The Bolivian Amazon foreland basin is controlled by i) the sediment input from the Andes and the topographic load; ii) the basinal uplift in the forebulge; and iii) the uplifting Fitzcarrald Arch in the north. These boundary conditions should lead to bending and active faulting of the crust. However, although the presence of active faults has been inferred from geomorphological studies, neither seismicity

One of the main tasks of Very Long Baseline Interferometry (VLBI) is the rapid determination of the highly variable Earth’s rotation expressed through the difference between Universal Time UT1 and Coordinated Universal Time UTC (dUT1). For this reason, dedicated one hour, single baseline sessions, called “Intensives”, are observed on a daily basis. Thus far, the optimal geometry of Intensive sessions

Molecular clocks suggest that at least some heterotrophic protists, such as foraminifera, testate amoebae and tintinnids, were present in ecosystems as far back as 1000 Ma. Vase-shaped microfossils of earlier testate amoebae from the supergroups Amoebozoa and Rhizaria occur globally in pre-Sturtian shallow marine deposits, and have thus been considered as a potential biostratigraphic tool, especially

Quantification of soil stocks and ecosystem services (ES) under changing climate need time series of climate and soil data. Here, we propose a new approach combining process-based soil and climate simulations to obtain such time series, and apply it onto a recent interglacial, Marine Isotope Stage (MIS) 5e. We combined the LOVECLIM climate model and the SoilGen2 soil evolution model to simulate soil

With the increasing demand of autonomous systems, pixelwise semantic segmentation for visual scene understanding needs to be not only accurate but also efficient for potential real-time applications. In this paper, we propose Context Aggregation Network, a dual branch convolutional neural network, with significantly lower computational costs as compared to the state-of-the-art, while maintaining a

Alpine glaciers in the low- and mid-latitudes respond more quickly than large polar ice sheets to changes in temperature, precipitation, cloudiness, humidity, and radiation. Many high-altitude glaciers are monitored by ground observations, aerial photography, and satellite-borne sensors. Regardless of latitude and elevation, nearly all nonpolar glaciers and ice caps are undergoing mass loss, which

A continuously operating GNSS station within a lake interior is uncommon, but advantageous for testing the GNSS Interferometric Reflectometry (GNSS-IR) technique. In this research, GNSS-IR is used to estimate ten years of lake surface heights for Lake Taupō in New Zealand. This is achieved using data collected from station TGHO, approximately 4 km from the lake’s shoreline. Its reliability is assessed

ABSTRACT In this study, the relationship between vegetation dynamics and atmospheric patterns over Iceland from 2001-2019 has been assessed using remote sensing. This study is based on MODIS NDVI images, NCEP/NCAR reanalysis dataset and values of the North Atlantic Oscillation (NAO). The results show that the vegetation coverage in Iceland reaches a maximum in the period from the middle of July to

ABSTRACT Forage supply of savanna grasslands plays a crucial role for local food security and consequently, a reliable monitoring system could help to better manage vital forage resources. To help installing such a monitoring system, we investigated whether in-situ hyperspectral data could be resampled to match the spectral resolution of multi- and hyperspectral satellites; if the type of sensor affected

The polar format algorithm (PFA) is a frequency-domain algorithm with high computational efficiency especially for the high resolution imaging applications in wide-angle staring synthetic aperture radar (WAS-SAR). Because of the image distortion and defocus caused by plane wave assumption, the effective imaging scene is limited to a small region which is defined as the defocus-negligible region. In

Higher resolution building mapping from lower resolution remote sensing images is in great demand due to the lack of higher resolution data access, especially in the context of disaster assessment. High resolution building layout map is crucial for emergency rescue after the disaster. The emergency response time would be reduced if detailed building footprints were delineated from more easily available

Lake surface water temperature (LSWT) is an important factor of water ecological environment. As global warming, LSWT is also on the rise. Research on the main reasons of LSWT rising is the basis for controlling and improving the regional ecological environment. However, it is difficult for the existing remote sensing images to take into account the temporal and spatial resolution. Low-resolution images

High-frequency surface wave radar (HFSWR) is of great significance for maritime detection, but in the HFSWR echo signal, ship targets are often submerged in a variety of clutter and interference, making it difficult to detect vessels. In this paper, we propose an intelligent detection algorithm for targets concealed in strong clutter and complex interference environments. The algorithm has two stages:

