<p>Understanding and assessing the impact of watershed intervention on surface soil moisture (SSM) is essential for managing sustainable water resources and climate change adaptation. In this work, we showed how useful a remote sensing-based approach is for assessing how SSM in the Kulfo watershed, Ethiopia, is affected by watershed management activities. This study used remotely sensed data&#xa0;(Landsat images) to construct and apply the soil moisture index (SMI) model. The land surface temperature and vegetation index (LST-VI) spatial pixel distribution are interpreted via the trapezoid approach, which forms the basis of the model. From January 2021 to May 2022, we used 42 sample points worth of ground-based moisture measurements to validate the model’s performance. Following validation, the surface soil moisture from 1990 to 2022, including&#xa0;before, during, and after watershed improvements were implemented in Kulfo, was examined. The results revealed strong agreement between the SSM predicted by the model and the SSM observed on the ground. This was demonstrated by the low root mean squared error (0.019&#xa0;cm³&#xa0;cm⁻³) and high <i>R</i>² (0.81). The surface soil moisture of the research area was increased following extensive physical interventions. Hence, to assess the effects of interventions, a remote sensing approach can detect and quantify SSM. We urge scholars to assess and implement the model for additional watersheds to demonstrate the value of substantial investments in watershed management.</p>