Optical change detection is limited by imaging conditions, hindering real-time applications. Synthetic Aperture Radar (SAR) overcomes these limitations by penetrating clouds and being unaffected by lighting, enabling all-weather monitoring when combined with optical data. However, existing heterogeneous change detection datasets lack complexity, focusing on single-scene targets. To address this gap

One of the key solutions to the challenge of collecting training labels for high-resolution remote sensing images is to leverage prior information from historical land cover products, which includes knowledge derived from both same- and low-resolution land cover products (relative to the targeted images). However, employing these products as training labels directly fails to yield encouraging results

Novel Class Discovery (NCD) in 3D semantic segmentation is crucial for applications requiring the ability to learn and segment previously unknown classes in point cloud data, such as autonomous driving and urban planning. Traditional 3D semantic segmentation methods often build upon a fixed set of known classes, which restricts their ability to discover classes not covered in the original training

The texture reconstruction algorithm uses multiview images and 3D geometric surface models as data sources to establish the mapping relationship and texture consistency constraints between 2D images and 3D geometric surfaces to produce a 3D surface model with color reality. The existing algorithms still have challenges in terms of texture quality when faced with dynamic scenes with complex outdoor

Ground ice melt can reshape permafrost environments, with repercussions for Northern livelihoods and infrastructure. However, fine-scale permafrost ground ice products are lacking, limiting environmental change predictions. We propose an InSAR-based approach for estimating ground ice near the permafrost table in sparsely vegetated terrain underlain by continuous permafrost. The Bayesian inversion retrieves

Vehicle detection in infrared aerial imagery is essential for military and civilian applications due to its effectiveness in low-light and adverse scenarios. However, the low spectral and pixel resolution of long-wave infrared (LWIR) results in limited information compared to visible light, causing significant background interference. Moreover, varying thermal radiation from vehicle movement and environmental

Remote-sensing (RS) image compression at extremely low bitrates has always been a challenging task in practical scenarios like edge device storage and narrow bandwidth transmission. Generative models including VAEs and GANs have been explored to compress RS images into extremely low-bitrate streams. However, these generative models struggle to reconstruct visually plausible images due to the highly

The robust and accurate matching of multimodal remote sensing images (MRSIs) is crucial for realizing the fusion of multisource remote sensing image information. Traditional matching methods fail to exhibit effective performance when confronted with significant nonlinear radiometric distortions (NRDs) and geometric differences in MRSIs. To address this critical issue, we propose a novel framework called

Oriented object detection has been rapidly developed in the past few years, but most of these methods assume the training and testing images are under the same statistical distribution, which is far from reality. In this paper, we propose the task of domain generalized oriented object detection, which intends to explore the generalization of oriented object detectors on arbitrary unseen target domains

Satellites continuously capture vast amounts of data daily, including multispectral remote sensing images (MSRSI), which facilitate the analysis of planetary processes and changes. New machine-learning techniques are employed to develop models to identify regions with significant changes, predict land-use conditions, and segment areas of interest. However, these methods often require large volumes

Satellite measurements of the column-averaged dry air mole fraction of carbon dioxide (XCO2) have been successfully employed to quantify anthropogenic carbon emissions under clean atmospheric conditions. However, for some large anthropogenic sources such as megacities or coal-fired power plants, which are often accompanied by high aerosol loads, especially in developing countries, atmospheric XCO2

Time series from optical sensors are frequently used to retrieve phenology information of forests. While SAR (synthetic aperture radar) sensors can potentially provide even denser time series than optical data, their potential to retrieve phenological information of forests is still underexplored. In addition, the backscatter information from SAR is frequently exploited in the same way (e.g., via dynamic

A global event known as Meltwater Pulse 1 A (MWP-1 A) during the last deglaciation contributed to sea level rise by 10 % in just over 0.3 ka. Different methods are being adopted to understand the source of meltwater pulses. This article provides a review of the sea-level fingerprinting technique and its application in understanding meltwater pulses with a specific focus on MWP-1 A. Sea level fingerprinting

The early Ediacaran witnessed the rise of complex macroscopic eukaryotes in the ecosystem including the naissance of metazoans, which may have been triggered by the Neoproterozoic Oxygenation Event. However, local anoxia and/or euxinia likely persisted and dominated in deep waters and restricted environments during this period. Whether and how marine redox changes were related to this evolutionary

With the advancement of artificial intelligence (AI) technologies, vehicle‐mounted mobile monitoring systems have become increasingly integrated into wildlife monitoring practices. However, images captured through these systems often present challenges such as low resolution, small target sizes, and partial occlusions. Consequently, detecting animal targets using conventional deep‐learning networks

