Synthetic aperture radar (SAR) on unmanned aerial vehicles (UAVs) enables high-resolution sensing in low-altitude wireless networks, while requiring reliable uplink data backhaul to ground base stations under dynamic channel conditions. Conventional orthogonal frequency division multiplexing (OFDM)-based SAR systems rely on pilot or deterministic signaling, which occupies only a small fraction of the available timefrequency (TF) resources and limits imaging performance. This paper develops a data-aided OFDM-SAR imaging framework that reuses uplink communication data symbols for sensing, thereby exploiting the dominant TF resources of the UAV backhaul link. However, the randomness of data symbols disrupts the coherent structure required for SAR imaging, especially in highly dynamic channels with strong TF coupling, leading to severe degradation in range-Doppler focusing. To address this issue, we establish a unified TF domain filtering framework to suppress data-induced randomness and recover an equivalent deterministic imaging channel. Within this framework, reciprocal, matched, and Wiener filtering are interpreted under a common formulation, enabling a systematic characterization of their impact on imaging performance. A normalized mean square error (NMSE) metric of a reference point target's profile is further adopted to quantify the joint effects of randomnessinduced distortion and noise amplification. Simulation results based on 5G NR parameters show that the proposed dataaided scheme significantly outperforms pilot-only approaches by leveraging uplink data resources, demonstrating that effective TF-domain filtering is essential to ensure high-resolution imaging in dynamic UAV channels.