bluuebunny/crossref_metadata_2025 huggingface dataset:
“I Discovered I Love to Pray Alone Too”: Pluralist Muslim Women’s Approaches to Practicing Islam during and after Ramadan 2020
Authors: Piela Anna , Krotofil Joanna
Published: September 2021

Public health guidelines implemented during the COVID-19 pandemic have changed the way many people practice religion. In the realm of Islam, practices from the margins—attending online mosques and prayer groups, or praying alone—suddenly became commonplace. This paper addresses the question: What religious processes have become more evident among pluralist Muslim women during the pandemic? Based on 34 open-ended online surveys completed by pluralist Muslim women living chiefly in the USA and the UK, our analysis evidences the existence of four narratives that reflect fluctuations in the intensity and type of religious practice. The first and most prominent narrative in our dataset conveys enthusiastic embrace of social-distanced practices; the second describes a profound sense of aberration impossible to overcome in spiritual ways. The third highlights that for some Muslims, the pandemic brought no changes, as they continued to be isolated from their communities. The fourth is focused on an affirmation of a “remote” sociality experienced online. While some respondents acknowledge the increased individuation in their religious practice, they also find fulfilment in collective, if transformed, sociality. The changes in social interaction have led to a re-evaluation of salient aspects of their religious identity or, alternatively, highlighted longstanding modalities of exclusion.
Jin Worship, Founders’ Cults, and Social Relations in Tidore, Indonesia
Authors: Song Seung-Won
Published: September 2021

This article examines perceptions of jin rituals in Tidore in order to explore how Austronesian perceptions of founders’ cults, arrival-order precedence, and stranger-kingship operate in determining social relations. Tidore origin narratives are significant historical texts that encode the social order and its power relations and so must be explored in greater depth. I analyzed rituals, origin narratives, and public discourse through interviews conducted with locals and particularly with four sowohi, the ritual specialists of jin worship. Additionally, I observed the public aspects of the jin ritual of inauguration of the sultan. The jin are the ancestral spirits and “true owners” of Tidore. Both the jin and sowohi are associated with the land and thus are the autochthonous leaders on the island. The sultan belongs to the stranger-king category, which was formed by later immigrant groups. During jin rituals of worship, the jin bless the sultan through the sowohi, who serve as mediums; this symbolizes the autochthonous flow of blessings to later immigrant groups. The rituals are also a recollection of a more primordial social order of heterogenous groups, which is based on the arrival-order precedence on Tidore.
Platinum Group Metals: A Review of Resources, Production and Usage with a Focus on Catalysts
Authors: Hughes Anthony E. , Haque Nawshad , Northey Stephen A. , Giddey Sarbjit
Published: September 2021

The major applications of PGMs are as catalysts in automotive industry, petroleum refining, environmental (gas remediation), industrial chemical production (e.g., ammonia production, fine chemicals), electronics, and medical fields. As the next generation energy technologies for hydrogen production, such as electrolysers and fuel cells for stationary and transport applications, become mature, the demand for PGMs is expected to further increase. Reserves and annual production of Ru, Rh, Pd, Ir, and Pt have been determined and reported. Based on currently available resources, there is around 200 years lifetime based on current demand for all PGMs, apart from Pd, which may be closer to 100 years. Annual primary production of 190 t/a for Pt and 217 t/a for Pd, in combination with recycling of 65.4 t/a for Pt and 97.2 t/a for Pd, satisfies current demand. By far, the largest demand for PGMs is for all forms of catalysis, with the largest demand in auto catalysis. In fact, the biggest driver of demand and price for Pt, Pd, and Rh, in particular, is auto emission regulation, which has driven auto-catalyst design. Recovery of PGMs through recycling is generally good, but some catalytic processes, particularly auto-catalysis, result in significant dissipation. In the US, about 70% of the recycling stream from the end-of-life vehicles is a significant source of global secondary PGMs recovered from spent auto-catalyst. The significant use of PGMs in the large global auto industry is likely to continue, but the long-term transition towards electric vehicles will alter demand profiles.
Change Detection from SAR Images Based on Convolutional Neural Networks Guided by Saliency Enhancement
Authors: Li Liangliang , Ma Hongbing , Jia Zhenhong
Published: September 2021

Change detection is an important task in identifying land cover change in different periods. In synthetic aperture radar (SAR) images, the inherent speckle noise leads to false changed points, and this affects the performance of change detection. To improve the accuracy of change detection, a novel automatic SAR image change detection algorithm based on saliency detection and convolutional-wavelet neural networks is proposed. The log-ratio operator is adopted to generate the difference image, and the speckle reducing anisotropic diffusion is used to enhance the original multitemporal SAR images and the difference image. To reduce the influence of speckle noise, the salient area that probably belongs to the changed object is obtained from the difference image. The saliency analysis step can remove small noise regions by thresholding the saliency map, and interest regions can be preserved. Then an enhanced difference image is generated by combing the binarized saliency map and two input images. A hierarchical fuzzy c-means model is applied to the enhanced difference image to classify pixels into the changed, unchanged, and intermediate regions. The convolutional-wavelet neural networks are used to generate the final change map. Experimental results on five SAR data sets indicated the proposed approach provided good performance in change detection compared to state-of-the-art relative techniques, and the values of the metrics computed by the proposed method caused significant improvement.
Satellite Availability and Service Performance Evaluation for Next-Generation GNSS, RNSS and LEO Augmentation Constellation
Authors: Cui Haomeng , Zhang Shoujian
Published: September 2021

