["Transactions in GIS, Volume 30, Issue 4, June 2026. ", "\nABSTRACT\nAccurate and automatic determination of the boundaries of agricultural parcels is essential for planning sustainable agricultural policies, managing agricultural production, tracking products and enabling precision farming applications. In recent years, pre‐trained visual base models with zero‐shot segmentation capabilities, such as the Segment Anything Model (SAM), have introduced a new paradigm to the field. Integrating such models with satellite images of varying characteristics opens up new possibilities in terms of both accuracy and operational efficiency. While traditional satellite systems such as Sentinel‐2, PlanetScope, and Pleiades provide data at specific resolution ranges, super‐resolution (SR) like S2DR3 and Sen2SR, which have been developed in recent years, can enhance the spatial detail of existing data for analysis. In this study, we tested satellite images with different spatial resolutions for the automatic extraction of agricultural parcel boundaries using the SAM model. We also evaluated the performance and contribution of super‐resolution datasets like S2DR3 and Sen2SR. Additionally, the impact of the model's hyperparameter configurations on segmentation accuracy was analyzed, and pre‐ and post‐processing steps were applied to ensure that only agricultural parcel areas were obtained. The findings revealed that resolution directly impacts segmentation performance, though not in a linear manner. Although Sen2SR images have a higher spatial resolution than PlanetScope data, the former produced weaker results, particularly with regard to geometric error metrics (GOSE: 0.318, GUSE: 0.373). Although Pleiades images achieved the highest numerical accuracy values (intersection over union (IoU): 0.942; Dice coefficient: 0.971), they were found to be limited in practical applications due to issues such as over segmentation, processing time, and cost. In contrast, S2DR3 data provided balanced segmentation results, both visually and numerically (IoU: 0.920; DICE: 0.958), and was found to be the most operationally applicable solution due to its relatively low computational demand and short processing time. Hyperparameter analyses showed that accuracy increased by up to 40% in low‐resolution Sentinel‐2 images. However, improvements beyond the default configuration were limited in high‐resolution datasets (points_per_side: 64; crop_n_layers: 2). Considering the trade‐off between processing time and accuracy, the default configurations (points_per_side = 32 and crop_n_layers = 0) were found to provide sufficient accuracy for many scenarios. In conclusion, this study demonstrates that powerful base models such as SAM can be effectively applied to satellite images enhanced with super‐resolution techniques, and that synthetic data such as S2DR3 offer the most operationally feasible solutions when criteria such as cost, hardware, processing time, and accuracy are considered. The study provides practical recommendations for practitioners regarding the resolution–hyperparameter relationship and makes practical contributions to large‐scale or resource‐constrained agricultural monitoring scenarios.\n"]