@article{fdi:010093487,
  title = {{R}oot zone soil moisture mapping at very high spatial resolution using radar-derived surface soil moisture product},
  author = {{O}uaadi, {N}. and {C}hehbouni, {A}bdelghani and {A}yari, {E}. and {H}ssaine, {B}. {A}. and {E}l{F}arkh, {J}. and {L}e {P}age, {M}ichel and {E}r-{R}aki, {S}. and {B}oone, {A}.},
  editor = {},
  language = {{ENG}},
  abstract = {{R}oot zone soil moisture ({RZSM}) is a key variable controlling the soil-vegetation-atmosphere exchanges. {I}ts estimation is vital for monitoring hydrological, meteorological and agricultural processes. {A} number of large-scale products exist but with a coarse resolution (>1 km), which is not suitable for plot-scale studies. {T}he aim of this work is to map {RZSM}, for the first time, at very high spatial resolution using a very high spatial resolution surface soil moisture ({SSM}) product and a recursive exponential filter. {SSM} is estimated from {S}entinel-1 data using the water cloud model at a resolution of approximately 50 m. {T}he approach was evaluated on a database consisting of 12 fields, including 7 winter wheat and 5 summer maize fields, irrigated using different techniques. {T}he results show that the approach performs reasonably well using {S}entinel-1 {SSM} product with correlation coefficient ({R}) between 0.3 and 0.82, root-mean-square error ({RMSE}) between 0.05 and 0.12 m(3)/m(3) and a bias in the range -0.1-0.07 m(3)/m(3), at 15-20 cm depth. {T}his is equivalent to {R} = 0.6, {RMSE} = 0.12 m(3)/m(3) and bias = 0.07 m(3)/m(3) using the entire database, which is quite low compared to the use of in situ {SSM} measurements ({R} = 0.81, {RMSE} = 0.07 m(3)/m(3) and bias = 0.03 m(3)/m(3)). {T}his is related to inaccuracies in the {SSM} product, where fields with good {SSM} estimation also resulted in good {RZSM} estimation and conversely. {I}n addition to {SSM}, the approach is also sensitive to its time constant {T}. {A}nalysis of {RZSM} sensitivity to {T} shows that the optimum {T} value depends on soil texture, climate and measurement depth. {I}n particular, low optimum {T} values (1 day) are obtained for loamy and sandy loam soils, while higher values (5-10 days) are optimal for soils with a high clay fraction, at 15-20 cm depth. {T}hese values increase with soil depth and are influenced by seasonal atmospheric demand. {C}ombined to reasonable statistical metrics, the spatial variability depicted by the {RZSM} maps opens up prospects for high-resolution {RZSM} mapping from {S}entinel-1 {SSM} data using a simple approach over annual crops. {T}his is of prime relevance for agricultural applications requiring very high-resolution estimation at plot scale, such as crop yield, irrigation and fertilizer management, as well as for the assessment of inter-plot variability.},
  keywords = {{R}oot zone soil moisture ; {S}urface soil moisture ; {R}adar remote sensing ; {E}xponentiel filter ; {C}rops ; {MAROC} ; {ESPAGNE}},
  booktitle = {},
  journal = {{A}gricultural {W}ater {M}anagement},
  volume = {314},
  numero = {},
  pages = {109507 [16 p.]},
  ISSN = {0378-3774},
  year = {2025},
  DOI = {10.1016/j.agwat.2025.109507},
  URL = {https://www.documentation.ird.fr/hor/fdi:010093487},
}