Positioning accuracy is affected by the combined effect of user range errors and the geometric distribution of satellites. Dilution of precision (DOP) is defined as the geometric strength of visible satellites. DOP is calculated based on the satellite broadcast or precise ephemerides. However, because the modernization program of next-generation navigation satellite systems is still under construction, there is a lack of real ephemerides to assess the performance of next-generation constellations. Without requiring real ephemerides, we describe a method to estimate satellite visibility and DOP. The improvement of four next-generation Global Navigation Satellite Systems (four-GNSS-NG), compared to the navigation constellations that are currently in operation (four-GNSS), is statistically analyzed. The augmentation of the full constellation the Quasi-Zenith Satellite System (7-QZSS) and the Navigation with Indian Constellation (11-NavIC) for regional users and the low Earth orbit (LEO) constellation enhancing four-GNSS performance are also analyzed based on this method. The results indicate that the average number visible satellites of the four-GNSS-NG will reach 44.86, and the average geometry DOP (GDOP) will be 1.19, which is an improvement of 17.3% and 7.8%, respectively. With the augmentation of the 120-satellite mixed-orbit LEO constellation, the multi-GNSS visible satellites will increase by 5 to 8 at all latitudes, while the GDOP will be reduced by 6.2% on average. Adding 7-QZSS and 11-NavIC to the four-GNSS-NG, 37.51 to 71.58 satellites are available on global scales. The average position DOP (PDOP), horizontal DOP (HDOP), vertical DOP (VDOP), and time DOP (TDOP) are reduced to 0.82, 0.46, 0.67 and 0.44, respectively.
Reduced-Dynamic Precise Orbit Determination of Haiyang-2B Altimetry Satellite Using a Refined Empirical Acceleration Model
Authors: Wang Youcun , Li Min , Jiang Kecai , Li Wenwen , Qin Geer , Zhao Qile , Peng Hailong , Lin Mingsen
Published: September 2021

The Haiyang 2B (HY-2B) satellite requires precise orbit determination (POD) products for geodetic remote sensing techniques. An improved set of reduced-dynamic (RD) orbit solutions was generated from the onboard Global Positioning System (GPS) measurements over a 14-month period using refined strategies and processing techniques. The key POD strategies include a refined empirical acceleration model, in-flight calibration of the GPS antenna, and the resolution of single-receiver carrier-phase ambiguities. In this study, the potential periodicity of empirical acceleration in the HY-2B POD was identified by spectral analysis. In the along-track direction, a noticeable signal with four cycles per revolution (CPR) was significant. A mixed spectrum was observed for the cross-track direction. To better understand the real in-flight environment, a refined empirical acceleration model was used to cope with the time variability of empirical accelerations in HY-2B POD. Three POD strategies were used for the reprocessing for superior orbit quality. Validation using over one year of satellite laser ranging (SLR) measurements demonstrated a 5.2% improvement in the orbit solution of the refined model. Reliable correction for the GPS antenna phase center was obtained from an over-420-day dataset, and a trend in radial offset change was observed. After application of the in-flight calibration of the GPS antenna, a 26% reduction in the RMS SLR residuals was achieved for the RD orbit solution, and the carrier phase residuals were clearly reduced. The integer ambiguity resolution of HY-2B led to strong geometric constraints for the estimated parameters, and a 15% improvement in the SLR residuals could be inferred compared with the float solution.
Changing Pattern of Water Level Trends in Eurasian Endorheic Lakes as a Response to the Recent Climate Variability
Authors: Zhang Xin , Kurbaniyazov Abilgazi , Kirillin Georgiy
Published: September 2021

Lake level is a sensitive integral indicator of climate change on regional scales, especially in enclosed endorheic basins. Eurasia contains the largest endorheic zone with several large terminal lakes, whose water levels recently underwent remarkable variations. To address the patterns of these variations and their links to the climate change, we investigated the variability of levels in 15 lakes of three neighboring endorheic regions—Central Asia, Tibetan Plateau, and Mongolian Plateau. Satellite altimetry revealed a heterogeneous pattern among the regions during 1992–2018: lake levels increased significantly in Central Asia and the Tibetan Plateau but decreased on the Mongolian Plateau. The shifts to the increasing trend were detected since 1997 in Central Asia, since 1998 in the southern part of the Tibetan Plateau, and since 2005 in its northern part. The shift in air temperatures around 1997 and the precipitation shifts around 1998 and 2004 contributed to the trend’s turning points, with precipitation being the major contributor to the heterogeneous pattern of lake levels. Our findings reveal the linkage of the heterogeneous pattern of lake levels to climatic factors in the endorheic basins, providing a further understanding of the hydrological regime in the Eurasian endorheic zone and its sensitivity to climate change.
A Novel Generation Method of High Quality Video Image for High Resolution Airborne ViSAR
Authors: Chen Jingwei , An Daoxiang , Wang Wu , Chen Leping , Feng Dong , Zhou Zhimin
Published: September 2021

