Although the summer monsoon is the dominant source of precipitation in the East Asian monsoon region (EAMR), there is long-standing uncertainty about the spatial and temporal variability of precipitation on the centennial time scale. In this study we analyzed two well-dated, high-resolution, published paleoclimatic records in order to determine the variability of precipitation in the EAMR during the

Widespread oceanic anoxia has been implicated as a killing mechanism during the end-Permian mass extinction (EPME). A high-resolution investigation of pyrite framboids and bioturbation structures in the Shangsi section of South China has revealed repeated and frequent fluctuations of coupled anoxic/euxinic to dysoxic/oxic episodes in the latest Permian, immediately preceding the EPME. Anoxic bottom

Conodonts, one of the longest-lived early groups of vertebrates, have a very complete fossil record ranging from the late Cambrian to the end of the Triassic and persisted through many global climatic and biotic events. In this paper, we analyse a large dataset harvested from the Paleobiology Database to compute global diversity curves at the generic level and explore patterns of conodont paleogeographic

A large proportion of the world's population lives in coastal areas. These zones play a vital role in the socioeconomic aspects of coastal communities. The evolution of shoreline along the zones is of great importance to scientists, and engineers as well as coastal management. This research aims to assess and understand the dynamics of shoreline along the Red Sea coast between Al Lith and Ras Mahāsin

The C-band Sentinel-1 A/B satellites in TOPS mode provide unprecedented opportunities for continuous radar mapping of the earth with enhanced revisit frequency. The reliability for routine operational services relies on a very stringent azimuth co-registration accuracy. However, while the enhanced spectral diversity (ESD) technique achieves a co-registration accuracy of better than 0.001 pixels, the

A 154 year-long, tree-ring based stable oxygen isotope time series (δ18OTRC) of the evergreen southern beech (Nothofagus betuloides) was established for the southernmost part of Patagonia. The trees grew on the terminal moraine system of the Schiaparelli Glacier (54.4°S) within the windward and hyper-humid zone of the Austral Andes. The annually resolved δ18OTRC record spans the common period 1861–2015

The India–Asia collision and subsequent uplift of the Tibetan Plateau during the Cenozoic were the main drivers of Asian monsoon development and evolution. However, the climate and possible existence of a desert landscape in southern China prior to the significant uplift of the Tibetan Plateau remain uncertain, because of a lack of reliable proxies or direct geological evidence. The well–preserved

Recovering a high-quality image from noisy indirect measurements is an important problem with many applications. For such inverse problems, supervised deep convolutional neural network (CNN)-based denoising methods have shown strong results, but the success of these supervised methods critically depends on the availability of a high-quality training dataset of similar measurements. For image denoising

As a non-invasive and radiation-free imaging modality, electrical impedance tomography (EIT) has attracted much attention in the field of industrial measurement. However, image reconstruction with EIT is a non-linear and ill-posed inverse problem, causing it to suffer from low spatial resolution and high noise sensitivity. To overcome this problem, a point-constrained framework is proposed to guide

Small watersheds are ideal objects for studying the evolution of hydrological and water resources at small scales. Whether precipitation products can meet the runoff simulation of small watersheds is the main purpose of this study. With NOAA-CPC-US precipitation as a reference, in nine small watersheds of the United States, accuracy of the precipitation products such as PERSIANN, PERSIANN-CDR, TRMM-3B42V7

The supplementary international comparison of national gas flow standards COOMET.M.FF-S9 was carried out from May 2019 to January 2020. The comparison were made in the range of gas flow from 20 to 6500 m 3 /h. Six European National Metrological Institutes (VNIIM-VNIIR, PTB, IFSM, BelGIM, LEI and LABSAGAS) took part in the comparison. The VNIIM-VNIIR acted as a pilot laboratory. The participants used

This report describes the results of comparison COOMET.M.H-S5. The comparison measurements between the five participants NSC "IM" (pilot laboratory), PTB, CMI, BelGIM, RSE "KazInMetr" were started in November 2013 and ended in May 2015. In the RMO SC, one set of hardness reference blocks were used consisting of 5 (five) blocks of the Rockwell scales and hardness levels 80-86 HRA, 80-100 HRBW, 20-30