Robust fitting is a basic technique and has been widely applied in photogrammetry and remote sensing, such as geometric correction. As known, typical robust estimators (include M-estimators, S-estimators, MM-estimators, etc.) often fail when outlier rate is higher than 50%, even if the outliers are uniformly distributed. In this article, we propose simple yet effective estimators, called adaptive M-estimators

In this article, we address the super-resolution problems, which estimate the high-resolution multispectral images from the multispectral Sentinel-2 (S2) images with different resolutions. Since S2 images can be naturally represented by tensors, we reformulate the degradation process as the tensor-based form. Based on the degradation mechanism, we build a tensor-based optimization model for S2 images

The nonlinear trajectory and bistatic characteristics of general bistatic synthetic aperture radar (SAR) can cause severe two-dimensional space-variance in the echo signal, and therefore it is difficult to focus the echo signal directly using the traditional frequency-domain imaging algorithm based on the assumption of azimuth translational invariance. At present, the state-of-the-art nonlinear trajectory

Recently, deep learning (DL)-based hyperspectral image classification (HSIC) has attracted substantial attention. Many works based on the convolutional neural network (CNN) model have been certificated to be significantly successful for boosting the performance of HSIC. However, most of these methods extract features by using a fixed convolutional kernel and ignore multiscale features of the ground

Object detection is a focal point in remote sensing applications. Remote sensing images typically contain a large number of small objects and a wide range of orientations across objects. This results in great challenges to small object detection approaches based on remote sensing images. Methods directly employ channel relations with equal weights to construct information features leads to inadequate

Deep learning approaches have been widely applied to building footprint extraction using high-resolution imagery. However, the traditional fully convolution network still has problems in recovering spatial details and discriminating buildings with varying sizes and styles. We propose a novel multipath hybrid attention network (MHA-Net) to address these challenges. We design a separable convolution

In this article, we propose a new weak maneuvering target detection method in a sea environment with a Radon-fractional Fourier transform (RFrFT) canceller, which can efficiently suppress the sea clutter and detect targets with low computations. To accomplish sea clutter suppression, we design a canceller based on the RFrFT in the detection process, which utilizes different representations of the maneuvering

The consistent and long-term spaceborne synthetic aperture radar (SAR) missions such as Sentinel-1 (S-1) provide high-quality dual-polarized $C$ -band images particularly suited to sea ice monitoring. SAR data are currently the primary source of information for sea ice charting made by human ice experts as a result of its integration with multiple information sources at different scales (mainly radiometers)

Multioriented object detection, an important yet challenging task because of the bird's-eye-view perspective, complex background, and densely packed objects, is in the spotlight of detection in remote sensing images. Although existing methods have recently experienced substantial progress based on oriented head, they learn little about essential rotation invariance of the objects. In this article,

Building detection is a crucial task in the field of remote sensing, which can facilitate urban construction planning, disaster survey, and emergency landing. However, for large-size remote sensing images, the great majority of existing works have ignored the image tilt problem. This problem can result in partitioning buildings into separately oblique parts when the large-size images are partitioned

Scene classification of high-resolution remote sensing (RS) images has attracted increasing attentions due to its vital role in a wide range of applications. Convolutional neural networks (CNNs) have recently been applied on many computer vision tasks and have significantly boosted the performance including imagery scene classification, object detection, and so on. However, the classification performance

In this article, we address class incremental learning (IL) in remote sensing image analysis. Since remote sensing images are acquired continuously over time by Earth's observation sensors, the land-cover/land-use classes on the ground are likely to be found in a gradational manner. This process restricts the deployment of stand-alone classification approaches, which are trained for all the classes

Mosaicking of remote sensing images stitches images of different moments or sensors to produce a new image under a uniform geographic coordinate system. In a mosaicking process, the critical enblending operation is divided into color balance, seamline finder, and fusion of overlapping areas, which is still challenging to maintain color consistency and data fidelity. In this article, a new mosaicking

Tree-crown and height are primary tree measurements in forest inventory. Convolutional neural networks (CNNs) are a class of neural networks, which can be used in forest inventory; however, no prior studies have developed a CNN model to detect tree crown and height simultaneously. This study is the first-of-its-kind that explored training a mask region-based convolutional neural network (Mask R-CNN)

Continental shelves host 90% of modern Organic Carbon (OC) burial and play a key role in the sequestration of terrigenous OC over geological timescales. The efficiency of OC burial in these systems, however, varies greatly depending on the duration of exposure to oxic-suboxic conditions during sediment transport. In this study, we use observations across a wide range of stratigraphic and sedimentological