Video synthetic aperture radar (ViSAR) can provide long-time surveillance of a region of interest (ROI), which is one of the hotspot directions in the SAR field. In order to better display ViSAR, a high resolution and high frame rate are needed. Azimuth integration angle and sub-aperture overlapping ratio, which determine the image resolution and frame rate, respectively, are analyzed in depth in this paper. For SAR imaging algorithm, polar format algorithm (PFA) is applied, which not only has high efficiency but is also easier to integrate with autofocus algorithms. Due to sensitivity to motion error, it is very difficult to obtain satisfactory focus quality, especially for SAR systems with a high carrier frequency. The three-step motion compensation (MOCO) proposed in this paper, which combines GPS-based MOCO, map-drift (MD) and phase gradient autofocus (PGA), can effectively compensate for motion error, especially for short wavelengths. In ViSAR, problems such as jitter, non-uniform grey scale and low image signal noise ratio (SNR) between different aspects images also need to be considered, so a ViSAR generation method is proposed to solve the above problems. Finally, the results of ViSAR in THz and Ku band demonstrate the effectiveness and practicability of the proposed method.
Looking for Change? Roll the Dice and Demand Attention
Authors: Diakogiannis Foivos I. , Waldner François , Caccetta Peter
Published: September 2021

Change detection, i.e., the identification per pixel of changes for some classes of interest from a set of bi-temporal co-registered images, is a fundamental task in the field of remote sensing. It remains challenging due to unrelated forms of change that appear at different times in input images. Here, we propose a deep learning framework for the task of semantic change detection in very high-resolution aerial images. Our framework consists of a new loss function, a new attention module, new feature extraction building blocks, and a new backbone architecture that is tailored for the task of semantic change detection. Specifically, we define a new form of set similarity that is based on an iterative evaluation of a variant of the Dice coefficient. We use this similarity metric to define a new loss function as well as a new, memory efficient, spatial and channel convolution Attention layer: the FracTAL. We introduce two new efficient self-contained feature extraction convolution units: the CEECNet and FracTALResNet units. Further, we propose a new encoder/decoder scheme, a network macro-topology, that is tailored for the task of change detection. The key insight in our approach is to facilitate the use of relative attention between two convolution layers in order to fuse them. We validate our approach by showing excellent performance and achieving state-of-the-art scores (F1 and Intersection over Union-hereafter IoU) on two building change detection datasets, namely, the LEVIRCD (F1: 0.918, IoU: 0.848) and the WHU (F1: 0.938, IoU: 0.882) datasets.
Multi-Object Segmentation in Complex Urban Scenes from High-Resolution Remote Sensing Data
Authors: Abdollahi Abolfazl , Pradhan Biswajeet , Shukla Nagesh , Chakraborty Subrata , Alamri Abdullah
Published: September 2021

Terrestrial features extraction, such as roads and buildings from aerial images using an automatic system, has many usages in an extensive range of fields, including disaster management, change detection, land cover assessment, and urban planning. This task is commonly tough because of complex scenes, such as urban scenes, where buildings and road objects are surrounded by shadows, vehicles, trees, etc., which appear in heterogeneous forms with lower inter-class and higher intra-class contrasts. Moreover, such extraction is time-consuming and expensive to perform by human specialists manually. Deep convolutional models have displayed considerable performance for feature segmentation from remote sensing data in the recent years. However, for the large and continuous area of obstructions, most of these techniques still cannot detect road and building well. Hence, this work’s principal goal is to introduce two novel deep convolutional models based on UNet family for multi-object segmentation, such as roads and buildings from aerial imagery. We focused on buildings and road networks because these objects constitute a huge part of the urban areas. The presented models are called multi-level context gating UNet (MCG-UNet) and bi-directional ConvLSTM UNet model (BCL-UNet). The proposed methods have the same advantages as the UNet model, the mechanism of densely connected convolutions, bi-directional ConvLSTM, and squeeze and excitation module to produce the segmentation maps with a high resolution and maintain the boundary information even under complicated backgrounds. Additionally, we implemented a basic efficient loss function called boundary-aware loss (BAL) that allowed a network to concentrate on hard semantic segmentation regions, such as overlapping areas, small objects, sophisticated objects, and boundaries of objects, and produce high-quality segmentation maps. The presented networks were tested on the Massachusetts building and road datasets. The MCG-UNet improved the average F1 accuracy by 1.85%, and 1.19% and 6.67% and 5.11% compared with UNet and BCL-UNet for road and building extraction, respectively. Additionally, the presented MCG-UNet and BCL-UNet networks were compared with other state-of-the-art deep learning-based networks, and the results proved the superiority of the networks in multi-object segmentation tasks.
An Investigation into Ground Movement on the Ventnor Landslide Complex, UK Using Persistent Scatterer Interferometry
Authors: O’Connor William , Mider Gosia , Lawrence James A. , Agar Stewart , Mason Philippa J. , Ghail Richard , Scoular Jennifer
Published: September 2021

