Earth and Environmental Sciences

Study region The Moniquirá–Sutamarchán River basin (19.69 km²) is located in the upper Suárez River system, Boyacá, Colombia, within a tropical inter-Andean Mountain environment characterized by fractured Mesozoic sedimentary formations and bimodal rainfall patterns. Study focus This study integrates the conceptual Thomas (ABCD) hydrological model with the classical GOD method to develop a composite GOD–Thomas (GOD–T) index for intrinsic aquifer vulnerability assessment. Groundwater recharge was simulated using hydrometeorological series (2000–2025) and spatially distributed at 12.5 m resolution within a GIS environment. Model calibration yielded satisfactory performance (NSE = 0.8363; R² = 0.8363; r = 0.956), supporting the reliability of simulated recharge as a dynamic vulnerability parameter. New hydrological insights for the region Results reveal that recharge is strongly controlled by lithological fracturing and topographic gradients, with the highest infiltration rates occurring in the Ritoque, Arcabuco, and Paja formations. Incorporating simulated recharge significantly improves the spatial discrimination of vulnerability classes compared to static approaches. Moderate vulnerability predominates (77%), while high and very high vulnerability zones (≈17%) coincide with shallow water tables and permeable fractured units. The GOD–T framework demonstrates that integrating dynamic recharge processes improves the hydrogeological understanding of tropical Andean basins and provides a reproducible approach for groundwater protection and territorial planning in data-scarce mountain regions.

In recent years, research on remote sensing scene classification (RSSC) has mainly focused on high-resolution imagery, which provides limited spectral information, whereas hyperspectral imaging (HSI) offers richer cues about material properties and compositional structure. Despite its potential, hyperspectral scene classification (HSI-SC) remains challenging because pixel- or patch-based representations fail to preserve spatial structures and regional boundaries. In addition, labeled hyperspectral samples are often scarce, making it difficult to learn stable class-discriminative representations from high-dimensional spectral observations. To address these issues, this paper proposes a dual-branch fusion framework. Superpixels are used to aggregate high-dimensional spectral signals into compact, boundary-aware tokens. The spectral branch is initialized with pretrained model weights and further adapted via a lightweight adaptation strategy for efficient transfer under limited supervision. In parallel, a pseudo-RGB spatial branch complements structural and textural information. Spectral and spatial features are fused additively to generate a more discriminative scene representation. Experimental results demonstrate that the proposed method outperforms compared hyperspectral scene classification approaches.

Detecting unmanned aerial vehicles (UAVs) remains a difficult task, primarily due to their tiny size, rapid motion, and complex backgrounds. Fusing visible and infrared imagery offers complementary advantages for robust detection, yet existing methods rely on spatial feature aggregation that overlooks spectral disparities, coupling noise with textures. Moreover, the small scale and high dynamics of UAVs hinder standard convolution from decoupling target signals from background interference due to limited receptive fields. To solve these limitations, the Wavelet-guided Frequency–Spatial Decoupling Network (WFSD-Net) is designed for visible–infrared UAV detection. First, to tackle fusion noise, the Discrete Wavelet Band-Differentiated Fusion (DWBF) module is designed to explicitly decouple noise-dominant sub-bands from information-rich components by performing spectral decomposition. It aligns low-frequency distributions via adaptive spatial weighting and disentangles high-frequency details using physics-aware rules, achieving source-level noise suppression. Second, an Axial Strip Contextual Attention (ASCA) module is proposed. By utilizing anisotropic strip convolution via orthogonal decomposition, this module captures global contextual dependencies to effectively decouple weak target features from background clutter, enhancing the spatial position encoding capability for weak targets. Finally, the proposed WFSD-Net method is validated on Anti-UAV300 and Multi-Sensor and Multi-View Fixed-Wing UAV (MMFW-UAV) datasets, and experiments demonstrate that the proposed method is superior to existing state-of-the-art (SOTA) methods.

High-resolution multispectral images are of great value in various fields. However, the physical limitations of satellite sensors hinder the simultaneous acquisition of high spatial resolution and high spectral resolution images. Deep learning has become a powerful tool for remote sensing image fusion, but its full potential has not been fully utilized. In order to maximize the quality of the high-resolution multispectral images generated by the improved model, this paper proposes a module called EBIFusion, which introduces an error backtracking mechanism to improve fusion performance. The module uses the intermediate results of the deep learning model to capture the information lost during the high-resolution image generation process, thereby guiding the optimization of model training. The experimental results on the GF-6 dataset show that the QNR index is increased by 2.22% after the introduction of the module. In addition, the spatial and spectral quality has been continuously improved on multiple datasets, including QuickBird and GF-2. The optimized models, such as PSGAN, PNN, GPPNN, and UCGAN, show stronger spatial details and spectral fidelity. The main contribution of this paper is to propose an error backtracking framework for recovering and compensating for the lost information in the process of remote sensing image fusion based on deep learning. Based on this, a lightweight and model-independent enhancement module EBIFusion is designed, which can be integrated into different deep learning fusion architectures. At the same time, the generalization ability of the module in multiple datasets and network paradigms is verified. In summary, the error backtracking module enhances the quality of the generated high-resolution multispectral images. In addition, it is not limited to specific models and data and can be used as the basis for a versatile and effective optimization component to improve the availability of high-resolution multispectral images.