In view of the increasing industrial need of reducing Wind Turbines (WTs) operational costs, improved Structural Health Monitoring (SHM) strategies are nowadays exploited and further boosted through innovative digitalization processes. The aim of these schemes consists in tracking and supporting blades status assessment not only in the field, but also from conceptualization, through certification tests, all the way to operation and end-of-life. Extensive testing is usually performed on blades prior to installation. Although more attention is usually placed on static and fatigue tests, dynamic tests are also adopted for identifying basic dynamic properties, essential for the structural integrity of the entire WT. The modal parameters obtained from dynamic tests, such as natural frequencies and mode shapes, are often used to update structural models and create test-validated digital twins. While effective, this conventional approach to model updating is inherently global, thus potentially introducing inaccuracies in capturing localized material behaviors. Additionally, this methodology predominantly relies on acceleration measurements, thereby excluding static model validation, which requires separate analysis. To address these limitations, this study proposes the use of strain measurements acquired via fiber-optic sensors, to update the Finite Element Model (FEM) of a small-scale Glass Fiber Reinforced Plastic (GFRP) wind turbine blade. This strain data is integrated into an optimization framework employing local criteria to assess model accuracy.