Highlights What are the main findings? OBCA identifies a compact, cross-site robust VNIR feature subset (three-band synergies) that strengthens salinity sensitivity under heterogeneous field conditions. The physically constrained S-WGAN-GP with Teacher reliability screening produces high-confidence synthetic spectra that improve LOOCV performance and stabilize prediction. What are the implications of the main findings? Reliable GAN-augmented training can mitigate small-sample bottlenecks while preserving spectral plausibility, enabling more transferable soil salinity inversion workflows. The resulting maps differentiate patchy, extensive, and edge-concentrated salinization patterns, supporting targeted management and site-specific remediation planning.Highlights What are the main findings? OBCA identifies a compact, cross-site robust VNIR feature subset (three-band synergies) that strengthens salinity sensitivity under heterogeneous field conditions. The physically constrained S-WGAN-GP with Teacher reliability screening produces high-confidence synthetic spectra that improve LOOCV performance and stabilize prediction. What are the implications of the main findings? Reliable GAN-augmented training can mitigate small-sample bottlenecks while preserving spectral plausibility, enabling more transferable soil salinity inversion workflows. The resulting maps differentiate patchy, extensive, and edge-concentrated salinization patterns, supporting targeted management and site-specific remediation planning.Abstract Soil salinity mapping in heterogeneous irrigation districts faces a dual challenge: the high dimensionality of hyperspectral data leads to redundancy, while the scarcity of ground-truth samples restricts the generalization of data-driven models. Traditional regression methods often struggle to capture non-linear spectral responses under such "small-sample" conditions. To address these limitations, this study proposes a semi-supervised retrieval framework coupling Optimal Band Combination Analysis (OBCA) with a Spectral Wasserstein GAN with Gradient Penalty (S-WGAN-GP). We constructed a robust feature set via cross-scenario evaluation and developed a rigorous "Uncertainty-Aware Filtering" protocol to screen synthetic samples generated by a teacher mechanism. The OBCA screening revealed that salinity-sensitive features are robustly clustered in the Green (550-570 nm) and Near-Infrared (NIR, 880-950 nm) regions, with NIR bands demonstrating superior stability across different sites. The proposed S-WGAN-GP successfully densified the feature manifold by generating 1186 high-fidelity synthetic samples. By incorporating these augmented data, the inversion accuracy was substantially improved: the R2 of the optimal SVR model increased from 0.36 (baseline) to 0.60 (+66.7%), and the RMSE decreased from 7.06 to 5.57 dSm-1. This study confirms that physically constrained generative augmentation, when combined with rigorous quality control, effectively bridges the distribution gap in limited datasets. The proposed framework offers a transferable and accurate solution for fine-scale soil salinity monitoring in data-scarce arid regions.