bluuebunny/crossref_metadata_2025 huggingface dataset:
Analysis of Heat Transfer and Flow Characteristics of a Helically Coiled Tube with Twisted Elliptical in a Low Reynolds Number Flow
Authors: Wang Jun , Liu Yaohui , Ding Ruoxi
Published: October 2022

In this paper, the heat transfer and flow characteristics of a helically coiled tube with twisted elliptical in a low Reynolds number (Re = 500–3000) flow were investigated numerically. The working fluid flowed in a laminar regime. Numerical results were compared with empirical correlations in the existing literature, demonstrating the accuracy of the analysis in this study. Firstly, we investigated the effects of geometric parameters and Reynolds number on the heat transfer and flow characteristics. The results showed that lower twist pitch length and semi-major axis length could induce sufficient fluid mixing and a larger temperature gradient near the tube wall, enhancing the heat exchange and producing larger friction resistance. Overall, the heat transfer performances were improved by about 1.04–1.21 times and 1.02–1.23 times for different semi-major axis lengths and different twist pitch lengths, respectively, compared to the smooth helical coil. Secondly, by changing the working fluid, it was found that the Nu when oil was chosen as the working fluid was all 6–6.8 times higher than that of water. Moreover, the effect of using Al2O3 nanofluids with a concentration of 0.10 wt.%, 0.25 wt.% and 0.50 wt.% was discussed. It can be seen that the heat transfer capacity of nanofluids increased by approximately 2–18% compared to the reference model with water as the working fluid, and the nanofluid solution with higher concentration has better heat transfer performance. Finally, Nu and f correlations are given to predict the heat transfer and pressure drop in practical applications.
Religious and Economic Soft Power in Ghana-Turkey Relations
Authors: Haynes Jeffrey
Published: October 2022

Turkey’s government seeks to apply both “religious soft power” and “economic soft power” to increase its influence in Ghana. Turkey’s religious soft power relationship with Ghana is exemplified by Turkey’s paying for construction of a new national mosque in the African country, at a cost of USD 10 million. Turkey’s economic soft power relationship with Ghana is exemplified by both considerable recent investments and in fast-growing bilateral trade. The overall aim of the government of Turkey is to increase the country’s influence in Ghana, part of a wider initiative to expand its regional influence in Africa. Ghana is important to Turkey as it is regarded as a strategically important African country, one of the region’s few democracies and an economic success. The paper assesses Turkey’s various forms of influence in Ghana and considers what Turkey hopes to achieve in foreign policy terms. The paper is in four sections. The first examines religious soft power and Turkey–Ghana relations, noting that recently they have become closer and more cordial, involving both religious and non-religious aspects. The second section examines Muslims’ traditionally marginal political position in Ghana and explains that over time Muslims have become more politically assertive, open to external religious influences, including from Turkey, a country well known to use religious soft power to try to expand its foreign policy influence. The third section assesses recent Ghana-Turkey relations, including the expansion of Turkey’s economic soft power, with three examples: the national mosque, encouraging Islamic education, and mutual desire to quell the activities of what the government of Turkey refers to as the “Fethullah Terrorist Organisation”. The section also considers the role of Turkey’s economic soft power in increasing the country’s presence in Ghana. The concluding section argues that the government of Turkey uses several techniques to increase its influence in Ghana, including both religious soft power and economic soft power. The government of Ghana broadly welcomes Turkey’s influence from both religious and economic perspectives: from a religious point of view, Turkey’s Sunni orthodoxy is seen as very unlikely to stimulate radicalization among Ghana’s Muslims, while Turkey’s economic presence is welcomed as an important means to help further build Ghana’s economy.
The Problem of Evil and God’s Moral Standing: A Rejoinder to James Sterba
Authors: Huffling J. Brian
Published: October 2022

This article is a rejoinder to James Sterba’s response to my previous article on the topic of his book, Is a Good God Logically Possible? Sterba argues that a good God is not logically possible given the amount of horrendous evil in the world. If God did exist, Sterba asserts, then he would be able to prevent such evils from happening while not losing any goods. My original article was a response to the notion that God is morally obligated to prevent such evil. The main points considered here are whether there really is a logical problem of evil and how God can have moral virtues ascribed to him while not being morally obligated in the sense that Sterba’s position requires.
What Comes Next: Continuing the Digital Ecclesiology Conversation in Response to the COVID-19 Pandemic
Authors: O’Lynn Rob
Published: October 2022

