@article{fdi:010083370,
  title = {{A}ssimilation of {SMAP} disaggregated soil moisture and {L}andsat land surface temperature to improve {FAO}-56 estimates of {ET} in semi-arid regions},
  author = {{A}mazirh, {A}. and {E}r-{R}aki, {S}. and {O}jha, {N}. and {B}ouras, {E}. and {R}ivalland, {V}. and {M}erlin, {O}. and {C}hehbouni, {A}bdelghani},
  editor = {},
  language = {{ENG}},
  abstract = {{A}ccurate estimation of evapotranspiration ({ET}) is of crucial importance in water science and hydrological process understanding especially in semi-arid/arid areas since {ET} represents more than 85% of the total water budget. {FAO}-56 is one of the widely used formulations to estimate the actual crop evapotranspiration ({ET}c (act)) due to its operational nature and since it represents a reasonable compromise between simplicity and accuracy. {I}n this vein, the objective of this paper was to examine the possibility of improving {ET}c act estimates through remote sensing data assimilation. {F}or this purpose, remotely sensed soil moisture ({SM}) and {L}and surface temperature ({LST}) data were simultaneously assimilated into {FAO}-dual{K}(c) . {S}urface {SM} observations were assimilated into the soil evaporation ({E}-s) component through the soil evaporation coefficient, and {LST} data were assimilated into the actual crop transpiration ({T}-c (act)) component through the crop stress coefficient. {T}he {LST} data were used to estimate the water stress coefficient ({K}-s) as a proxy of {LST} ({LST} proxy ). {T}he {FAO}-{K}s was corrected by assimilating {LST} proxy derived from {L}andsat data based on the variances of predicted errors on {K}-s estimates from {FAO}-56 model and thermal-derived {K}-s. {T}he proposed approach was tested over a semi-arid area in {M}orocco using first, in situ data collected during 2002-2003 and 2015-2016 wheat growth seasons over two different fields and then, remotely sensed data derived from disaggregated {S}oil {M}oisture {A}ctive {P}assive ({SMAP}) {SM} and {L}andsat-{LST} sensors were used. {A}ssimilating {SM} data leads to an improvement of the {ET}c act model prediction: the mot mean square error ({RMSE}) decreased from 0.98 to 0.65 mm/day compared to the classical {FAO}-dual{K}(c) using in situ {SM}. {M}oreover, assimilating both in situ {SM} and {LST} data provided more accurate results with a {RMSE} error of 0.55 mm/day. {B}y using {SMAP}-based {SM} and {L}andsat-{LST}, results also improved in comparison with standard {FAO} and reached a {RMSE} of 0.73 mm/day against eddy-covariance {ET}c act measurements.},
  keywords = {{E}vapotranspiration ; {D}ata assimilation ; {FAO}-dual{K}(c) ; {S}oil moisture ; {L}and surface temperature ; {MAROC} ; {ZONE} {SEMIARIDE} ; {TENSIFT} {REGION}},
  booktitle = {},
  journal = {{A}gricultural {W}ater {M}anagement},
  volume = {260},
  numero = {},
  pages = {107290 [14 p.]},
  ISSN = {0378-3774},
  year = {2022},
  DOI = {10.1016/j.agwat.2021.107290},
  URL = {https://www.documentation.ird.fr/hor/fdi:010083370},
}