Remote Sensing

In this study, Mask R-CNN was applied to 5-band raw reflectance images to detect potato plants in UAV images. The highest model performance across all metrics was achieved with a ResNeXt-101 backbone and transfer learning from the same model trained on apple orchard data. An F-1 score of 84.2% was achieved. To determine whether the plant was infected with PLB, two methods were used. In the first …

Accurately monitoring the surface stabilization of waste dumps in open-pit coal mines is critical for hazard prevention and ecological reclamation. In arid and semi-arid regions, traditional optical remote sensing vegetation indices suffer from a systematic “response lag” in assessing physical stability due to the slow establishment of pioneer vegetation. To overcome this biological limitation, t…

Leaf Area Index is a crucial biophysical variable, and its accurate estimation is essential for understanding vegetation dynamics. However, cloud cover significantly restricts optical remote sensing, hindering the generation of spatially continuous Leaf Area Index products. Remote sensing foundation models offer novel solutions to this challenge. This study presents an end-to-end framework based …

Land subsidence (LS) is a widespread geoenvironmental problem driven by both natural processes and human activities. Identifying the main factors controlling LS susceptibility and their spatial contribution patterns is essential for LS management and mitigation. In this study, an interpretable earth observation framework was developed for the North China Plain (NCP) to quantify both spatial and n…

Monitoring surface deformation at reclaimed airports under construction is crucial for ensuring construction safety. However, significant variations in surface scattering characteristics cause severe decorrelation, limiting the effectiveness of conventional single-polarization Interferometric Synthetic Aperture Radar (InSAR). To address the issue of insufficient coherent pixels, we propose a dual…

Accurate identification of tree species is important for forest management, biodiversity studies, and precision forestry. Near-range LiDAR point clouds provide detailed three-dimensional information about individual trees. However, the complex structure of the point clouds and the unbalanced distribution of species make automatic classification difficult. To address these issues, this study prese…

Atmospheric heatwaves (AHWs) and marine heatwaves (MHWs) are intensifying under climate change, yet their coupled behavior in the Red Sea remains insufficiently quantified. This study investigates the spatial and temporal characteristics of AHWs, MHWs, and their concurrent occurrence across the Red Sea from 1990 to 2024 using ERA5 surface air temperature (SAT) and NOAA OISST v2.1 satellite-derive…

Accurate reservoir dam detection in arid regions is challenging because of spectral similarity between dams and surrounding backgrounds, indistinct boundaries, and substantial target-scale variation. To address these issues, this study proposes a deep learning object detection with multi-conditional constraint assistance (DLOD&MCCA) framework that combines a dual deep enhancement YOLO network…

Sea surface cooling (SSC) induced by typhoons has a significant impact on typhoon intensity and regional air–sea interaction. This study develops a machine learning model based on a multilayer perceptron (MLP) to predict SSC during typhoon passage over the western North Pacific. The model uses pre-typhoon ocean background conditions and ocean states at the typhoon peak moment as inputs, including…

High-resolution remote-sensing semantic segmentation requires models to simultaneously capture global scene semantics and preserve fine-grained local structures. Although satellite-pretrained vision foundation models provide strong transferable representations, the features extracted by a frozen backbone remain insufficiently adapted to dense prediction, particularly for representing high-frequen…

Accurate forecasting of near-surface PM2.5 concentrations remains challenging due to the complex coupling between atmospheric vertical structure, thermodynamic stability, and pollutant accumulation processes. Most existing surface-based statistical and deep learning approaches struggle to represent the three-dimensional state of the atmosphere, which limits their robustness under complex meteorol…

Cross-border regions often exhibit complex vegetation-related land-cover patterns due to contrasting natural conditions and divergent development trajectories, causing multi-source land-cover products to suffer from disagreements in class assignment and boundary delineation, especially for cropland, forestland, and grassland. Because border zones are rarely mapping priorities, classification inst…

The primary productivity of phytoplankton (PPeu) is critical to the carbon cycle in aquatic ecosystems. However, in complex lakes covered by ice, the estimation of PPeu using remote sensing techniques is constrained. To address this limitation, this study developed an estimation model for ice-covered PPeu by incorporating optical parameters such as the ice surface refractive index and the extinct…

Milltailings deposition areas in abandoned mines are inherently unstable and spatially extensive and heterogeneous, making regional-scale field investigations challenging under intense rainfall. With the advancement of space–airborne remote sensing technologies, large-scale surface deformation monitoring has become feasible. In this study, a 22.02 km² abandoned mine in Lingqiu County, Shanxi Prov…

Accurate retrieval of visibility grades is critical for transportation safety. Due to the highly complex meteorological backgrounds, traditional global deep learning models frequently struggle with limited physical traceability and feature heterogeneity. To address these challenges by enhance physical traceability and reduces heterogeneity, this study proposes a scenario-adaptive visibility retri…

Optimizing olive orchard management requires timely, per-tree data to enhance productivity and sustainability. Unoccupied aerial vehicle (UAV)-based red, green, and blue (RGB) imagery offers a low-cost solution for acquiring high-resolution spatiotemporal insights for orchard management, which are not yet common in Tunisia. This study monitored tree structural parameters, leaf area index (LAI), a…

The Upper Zarafshon River Basin (UZRB) in Tajikistan hosts numerous glaciers, of which the Zarafshon glacier is the largest and most important source of meltwater for both Tajikistan and Uzbekistan. In this study, we analyzed glacier retreat, surface displacement, and the evolution of supraglacial features from 2016 to 2024 using multi-temporal, high-resolution satellite imagery from Gaofen-2 and…

Remote sensing change detection (RSCD), a fundamental task in Earth observation, aims to automatically identify land-cover changes (e.g., building construction, vegetation degradation) by comparing multitemporal satellite or aerial images of the same region. With the explosive growth of high-resolution remote sensing data, achieving real-time accurate change detection on edge computing devices (e…

Topsoil sand content (TSC) is a critical indicator of soil degradation in black soil regions, yet its long-term dynamics remain poorly quantified. To address this, we developed an automated Landsat-based framework on Google Earth Engine (GEE) for mapping cropland TSC across the Northeast China Black Soil Region (NCBSR) from 1984 to 2023. The methodology integrates a hierarchical bare-soil extract…

Object detection in optical remote sensing imagery has emerged as a crucial task in computer vision, with applications ranging between environmental monitoring to disaster management, precision agriculture, and urban planning. This review systematically examines current methodologies, categorising them into four principal approaches: (1) template matching-based methods, which leverage predefined …