Remote Sensing

The westward electric field plays an important role in the hmF2-declining type (where hmF2 denotes the peak height of the F2 layer) of postmidnight electron density (Ne) enhancements at equatorial ionization anomaly (EIA) latitudes through the compression effects, whereas its effectiveness in causing ionospheric storms has not been carefully investigated. As the counter equatorial electrojet (CEJ…

Small objects in remote sensing images often exhibit blurred edges and dense distributions. This makes it difficult to precisely localize object regions. These challenges are especially pronounced on devices with limited computational capacity, where accuracy and efficiency are both critical. To address these challenges, we propose MFRA-YOLOv11, an enhanced YOLOv11s-based network for remote sensi…

Global land abandonment drives secondary forest succession in mountains, yet long-term spatial dynamics remain poorly quantified. We traced 80 years (1945–2025) of forest expansion across Greece’s Rhodope Mountains using an integrated remote sensing framework. Historical baselines (>25% canopy closure) were derived via Object-Based Image Analysis of 1945 panchromatic orthophotos. Because grayscal…

Accurate land-cover mapping in tropical regions remains challenging because of high environmental heterogeneity, complex vegetation structure, and persistent cloud cover. Recent geospatial foundation models provide pre-computed Geo-embeddings that offer a new representation of Earth Observation data (EO), yet their potential for detailed tropical land-cover mapping and their performance relative …

Alluvial fan source-to-sink (S2S) systems in Mars-analog environments are crucial for investigating planetary surface processes. However, traditional S2S analyses lack continuous regional coverage and topographic physical constraints. This study integrates GF-5A spaceborne hyperspectral imagery with DEM data to establish a regional-scale hyperspectral mineral spatial correspondence framework on t…

Synthetic Aperture Radar (SAR) backscatter serves as a key tool for tracking surface water dynamics; however, single-source data dependencies introduce systematic bias tied to the specific physical limitations of the signal. A primary challenge in SAR analysis is the backscatter ambiguity created by ‘water look-alike’ surfaces, which frequently result in false-positive water detections. We show t…

The lunar south polar region’s extreme illumination conditions impose strict energy constraints for solar-powered rover operations. Traditional Sun-synchronous path planning relies on dynamic time-dependent illumination evaluation, leading to high computational costs. We present CIRsE-Net, a spatiotemporal deep learning model that generates 72 h continuous illumination maps from hourly sequential…

Semantic Change Detection (SCD) in high-resolution remote sensing images is challenged by appearance-induced pseudo-changes and highly imbalanced class transitions. To address these coupled difficulties, we propose FD-ProtoSCD, a decoupled SCD framework that combines Frequency-Domain Change Disentanglement (FDCD) with a Dynamic Class Prototype Decoder (DCPD). FDCD decomposes bi-temporal features …

Radar active jamming recognition is an essential component of radar anti-jamming processing and cognitive radar decision making. As jamming categories proliferate and electromagnetic environments become more complex, collecting sufficient labeled samples for every possible jamming condition can be challenging in practical scenarios. Thus, it becomes necessary to exploit the recognition capability…

Distributed Scatterer Interferometric Synthetic Aperture Radar (DSInSAR) technology has been widely applied in areas with complex terrain and dense vegetation. However, DSInSAR is computationally intensive and requires considerable processing time. When new observations become available, the entire dataset must be reprocessed without utilizing previously obtained results. This makes DSInSAR unsui…

Cobalt-rich ferromanganese crusts are an important deep-sea mineral resource, and the ore grade is a key indicator for evaluating their resource potential. Conventional ore-grade assessment relies on representative samples and extensive laboratory analyses, posing significant challenges due to limited sampling opportunities, high operational costs, and the pronounced structural heterogeneity of t…

Thick-cloud contamination severely limits the usability of optical remote sensing imagery because cloud-covered regions may suffer from complete loss of surface information. Synthetic aperture radar (SAR) imagery provides complementary structural cues due to its cloud-penetrating capability, but the substantial cross-modal discrepancy between SAR and optical images makes high-fidelity SAR–optical…

Accurate rice-lodging mapping from unmanned aerial vehicle (UAV) imagery supports post-disaster loss assessment, crop insurance, and precision field management. Existing deep-learning methods typically require dense pixel-level annotations, which are costly and time-consuming to produce. Moreover, RGB imagery alone often fails to distinguish lodged from healthy rice when their canopy colors and t…

Reliable monitoring of reclaimed cropland is hindered when one optical acquisition is unavailable or degraded. We formulate missing-modality bi-temporal optical–SAR change detection and propose the Modality-Availability-Aware Robust Change Network (MARC-Net), a new architecture that combines explicit availability conditioning, condition-aware temporal proxy stabilization, a shared residual input …

Dense partial-view LiDAR observations are attractive for outdoor perception, but limited overlap and viewpoint sensitivity make odometry and mapping less reliable than with spinning LiDARs. Many recent algorithms for this sensing regime are built as LiDAR-inertial odometry frameworks, whose localization and mapping performance can degrade or fail when the IMU state estimation becomes unstable. Th…

In seasonally arid systems, where water is a key limiting resource, unseasonal precipitation events may promote landscape greening. Because herbivores track spatial variation in fresh plant growth, dry-season precipitation could be an important catalyst of out-of-season foraging opportunities in seasonal arid environments. The eastern escarpment of California’s Sierra Nevada Mountains in the west…

Ecosystems and the services they provide are essential for life but continue to undergo degradation worldwide. Satellite remote sensing has been essential for environmental mapping for decades, but can suffer poor accuracy when applied to mapping terrestrial ecosystems. Geospatial Foundation Models (GeoFMs) integrate diverse spatial data, including image data, spatial context, and temporal dynami…

In complex inshore regions, ships are often densely berthed and arbitrarily oriented, while docks, shorelines, coastal facilities, and strong scatterers produce substantial background clutter and target-like scattering responses, leading to foreground-background confusion and localization ambiguity. In practical inshore synthetic aperture radar (SAR) ship detection, training and testing images of…

Segmentation of mining-disturbed land is of great significance for eco-geological environment monitoring. Although existing methods possess strong segmentation capabilities, mining-land exhibits irregular edges, different spatial size, and global texture variability, which lead to difficulties in extracting discriminative features, thereby limiting the accuracy performance. This study first built…

In recent years, U-Net-based architectures have been widely applied to polarimetric synthetic aperture radar (PolSAR) semantic segmentation. However, successive downsampling may lead to the loss of fine spatial details, while conventional U-Net-style decoder directly concatenates encoder features with the corresponding decoder features without explicitly accounting for their semantic discrepancy,…