River channels are fundamental geomorphological and hydrological features that play a critical role in regulating the Earth’s water cycle and ecosystems and influencing human activities. This study utilized Digital Elevation Model (DEM) data and multi-source remote sensing imagery (including GF-1 WFV, Sentinel-1, and Sentinel-2) to determine river channel dimensions. River water masks were obtained from multiple remote sensing imagery sources and processed through triangulation and segmentation to generate river reach results. Based on these segmented river reaches, buffer analysis was conducted. The buffer analysis results were then used to refine and clip the 5 m DEM and 12.5 m DEM datasets. Finally, river channels were extracted from the clipped DEM data using the natural breaks classification method. The classification accuracy was assessed using a confusion matrix. Experimental results demonstrate a high overall classification accuracy, reaching or exceeding 0.985, with classification consistency (Kappa coefficient) ranging from 0.78 to 0.81. The 5 m resolution DEM exhibited superior performance compared to the 12.5 m resolution DEM in river channel extraction, especially regarding the classification consistency (Kappa coefficient), with the 5 m resolution model outperforming the latter. This approach effectively delineates the river channel boundaries, transcends the constraints of a singular data source, enhances the precision and resilience of river extraction, and possesses several practical applications. The extracted data can support analyses of river evolution, facilitate hydrological modeling at the basin scale, improve flood disaster monitoring, and contribute to various other research domains.

Global precipitation products (GPPs) are vital in weather forecasting, efficient water management, and monitoring floods and droughts. However, the precision of these datasets varies considerably across different climatic regions and topographic conditions. Therefore, the accuracy assessment of the precipitation dataset is crucial at the local scale before its application. The current study initially compared the performance of recently modified and upgraded precipitation datasets, including Climate Research Unit Time-Series (CRU TS v4.08), fifth-generation ERA5-Land (ERA-5), Integrated Multi-satellite Retrievals for GPM (IMERG) final run (IMERG v07B), and Multi-Source Weighted-Ensemble Precipitation (MSWEP v2.8), against ground observations on the provincial basis across Pakistan from 2003 to 2020. Later, the study area was categorized into four regions based on the elevation to observe the impact of elevation gradients on GPPs’ skills. The monthly and seasonal precipitation estimations of each product were validated against in situ observations using statistical matrices, including the correlation coefficient (CC), root mean square error (RMSE), percent of bias (PBias), and Kling–Gupta efficiency (KGE). The results reveal that IMERG7 consistently outperformed across all the provinces, with the highest CC and lowest RMSE values. Meanwhile, the KGE (0.69) and PBias (−0.65%) elucidated, comparatively, the best performance of MSWEP2.8 in Sindh province. Additionally, all the datasets demonstrated their best agreement with the reference data toward the southern part (0–500 m elevation) of Pakistan, while their performance notably declined in the northern high-elevation glaciated mountain regions (above 3000 m elevation), with considerable overestimations. The superior performance of IMERG7 in all the elevation-based regions was also revealed in the current study. According to the monthly and seasonal scale evaluation, all the precipitation products except ERA-5 showed good precipitation estimation ability on a monthly scale, followed by the winter season, pre-monsoon season, and monsoon season, while during the post-monsoon season, all the datasets showed weak agreement with the observed data. Overall, IMERG7 exhibited comparatively superior performance, followed by MSWEP2.8 at a monthly scale, winter season, and pre-monsoon season, while MSWEP2.8 outperformed during the monsoon season. CRU TS showed a moderate association with the ground observations, whereas ERA-5 performed poorly across all the time scales. In the current scenario, this study recommends IMERG7 and MSWEP2.8 for hydrological and climate studies in this region. Additionally, this study emphasizes the need for further research and experiments to minimize bias in high-elevation regions at different time scales to make GPPs more reliable for future studies.

Accurate extraction of farmland boundaries is crucial for improving the efficiency of farmland surveys, achieving precise agricultural management, enhancing farmers’ production conditions, protecting the ecological environment, and promoting local economic development. Remote sensing and deep learning are feasible methods for creating large-scale farmland boundary maps. However, existing neural network models have limitations that restrict the accuracy and reliability of agricultural parcel extraction using remote sensing technology. In this study, we used high-resolution satellite images (2 m, 1 m, and 0.8 m) and the U²-Net++ model based on the RSU module, deep separable convolution, and the channel-spatial attention mechanism module to extract different types of fields. Our model exhibited significant improvements in farmland parcel extraction compared with the other models. It achieved an F1-score of 97.13%, which is a 7.36% to 17.63% improvement over older models such as U-Net and FCN and a more than 2% improvement over advanced models such as DeepLabv3+ and U²-Net. These results indicate that U²-Net++ holds the potential for widespread application in the production of large-scale farmland boundary maps.

