Remote Sensing, Volume 18, Issue 2

The spaceborne full-polarimetric (FP) synthetic aperture radar (SAR) is an advanced sensor for high-resolution Earth observation. However, FP data acquired by such a system are prone to distortions induced by ionospheric Faraday rotation (FR). From t...

Creeping landslides constitute the predominant form of long-term, slow-moving geohazards in high mountain gorge regions. Under the combined influence of gravity and external triggering factors, these landslides undergo persistent deformation, posing...

Change detection in remote sensing imagery plays a vital role in land use analysis, disaster assessment, and ecological monitoring. However, existing remote sensing change detection methods often lack a structured and tightly coupled interaction para...

Remote sensing enables large-scale, image-based assessments of river dynamics, offering new opportunities for hydrological monitoring. We present a publicly available dataset consisting of 281,024 satellite and aerial images of U.S. rivers, construct...

Climate-induced lake expansion on the Tibetan Plateau (TP) has led to two distinct hazard types: outburst floods and passive inundation. However, the divergent driving mechanisms behind these hazards remain insufficiently understood. This study analy...

Severe convective weather is marked by abrupt onset, rapid evolution, and substantial destructive potential, posing major threats to economic activities and human safety. To address this challenge, this study proposes MRKAN, a multi-parameter predict...

Satellite attitude is critical for both satellite antenna phase center offset and phase wind-up correction. However, during the eclipse season, the nominal satellite attitude is almost impossible to maintain, and the satellite attitude variability af...

Accurate monitoring of atmospheric carbon dioxide (CO₂) is critical for addressing climate change, as CO₂ is one of the dominant greenhouse gases. Satellite remote sensing remains the primary method for monitoring column-averaged CO₂ (XCO₂), yet diff...

Methane emissions from rice paddies account for over 11% of global atmospheric CH₄, making water management practices such as Alternate Wetting and Drying (AWD) critical for climate change mitigation. Remote sensing offers an objective approach to mo...

The highly squinted mode, as an operational configuration of synthetic aperture radar (SAR), fulfills specific remote sensing demands. Under equivalent conditions, it necessitates a higher pulse repetition frequency (PRF) than the side-looking mode b...

Detecting rip currents from satellite imagery offers valuable information for the characterization and assessment of this coastal hazard. While recent advances in deep learning have enabled automatic detection from close-view beach images, the broade...

The Global Navigation Satellite System-Scattering (GNSS-S) radar has a wide coverage and strong concealment, enabling large-scale and long-term monitoring of sea surface targets. However, its signal power is extremely low and susceptible to sea clutt...

Accurate quantification of forest aboveground biomass (AGB) is essential for monitoring terrestrial carbon stocks. While total AGB estimation is widely practiced, resolving component biomass such as canopy, branches, leaves, and trunks enhances the p...

Accurate and stable mapping of crop types is fundamental to agricultural monitoring and food security. However, inter-annual phenological shifts driven by variations in air temperature, precipitation, and sowing dates introduce systematic changes in...

Cloud-based Earth observation platforms, such as data cubes, enable reproducible analyses of long-term satellite time series for climate and urban studies. In parallel, Essential Climate Variables (ECVs) provide a standardised framework for monitorin...

The Arid Region of Northwest China (ARNC) functions as a critical ecological barrier for the Eurasian hinterland. To clarify the non-linear drivers of eco-environmental dynamics, a long-term (2000–2024) Remote Sensing Ecological Index (RSEI) ti...

Unmanned Aerial Vehicle (UAV) imagery has emerged as a critical data source in remote sensing, playing an important role in vehicle detection for intelligent traffic management and urban monitoring. Deep learning–based detectors rely heavily on...

Accurate river network mapping is essential for hydrological modeling, flood risk assessment, and watershed environment management. However, conventional methods based on either optical imagery or digital elevation models (DEMs) often suffer from riv...

Point clouds and digital surface models (DSMs) derived from unmanned aircraft system (UAS) imagery are widely used for plant height estimation in plant phenotyping and precision agriculture. However, comprehensive evaluations across multiple crops, f...

Recent studies have shown that lidar observations from the Ice, Clouds, and Land Elevation Satellite-2 (ICESat-2) enable seasonal snow depth retrieval over land through two primary approaches. The snow-on–off method estimates snow depth by diff...

Hyperspectral image (HSI) classification is pivotal in remote sensing, yet deep learning models, particularly Transformers, remain susceptible to spurious spectral–spatial correlations and suffer from limited interpretability. These issues stem...

Automated recognition of rock mass discontinuities in vegetated high-slope terrains remains a challenging task critical to geohazard assessment and slope stability analysis. This study presents an integrated framework combining close-range UAV photog...

Effective monitoring of maize phenology under stress conditions is crucial for optimizing agricultural management and mitigating yield losses. Crop prediction models constructed from Convolutional Neural Network (CNN) have been widely applied. Howeve...

Forest canopy near-infrared reflectance and mass-based canopy nitrogen concentration (canopy %N) have been shown to be positively correlated. While the mechanisms underpinning this relationship remain unresolved, the broad range of wavelengths involv...

Unmanned Aerial Vehicle (UAV)-captured images are easily affected by various degradations such as motion blur, noise, low illumination, haze, and raindrops in complex environments, and these degradations exhibit significant differences in the frequen...

