Agricultural field boundary information is important and often required for the geosciences and the agricultural sector. In this paper, a novel method is developed to extract sub-boundaries within the permanent boundaries of agricultural land parcels from high-resolution optical satellite imagery using an improved cluster-based snake model. The method takes the advantage of the results of an automatic fuzzy c-means (FCM) clustering and edge detection to compute external forces for an improved gradient vector flow (GVF) snake model. The GVF snake algorithm is improved by using an automatic seeding model based on clustering results and image moment functions. To seed the improved GVF algorithm, an ellipse is automatically delineated for each cluster within agricultural parcel by utilizing image moment functions (in particular silhouette moments). The GVF snake model is then implemented for each seed, one seed at a time. Active contours tend to have curve shapes rather than straight lines due to their structure that consists of several connected nodes within each contour. Therefore, the final accurate results are obtained after performing a three-stage line simplification operation. The experiments of the method were conducted on 20 test fields in a study area located near to the town of Karacabey, Turkey, using the 4-m resolution IKONOS multispectral (xs) image, the 2.44-m resolution QuickBird xs image, and the 0.61-m resolution QuickBird pan-sharpened (PS) image. Experimental results demonstrate that using both the clustering and edge detection results as external forces for the improved GVF snake model increases the accuracy of the results. In addition, the developed method showed a fairly good performance in extracting sub-boundaries for the fields comprising crops with an inherent high inner heterogeneity, such as rice and corn. The method can potentially be applied in the extraction of within-field sub-boundaries from high-resolution satellite imagery in agricultural areas.