@article{fdi:010073229,
  title = {{S}oil moisture estimation in {F}erlo region ({S}enegal) using radar ({ENVISAT}/{ASAR}) and optical ({SPOT}/{VEGETATION}) data},
  author = {{F}aye, {G}. and {F}rison, {P}. {L}. and {D}iouf, {A}. {A}. and {W}ade, {S}. and {K}ane, {C}. {A}. and {F}ussi, {F}. and {J}arlan, {L}ionel and {N}iang, {M}. {F}. {K}. and {N}dione, {J}. {A}. and {R}udant, {J}. {P}. and {M}ougin, {E}.},
  editor = {},
  language = {{ENG}},
  abstract = {{T}he sensitivity of the radar signal to the seasonal dynamics in the {S}ahel region is a considerable asset for monitoring surface parameters including soil moisture. {G}iven the sensitivity of the radar signal to vegetation mass production, roughness and soil moisture, the main problem has been to estimate the contribution of these three parameters to the signal. {T}his study aims to circumvent this problem by combining radar with optical data. {T}he {DMP} ({D}ry {M}ater {P}roduct) extracted from {SPOT} data allowed to estimate vegetation mass production. {S}urface roughness was estimated from radar data during the dry season. {B}ecause during the dry season, radar signal is only conditioned by soil roughness in this region a {R}adiative {T}ransfer {M}odel ({RTM}) was used: it consists in a microwave scattering model of layered vegetation based on the first-order solution of the radiative transfer equation and it accounts for multiple scattering within the canopy, surface roughness of the soil, and the interaction between canopy surface and soil. {T}his model was designed to account for the branch size distribution, leaf orientation distribution, and branch orientation distribution for each size. {I}n this study, the {RTM} has been calibrated with {ESCAT} ({E}uropean {R}adar {S}atellite {S}catterometer) data, and has been used in order to estimate soil moisture. {T}he results obtained have allowed to track the spatial and temporal dynamics of soil moisture on the one hand, and on the other hand the influence of geology and morphopedology on the spatial dynamics of the soil moisture variability. {T}hese results are promising despite the fact that the inversed {RTM} often faces difficulties to interpret the signal for saturated soils, giving an aberrant value of soil moisture more often than not.},
  keywords = {{S}oil moisture ; {R}adar remote sensing ; {ASAR} ; {ERS} ; {SPOT}-{VEGETATION} ; {F}erlo ; {SENEGAL} ; {FERLO}},
  booktitle = {},
  journal = {{E}gyptian {J}ournal of {R}emote {S}ensing and {S}pace {S}ciences},
  volume = {21},
  numero = {1},
  pages = {{S}13--{S}22},
  ISSN = {1110-9823},
  year = {2018},
  DOI = {10.1016/j.ejrs.2017.11.005},
  URL = {https://www.documentation.ird.fr/hor/fdi:010073229},
}