Accurate estimation of soil moisture (SM) and vegetation optical depth (VOD) is essential for understanding land–atmosphere interactions, climate dynamics, and ecosystem processes. While passive microwave missions such as SMAP and SMOS provide...

Simulated impact multipath (SIM) occurs when forward operators propagate Global Navigation Satellite System (GNSS) radio occultation (RO) signals through strongly nonspherical atmospheric structures, producing multivalued bending angles that cannot b...

Real-Time Precise Orbit Determination (RTPOD) of Low Earth Orbit (LEO) satellites relies primarily on onboard GNSS observations and may suffer from degraded performance when observation geometry weakens or tracking conditions deteriorate within satel...

Citrus, as a globally important economic crop, requires accurate assessment of premium fruit production potential for precise orchard management and enhanced economic benefits. This study develops a method for assessing the production potential of pr...

The photovoltaic (PV) bands on the mid-wave and long-wave infrared (MWIR and LWIR) cold focal plane assemblies of Terra and Aqua MODIS have suffered from gradually increasing electronic crosstalk contamination as both instruments have continued to op...

The scale and magnitude of urban heating are often assessed using Satellite-Derived Land Surface Temperature (SD-LST). Yet, discrepancies in spatial resolution limit SD-LST’s ability to reflect pedestrian thermal experience, potentially leading...

Water quality monitoring is essential for assessing a freshwater ecosystem’s status. This knowledge is indispensable for selecting restoration measures to ensure the provision of ecosystem services and sustainable growth of human communities. R...

Forest management interventions in fire-prone western U.S. forests aim to restore structural heterogeneity, yet tracking treatment efficacy at landscape scales remains a persistent challenge. Traditional monitoring tools often lack the spatial resolu...

Complex-valued convolutional neural networks (CVCNNs) have demonstrated strong capabilities for polarimetric synthetic aperture radar (PolSAR) image classification by effectively integrating both amplitude and phase information inherent in polarimetr...

The survival and growth of mangroves along coastal China is threatened by invasive smooth cordgrass (Spartina alterniflora). Due to the high mortality and frequent replanting of mangrove trees and the impacts of invasive smooth cordgrass, the exact m...

Shadow detection has become a key technology for ground-based moving target indication in video synthetic aperture radar (SAR). However, single-platform video SAR faces the issue of moving-target shadows being occluded. This paper proposes a new dyna...

The mineral-mapping capability of three spaceborne sensors with different spatial and spectral resolutions, the Environmental Mapping and Analysis Program (EnMap), Sentinel-2, and World View-3 (WV3), is assessed regarding bauxite mining wastes in Amp...

In recent years, unmanned aerial vehicles (UAVs) have become increasingly prevalent across diverse application scenarios due to their high maneuverability, compact size, and cost-effectiveness. However, these advantages also introduce significant cha...

Accurate brightness temperature (TB) measurements and microwave emissivity retrieval in passive microwave sensing conventionally rely on absolute radiometric calibration, which often requires additional hardware and complex procedures. Under well-def...

Accurate cotton yield estimation in arid oasis regions faces challenges from landscape fragmentation and the conflict between monitoring precision and computational costs. To address this, we developed a robust integrated framework combining multi-so...

Synthetic Aperture Radar (SAR) has unique advantages in ship monitoring at sea due to its all-weather imaging capability. However, its unique imaging mechanism presents two major challenges. First, speckle noise in the frequency domain reduces the co...

Using the near-space platform to conduct radiometric calibrations of ocean color sensors is a promising method for refining calibration precision, but there is knowledge gap about the radiance contributions above near-space over the open ocean. We us...

Under sparse aperture (SA) conditions, inverse synthetic aperture radar (ISAR) imaging becomes a severely ill-posed inverse problem due to undersampled and noisy measurements, leading to pronounced degradation in azimuth resolution and image quality....

Sea fog is a major meteorological hazard that severely disrupts maritime transportation and economic activities in the South China Sea. As China’s next-generation geostationary meteorological satellite, Fengyun-4B (FY-4B) supplies continuous ob...

Classifying optical water types (OWTs), particularly concerning different phytoplankton bloom types, is critically important because dominant phytoplankton groups govern key marine ecosystem functions and biogeochemical processes, including nutrient...

Accurate registration of optical and synthetic aperture radar (SAR) images is a fundamental prerequisite for multi-source remote sensing data fusion and analysis. However, due to the substantial differences in imaging mechanisms, optical–SAR im...

In building measurement using terrestrial laser scanners (TLSs), acquired 3D point clouds (3DPCs) often contain significant reflection artifacts caused by reflective glass surfaces. Such reflection artifacts significantly degrade the performance of d...

Rapid urbanization complicates the precise, timely quantification of urban spatial morphology. This study examined urban spatial morphology in Fujian Province, integrating DMSP-OLS and NPP-VIIRS nighttime light imagery from 1992 to 2022 to extract th...

Deep learning has significantly advanced PolSAR image processing, with a growing trend of integrating mathematical theories into deep neural networks to enhance their capabilities with regard to complex data. Kolmogorov–Arnold networks (KANs),...

Synthetic Aperture Radar (SAR) and Interferometric SAR (InSAR) techniques have received rapid advance in recent years, and the Multi-temporal InSAR (MT-InSAR) has been widely applied in various earth observations. Distributed scatterer (DS) InSAR is...