UAV-Based Soil Salinity Estimation Using Stagewise Feature Optimization and Dual-Backbone Deep Learning Fusion

Accurate soil salinity estimation under small-sample agricultural conditions continues to pose a formidable challenge, attributed to the scarcity of labeled data, inherent representational limitations of single-backbone neural networks, and the heightened complexity of subsurface salinity inversion. To mitigate these intertwined challenges, this study developed a UAV-enabled soil salinity estimation framework that integrated lightweight convolutional neural networks and staged feature optimization, leveraging both RGB and multispectral imagery. A feature selection framework integrating random forest recursive feature elimination (RF-RFE), the one-standard-error (One-SE) criterion, and variance inflation factor (VIF) analysis was employed to reduce 129 candidate variables to a unified 16-channel feature set, which served as the common input for estimating both surface and subsurface soil salinity. Three lightweight single-backbone (VGGNet, ResNet, and DenseNet) and dual-backbone feature-level fusion networks (DenseResNet, DenseVGGNet, and ResVGGNet) were constructed and systematically evaluated for their performance in estimating both surface and subsurface soil salinity. Among the single-backbone networks, ResNet yielded the highest overall statistical accuracy, while DenseNet exhibited superior performance in preserving estimation trends. For surface soil salinity estimation, ResVGGNet achieved the best performance among all evaluated models, with an R2 of 0.820, RMSE of 0.626 g/kg, MAE of 0.409 g/kg, and RPD of 2.31 on the test dataset. SHAP analysis further highlighted the dominant role of vegetation and salinity-sensitive indices, together with selected spectral mean features, and revealed spatially complementary response patterns among major input channels. Collectively, the integration of lightweight multi-backbone feature-level fusion with streamlined feature optimization strategies effectively enhances the accuracy, robustness, and interpretability of UAV-enabled soil salinity estimation, particularly under the constraint of small agricultural sample sizes.