The gravitational field of a planetary body is most often modeled by an exterior spherical harmonic series, which is uniformly convergent outside the smallest mass-enclosing sphere centered at the origin of the coordinate system, known as the Brillouin sphere. The model can become unstable inside the spherical boundary. Rarely deliberated or emphasized is an obvious fact that the radius of the Brillouin

Satellite-derived actual evapotranspiration (ETa) maps are essential for the development of innovative water management strategies. Over the past decades, multiple novel satellite remote sensing-based surface energy balance (SEB) ETa modeling tools have been widely used to account for field-scale crop water use and irrigation monitoring. However, their predictive capabilities for intensively irrigated

Tomographic synthetic aperture radar (TomoSAR) technology, as an extension of interferometric SAR (InSAR), solves the layover problem and realizes three-dimensional (3-D) imaging. Now, it is an important research direction in the field of radar imaging. However, TomoSAR usually requires the SAR sensor to make enough acquisitions at different spatial locations to achieve high-quality 3-D imaging, which

Street view images (SVIs) coupled with computer vision (CV) techniques have become powerful tools in the planning and related fields for measuring the built environment. However, this methodology is often challenging to be implemented due to challenges in capturing a comprehensive set of planning-relevant environmental attributes and ensuring adequate accuracy. The shortcomings arise primarily from

The Neoproterozoic Era (1000–539 Ma) saw extreme changes in climate and biogeochemical cycles, but the drivers of these changes remain poorly understood. In this paper, we extend the Spatial Continuous Integration (SCION) global climate-biogeochemical model beyond the Phanerozoic and into the Neoproterozoic using a set of GCM simulations to update the model's climate emulator and a plate tectonic model

For a rapid retrieval of zenith wet delay (ZWD) and multi-global navigation satellite system (GNSS) precise point positioning (PPP) enhancement, a lightweight ZWD retrieval model was constructed by combining ground-based GNSS observations and precipitable water vapor (PWV) data provided by the European Center for Medium-Range Weather Forecasts Reanalysis (ERA5). The proposed model can rapidly produce

Cyanobacteria blooms are a threat to water quality of lakes and reservoirs worldwide, requiring scalable monitoring solutions. Existing approaches for remote sensing of cyanobacteria focus on quantifying (accessory) photosynthetic pigment to map surface accumulations. These approaches have proven challenging to validate against in situ observations, limiting uptake in water quality management. Optical

The Synthetic Aperture Radar (SAR) altimeter is an active sensor, which is widely used in satellite microwave remote sensing. It can be also used for geophysical localization by evaluating the similarity between the acquired terrain profile and the prior data. However, typical factors, such as the linear assumption of terrain, high variation of the ground elevation, and wide beam width will degrade

Light Detection and Ranging (LiDAR) technology has demonstrated significant effectiveness in forest remote sensing. Terrestrial Laser Scanning (TLS) and Drone Laser Scanning (DLS) systems reconstruct forest point clouds from distinct perspectives. However, a single-platform point cloud is insufficient for a comprehensive reconstruction of multi-layered forest structures. Therefore, registration of

The United Nations' sustainable development goal to designate 30% of the oceans as marine protected areas by 2030 requires practical management tools, and in turn ecologically meaningful mapping of the seafloor. Particularly challenging is the mesophotic zone, a critical component of the marine system, a biodiversity hotspot, and a potential refuge. Here, we introduce a novel seafloor habitat management

Aerial photogrammetry is a popular non‐invasive tool to measure the size, body morphometrics and body condition of wild animals. While the method can generate large datasets quickly, the lack of efficient processing tools can create bottlenecks that delay management actions. We developed a machine learning algorithm to automatically measure body morphometrics (body length and widths) of southern right

Global Navigation Satellite System (GNSS) products are an integral part of a wide range of scientific and commercial applications. The creation of such products requires processing software capable of solving a combined station position and GNSS satellite orbit estimation by least squares adjustment, also known as global GNSS processing. Such processing is routinely performed by the International GNSS

Cold-water coral (CWC) mounds are commonly found in the Alboran Sea (westernmost Mediterranean), specifically in the so-called East and West Melilla mound provinces. This study presents a multi-proxy analysis on the environmental changes that occurred in west Melilla since the last deglaciation (∼14 kyr), based on the on-mound core MD13–3451 (∼370 m). The analyses performed include geochemical measurements

An integrated high-resolution climate framework is reconstructed at the Monte San Nicola GSSP section (Southern Sicily), the type-section for the Lower Pleistocene Gelasian Stage. Our multiproxy record is based on planktonic foraminiferal stable oxygen isotopes, alkenones and calcareous plankton assemblages, focusing on the interval between ∼2.7 and 2.5 Ma, which includes the Gelasian GSSP. Calcium