We study the characteristics of wintertime Arctic amplification (AA) and the inter-model spread in model output from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) and the Community Earth System Model (CESM) Large Ensemble Project. The CMIP6 models exhibit a large spread in AA intensity (as measured by an AA index, ranging from about −0.2 to about 1.1 °C/decade in the Historical

At present, global warming is an indisputable fact, and more and more attention has been paid to the impacts of climate warming on global ecological environments. Forests play increasing significant roles in regulating global carbon balance and mitigating climate change. Therefore, to understand the response mechanisms of the carbon budget of global forest ecosystems to future climate change, an improved

Linking the long-term with the short-term exhumation history of a mountain range is vital for understanding the evolution of orogenic topography. Further, erosion rates could be controlled by the variability of climatic and/or tectonic processes over geological time. For example, in the Eastern Cordillera of the Colombian Andes, until recently climate forcing has been hypothesized as the primary driver

A dramatic shift in microbial reefs occurred around the Ediacaran–Cambrian boundary. Here we describe changes in the composition, construction, and texture of microbial reefs in the Zavkhan Terrane of Gobi-Altai Province, western Mongolia, during the late Ediacaran and early Cambrian. Stromatolites consisting of peloids, micritic clots, and homogeneous lime mud without calcified microbes (calcimicrobes)

The dearth of frequency-specific satellite antenna phase centers (APCs), such as GPS Block IIF L5 phase center offsets (PCOs) and BeiDou System (BDS) phase variations (PVs), inconveniences multi-frequency precise point positioning (PPP). We find that the GNSS observation biases caused by incorrect frequency-specific APCs are both spatially incoherent and time variable. This spatiotemporal incoherency

The Computed Tomography Imaging Spectrometer (CTIS) permits a snapshot acquisition of a hyperspectral cube, through the creation of an image of indirect measurements which is then traditionally used for reconstruction of the cube. This reconstruction step is time-consuming and only yields an approximation of the original cube. Following a compressed learning framework, we compare the performance of

In cell and molecular biology, the fusion of green fluorescent protein (GFP) and phase contrast (PC) images aims to generate a composite image, which can simultaneously display the functional information in the GFP image related to the molecular distribution of biological living cells and the structural information in the PC image such as nucleus and mitochondria. In this paper, we propose a detail

Abstract A method is described for providing a levelling datum to a sea level record containing hourly heights (or similar) with the use of a second record from a nearby location consisting of high waters only but measured to a known datum. The method is tested using data from a pair of stations in the Thames estuary where there is a predominantly semidiurnal tide. It is then applied to the determination

Inconsistent measurements of city-size and a lack of time-series information on urban socioeconomic development have hindered determining whether China's city-size distribution (CD) follows a Pareto distribution according to multiple perspectives. This article has attempted to evaluate China's CD based on the defense meteorological satellite program- operational line-scan system (DMSP-OLS) nighttime

We present a framework to quantify the radiation from tropical cyclones (TCs) in shortwave (SW, wavelength smaller than 3 micron) and longwave (LW, wavelength larger than 3 micron) portions of the electromagnetic spectrum. The framework includes two stages: segmentation of TC clouds and calculation of the radiation effects attributable to TC clouds. The segmentation task is accomplished by an algorithm

Soil moisture is a key parameter affecting crop growth. Gaofen-3 satellite is the first C-band synthetic aperture radar produced by China, which provides full-polarization data sources for soil moisture estimation. This article evaluated the potential of estimating soil moisture via GF-3 SAR over agricultural area using different polarimetric decomposition models, namely, the modified Freeman–Durden

Monitoring of rivers is of major scientific and societal importance due to the crucial resource they provide to human activities and the threats caused by flood events. Rapid revisit synthetic aperture radar (SAR) sensors such as Sentinel-1 or the future surface water and ocean topography (SWOT) mission are indispensable tools to achieve all-weather monitoring of water bodies at the global scale. Unfortunately

Radar echo extrapolation is a basic but essential task in meteorological services. It could provide radar echo prediction results with high spatiotemporal resolution in a computationally efficient way, and effectively enhance the operational system's forecasting capability for meteorological hazards. Traditional methods perform extrapolation by estimating echo motions between contiguous radar data

