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Antarctica plays an important role in research on global change, and its unique geography, ocean, climate, and environment provide an ideal place for humankind to understand Earth’s evolution. Remote sensing provides an effective means to monitor and observe large-scale processes on the continent. Synthetic aperture radar (SAR) in particular provid...
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... task scheduling software batched 24 virtual machines to process the SAR images. The data processing flow of each virtual machine is shown in Figure 3. ...
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We have described an efficient approach to radiometrically flatten geocoded stacks of calibrated synthetic aperture radar (SAR) data for terrain-related effects. We have used simulation to demonstrate that, for the Sentinel-1 mission, one static radiometric terrain-flattening factor derived from actual SAR imaging metadata per imaging geometry is s...
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... The method of Mazur et al. (2017), however, required operator input for each scene in terms of threshold determination and, given the variable nature of SAR imagery it would not be suitable for automated deployment. To contextualise amount of imagery available and the need for methods that do not require operator input, Liang et al. (2021) collated 69586 coastal scenes across Antarctica between 2014 and 2020. ...
Accurate quantification of iceberg populations is essential to inform estimates of Southern Ocean freshwater and heat balances as well as shipping hazards. The automated operational monitoring of icebergs remains challenging, largely due to a lack of generality in existing approaches. Previous efforts to map icebergs have often exploited synthetic aperture radar (SAR) data but the majority are designed for open water situations, require significant operator input, and are susceptible to the substantial spatial and temporal variability in backscatter that characterises SAR time-series. We propose an adaptive unsupervised classification procedure based on Sentinel 1 SAR data and a recursive Dirichlet Process implementation of Bayesian Gaussian Mixture Model. The approach is robust to inter-scene variability and can identify icebergs even within complex environments containing mixtures of open water, sea ice and icebergs of various sizes. For the study area in the Amundsen Sea Embayment, close to the calving front of Thwaites Glacier, our classifier achieved a mean pixel-wise F1 score against manual iceberg delineations from the SAR scenes of 0.960 +- 0.018 with a corresponding object-level F1 score of 0.729 +- 0.086. The method provides an excellent basis for estimation of total near-shore iceberg populations and has inherent potential for scalability that other approaches lack.
... Furthermore, target detection algorithms have evolved from the traditional machine learning era, which was characterized by numerous practical applications, to the current stage, which is characterized by the widespread application of deep learning (Z. Liang et al. 2022). First, R-CNN (region convolutional neural networks) pioneered the utilization of deep learning for target detection (Cai and Vasconcelos 2018;Girshick et al. 2014); second, the traditional two-stage detection model, namely Faster R-CNN (Ren et al. 2017), emerged; third, the YOLO and SSD (Single Shot Detector) model series that focus on efficiency and computational speed (W. ...
Remote sensing and deep learning are being widely combined in tasks such as urban planning and disaster prevention. However, due to interference occasioned by density, overlap, and coverage, the tiny object detection in remote sensing images has always been a difficult problem. Therefore, we propose a novel TO–YOLOX(Tiny Object–You Only Look Once) model. TO–YOLOX possesses a MiSo(Multiple-in-Single-out) feature fusion structure, which exhibits a spatial-shift structure, and the model balances positive and negative samples and enhances the information interaction pertaining to the local patch of remote sensing images. TO–YOLOX utilizes an adaptive IOU-T (Intersection Over Uni-Tiny) loss to enhance the localization accuracy of tiny objects, and it applies attention mechanism Group-CBAM (group-convolutional block attention module) to enhance the perception of tiny objects in remote sensing images. To verify the effectiveness and efficiency of TO–YOLOX, we utilized three aerial-photography tiny object detection datasets, namely VisDrone2021, Tiny Person, and DOTA–HBB, and the following mean average precision (mAP) values were recorded, respectively: 45.31% (+10.03%), 28.9% (+9.36%), and 63.02% (+9.62%). With respect to recognizing tiny objects, TO–YOLOX exhibits a stronger ability compared with Faster R-CNN, RetinaNet, YOLOv5, YOLOv6, YOLOv7, and YOLOX, and the proposed model exhibits fast computation.