This essay seeks to add to the emerging conversation regarding digital ecclesiology. In short, digital ecclesiology is an ongoing conversation not only about how congregations use technology but craft digital spaces for worship and ministry. This essay will seek to add in four ways. First, this essay will explore the concern of techno-ontology. As articulated by Ashley John Moyse, techno-ontology occurs when humans lose their identity to technology by being conformed to the limits of technology. Concerns such as “Zoom fatigue” and content proliferation will be given attention here. Next, this essay will explore a homiletic response which was adopted largely wholesale, whether done so critically or uncritically, during the COVID-19 pandemic—conversational preaching. Then, this concern will come into focus through a brief textual analysis of Hebrews 10:19–25. Finally, a way forward—the “what comes next”—will be considered and proposed. This way forward will be articulated in two forms. First, there will be the overall ecclesiastical, or congregational, focus. Second, there will be the specific homiletic and liturgical focus. The essay will conclude with an invitation for continued conversation.
Fully Ordained Nuns in Fourteenth-to-Seventeenth Tibetan Hagiographical Narratives
Authors: Wu Fan
Published: October 2022

Many contemporary efforts have been put to (re-)establish the order of fully ordained nuns in Tibetan Buddhism. Those who are in favor of such practice often refer to premodern Tibetan hagiographies to claim the existence of indigenous fully ordained nuns in the past. A series of female practitioners, indeed, appear as fully ordained nuns in such narratives dating from approximately the fourteenth century to the seventeenth century. Their monastic identities as such, however, are contested by Tibetan Buddhist masters because the methods of their ordinations, seemingly conferred by the male saṃgha alone, do not strictly follow the Mūlasarvāstivāda Vinaya tradition, which is observed by the Tibetan Buddhists. In an effort to investigate as to how these female practitioners were fully ordained and the purposes of composing such narratives about their ordinations, this article revisits relevant hagiographies with particular reference to The biography of Chokyi Dronma, the Third incarnation of the WisdomḌākinī Sonam Peldren (Ye shes mkha’ ‘gro bsod nams dpal ‘dren gyi sku skye gsum pa rje btsun ma chos kyi sgron ma’i rnam thar) and a detailed exposition of The biography of Shākya Chokden (Shākya mchog ldan gyi rnam thar zhib mo rnam par ‘byed pa). It suggests that depicting these personas as fully ordained nuns serves the purpose of highlighting the hagiography subjects’ outstanding spiritual performance, while the recognition of monastic identity as such may not go beyond the context of these writings.
The Rising Impacts of the COVID-19 Pandemic and the Russia–Ukraine War: Energy Transition, Climate Justice, Global Inequality, and Supply Chain Disruption
Authors: Allam Zaheer , Bibri Simon Elias , Sharpe Samantha A.
Published: October 2022

This perspective paper explores the rising impacts of the COVID-19 and the Russia–Ukraine war from different perspectives, with an emphasis on the role of climate financing in achieving equitable and just transition mechanisms and that of peace in expediting this pursuit and sustaining this drive. It is motivated by the realization that there is an urgent need for accelerating the decarbonisation agenda, as highlighted in pre-COP26 debates and in the resulting Glasgow Climate Pact, through the mitigation measures that can be unpacked at both cost and scale. This is further reiterated in the third instalment of Assessment Report 6 (AR6) the Intergovernmental Panel on Climate Change (IPCC) report, dwelling on Mitigation of Climate Change, underlining the required policy shifts and technology developmental needs. Green technology, however, comes at a green premium, being more expensive to implement in geographies that cannot absorb its cost in the immediate short term. This engenders an inequitable and unjust landscape, as those that require green technology are unable to have access to it but are most often on the frontlines of the impacts of climate change. While it is urgent to review this issue and to encourage more cooperation for technology development and transfer, the COVID-19 pandemic and the Russia–Ukraine war are posing mounting challenges for achieving these objectives. These two crises are causing an unprecedented rise in commodities and labour pricing, with further knock-on impacts on global supply chains for technology. This is in turn rendering green technology unattainable for developing and less developed countries and Small Island Developing States (SIDS).
Estimation of Nearshore Wind Conditions Using Onshore Observation Data with Computational Fluid Dynamic and Mesoscale Models
Authors: Konagaya Mizuki , Ohsawa Teruo , Mito Toshinari , Misaki Takeshi , Maruo Taro , Baba Yasuyuki
Published: October 2022

