Atmospheric and Oceanic Sciences

Late Cretaceous phosphogenic systems are widely documented along the Tethyan and Atlantic margins. However, the paleoenvironmental conditions controlling phosphorite formation along the Brazilian Equatorial Margin remain poorly understood. This study reconstructs the paleoenvironmental, depositional, and phosphogenic evolution of the Itamaracá, Gramame, and Jandaíra formations along the equatorial South Atlantic margin during the late Campanian–early Maastrichtian, integrating microfacies, foraminiferal assemblages, and quantitative paleoenvironmental proxies. The results reveal distinct but interconnected depositional systems governed by variations in nutrient flux, water-mass stratification, and bottom-water oxygenation. The upper Campanian stratigraphic sequence limit KCa7 records intensified upwelling, enhanced productivity, and the onset of dysoxic conditions, triggering the initial stages of phosphogenesis in both basins. The early Maastrichtian stratigraphic sequence limit KMa1 interval corresponds to maximum flooding, extreme sedimentary condensation, expansion of the oxygen minimum zone, leading to pervasive phosphatization in the basal Gramame Formation analogous to the classical Tethyan and North African phosphorite systems. In contrast, the early Maastrichtian stratigraphic sequence limit KMa2 reflects improved ventilation, oscillatory nutrient supply, and reduced phosphogenic intensity. These findings support regional and intercontinental correlations between the Late Cretaceous phosphatic deposits of northeastern Brazil and phosphogenic systems developed in the Neo-Tethyan domain. They further refine the role of upwelling-driven productivity and redox instability in controlling the development of Campanian–Maastrichtian carbonate–phosphate systems.

Introduction This study examines the long-run and non-linear effects of key agricultural activities on total greenhouse gas (GHG) emissions in Saudi Arabia, a country pursuing rapid agricultural modernization under Vision 2030 amid rising environmental pressures. Although agriculture is increasingly recognized as a significant contributor to GHG emissions, existing evidence for Saudi Arabia remains limited, with little attention to asymmetric and non-linear responses of emissions to agricultural inputs and land-use dynamics. To address this gap, the study investigates how fertilizer consumption, forest rents, and crop production influence total GHG emissions, while accounting for the roles of arable land, agricultural land, and total fisheries production as control variables. Methods Annual data spanning 2000–2023 are analyzed using both linear ARDL and non-linear ARDL (NARDL) frameworks to capture long-run relationships, short-run adjustments, and asymmetric effects. Results The results confirm a stable long-run cointegrating relationship among the variables and reveal that fertilizer consumption and crop production significantly increase GHG emissions, with positive shocks exerting stronger environmental pressure than negative shocks. Forest rents also intensify emissions, reflecting land-use and resource-extraction effects, while control variables exhibit heterogeneous influences. Discussion These findings are consistent with Sustainable Intensification Theory, highlighting trade-offs between productivity gains and environmental sustainability. The study’s novelty lies in its integrated linear–non-linear analysis of agricultural drivers of total GHG emissions in Saudi Arabia. Practically, the results offer targeted policy insights for designing climate-smart agriculture, land-use regulation, and input-management strategies aligned with Vision 2030 and applicable to other emerging economies.

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.