Integrating Deep Generative AI and Hyperspectral–Multispectral Data Fusion for Enhancing Digital Soil Mapping

Integrating high-resolution hyperspectral remote sensing with deep generative artificial intelligence (AI) offers a promising method for accurate soil mapping under limited sampling conditions. While the EnMAP satellite provides hyperspectral data for mapping soil properties, its coarse spatial resolution (30 m) restricts its applications in digital soil mapping (DSM). This study investigates the potential of an integrated framework that combines hyperspectral–multispectral satellite data fusion and deep generative AI for high-resolution DSM. A total of 110 surface soil samples (0–30 cm) were collected from an agricultural farm in Ismailia (Egypt) and were analysed for soil organic matter (OM), electrical conductivity (EC), and available phosphorus (P). EnMAP hyperspectral and SuperDove multispectral images were pre-processed and fused using a 1D U-Net-based convolutional neural network (CNN) to generate a hyperspectral high-resolution (3 m) image. A conditional Wasserstein generative adversarial network (GAN) with gradient penalty (cWGAN-GP) was used to generate soil spectra at different levels of augmentation. The generated spectra were combined with 70% of real spectra to create different calibration datasets that were filtered to preserve spectral diversity and avoid spectral duplication. Two predictive models, random forest (RF) and CNN, were developed based on the optimal combined calibration datasets. The prediction results based on the independent prediction dataset (30%) showed that GAN–CNN outperformed GAN–RF at the highest augmentation level (5×), with increases in coefficient of determination (R²) by 31.3, 25.8, and 9.0%, and reductions in root mean square error (RMSE) by 33.2, 22.1 and 8.2% for EC, OM, and P, respectively. The optimal GAN–CNN model was used to produce soil maps at 3 m resolution based on the fused high-resolution hyperspectral image. The results indicate the potential of fusing hyperspectral and multispectral data combined with deep generative AI to overcome limited soil sampling and advance DSM for precision agriculture applications.