As greenhouse warming is predicted to intensify the El Niño–Southern Oscillation (ENSO), it is key to understand relationships between the magnitudes and spatial distribution of ENSO events and associated extreme sea levels (ESLs). Current understanding is lacking for river deltas, where human societies and ecosystems are particularly vulnerable to coastal hazards. Using long-term tide gauge records

The BeiDou Navigation Satellite System (BDS) is essentially a precise time measurement and time synchronization system for a large-scale space near the Earth. General relativity is the basic theoretical framework for the information processing in the master control station of BDS. Having introduced the basic conceptions of relativistic space–time reference systems, the space–time references of BDS

In this paper, we propose a method for the generation of real-time global ionospheric map (RT-GIM) of vertical total electron content (VTEC) from GNSS measurements. The need for interpolation arises from the fact that the ionospheric pierce point (IPP) measurements from satellites to stations are not distributed uniformly over the ionosphere, leaving unfilled gaps at oceans or poles. The method we

As remote sensing (RS) data obtained from different sensors become available largely and openly, multimodal data processing and analysis techniques have been garnering increasing interest in the RS and geoscience community. However, due to the gap between different modalities in terms of imaging sensors, resolutions, and contents, embedding their complementary information into a consistent, compact

Lake Balaton (Hungary), the largest lake in Central Europe, formed by the interplay of tectonic and external forces. Its shallow water and young soft sediments together allow to carry out ultra-high resolution reflection seismic surveys to investigate shallow tectonic structures and near surface stratigraphy at depth of ca. 0–30 m. To document neotectonics of the eastern lake basin and its onshore

The paper presents local gravity field modelling in spatial domain using the finite element method (FEM). FEM as a numerical method is applied for solving the geodetic boundary value problem with oblique derivative boundary conditions (BC). For such a problem, we derive a new numerical scheme where the oblique derivative BC are considered directly at computational nodes on the discretized Earth’s topography

We introduce a new algorithm for complex image reconstruction with separate regularization of the image magnitude and phase. This optimization problem is interesting in many different image reconstruction contexts, although is nonconvex and can be difficult to solve. In this work, we first describe a novel implementation of the previous proximal alternating linearized minimization (PALM) algorithm

Given a spectral library, sparse unmixing aims to estimate the fractional proportions in each pixel of a hyperspectral image scene. However, the ever-growing dimensionality of spectral dictionaries strongly limits the performance of sparse unmixing algorithms. In this study, we propose a novel dictionary pruning (DP) approach to improve the performance of sparse unmixing algorithms, making them more

The urban heat island (UHI) effect in cities and its driving factors have long been investigated. 3-D buildings are key components of urban structures and have notable effect on the UHI effect. However, due to the incomplete 3-D building information in urban database, only a few studies investigated the impact of 3-D building morphology factors on the land surface temperature (LST). In this study,

Semantic segmentation for very-high-resolution remote sensing images has been a research hotspot in the field of remote sensing image analysis. However, most existing methods still suffer from a challenge that object boundaries cannot be finely recovered. To tackle the problem, we develop a dual-stream network based on the U-Net architecture, Instead of the traditional skip connections, a boundary

Polarimetric Doppler frequency-modulated continuous-wave weather radar produces a tremendous amount of data during the observation of atmospheric conditions. Using conventional signal processing method, the beat signals are sampled at Nyquist rate, so that the range signal can be reconstructed perfectly. We propose compressive sampling (CS) technique to sample and reduce the data simultaneously by

Information about forest structures is becoming crucial to earth's global carbon cycle, forest habitats, and biodiversity. The Global Ecosystem Dynamics Investigation (GEDI) provides 25-m diameter footprints of the surface for 3-D structure measurements. The main goal of this study is to compare 12 031 footprints of GEDI data with other airborne and spaceborne digital elevation models (DEMs) for Southwest

The index of ozone anomaly (IOA) has been proposed to detect changes in tropospheric ozone associated with strong earthquakes. The tropospheric ozone prior and after the 2008 Wenchuan earthquake has been analyzed using IOA. Atmospheric infrared sounder ozone volume mixing ratio (O3 VMR) at different pressure levels (600, 500, 400, 300, 200 hPa) for an 18-year period 2003–2020 has been considered to

High-precision canopy chlorophyll content (CCC) inversion for marsh vegetation is of great significance for marsh protection and restoration. However, it is difficult to collect the CCC measured data for marsh vegetation that matches the pixel scale of remote sensing image. This article proposes a new method based on unmanned aerial vehicle (UAV) multispectral images to obtain multiscale marsh vegetation