Crop classification plays a vital role in acquiring the spatial distribution of agricultural crops, enhancing agricultural management efficiency, and ensuring food security. With the continuous advancement of remote sensing technologies, achieving efficient and accurate crop classification using remote sensing imagery has become a prominent research focus. Conventional approaches largely rely on empirical rules or single-feature selection (e.g., NDVI or VV) for temporal feature extraction, lacking systematic optimization of multimodal feature combinations from optical and radar data. To address this limitation, this study proposes a crop classification method based on feature-level fusion of multimodal remote sensing data, integrating the complementary advantages of optical and SAR imagery to overcome the temporal and spatial representation constraints of single-sensor observations. The study was conducted in Story County, Iowa, USA, focusing on the growth cycles of corn and soybean. Eight vegetation indices (including NDVI and NDRE) and five polarimetric features (VV and VH) were constructed and analyzed. Using a random forest algorithm to assess feature importance, NDVI+NDRE and VV+VH were identified as the optimal feature combinations. Subsequently, 16 scenes of optical imagery (Sentinel-2) and 30 scenes of radar imagery (Sentinel-1) were fused at the feature level to generate a multimodal temporal feature image with 46 channels. Using Cropland Data Layer (CDL) samples as reference data, a U-Net deep neural network was employed for refined crop classification and compared with single-modal results. Experimental results demonstrated that the fusion model outperforms single-modal approaches in classification accuracy, boundary delineation, and consistency, achieving training, validation, and test accuracies of 95.83%, 91.99%, and 90.81% respectively. Furthermore, consistent improvements were observed across evaluation metrics, including F1-score, precision, and recall.

This paper introduces an enhanced U-Net network designed to extract fine crack features against complex backgrounds and balance crack pixel categories for bridge crack segmentation. Initially, the model integrates an ECA (Efficient Channel Attention) module with the original U-Net to dynamically calibrate channel features. This enhances the detection of crack-related features and suppresses irrelevant textures. The architecture is further enhanced by a DSConv (Dynamic Snake Convolution) layer, which the employs learnable offset vectors to adaptively deform convolutional kernels. This allows for precise mapping along the contours of curvilinear cracks. Finally, the model utilizes a weighted combination loss function to achieve a balance between pixel-level classification and global shape consistency, effectively addressing gradient conflicts in scenarios with class imbalance. This study’s algorithm was validated for crack segmentation using a custom dataset. Experimental results show that it outperforms other algorithms in accuracy and improves upon the original U-Net across all metrics, with mIoU (Mean Intersection over Union) and F1-Score increasing by 1.23% and 1.89%, respectively.

In response to the inability of traditional remote sensing technology to accurately capture the micro-topographic features of landslide surfaces in vegetated areas under complex terrain conditions, this paper proposes a method for enhanced landslide terrain display and trace recognition based on airborne LiDAR technology. Firstly, a high-precision LiDAR-DEM is constructed using preprocessed LiDAR point cloud data, and visual images are generated using visualization methods, including hillshade, slope, openness, and Sky View Factor (SVF). Secondly, pixel-level image fusion methods are applied to the visual images to obtain enhanced display images of the landslide terrain. Finally, a threshold is determined through a fractal model, and the Mean-Shift algorithm is utilized for clustering and denoising to extract landslide traces. The results indicate that employing pixel-level image fusion technology, which combines the advantageous features of multiple terrain visualization images, effectively enhances the display of landslide micro-topography. Moreover, based on the enhanced display images, the fractal model and the Mean-Shift algorithm are applied for denoising to extract landslide traces. Compared to orthophotos, this method can effectively and accurately extract landslide traces. The findings of this study provide valuable references for the enhanced display and trace recognition of landslide terrain in densely vegetated areas within complex mountainous areas, thereby providing technical support for emergency investigations of landslide disasters.

Against the backdrop of the continuous growth in global demand for mineral resources, efficient and accurate mineral exploration technologies are of paramount importance. Therefore, utilizing remote sensing technology, which features wide coverage, a non-contact nature, and multi-source data acquisition, is of great significance for conducting mineral resource exploration and prospecting research. This study focuses on the Tsagaankhairkhan copper–gold mining area in Mongolia and proposes a structural-alteration anomalies coupling mining prediction method based on the collaboration of multi-source remote sensing data. By comprehensively utilizing multi-source image data from Landsat-8, GF-2, and Sentinel-2, and employing methods such as principal component analysis (PCA), band ratio, and texture analysis, we effectively extracted structural information closely related to mineralization, as well as alteration anomaly information, including hydroxyl alteration anomalies and iron-staining alteration anomalies. Landsat-8 and Sentinel-2 data were employed to extract and mutually validate hydroxyl and iron-staining alteration anomaly information in the study area, thereby delineating alteration anomaly zones. By integrating the results of structural interpretation, the distribution of alteration anomaly information, and their spatial coupling characteristics, we explored the spatial coupling relationship between structural and alteration anomalies, analyzed their mineral control patterns, and identified 7 prospecting target areas. These target areas exhibit abundant mineral anomalies and favorable structural settings, indicating high metallogenic potential. The research findings provide crucial clues for the exploration of the Tsagaankhairkhan copper–gold mine in Mongolia and can guide future mineral exploration and development efforts.

Lake spatial-temporal variations are sensitive records of climate and environmental changes, directly reflecting global changes. Lakes have great ecological and economic benefits, which play an important role in improving the ecological environment of the river basin, regulating surface and underground runoff, irrigating farmland, breeding aquatic animals and plants, maintaining biodiversity, flood control and disaster reduction, and producing and drinking water. In this paper, we took lakes in central Yunnan province as the research object, based on Landsat TM, ETM+ and OLI remote sensing images data in the period from 1987 to 2017, the vectorized boundary of 5 lakes with areas greater than 30 km² in central Yunnan were obtained by artificial visual interpretation technology and the causes of lake variations were analysed. Several conclusions can be drawn from the present study: (1) From 1987 to 2017, the total lake area in central Yunnan increased from 603.86km² to 605.71km².(2) During the study period, the lake area in central Yunnan increased as a whole, but also showed obvious spatial differences. According to the change of lake area, the study area can be divided into three types: stable, expanding and shrinking. Dian chi lake area is a lake area expanding area. Yang zonghai lake area is a shrinking lake area. (3) Influenced by the complex natural and cultural environment in the study area, the reasons for the change of lake area in different areas are different.

Aerosol plays an important role in global climate effect, and Aerosol Optical Depth (AOD), as one of its important parameters, can not only monitor the turbidity of the atmosphere, but also is an important index of atmospheric correction quality in remote sensing. Urban agglomeration in the middle reaches of the Yangtze River is a key national-level urban agglomerations in China, and its rapid urbanization leads to the aggravation of urban diseases such as disorderly development of cities, waste of resources and environmental pollution, and the research on ecological environment problems of urban agglomerations is also the current key project.In view of the above problems, this paper studies the AOD inversion and its spatial and temporal distribution in the middle reaches of the Yangtze River, in order to provide important scientific materials for environmental monitoring and satellite atmospheric correction quality inspection.