Acoustic recordings have emerged as a promising tool to monitor nocturnal bird migration, as it can uniquely provide species‐level detection of migratory movements under the darkness of the night sky. This study explores the use of acoustics to quantify nocturnal bird migration across Europe, a region where research on the topic remains relatively sparse. We examine three migration intensity measures

Coral bleaching, often triggered by oceanic warming, has a devastating impact on coral reef systems, resulting in substantial alterations to biodiversity and ecosystem services. For conservation management, an effective technique is needed to not only detect and monitor coral bleaching events but also to predict their severity levels. By combining high‐resolution satellite measurements (Sentinel‐2

Errors in ocean tide and non-tidal atmospheric and oceanic models are among the largest error sources in gravity field recovery from space. We co-estimate corrections to these background models subject to uncertainty constraints during the adjustment procedure of gravity field spherical harmonic coefficients. Simulations are performed for the Mass-Change and Geoscience International Constellation to

The stochastic modelling of a finite mass distribution can provide a new perspective on the dynamic evaluation of time variable gravity fields. The algorithm for estimating variations of spherical harmonic coefficients implied by corresponding shape changes is implemented for the first-order derivatives of the gravitational potential. The described algorithm uses the spherical harmonic synthesis formula

The relationship of marine productivity in the Southern Ocean (SO) to dynamics of the Antarctic icesheet (AIS) since the Late Pleistocene remains uncertain. Here, we investigated Late Pleistocene variation in productivity in the Antarctic Zone of the SO, and the response of the AIS to various potential forcing mechanisms. We analyzed secular variation in multiple biogenic components and ice-rafted

Temperature variability refers to temperature fluctuations around the mean state, which closely connects with the probability of extreme events. Examining temperature variability during past warm periods in Earth's history is essential for elucidating its future trends. We quantitatively analyze the interannual variability changes in near-surface air temperature and associated mechanisms during the

Multi-View Photometric Stereo (MVPS) is a popular method for fine-detailed 3D acquisition of an object from images. Despite its outstanding results on diverse material objects, a typical MVPS experimental setup requires a well-calibrated light source and a monocular camera installed on an immovable base. This restricts the use of MVPS on a movable platform, limiting us from taking MVPS benefits in

Reducing the reliance of remote sensing semantic segmentation models on labeled training data is essential for practical model deployment. Self-supervised pre-training methods, which learn representations from unlabeled data by designing pretext tasks, provide an approach to address this requirement. One inconvenience of the currently contrastive learning-based and masked image modeling-based self-supervised

The Equatorial Atlantic Gateway (EAG) was critical to Earth's climatic and oceanographic evolution during the Mesozoic, yet its early opening history remains enigmatic. Here, we present new 2D seismic reflection data and biostratigraphic ages from DSDP Site 367, integrated with tectonic reconstruction models, to constrain the sedimentary response to the evolution of the gateway. Seismic analysis reveals

Land Surface Temperature (LST) is a vital meteorological variable for assessing hydrological, ecological, and climatological dynamics, as well as energy exchanges at the land–atmosphere interface. Accurate and frequent LST measurement is essential for meteorological satellites. However, existing retrieval algorithms often fail to capture the nuances of diurnal temperature variations. This study utilizes

Tropospheric delays present a significant challenge to accurately mapping the Earth’s surface movements using interferometric synthetic aperture radar (InSAR). These delays are typically divided into stratified and turbulent components. While efforts have been made to address the stratified component, effectively mitigating turbulence remains an ongoing challenge. In response, this study proposes a

InSAR is an effective tool for indirectly monitoring large-scale hydrological-thermal dynamics of the active layer and permafrost by detecting the surface deformation. However, the conventional time-series models of InSAR technology do not consider the distinctive and pronounced seasonal characteristics of deformation over permafrost. Although permafrost-tailored models have been developed, their performance

This paper introduces a novel task of visual grounding for high-resolution synthetic aperture radar images (SARVG). SARVG aims to identify the referred object in images through natural language instructions. While object detection on SAR images has been extensively investigated, identifying objects based on natural language remains under-explored. Due to the unique satellite view and side-look geometry

A large quantity of CO2 produced in the Earth's interior is emitted to the atmosphere via soil diffusion, especially in active tectonic areas. Due to the lack of extensive in situ measurements, however, estimations of soil CO2 output have been poorly constrained thus far, leading to the perception that soil CO2 seems to be a marginal source of global carbon emissions. Here, the contribution of soil

Bounding box and point annotations are widely used in deep learning-based animal detection from remote sensing imagery, yet their impact on model performance and training efficiency remains insufficiently explored. This study systematically evaluates the influence of these two annotation methods using aerial survey datasets of African elephants and antelopes across three commonly employed deep learning