Analysis of ground movement rates along the coastline and upper sections of the Ventnor landslide complex was carried out utilizing Persistent Scatterer Interferometric Synthetic Aperture Radar methods using Sentinel-1 SAR data from 2015 to 2019 (four years). Results were compared with rainfall data, historical ground investigation records and monitoring surveys carried out at Ventnor to relate observations to geology, geomorphology and rainfall. Decomposition of InSAR viewing geometries to vertical and horizontal aligned well with previous ground-based studies. Subsidence of −9.8 mm a−1 at the Lowtherville Graben and heave of +8.5 mm a−1 along the coastline south of Ventnor Park were observed. Decomposition to east-west geometry results showed an eastward displacement of approximately 12.4 mm a−1 along the coastline south of Ventnor Park, and a westward displacement of −3.7 mm a−1 throughout built up sections of Ventnor town, indicating the landslide was displacing more in an eastern direction than vertically. The cause of this movement was investigated by using publicly available intrusive boreholes paired with Persistent Scatterer Interferometry, and a new ground model spanning east-west parallel to the coastline was presented. No evidence of significant ground movement was observed along heavily protected sections of the coastline, suggesting coastal defences comprised of concrete aprons and rip rap appear to be an effective coastal management/landslide stabilisation tool when compared to rip rap alone. The mechanism of this increased stability is likely due to the combination of toe weighting and reduced toe erosion. A lag of approximately 13–20 days was observed between high rainfall events and subsequent peaks in ground displacement, which was shorter than a 29 day lag observed in a previous study. Similar observations of prolonged rainfall resulting in prolonged displacements were also observed. The study demonstrates the capabilities of the PSI methodology in identifying the same ground movements that conventional methods provide. By providing detailed analysis of ground deformation of the Ventnor landslide, we demonstrate small ground movements, validated with existing ground movement surveys. Similar methodology can be applied to coastal landslides in urban environments worldwide, providing a relatively cheap and rapid resource for coastal landslide monitoring.
Dual Attention Feature Fusion and Adaptive Context for Accurate Segmentation of Very High-Resolution Remote Sensing Images
Authors: Shi Hao , Fan Jiahe , Wang Yupei , Chen Liang
Published: September 2021

Land cover classification of high-resolution remote sensing images aims to obtain pixel-level land cover understanding, which is often modeled as semantic segmentation of remote sensing images. In recent years, convolutional network (CNN)-based land cover classification methods have achieved great advancement. However, previous methods fail to generate fine segmentation results, especially for the object boundary pixels. In order to obtain boundary-preserving predictions, we first propose to incorporate spatially adapting contextual cues. In this way, objects with similar appearance can be effectively distinguished with the extracted global contextual cues, which are very helpful to identify pixels near object boundaries. On this basis, low-level spatial details and high-level semantic cues are effectively fused with the help of our proposed dual attention mechanism. Concretely, when fusing multi-level features, we utilize the dual attention feature fusion module based on both spatial and channel attention mechanisms to relieve the influence of the large gap, and further improve the segmentation accuracy of pixels near object boundaries. Extensive experiments were carried out on the ISPRS 2D Semantic Labeling Vaihingen data and GaoFen-2 data to demonstrate the effectiveness of our proposed method. Our method achieves better performance compared with other state-of-the-art methods.
Deep Learning for Chlorophyll-a Concentration Retrieval: A Case Study for the Pearl River Estuary
Authors: Ye Haibin , Tang Shilin , Yang Chaoyu
Published: September 2021

The abundance of phytoplankton is generally estimated by measuring the chlorophyll-a concentration (Cchla), which is an important factor in photosynthesis and can be used to analyze the density and biomass of phytoplankton in the ecosystem. The band-ratio-based empirical or semi-analytical algorithms are operationally applied to retrieve Cchla in global oceans, which generally experience difficulties from the diversity of optical properties and the complexity of the radiative transfer equations in analytical analyses, respectively. With an attempt to develop an accurate Cchla retrieval model for the optically complex coastal and estuarine waters, this study aimed to explore the deep learning (DL) methods in satellite retrieval of Cchla. A two-stage convolutional neural network (CNN), named Cchla-Net, was proposed, which utilized the spectral information of remote sensing reflectances at MODIS/Aqua’s visible bands. In the first-stage phase, the Cchla-Net was pretrained by a set of remote sensing patches, in which the Cchla was generated from an existing model (OC3M). The pretrained results were than used as the initial values to refine the network with the synthetic oversampled in-situ dataset in the second-stage training phase. Using in-situ samples for training with the new initial values has a higher probability to reach the global optimum. The quantitative analyses showed that the two-stage training was more likely to achieve a global optimum in the optimization than the one-stage training. Matchups of the in-situ Cchla measurements were used to evaluate the retrieval models. Results showed that the proposed Cchla-Net produced obvious better performance than the empirical and semi-analytical algorithms, implying the DL method was more effective for optically complex waters with extremely high Cchla. This study provided an applicable method for remote sensing retrieval of Cchla, which should be helpful for studying the spatial distribution and temporal variability in the productive Pearl River estuary (PRE) waters.
Determining Evapotranspiration by Using Combination Equation Models with Sentinel-2 Data and Comparison with Thermal-Based Energy Balance in a California Irrigated Vineyard
Authors: D’Urso Guido , Bolognesi Salvatore Falanga , Kustas William P. , Knipper Kyle R. , Anderson Martha C. , Alsina Maria M. , Hain Christopher R. , Alfieri Joseph G. , Prueger John H. , Gao Feng , McKee Lynn G. , De Michele Carlo , McElrone Andrew J. , Bambach Nicolas , Sanchez Luis , Belfiore Oscar Rosario
Published: September 2021

