2025 November-2 - 124 articles
Accurately assessing grazing impacts is essential for sustaining alpine grasslands. Conventional approaches often rely on total forage productivity, an indirect and uncertain proxy for forage availability. In this study, we propose a novel framework...
Monitoring the horizontal distribution of PM2.5 within urban areas is of great significance, not only for environmental management but also for providing essential data to understand the distribution, formation, transport, and transformation of PM2.5...
Sand dune movements represent a critical global environment challenge. While previous studies have mainly focused on horizontal deformation, this study applies the bistatic InSAR technique to reconstruct high-precision digital elevation models (DEMs)...
Ground deformation data are crucial for understanding the processes driving volcanic unrest. The current unrest at the Campi Flegrei caldera, Italy, presents a significant challenge, primarily due to the presence of inconsistencies between seismic da...
Soil salinity severely constrains agricultural productivity and soil health, particularly in arid and semi-arid regions. Conventional salinity assessment methods are labor-intensive, time-consuming, and spatially limited. This study developed a data-...
Radar altimeters receive radio-wave reflections from nadir and determine surface parameters from the strength and shape of the return signal. Over the oceans, the strength of the return, termed sigma0 (
Synthetic Aperture Radar (SAR) images generated via range-Doppler (RD) model-based geometric correction often suffer from non-negligible systematic geolocation errors due to cumulative impacts of platform positioning inaccuracies, payload time synchr...
Object detection in high-resolution satellite imagery is a critical technology for various applications, yet it faces persistent challenges due to extreme variations in object scale, orientation, and density. The development of numerous public datase...
Accurate rainfall estimation remains a critical challenge in hydrology, particularly in Italy, where complex topography and uneven rain-gauge distribution introduce major uncertainties. To address this gap, this study assessed five widely used satell...
A truly operational SAR was first flown in space in 1978 on board the Seasat satellite. Since then, its utilization first expanded to Canada, Europe and Japan, and then to countries with emerging space industries later on. Many technological developm...
The performance of remote sensing object detectors often degrades severely when deployed in new operational environments due to covariate shift in the data distribution. Existing evaluation paradigms, which primarily rely on aggregate performance met...
In recent years, the continuous improvement of remote sensing technologies has
profoundly strengthened our capacity to investigate the active deformation of the Earth’s
crust [1] [...]
The clumping index (CI) describes the level of foliage grouping relative to the random distribution within the canopy. It plays a vital role in the derivation of other important parameters (e.g., the leaf area index, (LAI)) that are usually employed...
Infrared–visible-oriented object detection aims to combine the strengths of both infrared and visible images, overcoming the limitations of a single imaging modality to achieve more robust detection with oriented bounding boxes under diverse en...
Recent advances in UAV (Unmanned Aerial Vehicle)-based remote sensing have significantly enhanced the efficiency of monitoring and managing agricultural and forested areas. However, the low-altitude and narrow-field-of-view characteristics of UAVs ma...
Urban heat island pose a significant threat to public health and urban livability. UHI maps are created using satellite thermal data, a crucial source for earth monitoring and for delivering mitigation strategies. Nowadays there is still a resolution...
Soil temperature (ST) is a key regulator of crop growth, microbial activity, and soil biogeochemical processes, making its accurate estimation critical for agricultural monitoring. Focusing on the Huang-Huai-Hai (HHH) Plain, a major grain-producing r...
Sea surface temperature (SST) gradients modulate surface wind variability at the mesoscale O(100 km), with relevant impacts on surface fluxes, rainfall, cloudiness and storms. The dependence of the SST-wind coupling mechanisms on physical environment...
The performance of deep learning models largely depends on the scale and quality of training data. However, acquiring sufficient, high-quality samples for specific observation scenarios is often challenging due to high acquisition costs. Unlike optic...
Accurate and large-scale crop mapping is crucial for global food security, yet its performance is often hindered by domain shift when models trained in one region are applied to another. This is particularly challenging in cross-continental scenarios...
The multi-source fusion localization algorithm demonstrates advantages in achieving continuous localization. However, its reliability and robustness could not be guaranteed and still with some insufficiencies in complex environments, especially for s...
Phytoplankton are fundamental to sustaining marine ecosystems and significantly influence the global carbon cycle. However, identifying their types accurately from satellite imagery remains a challenge. This study presents machine learning approaches...
This paper presents the findings related to the design solution options for a next-generation C-band Synthetic Aperture Radar (SAR) mission, developed to address the Harmonized User Needs (HUN) in Earth observation (EO) data as defined by several dep...
High-resolution LiDAR-derived three-dimensional (3D) digital outcrop models are crucial for detailed geological analysis. However, their massive data volumes often exceed the rendering and memory capacities of standard computer systems, posing signif...
The Antarctic ice sheet, Earth’s largest ice mass, is vital to the global climate system. Analyzing its thermal behavior is crucial for sea-level projections and ice shelf assessments; however, internal temperature studies remain challenging du...
