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Irrigated oases are the main water consumers in arid and semi-arid regions. As plant evapotranspiration (ET) in these regions mainly depends on irrigated water, accurate quantification of evapotranspiration (ET) on the irrigated oases is crucial for allocation and management of irrigation water resources. In this study, we integrated the soil moisture retrieved from Polarimetric L-band Multibeam Radiometer (PLMR) into the Surface Energy Balance System (SEBS) model for improving ET estimates under water stress conditions. The study area is the irrigated oasis in the middle reaches of the Heihe River where airborne and satellite-borne remotely sensed data as well as in situ observations are available through the Heihe Watershed Allied Telemetry Experimental Research (HiWATER). The main goal of this experiment is to monitor the energy-water exchange between near-surface atmosphere and land surface, and to assess its influencing factors within the oasis–desert ecosystem. The soil moisture data were retrieved using the L-band Microwave Emission of the Biosphere (L-MEB) model fed with the airborne dual-polarized and multi-angular viewing of PLMR. The comparison of soil moisture retrieval from PLMR data with the soil moisture measured by a wireless sensor network (WSN) showed good consistency, with an absolute mean error (ME) <0.004 cm 3 cm −3 and a root mean square error (RMSE) value <0.05 cm 3 cm −3 . Further, the actual daily evapotranspiration was estimated using the soil moisture integrated (SM-integrated) SEBS algorithm fed with the Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) images and soil moisture data retrieved from PLMR data. The sensible heat fluxes and daily evapotranspiration ( ET daily ) obtained by the SM-integrated SEBS and the original SEBS were compared with the eddy correlation (EC) measurements collected from HiWATER experiment. The results indicate an obvious improvement when soil moisture information is integrated into the SEBS. This method overcomes the weakness of remote sensing based (RS-based) surface energy balance models of overestimating evapotranspiration particularly in semi-arid and arid regions. It shows a prospect that the combination of optical and microwave remote sensing can further improve the RS-based ET estimation. [ABSTRACT FROM AUTHOR]