This study develops an innovative sigmoid-based vegetation index, Sigmoid-based Tree Canopy Index (STCI), for tree canopy extraction, aiming to effectively separate tree canopies from surrounding vegetation. STCI enhances vegetation index through a targeted sigmoid transformation to selectively amplify canopy-structure-related spectral signals. The results demonstrate that STCI exhibits strong robustness across different land-cover types and optical sensors, showing stable performance comparable to traditional indices, particularly within urban forest environments. Furthermore, STCI not only distinguishes tree canopy and other vegetation but also improves vegetation classification accuracy, achieving an enhancement of canopy classification accuracy approximately 1–13% compared with traditional indices. This improvement remains consistent cover whole Taiwan, as well as across different seasons, indicating strong spatial–temporal robustness. STCI was also tested on multiple high-resolution satellite images from different sensors, maintaining good applicability despite minor reductions in performance due to differences in spatial resolution and sensor-specific spectral responses. In addition, both the cross-sensor evaluation and the equation-level analysis highlight the essential role of the blue band in distinguishing tree canopies from adjacent vegetation. Overall, this study introduces a simple in vegetation index design and demonstrates that incorporating band-ratio-based sigmoid functions can substantially enhance tree canopy extraction.
Groundwater in the Al Hisy-Thadiq sector, northwest of Riyadh, is strategic for agriculture and local development, yet its quality and controlling processes remain unclear. This study evaluated 25 groundwater samples using physicochemical parameters, major ions, nutrients, trace elements, irrigation indices, and multivariate statistics. Groundwater is neutral to slightly alkaline (pH 7.48–8.10; mean 7.74), mineralised, with EC of 2800–7230 µS/cm, TDS of 1804–4600 mg/L and total hardness of 925–2675 mg/L as CaCO3. Chemistry is dominated by Na+, Mg2+, Ca2+, Cl− and SO42−, and Piper facies are mainly Na+ + K+-Cl− and Mg2+-Cl−. Some samples have screening values of EC, TDS, hardness, Cl− and SO42−, whereas NO3− and F− remain acceptable. Trace elements are generally low. SAR and KR indicate a low sodicity hazard, whereas Na%, PI, and especially MR indicate irrigation restrictions. Cross-consistency among facies, spatial maps, correlations, PCA, K-means, and HCA identifies a salinity-mineralisation axis and one outlier. The groundwater is suitable for irrigation and requires quality-based allocation, salinity management, crop selection, leaching control, and soil monitoring.
Optical and Synthetic Aperture Radar (SAR) imagery provides complementary resources for land cover classification via spectral richness and all-weather sensing. Despite their potential, integrating these modalities remains challenging due to significant disparities in spatial resolution, imaging physics, and the inherent speckle noise in SAR data. Existing fusion frameworks often employ homogeneous feature extraction strategies that fail to account for these heterogeneous characteristics, leading to insufficient information integration and blurred semantic boundaries. To address these issues, we propose a Cross-Resolution dual-branch multi-task Fusion Network (CRFNet). The methodology features an asymmetric encoding architecture that utilises ResNet for high-resolution optical data and a Swin Transformer equipped with an Attention-Guided SAR Enhancement (AGSE) block for SAR data. Furthermore, a Modal-aware Adaptive Fusion Module (MAFM) is introduced to dynamically modulate feature injection, while a Multi-Scale Context Aggregation (MSCA) module facilitates deep feature enhancement. CRFNet achieves the highest overall performance among the compared methods, with an Overall Accuracy (OA), mean Intersection over Union (mIoU), and Mean F1-Score (M-F1) of 86.42%, 76.07%, and 81.48%, respectively, on the Shandong dataset, and 71.86%, 55.43%, and 66.28%, respectively, on the Jiangsu dataset. Cross-region tests without target-region fine-tuning, CRFNet obtains mIoU values of 49.32% (Shandong-to-Jiangsu) and 63.05% (Jiangsu-to-Shandong).
Agricultural greenhouse gas (GHG) emissions contribute similar to 10% to total emissions in the United States, which has resulted in a need for conservation-focused agricultural practices. The objective of this field study was to evaluate the effects of reduced tillage with cover crop (CC) compared to conventional tillage with no CC on carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) fluxes, season-long emissions, and global warming potential (GWP) over two consecutive growing seasons in a soybean (Glycine max)-corn (Zea mays) rotation in southeast Arkansas. Weekly GHG measurements were made throughout the 2024 (soybean) and 2025 (corn) growing seasons with a chamber and field-portable gas analysers. Season-long GHG emissions, crop yield, and GWP did not differ (P > 0.05) between field treatments in either growing season. Global warming potential from the 2025 corn growing season was 16,176 and 17,025 kg CO2 equivalents ha(-1) from the conventional and conservation field treatments, respectively. Despite no large-scale differences (i.e. emissions and GWP), smaller-scale CH4 and N2O flux differences occurred between treatments over time (P < 0.01) during both growing seasons, and for CO2 during the 2025 corn growing season. Results contribute to improving the understanding of practical management for GHG mitigation from upland cropping systems.
The Taihang Mountains are located in the central part of the North China Craton. The genesis of this mineralisation is closely associated with several Mesozoic plutons and coeval dykes. The preservation and alteration of these deposits depend to a large extent on the regional uplift and exhumation processes that occurred subsequent to their formation. This study applies apatite and zircon fission track thermochronology to the Mapeng-Chiwawu plutons. AFT central ages: 33 +/- 2-14 +/- 2 Ma, with mean fission track lengths: 12.4 +/- 1.7-13.2 +/- 2.2 mu m. ZFT central ages: 110 +/- 4-33 +/- 2 Ma. Combined with previous zircon U-Pb and whole-rock Rb-Sr age data, our new data reveal three distinct cooling episodes: (1) Late Cretaceous (110-68 Ma), (2) Palaeocene to Eocene (65-36 Ma), (3) Late Eocene to Miocene (35-5 Ma). A comprehensive analysis employing the mineral pair method and thermal history modelling method indicates that the Mapeng-Chiwawu plutons have undergone approximately 3.1-3.5 km of uplift and exhumation since its emplacement and crystallisation. This value is significantly lower than the mineralisation depths of the Chiwawu Cu-Mo Deposit and Shihu Gold Deposit (3-5 km and 5-8 km, respectively), which are closely genetically associated with the intrusion, indicating that this area holds important potential for mineral exploration.