This study aimed to establish numerical models to replicate wind conditions for nearshore waters, sensitive to onshore topography, and to compare the characteristics of computational fluid dynamic (CFD) and mesoscale models. Vertical Doppler light detection and ranging (LiDAR) observation data were measured at an onshore site, which showed that wind conditions were affected by thermodynamic phenomena, such as land and sea breeze, and dynamical effects from neighboring onshore topography. The estimation accuracy of the CFD model depended on the height of the LiDAR data input. A height close to the target, such as the hub height of wind turbines, seemed appropriate as input data, considering that the accuracy of the wind speed shear replicated in a CFD numerical model may be uncertain. The mesoscale model replicated the wind through the thermodynamic effect and reliably estimated wind speed over nearshore waters without observation correction. Larger estimation errors were detected in the CFD model than in the mesoscale model, as the former could not account for thermodynamic effects. Wind conditions in water areas near complex coastlines may also be formed by thermodynamic factors, making analysis using a mesoscale model advantageous.
Lineament Extraction from Digital Terrain Derivate Model: A Case Study in the Girón–Santa Isabel Basin, South Ecuador
Authors: Villalta Echeverria Michelle Del Pilar , Viña Ortega Ana Gabriela , Larreta Erwin , Romero Crespo Paola , Mulas Maurizio
Published: October 2022

Geological lineaments are linear or curvilinear surfaces that are considered a superficial expression of discontinuities on the earth’s surface. The extraction of lineaments from remotely sensed satellite data is one of the most frequently used applications of remote sensing in geology. This study focuses on the semi-automatic extraction of lineaments in the Girón–Santa Isabel basin using a Topographic Position Index (TPI). The lineaments were extracted in the PCI Geomatics 2016 software and analyzed in the ArcGIS and Rockworks software. Statistical and density map analyses of the lineaments were performed; then, these results were interpreted to obtain the geological lineaments. Finally, a bibliographic verification was carried out, and structures such as faults and folds were defined. The total number of geological lineaments was 76, and 71 of them were defined as faults, the longest with a length of 33 km. It was determined that the preferential orientation of the lineaments is NE–SW, which is consistent with the axis of the Girón–Santa Isabel basin. This methodology can be useful to optimize time and reduce costs when gathering the structural information of the study area in the first stage of geological and mining prospecting or the educational field.
On Tide Aliasing in GRACE Time-Variable Gravity Observations
Authors: Li Zhen , Zhang Zizhan , Wang Hansheng
Published: October 2022

Aliasing error induced by tide-related high frequency mass variations is one of the most significant errors in the Gravity Recovery and Climate Experiment (GRACE). In the present work, we evaluated the 161.0-day S2, 171.2-day P1, and 322.1-day S1 ocean tide aliasing in GRACE latest RL06 data based on nearly 15 years of observation from 2002 to 2017. Tide aliasing was still obvious for current GRACE observations, especially for S2 and P1 aliasing. S2 aliasing was mostly evident over West Antarctica, and was a clearly eastward propagation that travelled around Antarctica in about 2 years, while P1 showed strongest aliasing over South Greenland. More seriously, we found that GRACE mascon data showed an extremely large aliasing error. The mascon data may have unintentionally amplified the aliasing error on land due to the regularization (or constraint) applied for reducing signal leakage. Enough attention must be paid to tide aliasing when using GRACE for assessing mass variations at high latitudes (e.g., glaciers in polar regions) which can cause potential obstacles to estimation of actual seasonality.
Analysis of Comprehensive Multi-Factors on Station Selection for Moon-Based Earth Observation
Authors: Chen Guoqiang , Guo Huadong , Jiang Huiping , Han Chunming , Ding Yixing , Wu Kai
Published: October 2022