Due to the harsh construction environment of tunnels, the visual system must be fitted with a sphere cover of a certain thickness. The visual system with an optical sphere cover invalidates conventional measurement methods. Therefore, this paper provides a comprehensive visual measurement method using spherical glass refraction. First, the spherical glass refraction imaging is modeled using a geometry-driven

This work presents an approach to generating LiDAR point clouds with empirical intensity data on a massively parallel scale. Our primary aim is to complement existing real-world LiDAR datasets by simulating a wide spectrum of attributes, ensuring our generated data can be directly compared to real point clouds. However, our emphasis lies in intensity data, which conventionally has been generated using

Image–text retrieval (ITR) is crucial for making informed decisions in various remote sensing (RS) applications, including urban development and disaster prevention. However, creating ITR datasets that combine vision and language modalities requires extensive geo-spatial sampling, diverse categories, and detailed descriptions. To address these needs, we introduce the LuojiaHOG dataset, which is geospatially

Marine heatwaves are prolonged periods of anomalously high water temperatures and have significant ecological and economic impacts. While these events are well-documented in open ocean systems, their characteristics and drivers in estuarine environments, particularly within upwelling systems, are less understood. This study analyzes estuarine marine heatwaves (EMHWs) in the Ría de Arousa, a productive

Hainan Island, situated at the northern margin of the South China Sea, is highly vulnerable to typhoon impacts, particularly super typhoons, which have caused catastrophic damage. Despite their persistent threats, research on these extreme events remains limited. At the Xiaodonghai Fringing Reef (XFR) in southern Hainan Island, coral boulders (CBs) distributed across the reef flat are interpreted as

To assess the ability of a satellite instrument to detect submerged targets, we constructed a semi-analytical relationship to link target reflectance and the contrast threshold of the satellite instrument to visibility ranges. Using numerical simulation, we found that the contrast threshold of the satellite instrument was equal to 50 % of the residual error contained in satellite Rrs data. We evaluated

Knowledge distillation (KD) has become a promising technique for obtaining a performant student detector in remote sensing images by inheriting the knowledge from a heavy teacher detector. Unfortunately, not every pixel contributes (even detrimental) equally to the final KD performance. To dispel this problem, the existing methods usually derived a distillation mask to stress the valuable regions during

To investigate paleo-ocean environmental variations during the Paleocene Eocene Thermal Maximum (PETM), we conducted a geochemical analysis of high-resolution sediment samples from ODP Site 1267 in the Southwest Atlantic. Our results suggest that volcanic materials may have been introduced into the ocean prior to the negative carbon isotope excursion (CIE), while terrestrial debris became the predominant

This contribution analyzes the processes related to the development of small, temporary, non-floodplain wetlands (NFWs) of neotectonic origin in the loessic North Pampa Plain under wet conditions. The study focuses on the Vila-Cululú upstream sub-basin (973 km2), a tributary of the Salado River belonging to the Paraná River basin. Under wet conditions, the flat landscape influences the surface runoff

Exploring the intricate dynamics of organic carbon (OC) in marine sediments is essential for understanding global carbon cycling. This study examines how abrupt climate events, monsoonal variations, and mineral composition influence the delivery and burial of terrestrial organic matter (OM) in the western Bay of Bengal (BoB), focusing on the Bengal Fan (BF). Grain size and mineralogical composition

The Huai River Basin, located in the transition zone between the Yangtze and Yellow River basins, plays a significant role in the integration and development of Neolithic cultures. Lack of precipitation reconstructions from the Huai River Basin limits our knowledge on patterns and mechanisms of precipitation in the East Asian monsoon (EAM) region during the 4.2 ka event, which also hinders our understanding

Marine organic matter (OM) plays a crucial role in regulating global carbon cycling and climate change; however, its significance is often underestimated or even overlooked due to the relatively low proportion of organic carbon (OC) within marine carbon pool and the insufficient documentation of coupled relationships between marine OM processes and atmospheric CO2 changes during major climatic events

The current digital elevation model super-resolution (DEM SR) methods are unstable in regions with significant spatial heterogeneity. To address this issue, this study proposes a regional DEM SR method based on an ensemble learning strategy (ELSR). Specifically, we first classified geographical regions into 10 zones based on their terrestrial geomorphologic conditions to reduce spatial heterogeneity;

Hyperspectral reflectance as well as thermal infrared emittance unmanned aerial vehicle (UAV)-borne imagery are widely used for determining plant status. However, they have certain limitations to distinguish crops subjected to combined environmental stresses such as nitrogen and water deficiencies. Studies on combined stresses would require a multimodal analysis integrating remotely sensed information