Accurate and timely estimation of crop yield at a small scale is of great significance to food security and harvest management. Recent studies have proven remote sensing is an efficient method for yield estimation and machine learning, especially deep learning, can infer a good prediction by integrating multisource datasets such as satellite data, climate data, soil data, and so on. However, there

Hyperspectral unmixing refers to a source separation problem of decomposing a hyperspectral imagery (HSI) to estimate endmembers, and their corresponding abundances. Recently, matrix–vector nonnegative tensor factorization (MV-NTF) was proposed for unmixing to avoid structure information loss, which is caused by the HSI cube unfolding in nonnegative matrix factorization (NMF)-based methods. However

Urban areas tend to be warmer than their rural surroundings, well-known as the “urban heat island” effect. Higher nocturnal air temperature ( T air ) is associated with adverse effects on human health, higher mortality rates, and higher energy consumption. Prediction of the spatial distribution of T air is a step toward the “Smart City” concept, providing an early warning system for vulnerable populations

The interpretation of deep learning network is an important part in understanding the convolutional neural networks (CNNs). As an exploratory research, this article explored the interpretation method in 3-D point cloud deep learning networks, for the purpose of evaluating the performance of convolution functions in 3-D point cloud CNNs. Specifically, a 3-D point cloud classification network with two

Satellite-based active fire (AF) products provide opportunities for constructing continuous fire progression maps, a critical dataset needed for improved fire behavior modeling and fire management. This study aims to investigate the geospatial interpolation techniques in mapping the daily fire progression and assess the accuracy of the derived maps from multisensor AF products. We focused on 42 large

Recent advances in machine learning and computer vision have enabled increased automation in benthic habitat mapping through airborne and satellite remote sensing. Here, we applied deep learning and neural network architectures in NASA NeMO-Net, a novel neural multimodal observation and training network for global habitat mapping of shallow benthic tropical marine systems. These ecosystems, particularly

Hyperspectral images (HSIs) have fine spectral information, and rich spatial information, of which the feature quality is one of the key factors that affect the classification performance. Therefore, how to extract essential features, and eliminate redundant features from hyperspectral data are the main research focus of this article. Here, we propose a spectral-spatial feature extraction method based

Time series prediction (TSP) of land use/land cover (LULC) is an important scientific issue, but forecasting LULC changes at lead times of multiple time steps at fine time scales remains problematic. Especially in the context of current rapid economic and social development, the traditional one-step prediction models with a five-year or ten-year cycle cannot meet the application needs of land management

Spectral unmixing is an important technique for quantitatively analyzing hyperspectral remote sensing images. Recently, constrained nonnegative matrix factorization (NMF) has been demonstrated to be a powerful tool for spectral unmixing. However, acquiring the problem-dependent prior knowledge and incorporating it into NMF as effective constraints is a challenging task. In this article, a multiple

The spectral resolution of hyperspectral images (HSIs) is very high. Nevertheless, their spatial resolution is low due to various hardware limitations. Therefore, it is important to study HSI super resolution to improve their spatial resolution. In this article, for hyperspectral single-image super resolution, we propose a multiscale feature fusion and aggregation network with 3-D convolution (MFFA-3D)

Convolutional neural networks (CNNs) have outstanding advantages in the classification of remote sensing scenes. Deep CNN models with better classification performance typically have high complexity, whereas shallow CNN models with low complexity rarely achieve good classification performance for remote sensing images with complex spatial structures. In this article, we proposed a new lightweight CNN

Northeast China is one of the primary snow-covered regions, and its forest coverage is over 40%. Forest snow identification is usually a challenging problem, and the SNOMAP algorithm tends to underestimate the amount of snow cover in forest regions for the lower normalized difference snow index. In this article, an improved method of the snow-cover identification based on the Landsat operational land

Most image fusion algorithms based on hyperspectral unmixing perform worse with the lower spatial resolution of hyperspectral image (HSI) for the reason that the estimated endmembers and abundance deviate from the truth value. Therefore, it is more meaningful to unmix the low spatial resolution hyperspectral image (LRHSI) accurately, which is also helpful to improve the image fusion performance. In

Due to synthetic aperture radar (SAR) imaging geometry and scattering characteristics, there are some low coherence areas in interferometric SAR (InSAR) data, such as shadow and water, where the interferometric phase is discontinuous. Therefore, it is difficult to prevent the phase error spreading in the process of phase unwrapping. In this article, the characteristics of the interferometric phase