A new approach is proposed to derive evapotranspiration (E) and irrigation requirements by implementing the combination equation models of Penman–Monteith and Shuttleworth and Wallace with surface parameters and resistances derived from Sentinel-2 data. Surface parameters are derived from Sentinel-2 and used as an input in these models; namely: the hemispherical shortwave albedo, leaf area index and water status of the soil and canopy ensemble evaluated by using a shortwave infrared-based index. The proposed approach has been validated with data acquired during the GRAPEX (Grape Remote-sensing Atmospheric Profile and Evapotranspiration eXperiment) in California irrigated vineyards. The E products obtained with the combination equation models are evaluated by using eddy covariance flux tower measurements and are additionally compared with surface energy balance models with Landsat-7 and -8 thermal infrared data. The Shuttleworth and Wallace (S-W S-2) model provides an accuracy comparable to thermal-based methods when using local meteorological data, with daily E errors < 1 mm/day, which increased from 1 to 1.5 mm/day using meteorological forcing data from atmospheric models. The advantage of using the S-W S-2 modeling approach for monitoring ET is the high temporal revisit time of the Sentinel-2 satellites and the finer pixel resolution. These results suggest that, by integrating the thermal-based data fusion approach with the S-W S-2 modeling scheme, there is the potential to increase the frequency and reliability of satellite-based daily evapotranspiration products.
Data Quality Evaluation of Sentinel-1 and GF-3 SAR for Wind Field Inversion
Authors: Wan Yong , Guo Sheng , Li Ligang , Qu Xiaojun , Dai Yongshou
Published: September 2021

Synthetic aperture radar (SAR) is an important means to observe the sea surface wind field. Sentinel-1 and GF-3 are located on orbit SAR satellites, but the SAR data quality of these two satellites has not been evaluated and compared at present. This paper mainly studies the data quality of Sentinel-1 and GF-3 SAR satellites used in wind field inversion. In this study, Sentinel-1 SAR data and GF-3 SAR data located in Malacca Strait, Hormuz Strait and the east and west coasts of the United States are selected to invert wind fields using the C-band model 5.N (CMOD5.N). Compared with reanalysis data called ERA5, the root mean squared error (RMSE) of the Sentinel-1 inversion results is 1.66 m/s, 1.37 m/s and 1.49 m/s in three intervals of 0~5 m/s, 5~10 m/s and above 10 m/s, respectively; the RMSE of GF-3 inversion results is 1.63 m/s, 1.45 m/s and 1.87 m/s in three intervals of 0~5 m/s, 5~10 m/s and above 10 m/s, respectively. Based on the data of Sentinel-1 and GF-3 located on the east and west coasts of the United States, CMOD5.N is used to invert the wind field. Compared with the buoy data, the RMSE of the Sentinel-1 inversion results is 1.20 m/s, and the RMSE of the GF-3 inversion results is 1.48 m/s. The results show that both Sentinel-1 SAR data and GF-3 SAR data are suitable for wind field inversion, but the wind field inverted by Sentinel-1 SAR data is slightly better than GF-3 SAR data. When applied to wind field inversion, the data quality of Sentinel-1 SAR is slightly better than the data quality of GF-3 SAR. The SAR data quality of GF-3 has achieved a world-leading level.
MSNet: A Multi-Stream Fusion Network for Remote Sensing Spatiotemporal Fusion Based on Transformer and Convolution
Authors: Li Weisheng , Cao Dongwen , Peng Yidong , Yang Chao
Published: September 2021

Remote sensing products with high temporal and spatial resolution can be hardly obtained under the constrains of existing technology and cost. Therefore, the spatiotemporal fusion of remote sensing images has attracted considerable attention. Spatiotemporal fusion algorithms based on deep learning have gradually developed, but they also face some problems. For example, the amount of data affects the model’s ability to learn, and the robustness of the model is not high. The features extracted through the convolution operation alone are insufficient, and the complex fusion method also introduces noise. To solve these problems, we propose a multi-stream fusion network for remote sensing spatiotemporal fusion based on Transformer and convolution, called MSNet. We introduce the structure of the Transformer, which aims to learn the global temporal correlation of the image. At the same time, we also use a convolutional neural network to establish the relationship between input and output and to extract features. Finally, we adopt the fusion method of average weighting to avoid using complicated methods to introduce noise. To test the robustness of MSNet, we conducted experiments on three datasets and compared them with four representative spatiotemporal fusion algorithms to prove the superiority of MSNet (Spectral Angle Mapper (SAM) < 0.193 on the CIA dataset, erreur relative global adimensionnelle de synthese (ERGAS) < 1.687 on the LGC dataset, and root mean square error (RMSE) < 0.001 on the AHB dataset).
An Improved Method of Soil Moisture Retrieval Using Multi-Frequency SNR Data
Authors: Chen Kun , Cao Xinyun , Shen Fei , Ge Yulong
Published: September 2021

