Assimilation of SMAP disaggregated soil moisture and Landsat land surface temperature to improve FAO-56 estimates of ET in semi-arid regions - Archive ouverte HAL Access content directly
Journal Articles Agricultural Water Management Year : 2022

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

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 (ET c 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 ET c 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 ET c act model prediction: the root 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 ET c act measurements.
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Dates and versions

hal-03878614 , version 1 (14-12-2022)

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Abdelhakim Amazirh, Salah Er-Raki, Nitu Ojha, El Houssaine Bouras, Vincent Rivalland, 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, 2022, 260, pp.107290. ⟨10.1016/j.agwat.2021.107290⟩. ⟨hal-03878614⟩
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