In the course of global urbanization, poverty in cities has been observed to increase, especially in the Global South. Poverty is one of the major challenges for our society in the upcoming decades, making it one of the most important issues in the Sustainable Development Goals defined by the United Nations. Satellite-based mapping can provide valuable information about slums where insights about the

To improve the accuracy of rainfall estimation by microwave links, this article presents a method for classifying wet and dry periods based on the support vector machine (SVM). The average, minimum, and maximum attenuation measurements in 5 min are applied as the feature vector of the SVM after the analysis of the relation between the statistical parameters of the attenuation measurements from seven

Land surface temperature (LST) is crucial to wide varieties of environmental issues, whereas its low tolerance to cloud contamination greatly challenges its applications. Passive microwave (PMW) measurements are employed to retrieve LST due to its great capability of penetrating clouds. Despite great efforts from previous studies, their further applications are limited by coarse resolution of PMW,

Remote sensing (RS) systems have been collecting massive volumes of datasets for decades, managing and analyzing of which are not practical using common software packages and desktop computing resources. In this regard, Google has developed a cloud computing platform, called Google Earth Engine (GEE), to effectively address the challenges of big data analysis. In particular, this platform facilitates

Front-line remote sensing tools, coupled with machine learning (ML), have a significant role in crop monitoring and disease surveillance. Crop type classification and a disease early warning system are some of these remote sensing applications that provide precise, timely, and cost-effective information at different spatial, temporal, and spectral resolutions. To our knowledge, most disease surveillance

Global or quasi-global digital elevation model (DEM) datasets provide three-dimensional information on terrain surface, and they have been extremely useful in geoscience research and applications. However, the wide application of DEMs is constrained by differences in the means of observation and processing, and in the resolution of global public DEM datasets. An adaptive regularization variation model

Information on rice phenological stages from Synthetic Aperture Radar (SAR) images is of prime interest for in-season monitoring. Often, prior in-situ measurements of phenology are not available. In such situations, unsupervised clustering of SAR images might help in discriminating phenological stages of a crop throughout its growing period. Among the existing unsupervised clustering techniques using

One main challenge in detecting built-up land cover changes using synthetic aperture radar (SAR) instruments is that complicated backscattering behaviours and the superimposition of speckles on rich textures cause a large number of false alarms. Using trajectory-based analyses from time-series SAR imagery can mitigate false alarms since the temporal variability in backscattering during construction

Retreating Arctic sea ice under rapid warming is projected to continue. A new transarctic route, the Northeast Passage (NEP), may open in the near future, with considerable impacts on global shipping transportation. Comprehensive research on the past changes in sea ice in September and future accessibility along the NEP is essential. In this investigation, an unstructured-grid model was used for accurate

Soil resource characterization of Kumaon hills of Himalayas was carried out through soil survey and established soil–physiography relationship after identification and delineation of six physiographic units in Khulgad watershed of Almora district, Uttarakhand. The satellite image characteristics identified through the visual/digital interpretation of IRS-1D LISS-III fused with PAN data on 1:12,500

Ağrı ice cap is the biggest glacier in Turkey, but there are only a few published studies on Ağrı glacier. Many of them are focused on the spatial changes of the glacier detected by temporal data. In addition to the spatiotemporal changes of glacier, the main contribution of this study is to investigate the relation between Glacier Surface Temperature Anomalies (GSTA) and glacier retreat. The study

During the Paleocene–Eocene Thermal Maximum (PETM, ~56 Ma), a large, negative carbon-isotope excursion (CIE) testifies to a massive perturbation of the global carbon cycle. Shallow-marine settings are crucial to understand the environmental and ecological changes associated with the PETM and the connection between continental and open-marine environments. Detailed sedimentological, paleontological

Global Positioning System (GPS) position verification and legal traceability in Australia supports industry, trade, science and innovation and is trusted and recognized domestically and internationally. At the end of 2017, the Australia’s national datum was transitioned from the Geocentric Datum of Australia 1994 (GDA94) to the Geocentric Datum of Australia 2020 (GDA2020). As such, the datum for the