Soil moisture monitoring using Global Navigation Satellite System (GNSS) multipath signals has gained continuous interests in recent years. However, traditional GNSS-interferometric reflectometry (GNSS-IR) soil moisture retrieval methods generally utilize a single frequency or single satellite, which fail to take full advantage of different and complementary of satellite signals with different frequencies. An improved algorithm for soil moisture retrieval based on principal component analysis (PCA) and entropy method using multi-frequency amplitude and phase offset fusion data was proposed in this research. The performance of the proposed soil moisture retrieval method was evaluated using data recorded by Plate Boundary Observatory (PBO) H2O networks and a self-built site in Henan, China. The results from GPS and BeiDou both showed that the retrieved soil moisture has a stronger correlation with in situ soil moisture, which can better reflect the fluctuation of ground truth measurements. Compared with the traditional method, the retrieval accuracy of the proposed method in terms of root-mean-square error (RMSE) was improved by 50.93%, and the average correlation coefficient were increased by 11.71%. This research proved that the proposed method could effectively improve retrieval accuracy due to the increasing number of frequencies and tracks clustering. Moreover, this study has illustrated the feasibility of BeiDou signals to precisely estimate surface soil moisture.
Pass-by-Pass Ambiguity Resolution in Single GPS Receiver PPP Using Observations for Two Sequential Days: An Exploratory Study
Authors: Xi Ruijie , Chen Qusen , Meng Xiaolin , Psimoulis Panos , Jiang Weiping , Xu Caijun
Published: September 2021

“Pass-by-pass” or “track-to-track” ambiguity resolution removes Global Navigation Satellite System (GNSS) satellite hardware delays between adjacent undifferenced (UD) ambiguities, which is often applied in precise orbit determination (POD) for Low Earth Orbit (LEO) satellites to improve the accuracy of orbits. In this study, we carried out an exploratory study to use the “pass-by-pass” ambiguity resolution by differencing the undifferenced ambiguity candidates for two adjacent passes in sidereal days for a single Global Positioning System (GPS) receiver static Precise Point Positioning (PPP). Using the GPS observations from 132 globally distributed reference stations of International GPS Service (IGS), we find that 99.08% wide-lane (WL) and 97.83% narrow-lane (NL) double-difference ambiguities formed by the “pass-by-pass” method for all stations can be fixed to their nearest integers within absolute fractional residuals of 0.2 cycles. These proportions are higher than the corresponding values of network solution with multiple receivers with 97.39% and 91.20%, respectively. About 97% to 98% of ambiguities can be fixed finally on average. The comparison of the estimated station coordinates with the IGS weekly solutions reveals that the Root Mean Square (RMS) in East and North directions are 2-4 mm and is about 6 mm in the Up direction. For hourly data, it is found that the mean positioning accuracy improvement can achieve to about 10% after ambiguity resolution. From a dam deformation monitoring application, it shows that the fixing rate of WL and NL ambiguity can be closed to 100% and higher than 90%, respectively. The time series generated by PPP are also in agreement with the short baseline solutions.
Predicting Arbitrary-Oriented Objects as Points in Remote Sensing Images
Authors: Wang Jian , Yang Le , Li Fan
Published: September 2021

To detect rotated objects in remote sensing images, researchers have proposed a series of arbitrary-oriented object detection methods, which place multiple anchors with different angles, scales, and aspect ratios on the images. However, a major difference between remote sensing images and natural images is the small probability of overlap between objects in the same category, so the anchor-based design can introduce much redundancy during the detection process. In this paper, we convert the detection problem to a center point prediction problem, where the pre-defined anchors can be discarded. By directly predicting the center point, orientation, and corresponding height and width of the object, our methods can simplify the design of the model and reduce the computations related to anchors. In order to further fuse the multi-level features and get accurate object centers, a deformable feature pyramid network is proposed, to detect objects under complex backgrounds and various orientations of rotated objects. Experiments and analysis on two remote sensing datasets, DOTA and HRSC2016, demonstrate the effectiveness of our approach. Our best model, equipped with Deformable-FPN, achieved 74.75% mAP on DOTA and 96.59% on HRSC2016 with a single-stage model, single-scale training, and testing. By detecting arbitrarily oriented objects from their centers, the proposed model performs competitively against oriented anchor-based methods.
Indoor Experiments of Bistatic/Multistatic GB-SAR with One-Stationary and One-Moving Antennae
Authors: Lee Hoonyol , Moon Jihyun
Published: September 2021

