Soil moisture (SM) is a critical variable in energy and water partitioning at the interface between the land surface and atmosphere. In this study, we provided a robust method to retrieve soil moisture using optimal remotely sensed soil evaporative efficiency (SEE) information. Specifically, SEE was deduced from the triangle space constituted by remotely sensed land surface temperature (LST) and fractional vegetation cover (Fc). Theoretical solutions of the dry and wet boundaries were derived by annual-scale optimization and microwave SM calibration. The two limits of SM were obtained by linear fit function between SEE and microwave-based SM. The proposed method was validated at the Liaoning Province of China in the year 2011 by using MODerate Resolution Imaging Spectroradiometer (MODIS) and Soil Moisture and Ocean Salinity (SMOS) satellite images as input. Results indicated that the new method has not only bypassed the complex parametric scheme in the calculation of boundaries within the LST-Fc feature space but also performed superior in the estimation of soil moisture status at all-sky days. Besides, the optimal method has reproduced the spatial and temporal patterns of soil moisture reasonably well, with a root mean square error of 0.07 m³ m⁻³. Therefore, the proposed method can be regarded as a suitable tool to provide accurate and continuous monitoring of soil moisture.