The identification of plant species using spectrometry is utilized in various applications. Some researchers have reported the success of using the reflectance spectrum along with its derivatives and their coding to identify different Phenomena, including plants. In this study, based on these parameters, we developed a new method to improve the accuracy of identifying plant species. Two experimental

Supervised training of a deep neural network for semantic segmentation of point clouds requires a large amount of labelled data. Nowadays, it is easy to acquire a huge number of points with high density in large-scale areas using current LiDAR and photogrammetric techniques. However it is extremely time-consuming to manually label point clouds for model training. In this paper, we propose an active

Understanding the impact of climatic variations on Himalayan-Karakoram glaciers has now become a necessity for planning water resources of Indus-Ganga–Brahmaputra basins. One of the most important parameters to assess this is the mass balance of glaciers. Besides ground-based studies of mass balance, differencing of Digital Elevation Models (DEMs) is now commonly employed for estimating the long-term

Physically-based methods in remote sensing provide benefits over statistical approaches in monitoring biophysical characteristics of vegetation. However, physically-based models still demand large computational resources and often require rather detailed informative priors on various aspects of vegetation and atmospheric status. Spectral invariants and photon recollision probability theories provide

The ocean plays a crucial role in the global and regional climate. Many regional monsoon climates are interlinked by ocean currents and sea surface temperature (SST). The climate link between the East Asian and North American continent has been proposed and verified by many studies. A widely accepted hypothesis is that the precipitation of the monsoon marginal zone is strongly affected by the monsoon

For compressed sensing magnetic resonance imaging (CS-MRI) that utilize sparse representations, the regularization parameter establishes a trade-off between sparsity and data fidelity. While convergence to the desired solution is slow for mean squared error (MSE) optimal constant regularization, continuation using decreasing parameter values enables faster convergence. To derive an explicit rule for

Aerosols play an important role in climate change and environmental pollution, and monitoring of aerosols via remote sensing is of great significance. However, for urban areas that are closely related to people's lives, remote sensing monitoring of aerosol optical depth (AOD) faces major challenges. These surface types, with bidirectional reflection characteristics, have a high surface reflectivity

Tanks are one of the main sources for the irrigation in the country. Out of average net irrigated area of 62 Mha in India during 2001–2015, 3% of area is irrigated through tanks, as against 15% in the 1950s. The deterioration of tanks was a matter of concern for most of the states in South India. Government of Telangana launched five-year long programme called 'Mission Kakatiya' in 2014 to harness

The long-term trend in satellite retrieved phytoplankton size classes (PSC) such as picophytoplankton (PP), nanophytoplankton (NP), and microphytoplankton (MP) was studied in the three zones falling under different ecological provinces in open ocean regions of the Arabian Sea. A regionally tuned three-component “abundance” model was used to compute PSC biomass from Moderate Resolution Imaging Spectroradiometer

Shoreline change studies in small-scale beaches require high-resolution satellite images. In this regard, high-resolution satellite images from Google Earth (GE) would be an alternative source however novel studies are needed to verify the effectiveness and the efficiency of applying those images for shoreline change detection in small-scale beaches. Addressing this gap, the current study attempts

Submeter high-resolution remote sensing image land cover classification could provide significant help for urban monitoring, management, and planning. Deep learning (DL)-based models have achieved remarkable performance in many land cover classification tasks through end-to-end supervised learning. However, the excellent performance of DL-based models relies heavily on a large number of well-annotated

Remote Sensing is both an active research area and the source of valuable information for decision-making. Many actors play a fundamental role in Remote Sensing, from industry (public or private) to large or small research groups. From that intensive activity, methods, algorithms, and techniques are continuously published or broadcasted through papers, conference presentations, repositories, patents

Band selection and feature extraction are the two paradigms of hyperspectral dimensionality reduction. While feature extraction methods have many desirable properties, band selection methods keep the actual reflectances intact, which results in better interpretation of the behavior of the materials. Keeping this in mind, several feature extraction-based band selection methods have been proposed in

In the global change context, an efficient management of the available resources has become one of the most important topics particularly for the sustainable crop development. Many questions concern the evolution of the rice farming systems in Camargue in Southeastern France, which play a crucial role in controlling the soil salinity. Their surface area significantly decreased from 20 000 ha in 2010