Ground-based synthetic aperture radar (GB-SAR) is a useful tool to simulate advanced SAR systems with its flexibility on RF system and SAR configuration. This paper reports an indoor experiment of bistatic/multistatic GB-SAR operated in Ku-band with two antennae: one antenna was stationary on the ground and the other was moving along a linear rail. Multiple bistatic GB-SAR images were taken with various stationary antenna positions, and then averaged to simulate a multistatic GB-SAR configuration composed of a moving Tx antenna along a rail and multiple stationary Rx antennae with various viewing angles. This configuration simulates the use of a spaceborne/airborne SAR system as a transmitting antenna and multiple ground-based stationary antennae as receiving antennae to obtain omni-directional scattering images. This SAR geometry with one-stationary and one-moving antennae configuration was analyzed and a time-domain SAR focusing algorithm was adjusted to this geometry. Being stationary for one antenna, the Doppler rate was analyzed to be half of the monostatic case, and the azimuth resolution was doubled. Image quality was enhanced by identifying and reducing azimuth ambiguity. By averaging multiple bistatic images from various stationary antenna positions, a multistatic GB-SAR image was achieved to have better image swath and reduced speckle noise.
The Role of Satellite InSAR for Landslide Forecasting: Limitations and Openings
Authors: Moretto Serena , Bozzano Francesca , Mazzanti Paolo
Published: September 2021

The paper explores the potential of the satellite advanced differential synthetic aperture radar interferometry (A-DInSAR) technique for the identification of impending slope failure. The advantages and limitations of satellite InSAR in monitoring pre-failure landslide behaviour are addressed in five different case histories back-analysed using data acquired by different satellite missions: Montescaglioso landslide (2013, Italy), Scillato landslide (2015, Italy), Bingham Canyon Mine landslide (2013, UT, USA), Big Sur landslide (2017, CA, USA) and Xinmo landslide (2017, China). This paper aimed at providing a contribution to improve the knowledge within the subject area of landslide forecasting using monitoring data, in particular exploring the suitability of satellite InSAR for spatial and temporal prediction of large landslides. The study confirmed that satellite InSAR can be successful in the early detection of slopes prone to collapse; its limitations due to phase aliasing and low sampling frequency are also underlined. According to the results, we propose a novel landslide predictability classification discerning five different levels of predictability by satellite InSAR. Finally, the big step forward made for landslide forecasting applications since the beginning of the first SAR systems (ERS and Envisat) is shown, highlighting that future perspectives are encouraging thanks to the expected improvement of upcoming satellite missions that could highly increase the capability to monitor landslides’ pre-failure behaviour.
Evaluating the Applicability of Thermal Infrared Remote Sensing in Estimating Water Potential of the Karst Aquifer: A Case Study in North Adriatic, Croatia
Authors: Horvat Bojana , Rubinić Josip
Published: September 2021

One of the most prominent tourist destinations in the Adriatic coast, the city of Opatija, is facing a problem concerning seasonal drinking water shortages. The existing water resources are no longer sufficient, and attention is being given to alternative resources such as the underlying karstic aquifer and several coastal springs in the city itself. However, the water potential of the area still cannot be estimated due to the insufficient hydrological data. The goal of this research was to evaluate the use of thermal infrared (TIR) remote sensing as the source of valuable information that will improve our understanding of the groundwater discharge dynamics. Ten Landsat ETM+ (enhanced thematic mapper plus) and two Landsat TM (thematic mapper) images of the north Adriatic, recorded during 1999–2004 at the same time as the field discharge measurements, were used to derive sea surface temperature (SST) and to analyze freshwater outflows seen as the thermal anomaly in the TIR images. The approach is based on finding the functional relationship between the size of the freshwater thermal signatures and the measured discharge data, and to estimate the water potential of the underlying aquifer. It also involved analyzing the possible connection between the adjusted size of the spring’s thermal signatures and groundwater level fluctuations in the deeper karst hinterland. The proposed methodology resulted in realistic discharge estimates, as well as a good fit between thermal anomalies with measured discharges and the groundwater level. It should be emphasized that the results are site specific and based on a limited data set. However, they confirm that the proposed method can provide additional information on groundwater outflow dynamics and coastal springs’ freshwater quantification.
A Multi-Analytical Study for the Enhancement and Accessibility of Archaeological Heritage: The Churches of San Nicola and San Basilio in Motta Sant’Agata (RC, Italy)
Authors: Giuffrida Dario , Mollica Nardo Viviana , Neri Daniela , Cucinotta Giovanni , Calabrò Irene Vittoria , Pace Loredana , Ponterio Rosina Celeste
Published: September 2021

