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Assimilation of SMAP disaggregated soil moisture and Landsat land surface temperature to improve FAO-56 estimates of ET in semi-arid regions

Amazirh, Abdelhakim ; Er-Raki, Salah ; Ojha, Nitu ; Bouras, El houssaine LU orcid ; Rivalland, Vincent ; Merlin, Olivier and Chehbouni, Abdelghani (2022) In Agricultural Water Management 260.
Abstract

Accurate 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 (ETc act) due to its operational nature and since it represents a reasonable compromise between simplicity and accuracy. In this vein, the objective of this paper was to examine the possibility of improving ETc act estimates through remote sensing data assimilation. For this purpose, remotely sensed soil moisture (SM) and Land surface temperature (LST) data were simultaneously assimilated into... (More)

Accurate 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 (ETc act) due to its operational nature and since it represents a reasonable compromise between simplicity and accuracy. In this vein, the objective of this paper was to examine the possibility of improving ETc act estimates through remote sensing data assimilation. For this purpose, remotely sensed soil moisture (SM) and Land surface temperature (LST) data were simultaneously assimilated into FAO-dualKc. Surface SM observations were assimilated into the soil evaporation (Es) component through the soil evaporation coefficient, and LST data were assimilated into the actual crop transpiration (Tc act) component through the crop stress coefficient. The LST data were used to estimate the water stress coefficient (Ks) as a proxy of LST (LSTproxy). The FAO-Ks was corrected by assimilating LSTproxy derived from Landsat data based on the variances of predicted errors on Ks estimates from FAO-56 model and thermal-derived Ks. The proposed approach was tested over a semi-arid area in Morocco 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 Soil Moisture Active Passive (SMAP) SM and Landsat-LST sensors were used. Assimilating SM data leads to an improvement of the ETc act model prediction: the root mean square error (RMSE) decreased from 0.98 to 0.65 mm/day compared to the classical FAO-dualKc using in situ SM. Moreover, assimilating both in situ SM and LST data provided more accurate results with a RMSE error of 0.55 mm/day. By using SMAP-based SM and Landsat-LST, results also improved in comparison with standard FAO and reached a RMSE of 0.73 mm/day against eddy-covariance ETc act measurements.

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publishing date
type
Contribution to journal
publication status
published
subject
keywords
Data assimilation, Evapotranspiration, FAO-dualK, Land surface temperature, Soil moisture
in
Agricultural Water Management
volume
260
article number
107290
publisher
Elsevier
external identifiers
  • scopus:85119450202
ISSN
0378-3774
DOI
10.1016/j.agwat.2021.107290
language
English
LU publication?
no
additional info
Funding Information: This study was conducted within the Center of remote sensing applications ( https://crsa.um6p.ma/ ), at the Mohammed VI university-Morocco and funded by OCP (AS No 71). This work was also partly funded by the European Commission Horizon 2020 Programme for Research and Innovation (H2020) in the context of the Marie Sklodowska-Curie Research and Innovation Staff Exchange (RISE) action (REC project, grant agreement no: 645642), followed by ACCWA project, grant agreement no. 823965 ). The in situ data set was provided by the Joint International Laboratory TREMA ( http://trema.ucam.ac.ma ). PRIMA IDEWA and ALTOS projects are also acknowledged. Publisher Copyright: © 2021 Elsevier B.V.
id
a9652b42-00e5-4f79-9569-130b9444cb8f
date added to LUP
2023-01-04 09:45:39
date last changed
2023-01-20 15:06:30
@article{a9652b42-00e5-4f79-9569-130b9444cb8f,
  abstract     = {{<p>Accurate 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<sub>c act</sub>) due to its operational nature and since it represents a reasonable compromise between simplicity and accuracy. In this vein, the objective of this paper was to examine the possibility of improving ET<sub>c act</sub> estimates through remote sensing data assimilation. For this purpose, remotely sensed soil moisture (SM) and Land surface temperature (LST) data were simultaneously assimilated into FAO-dualK<sub>c</sub>. Surface SM observations were assimilated into the soil evaporation (E<sub>s</sub>) component through the soil evaporation coefficient, and LST data were assimilated into the actual crop transpiration (T<sub>c act</sub>) component through the crop stress coefficient. The LST data were used to estimate the water stress coefficient (K<sub>s</sub>) as a proxy of LST (LST<sub>proxy</sub>). The FAO-Ks was corrected by assimilating LST<sub>proxy</sub> derived from Landsat data based on the variances of predicted errors on K<sub>s</sub> estimates from FAO-56 model and thermal-derived K<sub>s</sub>. The proposed approach was tested over a semi-arid area in Morocco 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 Soil Moisture Active Passive (SMAP) SM and Landsat-LST sensors were used. Assimilating SM data leads to an improvement of the ET<sub>c act</sub> model prediction: the root mean square error (RMSE) decreased from 0.98 to 0.65 mm/day compared to the classical FAO-dualK<sub>c</sub> using in situ SM. Moreover, assimilating both in situ SM and LST data provided more accurate results with a RMSE error of 0.55 mm/day. By using SMAP-based SM and Landsat-LST, results also improved in comparison with standard FAO and reached a RMSE of 0.73 mm/day against eddy-covariance ET<sub>c act</sub> measurements.</p>}},
  author       = {{Amazirh, Abdelhakim and Er-Raki, Salah and Ojha, Nitu and Bouras, El houssaine and Rivalland, Vincent and Merlin, Olivier and Chehbouni, Abdelghani}},
  issn         = {{0378-3774}},
  keywords     = {{Data assimilation; Evapotranspiration; FAO-dualK; Land surface temperature; Soil moisture}},
  language     = {{eng}},
  month        = {{02}},
  publisher    = {{Elsevier}},
  series       = {{Agricultural Water Management}},
  title        = {{Assimilation of SMAP disaggregated soil moisture and Landsat land surface temperature to improve FAO-56 estimates of ET in semi-arid regions}},
  url          = {{http://dx.doi.org/10.1016/j.agwat.2021.107290}},
  doi          = {{10.1016/j.agwat.2021.107290}},
  volume       = {{260}},
  year         = {{2022}},
}