@article{fdi:010085177,
  title = {{E}stimating high-resolution soil moisture over mountainous regions using remotely-sensed multispectral and topographic data},
  author = {{F}an, {L}. and {A}l-{Y}aari, {A}men and {F}rappart, {F}. and {P}eng, {J}. and {W}en, {J}. {G}. and {X}iao, {Q}. and {J}in, {R}. and {L}i, {X}. {J}. and {L}iu, {X}. {Z}. and {W}ang, {M}. {J}. and {C}hen, {X}. {Z}. and {Z}hao, {L}. and {M}a, {M}. {G}. and {W}igneron, {J}. {P}.},
  editor = {},
  language = {{ENG}},
  abstract = {{A} surface soil moisture ({SM}) condition at high spatiotemportal resolutions is required by regional {E}arth system applications. {H}ere, we mapped daily 1-km {SM} in the {B}abao {R}iver {B}asin in the northwest of {C}hina during the summers from 2013 to 2015 using a random forest ({RF}) method by merging {SM} information retrieved from in situ measurements, optical/thermal remote sensing, and topographical indices. {R}elative importance analysis was used to determine the optimal predictors for estimating high-resolution {SM}. {A} specific {RF} model ({RFVI}+sup) was constructed using the optimal predictors including remote sensing albedo, apparent thermal inertia ({ATI}), normalized difference vegetation index, normalized difference infrared index 5, soil adjusted vegetation index, and topographical indices (aspect and elevation). {T}he {RFVI}+sup also accounted for missing observations of the thermal index (e.g., {ATI}) over the mountainous regions. {I}n the comparison between the {SM} estimates using the new {RFVI}+sup model and other {RF} models, the spatial coverage of available estimates increased from 14% to 64% over the study region, the correlation coefficient values were improved to 0.75, the unbiased root-mean-squared difference values decreased to 0.032 m(3)/m(3). {T}hus, the proposed {RF} method provided accurate {SM} estimates with high spatiotemporal resolution over the mountainous regions, by merging multiresource datasets from in situ measurements, remotely-sensed, and topographical indices.},
  keywords = {{I}ndexes ; {V}egetation mapping ; {R}adio frequency ; {O}ptical sensors ; {M}icrowave measurement ; {R}ivers ; {O}ptical reflection ; {H}igh resolution ; mountainous regions ; optical index ; random forest ({RF}) method ; soil moisture ({SM}) ; thermal index ; {CHINE} ; {BABAO} {RIVER} {BASSIN}},
  booktitle = {},
  journal = {{IEEE} {J}ournal of {S}elected {T}opics in {A}pplied {E}arth {O}bservations and {R}emote {S}ensing},
  volume = {15},
  numero = {},
  pages = {3637--3649},
  ISSN = {1939-1404},
  year = {2022},
  DOI = {10.1109/jstars.2022.3166974},
  URL = {https://www.documentation.ird.fr/hor/fdi:010085177},
}