Accurate terrain modeling from high-resolution digital surface models (DSM) is critical for geosciences, geology, geomorphology, earthquake studies, and applied geology. However, existing filtering methods such as progressive morphological filtering (PMF), cloth simulation filtering (CSF), and progressive TIN densification (TIN) often struggle with complex topography and urban structures, leading to either excessive ground loss or incomplete object removal. Furthermore, some of these algorithms are only specialized for point cloud data and are not optimized for grid data. To address these limitations, we propose an iterative morphological filtering (IMF) algorithm that introduces a binary surface edge-segmentation strategy. The method refines object–ground separation by combining iterative morphological operations with block-based graph-cut stitching, thus enhancing continuity and accuracy in challenging terrain. Validation on UAV-derived DSM over the Haihe Basin in China and the ISPRS Vaihingen dataset shows that IMF achieves notable accuracy improvements: the Vaihingen test areas yielded an average Type I error of 8.93%, Type II error of 3.09%, overall accuracy of 80.85%, and Kappa coefficient of 0.7524, while the Haihe Basin test areas achieved Type I and II errors of 2.22% and 1.87%, overall accuracy of 89.32%, and a Kappa coefficient of 0.8706. These results demonstrate that IMF outperforms conventional methods by reducing both Type I and Type II errors, producing terrains highly consistent with real conditions. This innovation provides a robust and scalable solution for digital elevation models (DEM) generation from gridded DSM, offering significant value for large-scale environmental monitoring and flood risk assessment.

The increasing threat of Glacial Lake Outburst Floods (GLOFs), intensified by climate change, underscores the urgency for developing advanced early warning systems. The near-annual, cyclical outbursts of Lake Merzbacher in the Tien Shan mountains present a severe downstream threat, yet its remote location and lack of instrumentation pose a significant challenge to traditional monitoring. To bridge this gap, we develop and validate a dynamic risk assessment framework driven entirely by remote sensing data. Methodologically, the framework introduces an innovative Ice-Water Composite Index (IWCI) to resolve the challenge of lake area extraction under mixed ice-water conditions. This is coupled with a high-fidelity 5 m resolution Digital Elevation Model (DEM) of the lake basin, autonomously generated from GF-7 Dual-Line Camera (DLC) imagery, which enables accurate daily volume retrieval. Through systematic feature engineering, nine key hydro-thermal drivers are quantified from MODIS and other products to train a Random Forest (RF) machine learning model, establishing the non-linear relationship between catchment processes and lake volume. The model demonstrates robust predictive performance on an independent validation set (2023–2024) (R² = 0.80, RMSE = 5.15 × 10⁶ m³), accurately captures the complete lake-filling cycle from initiation to near-peak stage. Furthermore, feature importance analysis quantitatively confirms that Positive Accumulated Temperature (PAT) is the dominant physical mechanism governing the lake’s storage dynamics. This end-to-end framework offers a transferable paradigm for GLOF hazard management, enabling a critical shift from static, regional assessments to dynamic, site-specific early warning in data-scarce alpine regions.

Accurate monitoring of water spatiotemporal dynamics is critical for hydrological process analysis and climate impact assessment. While remote sensing enables effective water monitoring, public satellite imagery is limited by mixed-pixel effects that hinder small river detection, and high-resolution commercial data suffers from low temporal frequency and restricted coverage. To address these limitations, this study proposes a deep learning-based super-resolution (SR) framework for multispectral remote sensing imagery. This paper constructs a matched dataset for GF2 and Sentinel-2 imagery and develops an Attention Enhanced Super Resolution Generative Adversarial Network (AESRGAN). By integrating attention mechanisms and a spectral-structural loss design, the network is optimized to adapt to the characteristics of multispectral remote sensing imagery. Experimental results demonstrate that AESRGAN achieves strong reconstruction performance, with a Peak Signal-to-Noise Ratio (PSNR) of 33.83 dB and a Structural Similarity Index Measure (SSIM) of 0.882. Water extraction based on the reconstructed imagery using the U-Net++ model achieved an overall accuracy of 0.97 and a Kappa coefficient of 0.92. In addition, the reconstructed imagery improved the estimation accuracy of river length, width, and area by 0.34%, 3.28%, and 8.51%, respectively. The proposed framework provides an effective solution for multi-source remote sensing data fusion and high-precision surface water monitoring, offering new potential for long-term hydrological observation using medium-resolution satellite imagery.