With the rapid development of Moon exploration, the concept of establishing Moon-based station for Earth observation (MBSEO) has received more and more attention. Compared with the space-borne Earth observation method, the MBSEO can observe Earth with advantages of higher stability, longer period, wider range, better integrity, and consistency. The MBSEO can not only cover the entire Earth disc, but also has good observation of the target area (TA) whose radius is several times that of the Earth radius (e.g., plasma-sphere/magnetosphere). Basically, site selection is a prerequisite for MBSEO. In this paper, the time coverage of TA (TCTA), e.g., the whole Earth disc or Earth-related plasma-sphere and magnetosphere, and the time coverage of sunlight (TCS) without topography are briefly presented to show their distribution. However, the unevenly distributed craters on the near side make TCTA and TCS badly affected by complicated topography, thereby causing those sites with a high selection possibility of a single factor to not necessarily be suitable for other factors. For example, potential sites at the low-middle latitude on the near side might have better TCTA and flat topography, but its TCS might be terrible for MBSEO. In order to evaluate the above factors comprehensively, a multi-factor fuzzy evaluation (MFE) method based on a multiple-input single-output (MISO) model will be utilized in the period of 18.6 nutation years. By using the proposed evaluation method, the sites near the dividing line, e.g., Amundsen, can still have the absolute possibility of exceeding 0.6, which was selected as MBSEO in low acceptability of sunlight. A similar situation exists in other areas with weaker sunlight constraint, e.g., Malapert A, in the case that the absolute selection possibility of areas becomes closer if more sunlight is tolerable. The results indicate that the areas with low thermal environment for radiation protection and relatively flat topography will have more possibility to be selected, and those potential sites unevenly distributed in craters can still be picked out through our proposed method.
Retrieving SPAD Values of Summer Maize Using UAV Hyperspectral Data Based on Multiple Machine Learning Algorithm
Authors: Sudu Bilige , Rong Guangzhi , Guga Suri , Li Kaiwei , Zhi Feng , Guo Ying , Zhang Jiquan , Bao Yulong
Published: October 2022

Using unmanned aerial vehicle (UAV) hyperspectral images to accurately estimate the chlorophyll content of summer maize is of great significance for crop growth monitoring, fertilizer management, and the development of precision agriculture. Hyperspectral imaging data, analytical spectral devices (ASD) data, and SPAD values of summer maize in different key growth periods were obtained under the conditions of a micro-spray strip drip irrigation water supply. The hyperspectral data were preprocessed by spectral transformation methods. Then, several algorithms including Findpeaks (FD), competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), and CARS_SPA were used to extract the sensitive characteristic bands related to SPAD values from the hyperspectral image data obtained by UAV. Subsequently, four machine learning regression models including partial least squares regression (PLSR), random forest (RF), extreme gradient boosting (XGBoost), and deep neural network (DNN) were used to establish SPAD value estimation models. The results showed that the correlation coefficient between the ASD and UAV hyperspectral data was greater than 0.96 indicating that UAV hyperspectral image data could be used to estimate maize growth information. The characteristic bands selected by different algorithms were slightly different. The CARS_SPA algorithm could effectively extract sensitive hyperspectral characteristics. This algorithm not only greatly reduced the number of hyperspectral characteristics but also improved the multiple collinearity problem. The low frequency information of SSR in spectral transformation could significantly improve the spectral estimation ability for SPAD values of summer maize. In the accuracy verification of PLSR, RF, XGBoost, and the DNN inversion model based on SSR and CARS_SPA, the determination coefficients (R2) were 0.81, 0.42, 0.65, and 0.82, respectively. The inversion accuracy based on the DNN model was better than the other models. Compared with high-frequency information, low-frequency information (DNN model based on SSR and CARS_SPA) had a strong estimating ability for SPAD values in summer maize canopy. This study provides a reference and technical support for the rapid non-destructive testing of summer maize.
Combining Dilution of Precision and Kalman Filtering for UWB Positioning in a Narrow Space
Authors: Guo Yunjian , Li Weihong , Yang Guang , Jiao Zhenhang , Yan Jiachen
Published: October 2022

