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As floating offshore wind farms move further offshore into more challenging environments, robust and scalable monitoring strategies become critical for maintaining operational safety and performance. This study investigates the use of Sentinel-2 satellite imagery to remotely track the positional offset of semi-submersible floating wind platforms and evaluate their dynamic response to varying metocean conditions. A convolutional neural network (U-Net) was developed to segment turbine platforms in optical imagery, enabling automated extraction of positional data. These positions were then correlated with wind and wave parameters using both linear and multivariate regression models. The results indicate a statistically significant but moderate global correlation between wind speed and platform horizontal displacement, which improves substantially when accounting for wind directionality, reaching <inline-formula> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> values of 0.73 for westerly winds. Horizontal displacement was found to be anisotropic and strongly dependent on the direction of environmental forcing, with both wind and wave contributions playing key roles. Clustering analysis further revealed four distinct offset regimes, each associated with characteristic wind patterns, confirming the potential for satellite-driven classification of operational states. Additionally, the method demonstrated value for anomaly detection, with deviations in turbine position highlighting possible mooring line failures. Independent in-situ position measurements (e.g., GNSS/INS, SCADA position channels, or mooring telemetry) were not available for the study period; therefore, we report uncertainty bounds and surrogate consistency evidence rather than a direct validation of absolute offsets. We demonstrate an end-to-end workflow for 1) extracting apparent platform offsets from Sentinel-2 imagery and 2) reporting descriptive associations between these offsets and farm-scale metocean forcing; any asset-health or degradation inference is presented only as an exploratory condition-indicator example and is not claimed as a validated diagnostic. These findings suggest that, despite limitations in spatial resolution, Sentinel-2 imagery can complement traditional in-situ monitoring systems by offering a cost-effective, wide-area surveillance layer. The approach supports not only real-time tracking but also the development of predictive maintenance tools, contributing to safer and more resilient offshore wind operations.