... SAR images have been an important data source for surface water investigations since they are capable of penetrating through clouds and capturing the Earth's surface information under all weather conditions (Strozzi et al. 2012;Li et al. 2018;Liang et al. 2022). However, SAR images contain speckle noise, which complicates surface water identification. ...
The surface water in the Qinghai–Tibet Plateau (QTP) region has undergone dramatic changes in recent decades. To capture dynamic surface water information, many satellite imagery-based methods have been proposed. However, these methods are still limited in terms of automation and accuracy and thus prevent surface water dynamic studies in large-scale QTP regions. In this study, we developed a new fully automatic method for accurate surface water mapping by using Sentinel-1 synthetic aperture radar (SAR) imagery and convolutional networks (ConvNets). Specifically, we built a new multiscale ConvNet structure to improve the model capability in surface water body extraction. Moreover, a gating mechanism is introduced to promote the efficient use of multiscale information. According to the accuracy assessment, the proposed gated multiscale ConvNet (GMNet) achieved the highest overall accuracy of 98.07%. We applied our GMNet for monthly surface water mapping on the QTP; accordingly, we found that the QTP region experienced significant surface water fluctuations over one year. The surface water also showed distinct spatial heterogeneity on the QTP; that is, the surface water fraction of the Inner Tibetan Basin was significantly higher than that of the Mekong Basin in both the wet and dry seasons.
... However, their re-entry cycles are longer than half a month. In contrast, sensors such as MODIS and AMSR-E can obtain daily surface temperature data with extended coverage, but the spatial resolution is 1-10 km [18]. Spatial details and temporal variations are challenging to capture over GrIS. ...
Monitoring ice/snow surface temperature (IST) variations with high spatial and temporal resolution data from satellites are essential for research on the mass balance of the Greenland ice sheet (GrIS). However, the tradeoff between satellite sensors' bandwidth and re-entry cycle, coupled with the influence of cloudy weather, limits their ability to fine-monitor IST. Spatiotemporal data fusion is a way of producing high spatiotemporal datasets. This study uses four spatiotemporal fusion algorithms to fuse the Landsat 8 IST data and the Moderate Resolution Imaging Spectrometer IST to generate fine spatial-temporal IST in the GrIS regions. The quantitative evaluation of the different fusion data shows that the R² are all above 0.9. The spatial and temporal nonlocal filter based fusion model (STNLFFM) dual-temporal algorithm provided the highest accuracy with a root mean square error of 2.427 K, followed by the STNLFFM mono-temporal algorithm, the spatial and temporal adaptive reflectance fusion model (STARFM), the flexible spatiotemporal data fusion model (FADSF), and enhanced STARFM (ESTARFM). From the results, the fusion data are accurate and detailed in different regions. That is, the spatiotemporal fusion technique has the potential to generate IST datasets that possess high spatial and temporal resolutions for Greenland.
... The Antarctic ice sheet is covered in snow and ice year-round, and the surface melts after absorbing enough heat, changing from solid to liquid (Liang et al. 2021b;Zhao et al. 2021). The melting of the Antarctic ice sheet is mainly caused by solar radiation, though the warming effect caused by foehn winds can also lead to local melting (Datta et al. 2019;Liang et al. 2021c). ...
... Though previous studies have focused on specific aspects independently in limited areas, such as the accuracy of radiometric data in inversing ice melt (Liang et al. 2021b) and statistical analysis of correlations between freeze-thaw and temperature (National Remote Sensing Center of China 2020), there is a lack of research measuring the spatiotemporal characteristics of temperature and freeze-thaw in representative Antarctic regions. This study focuses on the correlation and interaction between temperature and ice sheet freeze-thaw to reveal the response of the Antarctic ice sheet to global changes, using freeze-thaw information measured by microwave radiometer and temperature data derived from automatic weather stations while comparing data and trends between different selected regions. ...
