@article{PAR00008807,
  title = {{T}he {SMOS} {S}oil {M}oisture {R}etrieval {A}lgorithm},
  author = {{K}err, {Y}ann and {W}aldteufel, {P}. and {R}ichaume, {P}. and {W}igneron, {J}. {P}. and {F}errazzoli, {P}. and {M}ahmoodi, {A}. and {A}l {B}itar, {A}. and {C}abot, {F}. and {G}ruhier, {C}. and {J}uglea, {S}. {E}. and {L}eroux, {D}. and {M}ialon, {A}. and {D}elwart, {S}.},
  editor = {},
  language = {{ENG}},
  abstract = {{T}he {S}oil {M}oisture and {O}cean {S}alinity ({SMOS}) mission is {E}uropean {S}pace {A}gency ({ESA}'s) second {E}arth {E}xplorer {O}pportunity mission, launched in {N}ovember 2009. {I}t is a joint program between {ESA} {C}entre {N}ational d'{E}tudes {S}patiales ({CNES}) and {C}entro para el {D}esarrollo {T}ecnologico {I}ndustrial. {SMOS} carries a single payload, an {L}-{B}and 2-{D} interferometric radiometer in the 1400-1427 {MH}z protected band. {T}his wavelength penetrates well through the atmosphere, and hence the instrument probes the earth surface emissivity. {S}urface emissivity can then be related to the moisture content in the first few centimeters of soil, and, after some surface roughness and temperature corrections, to the sea surface salinity over ocean. {T}he goal of the level 2 algorithm is thus to deliver global soil moisture ({SM}) maps with a desired accuracy of 0.04 m3/m3. {T}o reach this goal, a retrieval algorithm was developed and implemented in the ground segment which processes level 1 to level 2 data. {L}evel 1 consists mainly of angular brightness temperatures ({TB}), while level 2 consists of geophysical products in swath mode, i.e., as acquired by the sensor during a half orbit from pole to pole. {I}n this context, a group of institutes prepared the {SMOS} algorithm theoretical basis documents to be used to produce the operational algorithm. {T}he principle of the {SM} retrieval algorithm is based on an iterative approach which aims at minimizing a cost function. {T}he main component of the cost function is given by the sum of the squared weighted differences between measured and modeled {TB} data, for a variety of incidence angles. {T}he algorithm finds the best set of the parameters, e. g., {SM} and vegetation characteristics, which drive the direct {TB} model and minimizes the cost function. {T}he end user {L}evel 2 {SM} product contains {SM}, vegetation opacity, and estimated dielectric constant of any surface, {TB} computed at 42.5 degrees, flags and quality indices, and other parameters of interest. {T}his paper gives an overview of the algorithm, discusses the caveats, and provides a glimpse of the {C}al {V}al exercises.},
  keywords = {{C}al/{V}al ; model ; {SMOS} ; soil moisture ; retrievals ; vegetation opacity},
  booktitle = {},
  journal = {{IEEE} {T}ransactions on {G}eoscience and {R}emote {S}ensing},
  volume = {50},
  numero = {5},
  pages = {1384--1403},
  ISSN = {0196-2892},
  year = {2012},
  DOI = {10.1109/tgrs.2012.2184548},
  URL = {https://www.documentation.ird.fr/hor/{PAR}00008807},
}