging from 0.5 to 128 days, using wavelet transforms. {T}hen, their degree of spatial representativeness is evaluated on a per-timescale basis by comparison to large spatial scale data sets (the in situ spatial average, {SMOS}, {AMSR}2, and {ECMWF}). {F}our methods are used for this: temporal stability analysis ({TS}tab), triple collocation ({TC}), percentage of correlated areas ({CA}rea), and a new proposed approach that uses wavelet-based correlations ({WC}or). {W}e found that the mean of the spatial representativeness values tends to increase with the timescale but so does their dispersion. {L}ocations exhibit poor spatial representativeness at scales below 4 days, while either very good or poor representativeness at seasonal scales. {R}egarding the methods, {TS}tab cannot be applied to the anomaly series due to their multiple zero-crossings, and {TC} is suitable for week and month scales but not for other scales where data set cross-correlations are found low. {I}n contrast, {WC}or and {CA}rea give consistent results at all timescales. {WC}or is less sensitive to the spatial sampling density, so it is a robust method that can be applied to sparse networks (one station per footprint). {T}hese results are promising to improve the validation and downscaling of satellite {SM} series and the optimization of {SM} networks.},
  keywords = {soil moisture ; spatial representativeness ; timescales ; spatial scales ; wavelet decomposition ; satellite validation},
  booktitle = {},
  journal = {{J}ournal of {G}eophysical {R}esearch : {A}tmospheres},
  volume = {123},
  numero = {1},
  pages = {3--21},
  ISSN = {2169-897{X}},
  year = {2018},
  DOI = {10.1002/2017jd027478},
  URL = {https://www.documentation.ird.fr/hor/fdi:010072032},
}