干旱区生态环境知识资源中心(Arid): Assimilation of SMAP disaggregated soil moisture and Landsat land surface temperature to improve FAO-56 estimates of ET in semi-arid regions

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DOI	10.1016/j.agwat.2021.107290
	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; Rivalland, Vincent; Merlin, Olivier; Chehbouni, Abdelghani
通讯作者	Amazirh, A (corresponding author), Mohammed VI Polytech Univ UM6P, Ctr Remote Sensing Applicat CRSA, Hay Moulay Rachid Bengue, Morocco.
来源期刊	AGRICULTURAL WATER MANAGEMENT
ISSN	0378-3774
EISSN	1873-2283
出版年	2022
卷号	260
英文摘要	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-dualK(c) . Surface 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. The LST data were used to estimate the water stress coefficient (K-s) as a proxy of LST (LST proxy ). The FAO-Ks was corrected by assimilating LST proxy derived from Landsat data based on the variances of predicted errors on K-s estimates from FAO-56 model and thermal-derived K-s. 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 mot mean square error (RMSE) decreased from 0.98 to 0.65 mm/day compared to the classical FAO-dualK(c) 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.
英文关键词	Evapotranspiration Data assimilation FAO-dualK(c) Soil moisture Land surface temperature
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000724198900001
WOS关键词	DUAL CROP COEFFICIENT ; PARTITIONING EVAPOTRANSPIRATION ; SENTINEL-1 RADAR ; EDDY-COVARIANCE ; HEAT-FLUX ; MODEL ; EVAPORATION ; PERFORMANCE ; EFFICIENCY ; PREDICTION
WOS类目	Agronomy ; Water Resources
WOS研究方向	Agriculture ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/374526
作者单位	[Amazirh, Abdelhakim; Er-Raki, Salah; Chehbouni, Abdelghani] Mohammed VI Polytech Univ UM6P, Ctr Remote Sensing Applicat CRSA, Hay Moulay Rachid Bengue, Morocco; [Er-Raki, Salah; Bouras, El Houssaine] Univ Cadi Ayyad, Fac Sci & Tech, Dept Phys Appl, ProcEDE, Marrakech, Morocco; [Ojha, Nitu; Bouras, El Houssaine; Rivalland, Vincent; Merlin, Olivier; Chehbouni, Abdelghani] Univ Toulouse, Ctr Etud Spatiales Biosphere CESBIO, UPS, CNRS,IRD,CNES, Toulouse, France
推荐引用方式 GB/T 7714	Amazirh, Abdelhakim,Er-Raki, Salah,Ojha, Nitu,et al. Assimilation of SMAP disaggregated soil moisture and Landsat land surface temperature to improve FAO-56 estimates of ET in semi-arid regions[J],2022,260.
APA	Amazirh, Abdelhakim.,Er-Raki, Salah.,Ojha, Nitu.,Bouras, El Houssaine.,Rivalland, Vincent.,...&Chehbouni, Abdelghani.(2022).Assimilation of SMAP disaggregated soil moisture and Landsat land surface temperature to improve FAO-56 estimates of ET in semi-arid regions.AGRICULTURAL WATER MANAGEMENT,260.
MLA	Amazirh, Abdelhakim,et al."Assimilation of SMAP disaggregated soil moisture and Landsat land surface temperature to improve FAO-56 estimates of ET in semi-arid regions".AGRICULTURAL WATER MANAGEMENT 260(2022).