Low-altitude air-ground communication channels are characterized by time-varying multipath propagation and dynamic switching of line-of-sight （LOS） and non-line-of-sight （NLOS） components. Traditional pure data-driven joint source-channel coding methods lack physical communication priors， leading to poor image reconstruction robustness and oscillating training convergence. This paper proposes an end-to-end image semantic communication architecture （UP-WITT） with model-data dual-driven design for low-altitude scenarios. Taking the time-domain baseband transmission model of low-altitude time-varying channels as the physical basis， the architecture adopts a two-branch collaborative structure. The data-driven branch employs Swin Transformer as the codec backbone to extract multi-scale global semantic features， which are further mapped into time-domain discrete baseband symbol sequences complying with transmission constraints. Based on projected gradient descent （PGD） theory， the model-driven branch unrolls the iterative optimization process into learnable network layers and constructs a configurable PGD reconstruction module. With the physical prior of time-domain channel transmission， the module performs effective compensation of channel impairments and reduces iterative complexity via frequency-domain fast computation， thereby alleviating the ill-posedness of the inverse problem under the experimental conditions considered. Experimental results on the CIFAR dataset show that compared with pure data-driven baseline models， the proposed method achieves better peak signal-to-noise ratio （PSNR） and multi-scale structural similarity （MS-SSIM） under different channel bandwidth ratios （CBRs） and channel conditions. It also exhibits a smoother training convergence process and better reconstruction performance within the trained channel distribution， providing a useful reference for reliable image semantic transmission in low-altitude applications.