Person re-identification (ReID) plays a crucial role in advancing UAV-based surveillance applications, enabling robust tracking and event analysis. However, existing methods in UAV scenarios primarily focus on individual pedestrians, requiring cumbersome annotation efforts and lacking seamless integration with ground-based surveillance systems. These limitations hinder the broader development of UAV-based monitoring. To address these challenges, this paper proposes an Unsupervised Aerial-Ground Re-identification from Pedestrian to Group (UAGRPG) framework. Specifically, we introduce a neighbor-aware collaborative learning (NCL) and gradual graph matching (GGC) strategy to uncover the implicit associations between cross-modality groups in an unsupervised manner. Furthermore, we develop a collaborative cross-modality association learning (CCAL) module to bridge feature disparities and achieve soft alignment across modalities. To quantify the optimal group similarity between aerial and ground domains, we design a minimum pedestrian distance transformation strategy. Additionally, we introduce a new AG-GReID dataset, and extensive experiments demonstrate that our approach achieves state-of-the-art performance on both pedestrian and group re-identification tasks in aerial-ground scenarios, validating its effectiveness in integrating ground and UAV-based surveillance.