In the coming years, Italy will need to take on a great challenge concerning the digitization of its archaeological and architectural heritage, one of the richest and most problematic in the world. The aim is to improve the knowledge, conservation, enhancement and accessibility of cultural assets and to make them a resource for national and local development. In this process, the next generation of 3D survey methods (laser scanning and photogrammetry), in combination with diagnostic techniques (spectroscopy analyses) and GIS/BIM (Geographic Information System/Building Information Modeling) solutions, represent a valid support. This work, part of a broader intervention launched by the Municipality of Reggio Calabria for the requalification of some archaeological sites located within its urban and metropolitan area, is focused on the study case of Motta S. Agata. The ancient settlement is located 8 km from Reggio C. in a hilly area difficult to reach and preserves numerous structures in a state of ruin. Among these, two interesting medieval churches are proposed for examination: the church of San Nicola, characterized by five hypogeal funeral crypts, and the chapel of San Basilio, which preserves the traces of a wall painting. A multi-methodological approach including close-range photogrammetry, laser scanning and chemical and thermal analyses was adopted in order to fulfill different tasks: creating a topographic model of the hillfort, mapping the archaeological evidence, digitizing and returning 3D models of the churches, characterizing materials through chemical analyses and monitoring the surfaces with thermal imaging. These combined applications have contributed to reaching the planned goals, i.e., study, conservation, diagnostics, preparation for restoration interventions, development of digital media and dissemination. In this way, a type of interactive museum (made up of virtual tours and informative digital models) has been made available in order to improve the site’s accessibility and inclusivity as well as to test the effect of digitization in attracting tourists and local people toward a place located outside of the usual tourist circuits.
Link Budget Analysis with Laser Energy for Time Transfer Using the Ajisai Satellite
Authors: Park Jong Uk , Lim Hyung-Chul , Sung Ki-Pyoung , Choi Mansoo
Published: September 2021

Two-way Laser Time Transfer (TLTT) using the Ajisai satellite has been considered as a more accurate and stable time transfer technique than existing methods; TLTT requires the kHz laser pulses to decrease the systematic restrictions for TLTT realization. However, because of the low energy of the kHz laser pulses as well as the low cross section due to the small size of the Ajisai reflecting mirror, the link budget is an important issue to establish the TLTT link between two ground stations. In this study, the TLTT link budget is investigated to find the optimal laser pulse energy via analysis of geometric effects using 30 days of orbital data of the Ajisai satellite from 29 March 2021 within a ground network consisting of four stations located in three countries. The geometric configuration reduces the TLTT link budget by three orders of magnitude due to free space loss, atmospheric transmission, and effective cross section; then, the pulse energy is required to be much higher than laser ranging to the Ajisai satellite. It is shown from the simulation that a few tens of mJ level of pulse energy at the transmitting station is quite enough for TLTT realization.
Identification of NO2 and SO2 Pollution Hotspots and Sources in Jiangsu Province of China
Authors: Wang Yu , Ali Md. Arfan , Bilal Muhammad , Qiu Zhongfeng , Mhawish Alaa , Almazroui Mansour , Shahid Shamsuddin , Islam M. Nazrul , Zhang Yuanzhi , Haque Md. Nazmul
Published: September 2021

Nitrogen dioxide (NO2) and sulfur dioxide (SO2) are important atmospheric trace gases for determining air quality, human health, climate change, and ecological conditions both regionally and globally. In this study, the Ozone Monitoring Instrument (OMI), total column nitrogen dioxide (NO2), and sulfur dioxide (SO2) were used from 2005 to 2020 to identify pollution hotspots and potential source areas responsible for air pollution in Jiangsu Province. The study investigated the spatiotemporal distribution and variability of NO2 and SO2, the SO2/NO2 ratio, and their trends, and potential source contribution function (PSCF) analysis was performed to identify potential source areas. The spatial distributions showed higher values (>0.60 DU) of annual mean NO2 and SO2 for most cities of Jiangsu Province except for Yancheng City (<0.50 DU). The seasonal analyses showed the highest NO2 and SO2 in winter, followed by spring, autumn, and summer. Coal-fire-based room heating and stable meteorological conditions during the cold season may cause higher NO2 and SO2 in winter. Notably, the occurrence frequency of NO2 and SO2 of >1.2 was highest in winter, which varied between 9.14~32.46% for NO2 and 7.84~21.67% for SO2, indicating a high level of pollution across Jiangsu Province. The high SO2/NO2 ratio (>0.60) indicated that industry is the dominant source, with significant annual and seasonal variations. Trends in NO2 and SO2 were calculated for 2005–2020, 2006–2010 (when China introduced strict air pollution control policies during the 11th Five Year Plan (FYP)), 2011–2015 (during the 12th FYP), and 2013–2017 (the Action Plan of Air Pollution Prevention and Control (APPC-AC)). Annually, decreasing trends in NO2 were more prominent during the 12th FYP period (2011–2015: −0.024~−0.052 DU/year) than in the APPC-AC period (2013–2017: −0.007~−0.043 DU/year) and 2005–2020 (−0.002 to −0.012 DU/year). However, no prevention and control policies for NO2 were included during the 11th FYP period (2006–2010), resulting in an increasing trend in NO2 (0.015 to 0.031) observed throughout the study area. Furthermore, the implementation of China’s strict air pollution control policies caused a larger decrease in SO2 (per year) during the 12th FYP period (−0.002~−0.075 DU/year) than in the 11th FYP period (−0.014~−0.071 DU/year), the APPC-AC period (−0.007~−0.043 DU/year), and 2005–2020 (−0.015~−0.032 DU/year). PSCF analysis indicated that the air quality of Jiangsu Province is mainly influenced by local pollution sources.