2025 May-2 - 147 articles
Utilizing 58 Landsat-7 images taken over 10 years, the current study investigated the relationship between the mitigation of surface urban heat islands (SUHIs) by NbSs (Nature-based Solutions) and influential variables such as physical variables of N...
Efficient and realistic large-scale scene modeling is an important application of low-altitude remote sensing. Although the emerging 3DGS technology offers a simple process and realistic results, its high computational resource demands hinder direct...
The codesign of the receive filter and transmit waveform under similarity constraints is one of the key technologies in frequency diverse array multiple-input multiple-output (FDA-MIMO) radar systems. This paper discusses the design of constant modul...
Direct Position Estimation (DPE) is an alternative GNSS positioning method that models received satellite signals as a function of the receiver’s navigation state, allowing for the direct estimation of position, velocity, and time within the na...
Microwave sounding observations obtained from the National Oceanic and Atmospheric Administration (NOAA) and the European Meteorological Operational Satellite Program (METOP) satellites have been used for retrieving the cloud ice water path (IWP). Ho...
The evolution of meteorological environments in global arid and semi-arid regions has significant impacts on regional ecological security and global climate regulation [...]
The semantic segmentation of remote sensing images is vital for accurate surface monitoring and environmental assessment. Multimodal remote sensing images (RSIs) provide a more comprehensive dimension of information, enabling faster and more scientif...
The combination of GNSS RTK and INS offers complementary advantages but faces significant challenges in urban canyons. Frequent cycle slips in carrier phase measurements and ambiguity resolution algorithms increase computational burden without improv...
Frequency agile radar (FAR) exhibits robust anti-jamming capabilities and a superior low probability of intercept performance due to its randomized carrier frequency (CF) and pulse repetition frequency (PRF) hopping sequences. The advent of compresse...
The Po Valley is depicted in the literature as a region with one of the most severe air pollution profiles in Europe, frequently exceeding the permitted statutory concentration limits for several air pollutants. The aim of this paper is to present an...
The urban heat island (UHI) effect significantly impacts urban environments and quality of life, yet research comparing coastal and inland cities is relatively lacking. This study, using the MYD11A2 dataset, analyzes the spatiotemporal evolution of l...
Dissolved organic carbon (DOC) is an essential form of carbon in lakes and has significant impact on thermal structure and carbon source-supporting food webs. Current remote sensing studies on DOC mainly focus on the retrieval of surface concentratio...
Cross-Track Interferometric Synthetic Aperture Radar (XTI-SAR) can utilize variations in interferometric phase to measure sea surface velocity along radar radial direction and sea surface height, which can be used for ocean wave parameter inversion....
The water status of plants is affected by abiotic and biotic environmental factors and influences the growth and yield formation of crops. Assessment of the leaf water content (LWC) of grapevine using hyperspectral imaging (1000–2500 nm) was in...
Deep learning-based methods for real-time small target detection are critical for applications such as traffic monitoring, land management, and marine transportation. However, achieving high-precision detection of small objects against complex backgr...
Remote sensing imaging technology is one of the safest and most effective tools for gas leakage monitoring in chemical parks, as it enables fast and accurate access to detailed information about the gas cloud (e.g., volume, distribution, diffusion, a...
The coupling coordination relationship between ecosystem services supply–demand and human well-being in arid inland regions is increasingly vulnerable to imbalance risks under the combined pressures of climate change and intensified anthropogen...
A comprehensive geophysical survey, combining magnetic gradiometry and ground-penetrating radar (GPR), was undertaken at the Roman fort of Aquis Querquennis to map buried archaeological structures, including potential walls and internal divisions, wi...
The bistatic frequency diverse array (FDA) radar system is designed to exploit the beam autoscanning of FDA radar, providing a novel solution to address spatial synchronization challenges in bistatic radar architecture, unleashing bistatic radar&rsqu...
Accurate early-season crop classification is critical for food security, agricultural applications and policymaking. However, when classification is performed earlier, the available time-series data gradually become scarce. Existing methods mainly fo...
Limited labels and detailed changed land-cover interpretation requirements pose challenges for time-series PolSAR change monitoring research. Accurate labels and supervised models are difficult to reuse between massive unlabeled time-series PolSAR da...
Understanding tidal changes and their potential forcing mechanisms enables a better assessment of non-stationary tidal effects for projecting extreme sea levels and nuisance flooding. In this study, we investigate the seasonal and interannual changes...
Understanding vegetation’s drought response helps predict ecosystem adaptations to climate change and offers scientific insights for managing extreme climate events. Using RS technology, this study systematically investigates the response mecha...
As a vital part of the Eurasian temperate grassland, the Chinese temperate grassland is primarily distributed in the Inner Mongolia Plateau. This paper focuses on mapping temperate grassland dynamics from the 1980s to the 2010s in Inner Mongolia, whi...
Seasonally dynamic plant communities present challenges for remote mapping, but estimating phenology can help identify periods of peak spectral distinction. While phenology is widely used in environmental and agricultural mapping, its broader ecologi...
