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

Accurate estimation of evapotranspiration (ET) is crucial importance in water science and hydrological process understanding especially semi-arid/arid areas since ET represents more than 85% the total budget. FAO-56 one widely used formulations to estimate actual crop (ETc act) due its operational nature it a reasonable compromise between simplicity accuracy. In this vein, objective paper was examine possibility improving ETc act estimates through remote sensing data assimilation. For purpose, remotely sensed soil moisture (SM) Land surface temperature (LST) were simultaneously assimilated into FAO-dualKc. Surface SM observations evaporation (Es) component coefficient, LST transpiration (Tc stress coefficient. The coefficient (Ks) as proxy (LSTproxy). FAO-Ks corrected by assimilating LSTproxy derived from Landsat based on variances predicted errors Ks model thermal-derived Ks. proposed approach tested over semi-arid area Morocco using first, situ collected during 2002–2003 2015–2016 wheat growth seasons two different fields then, disaggregated Soil Moisture Active Passive (SMAP) Landsat-LST sensors used. Assimilating leads an improvement prediction: root mean square error (RMSE) decreased 0.98 0.65 mm/day compared classical FAO-dualKc SM. Moreover, both provided accurate results with RMSE 0.55 mm/day. By SMAP-based Landsat-LST, also improved comparison standard FAO reached 0.73 against eddy-covariance measurements.