As the most sensitive and direct indicator of global climate change, the freezing and thawing of the Antarctic ice sheet is of great significance for research on surface mass and energy balance. In this study, four representative regions in Antarctica were selected and correlation analysis, Granger causality testing, and cluster analysis were applied to comprehensively analyze the correlation and response of spatiotemporal variation in freeze-thaw and temperature. The conclusions are as follows. (1) In the Antarctic Peninsula, a phenomenon was demonstrated that the summer shifts rearward. Hotter December and colder March temperatures were observed in the Amery Ice Shelf and Queen Maud Land. (2) The Antarctic Peninsula featured the most severe degree of melting among the four regions, with the largest melt area in the past 30 years appearing during the 2015/2016 season. However, the number of melt days in most areas of the Antarctic Peninsula was observed to have decreased. (3) There is a strong correlation between the freeze-thaw state of the Antarctic ice sheet and temperature, as well as spatial differences among regions, but the data were clustered at different time scales.
... In this platform, the spatiotemporal data reach up to the petabyte (PB) scale and include ground observation and scientific research data, remote sensing data products, models and assimilation data (Wu, Che, Li, & Zhu, 2021). And data on the platform will allow the dataset to be further utilized in other research and integrated with multiple datasets, data analysis, and mining services (Liang, Guo, Zhang, Li, & Wang, 2021). This will promote technological innovation in the field of big data and scientific discoveries in earth system science (Guo, 2019). ...
Big Earth Data refers to the multidimensional integration and association of scientific data, including geography, resources, environment, ecology, and biology. An effective data classification system and label management strategy are important foundations for long-term management of data resources. The objective of this study was to construct a classification system and realize multidimensional semantic data label management for the Big Earth Data Science Engineering Program (CASEarth). This study constructed two sets of classification and coding systems that realize classification by mapping each other; namely, the geosphere-level and Sustainable Development Goals (SDGs) indicator classifications. This technique was based on natural language processing technology and solved problems with subject-word segmentation, weight calculation, and dynamic matching. A prototype system for classification and label management was constructed based on existing CASEarth datasets of more than 1,100. Furthermore, we expect our study to provide the methodology and technical support for user-oriented classification and label management services for Big Earth Data.
Human activities increase significantly over Antarctic ice shelves. However, they are constantly faced with danger posed by the harsh environment. For decision-making, it is a prerequisite to have the macro-scale suitability information about human activity site selection. Here, we define a new index, the human activity suitability (HAS) index, to quantitatively analyze the locational suitability of human activity sites over Antarctic ice shelves. Two multi-criteria decision analysis methods (AHP + Entropy, TOPSIS) and three machine learning methods (support vector machine, random forest and logistic regression) are tested to develop HAS maps. Nine conditioning factors about ice surface features, ice shelf stability, meteorology and topography are generated as input parameters. The accuracy of the proposed models is evaluated using metrics such as the area under curve (AUC-ROC), root mean square error, overall accuracy and kappa index. The results indicate that the Random Forest performs best. The HAS map exhibits great heterogeneity driven by the synergistic influence of multiple factors. Areas in low HAS classes are concentrated at Crosson, Brunt, Thwaites and the edge of some ice shelves, implying the complex environment in these regions. The findings can provide a new insight for forecasting the potential human footprint and support sustainability research in Antarctica.
Accurately locating and studying grounding lines is essential for predicting the response of glaciers to climate change. However, it is challenging to find grounding lines since they are subglacial features. In this study, Sentinel-1 synthetic aperture radar (SAR) data were utilized to derive the grounding lines of the Riiser-Larsen Ice Shelf. A new method with inspiration drawn from multi-temporal baseline InSAR techniques is proposed. It takes advantage of the temporal consistency of the vertical displacement gradients and identifies grounding zones pixel-by-pixel on a stack of double differential interferograms, thereby providing grounding line proxies. As it fully exploits coherent signals in both spatial and temporal domains, the maximum possible number of grounding zone pixels can be obtained. Moreover, due to the introduction of the concept of the temporal consistency, the method can cope with short term grounding line fluctuations to some extent and may mitigate the influences of atmospheric disturbances and residual ice displacements. The resulting grounding lines are compared with the MEaSUREs Antarctic grounding line product. The comparison confirms the effectiveness of the proposed method and corroborates that the Riiser-Larsen Ice Shelf should have not undergone significant changes over the past few decades.