As the frequency of strong storms and cyclones increases, understanding wind risk in both existing and newly established plantation forests is becoming increasingly important. Recent advances in the quality and availability of remotely sensed data ha...
With the increasing complexity of the electromagnetic environment, radar waveform design needs to break through the limitation of traditional single-function architectures, prompting the emergence of integrated radar waveforms. Currently, the mainstr...
Ocean color remote sensing technology has proven to be an indispensable tool for monitoring ocean conditions, as it has consistently provided critical data on global ocean optical properties, color, and biogeochemical parameters over several decades....
Cross-calibration is an essential technique for calibrating Earth observation satellite sensors, which involves taking nearly simultaneous images of a ground target to compare an uncalibrated sensor to a well-calibrated reference sensor. This study i...
Disparity estimation in satellite stereo images is a highly challenging task due to complex terrain, occlusions caused by tall buildings and structures, and texture-less regions such as roads, rivers, and building roofs. Recent deep learning-based sa...
Tropospheric delay is the main error source that affects the further improvement of the accuracy of space geodesy. High-precision zenith tropospheric delay (ZTD) can be used as a prior value for precise point positioning (PPP) in global navigation sa...
Despite the growing availability of very-high-resolution (VHR) remote sensing imagery, extracting fine-grained urban features and materials remains a complex task. Land use/land cover (LULC) maps generated from satellite imagery often fall short in p...
The rapid progress of deep learning has significantly enhanced the development of ship detection using synthetic aperture radar (SAR). However, the diversity of ship sizes, arbitrary orientations, densely arranged ships, etc., have been hindering the...
The accurate detection of mowing events is important in many applications, including in agricultural contexts such as yield and fodder production, as well as biodiversity assessments, habitat modeling, and protected area monitoring. This work present...
Drone object detection serves as a fundamental task for more advanced applications. However, drone images typically exhibit challenges such as small object sizes, dense distributions, and high levels of overlap. Traditional object detection networks...
Satellite observations provide broad spatial coverage of complex coastal environments but may lack temporal resolution to capture rapid changes in these dynamic systems. This study explores the potential of the recently installed NASA Aerosol Robotic...
The single-site lightning detection system can provide timely and effective information on lightning activity in areas where a multi-site lightning network cannot be built. Using deep learning, the single-site lightning detection achieves better perf...
Photosynthetically Active Radiation (PAR) is a critical indicator of plant photosynthesis and ecosystem energy flux. Accurate measurement of PAR is essential in agriculture, ecological research, and environmental management. This paper reviews recent...
This study developed machine learning models using different algorithms, including support vector machine (SVM), random forest (RF), and backpropagation neural network (BPNN), to estimate the critical frequency of the F2 layer (foF2) and the maximum...
Soil moisture (SM) estimates are essential for drought monitoring, hydrological modeling, and climate resilience planning applications. While satellite and model-derived SM products effectively capture SM dynamics, their coarse spatial resolutions (~...
Recent advancements in deep-learning and computer vision technologies, coupled with the availability of large-scale remote-sensing image datasets, have accelerated the progress of remote-sensing object detection. However, large-scale remote-sensing i...
The monitoring and assessment of forest conditions has traditionally relied on continuous forest inventory (CFI) plots, where all plot trees are regularly measured at discrete locations, then plots are grouped as representative samples of forested ar...
The Global Navigation Satellite System (GNSS) has emerged as a critical spatiotemporal infrastructure for ensuring the integrity of remote sensing data links. However, traditional GNSS antenna arrays, typically configured with the antenna spacing of...
Climate change is threatening the sustainability of crop yields due to an increasing frequency of extreme weather conditions, requiring timely agricultural monitoring. Remote sensing facilitates consistent and continuous monitoring of field crops. Th...
Leaf biochemical traits are crucial for understanding plant physiological status and ecological dynamics. Partial least squares regression (PLSR) models have been widely used to estimate leaf biochemical traits from spectral reflectance information....
The Arctic is the fastest-warming region on Earth, exhibiting a pronounced “amplifying effect”, which has triggered widespread permafrost thaw and increased the risk of surface deformation. In the Arctic coastal lowlands, permafrost is al...
The particle absorption coefficient of pigments (aph(λ)), a critical indicator of phytoplankton spectral absorption properties, is essential for bio-optical models and water quality monitoring. To enhance the accuracy of aph(λ) retrieva...
Accurate prediction of the spatiotemporal distribution of chlorophyll-a (Chl-a) is essential for evaluating marine ecosystem health and predicting ecological disasters. Current methods struggle to capture short-term variability and periodic trends in...
Predicting macroseismic intensity from instrumental ground motion parameters remains a complex task due to the nonlinear relationship with observed damage patterns. An explainable machine learning model based on the XGBoost algorithm was developed to...
This study highlights the importance of incorporating temporal landscape dynamics in landslide susceptibility assessments (LSAs). Two models are compared: one integrates multi-temporal data, while the other relies solely on present conditions. Artifi...
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