Affected by the spatial environment, the accuracy and stability of ultra-wideband (UWB) positioning in a narrow space are significantly lower than those in the general indoor environment, which limits navigation and positioning services in a complex scene. To improve the positioning accuracy and stability of a narrow space, this study proposed a positioning algorithm by combining Kalman filter (KF) and dilution of precision (DOP). Firstly, we calculated the DOP values of the target narrow space by changing the location of the test nodes throughout the space. Secondly, the initial coordinate values of the test nodes were calculated by the weighted least squares (WLS) positioning algorithm and were used as the observation values of KF. Finally, the DOP values were adaptively introduced into KF to update the coordinates of the nodes to be tested. The proposed algorithm was tested in two narrow scenes with different length–width ratios. The experimental results showed that the DOP values of the narrow space were much higher than that of the wide space. Furthermore, even if the ranging error was low, the positioning error was high in the narrow space. The proposed fusion positioning algorithm reported a higher positioning accuracy in the narrow space, and the higher DOP values of the scene, the greater the accuracy improvement of the algorithm. This study reveals that no matter how the base stations are configured, the DOP values of the narrow space are much higher than that of the wide space, thus causing larger positioning errors. The proposed positioning algorithm can effectively suppress the positioning error caused by the narrow spatial structure, so as to improve the positioning accuracy and stability.
Investigating the Changes in Urban Green-Space Patterns with Urban Land-Use Changes: A Case Study in Hangzhou, China
Authors: Yin Jiadi , Fu Ping , Cheshmehzangi Ali , Li Zhichao , Dong Jinwei
Published: October 2022

Urban green space (e.g., parks, farmland, gardens, etc.) design in different urban functional regions (e.g., residential land, commercial land, etc.) depends on different planning purposes. The changes in urban green spaces are highly related to urban land-use changes (e.g., from residential land to commercial land). However, the investigation of urban green space patterns in response to urban land-use changes has been ignored. This research takes Hangzhou city, a typical example in terms of urbanization, population growth, economic development, and land-use changes, as the study site, aiming to explore the landscape patterns of urban green space changes with different urban land-use changes. The results showed that urban green spaces increased from 2017 to 2021, and the growth was mainly concentrated in the urban core area, indicating that the city has made remarkable achievements in the planning of green spaces. Specifically, the increase in urban green spaces in the first ring belt was mainly related to the old town transformation program in the residential land. The change from the residence parcels to the business parcels determined the increase in green spaces in the second and third ring belts, probably because of the attractiveness of customers. In addition, a large number of open-space parcels have changed to business parcels around the urban periphery, which might be due to the transformation from farmland to impervious surfaces. Combined with the urban land-use and green-space policies, the findings highlighted that a reasonable urban land-use layout can promote the optimization and layout of urban green spaces. The private sector (e.g., shopping malls) can also contribute to the increase in green spaces. The understanding of urban green landscapes with different urban land-use changes can provide references for analyzing and optimizing green space in other cities experiencing rapid urban land-use changes.
Climatology of TEC Longitudinal Difference in Middle Latitudes of East Asia
Authors: Sun Xingxin , Zhang Yuqiang , Feng Jian , Wu Zhensen , Xu Na , Xu Tong , Deng Zhongxin , Liu Yi , Zhang Fubin , Zhou Yufeng , Zhou Chen , Zhao Zhengyu
Published: October 2022

In this paper, a statistical analysis of the diurnal, seasonal and solar cycle variation in the TEC longitudinal difference in midlatitudes of East Asia is presented using CODE GIMs data in 2015–2019. Moreover, the empirical neutral wind model HWM-14 and geomagnetic field model IGRF-2020 were employed to analyze the influence of geomagnetic configuration-neutral wind mechanism on the TEC longitudinal difference, and the F2 layer peak electron density (NmF2) data from the Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) were also used to study the role of local electron density in the TEC longitudinal difference. For the high solar activity year, the results show that east-west TEC longitudinal difference index Re/w is negative in the noon and positive at evening-night. Moreover, the longitudinal difference of daytime TEC is most evident in summer, less in autumn and least in spring and winter, while the nighttime difference is most obvious in equinox, followed by summer and winter during nighttime. The model simulation shows that the TEC longitude difference around noon is mainly caused by the zonal wind-declination mechanism, and a 4-h time delay seems to be an optimal result for the vertical drift velocity to cause the longitudinal TEC difference during pre-noon hours. At night, the uplifting electron flux, which is a product of local electron density and vertical drift velocity, shows a good correlation with Re/w, indicating that the local electron density is also an important factor affecting the TEC longitudinal difference during the nighttime. Moreover, there was about a 3-h time delay between the TEC longitudinal variations and the uplifting electron flux at night. For the low solar activity years, the western TEC is greater than eastern TEC during most of the year except in the summer nighttime. The TEC diurnal variation in the east and west suggested that the nighttime Re/w should be related to other physical process, such as the midlatitude summer nighttime anomaly (MSNA) in the east and the ionospheric nighttime enhancement (INE). The current study provides evidence for the longitudinal difference of NmF2 in East Asian midlatitudes and geomagnetic configuration-neutral wind mechanism proposed in previous studies and finds some new features which need further studying to improve our current understanding of ionospheric longitudinal difference in the low solar activity years. The results provide new insight into TEC longitudinal variations at midlatitudes, and they can contribute to understanding the ionosphere-thermosphere coupling system.
Spatial-Temporal Relationship Analysis of Vegetation Phenology and Meteorological Parameters in an Agro-Pasture Ecotone in China
Authors: Fan Juncheng , Min Jie , Yang Qiang , Na Jiaming , Wang Xinyuan
Published: October 2022