Accurate, timely maps of post-fire burn severity are vital for rehabilitation, hydrologic hazard assessment, and ecosystem recovery in the western United States, where large, frequent wildfires and steep environmental gradients challenge model genera...
Lake water level variation, reflecting the dynamic balance between water input and loss, is a crucial indicator of climate change and regional hydrological cycles. This is particularly significant in arid Central Asia, where lakes are vital surface w...
Channel geometry, e.g., riverbed elevation and channel width, is the fundamental input for hydrodynamic simulations and conveys critical information for understanding fluvial processes. In remote or data-scarce areas, however, traditional field surve...
Accurate global crop type mapping is essential to ensure food security. However, large-scale crop-type mapping still poses challenges to commonly used classification strategies. Specifically, variation within crop types downgrades classification perf...
Aboveground biomass (AGB) is a key indicator reflecting the metabolic mechanisms of wetland plants. This study simulated the AGB of multi-community in Poyang Lake (PYL) wetland based on long-term high-resolution (30 m, 8 d) NDVI fused from MODIS and...
Triple/N-way collocation is a statistical analysis tool used to estimate the individual error variances of simultaneous observations of a physical quantity by three or more distinct systems. The tool is widely used to validate remote sensing data pro...
Hyperspectral imagery (HSI), characterized by the integration of both spatial and spectral information, is widely employed in various fields, such as environmental monitoring, geological exploration, precision agriculture, and medical imaging. Hypers...
Detection of small objects in aerial images captured by Unmanned Aerial Vehicles (UAVs) is a critical task in remote sensing. It is vital for applications like urban monitoring and disaster assessment. This task, however, is challenged by unique view...
Remote sensing video (RSV) provides continuous, high spatiotemporal earth observations that are increasingly important for environmental monitoring, disaster response, infrastructure inspection and urban management. Despite this potential, operationa...
To further achieve high-precision localization of lightning radiation sources and clear imaging of lightning plasma channel structures under existing radar systems, this study proposes a lightning localization and imaging method that combines the Ele...
Pansharpening seeks to reconstruct a high-resolution multi-spectral image (HR-MSI) by integrating the fine spatial details from the panchromatic (PAN) image with the spectral richness of the low-resolution multi-spectral image (LR-MSI). In recent yea...
Remote sensing image segmentation is essential for resource planning and disaster monitoring. Although RGB-based methods are widely adopted, they often exhibit suboptimal performance in distinguishing objects with similar color and texture characteri...
In Northern Croatia, the stability of slopes is increasingly compromised by a combination of anthropogenic pressures, seismic activity, and climate-driven changes in precipitation patterns. This study presents an integrated, multi-level investigation...
Accurate 3D object detection and localization in LiDAR point clouds are crucial for applications such as autonomous driving and UAV-based monitoring. However, existing detectors often suffer from the loss of critical geometric information during netw...
With an area of 13,000 km2 and more than 60 outlet glaciers (tidewater or lake-terminating), the Southern Patagonian Icefield (SPI) stores a substantial volume of freshwater, and its accelerating melt contributes to global sea level rise. In addition...
A major scientific challenge is understanding how precipitation systems will change under global warming. In particular, extreme precipitation events associated with hail and graupel are of significant concern. In this study, we evaluated the perform...
To enhance the comprehensive representation capability and fusion accuracy of remote sensing information, this paper proposes a multi-resolution hierarchical fusion network (MRLF) tailored to the heterogeneous characteristics of optical and synthetic...
Remotely sensed nighttime lights (NTLs) have become essential in urban and environmental research but are typically captured at fixed local times by sun-synchronous satellites, limiting their ability to capture changes throughout the night. In contra...
The use of Uncrewed Aerial Systems (UASs) for remote sensing applications has increased significantly in recent years due to their low cost, operational flexibility, and rapid advancements in sensor technologies. In many cases, UAS platforms are cons...
Accurate extraction of land cover information and effective classification strategies are crucial for reliable wetland mapping. Data-driven approaches, such as convolutional neural networks (CNNs), demonstrate strong capability in modeling complex no...
The ongoing launch of high-resolution satellites has led to a significant increase in the volume of synthetic aperture radar data, resulting in a high-resolution and high-revisit Earth observation that efficiently supports subsequent high-resolution...
The acquisition of maize tassel phenotypic information is important for studying maize growth and improving yield. Unfortunately, tassel detection remains challenging due to complex field conditions, including image resolution, light variation, culti...
Accurate segmentation of coastal aquaculture ponds from high-resolution remote sensing images is critical for applications such as coastal environmental monitoring, land use mapping, and infrastructure management. Semi-supervised learning (SSL) has e...
With the rapid development in semantic segmentation in remote sensing (RS) imagery, the balance between segmentation accuracy and computational efficiency has been recognized as a key challenge. Most existing approaches have predominantly been based...
In underground mining environments, 4D mmWave radar performance is severely constrained by ghost noise issues resulting from multipath reflections, metal structure interference, and complex terrain, creating significant challenges for target detectio...
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