Feature-constrained conditional generative data augmentation for hyperspectral retrieval of nitrogen and phosphorus...

Hyperspectral retrieval of total nitrogen (TN) and total phosphorus (TP) is constrained by the scarcity of field spectra paired with laboratory measurements. We developed a feature-constrained conditional generative adversarial network (F-CGAN) for a 46-site inland-river dataset. Its generator maps noise and target concentration to a 651-band spectrum, while a frozen partial least-squares regression (PLSR) supervisor applies a feature-consistency penalty within target-specific windows identified from Pearson and Spearman correlations. A 36-site development set was used for feature selection, model fitting, and tuning, and 10 sites were held out for final evaluation. Synthetic-spectral fidelity was assessed using t-distributed stochastic neighbor embedding (t-SNE), maximum mean discrepancy (MMD), and a principal-component-based Fréchet distance (PCA-FD); predictive utility was compared across five regression models. The hybrid one-dimensional convolutional neural network-Transformer (Hybrid) increased $R^2$ and reduced root mean square error (RMSE) at every augmentation ratio relative to the unaugmented baseline. Across 100-400%, RMSE reductions ranged from 6.19 to 13.68% for TN and from 2.80 to 16.94% for TP. At 400%, Hybrid achieved TN/TP $R^2$ values of 0.8726/0.8800 and RMSEs of 2.6820/0.0600 mg L $_{}^{-1}$ . In 10,000 paired Bootstrap resamples, the proportions favoring lower RMSE ranged from 83.47 to 98.83% for TN and from 92.79 to 99.61% for TP. After Holm correction across the eight target-ratio comparisons, only TP at 400% remained below 0.05; the other comparisons, including TN at 400%, were not statistically significant. The results indicate a broadly favorable but non-monotonic and architecture-dependent augmentation response, while uncertainty remains because of the small independent test set.