@article{fdi:010090716,
  title = {{A}ssessment of {SMOS} {R}oot {Z}one {S}oil {M}oisture : a comparative study using {SMAP}, {ERA}5, and {GLDAS}},
  author = {{O}jha, {N}. and {M}ahmoodi, {A}. and {M}ialon, {A}. and {R}ichaume, {P}. and {F}errant, {S}ylvain and {K}err, {Y}. {H}.},
  editor = {},
  language = {{ENG}},
  abstract = {{R}oot zone soil moisture ({RZSM}) refers to the amount of water present in the soil layer where plants can freely absorb water, and its information is crucial for various applications such as hydrology and agriculture. {SMOS} and {SMAP} remote sensing satellites provide soil moisture ({SM}) data on a global scale but limit their sensing capability to a depth of approximately 5 cm. {H}owever, for a comprehensive understanding of soil water content in the root zone, a deeper insight into {RZSM} is essential, which extends from 5 cm to 100 cm. {H}ence, to bridge the gap between the surface {SM} and {RZSM}, {SMOS} surface {SM} information (5 cm) was integrated into the root zone (100 cm) using a simple subsurface physical model. {SMOS} {RZSM} data are available on a global scale with 25 km sampling on {EASE} grid 2, which provides a daily temporal scale from 2010 to the present. {T}he main aims of this study were to i) check the efficacy of a simple model-based approach and ii) investigate the importance of remote sensing {SM} observations for the retrieval of {RZSM}. {H}ere, we investigate the benefit of the simple model-based approach by comparing {SMOS} {RZSM} (simple model) and {SMAP}, {ERA}5, and {GLDAS} {RZSM} (complex model or data-assimilation) with in-situ {SM}. {W}e then investigated the role of remote sensing {SM} observation in the retrieval of {RZSM} by comparing {SMOS} {RZSM} and {SMAP} {RZSM} products over rice-irrigated areas for dry seasons (minimal rainfall) in {T}elangana, {S}outh {I}ndia. {F}irst, {SMOS} {RZSM} was evaluated with in-situ {SM} data for four distinct networks: {SCAN}, {HOBE}, {SMOSMANIA}, and {A}mma catch from 2011 to 2017. {T}he results between {SMOS} {RZSM} and in-situ {SM} show an average correlation coefficient between 0.54 and 0.8 with an average unbiased root mean square difference (ub{RMSD}) within the threshold of 0.04 m3/m3. {T}he average correlation coefficient between the {RZSM} and in-situ {SM} for the {SMOS} and {SMAP} {RZSM} shows better performance in the range (0.55 to 0.93) than the {ERA}5 and {GLDAS} {RZSM} in the range (0.20 to 0.93). {F}inally, the outcomes of {SMOS} and {SMAP} {RZSM} over irrigated areas show that only {SMOS} {RZSM} captures changes in {SM} dynamics due to irrigation, particularly during the dry season.},
  keywords = {{L}and surface ; {S}oil measurements ; {R}ain ; {M}eteorology ; {S}oil moisture ; {S}patial resolution ; {S}atellites ; {S}urface soil ; {W}ater resources ; {S}urface soil moisture ; root zone soil moisture ; {SMOS} ; {SMAP} ; {GLDAS} ; {ERA}5},
  booktitle = {},
  journal = {{IEEE} {A}ccess},
  volume = {12},
  numero = {},
  pages = {76121--76132},
  ISSN = {2169-3536},
  year = {2024},
  DOI = {10.1109/access.2024.3404123},
  URL = {https://www.documentation.ird.fr/hor/fdi:010090716},
}