Vegetation phenology is a sensitive indicator of climate change, and can help understand the response of vegetation cycles to climate, which is important for understanding the impact of global climate change on terrestrial ecosystems. In this study, based on the enhanced vegetation index (EVI) time-series data, derived from the moderate-resolution imaging spectroradiometer (MODIS) data, the climate parameters were extracted using the Savitzky–Golay (S–G) filtering method to explore the spatial and temporal variation characteristics of the vegetation phenology in an agro-pasture ecotone in China, from 2000 to 2020. In addition, the response characteristics of the vegetation phenology to the climate elements (temperature and precipitation) were also analyzed. The results showed the following: (1) The start of the growing season (SOS) was widely advanced, and that was caused by climate change. The end of the growing season (EOS) was delayed, and the length of the growing season (LOS) was gradually extended with a large interannual fluctuation in the SOS and the LOS in the region; (2) the SOS showed significant negative correlations with the air temperature and precipitation. Precipitation was mainly positively correlated with the EOS, but there was no significant difference in the correlation between temperature and the EOS. In general, pre-season precipitation is the main driver of the vegetation phenology, while the influence of temperature on the phenology is less obvious; (3) in the semi-arid area and arid area, the phenology was mainly influenced by precipitation. The response of the vegetation phenology to the temperatures in different temperature zones was found to be regular, showing high spatial differences. In general, the higher the cumulus temperature, the lower the negative effect of the temperature on both the SOS and EOS. These results may provide new reference to study the non-systematic changes of the vegetation phenology in response to climate change.
Modeling and Forecasting Ionospheric foF2 Variation in the Low Latitude Region during Low and High Solar Activity Years
Authors: Bi Cheng , Ren Peng , Yin Ting , Xiang Zheng , Zhang Yang
Published: October 2022

Prediction of ionospheric parameters, such as ionospheric F2 layer critical frequency (foF2) at low latitude regions is of significant interest in understanding ionospheric variation effects on high-frequency communication and global navigation satellite system. Currently, deep learning algorithms have made a striking accomplishment in capturing ionospheric variability. In this paper, we use the state-of-the-art hybrid neural network combined with a quantile mechanism to predict foF2 parameter variations under low and high solar activity years (solar cycle-24) and space weather events. The hybrid neural network is composed of a convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM), in which CNN and BiLSTM networks extracted spatial and temporal features of ionospheric variation, respectively. The proposed method was trained and tested on 5 years (2009–2014) of ionospheric foF2 observation data from Advanced Digital Ionosonde located in Brisbane, Australia (27°53′S, 152°92′E). It is evident from the results that the proposed model performs better than International Reference Ionosphere 2016 (IRI-2016), long short-term memory (LSTM), and BiLSTM ionospheric prediction models. The proposed model extensively captured the variation in ionospheric foF2 feature, and better predicted it under two significant space weather events (29 September 2011 and 22 July 2012).
Evaluating Spatiotemporal Patterns of Post-Eruption Vegetation Recovery at Unzen Volcano, Japan, from Landsat Time Series
Authors: Lai Roxanne , Oguchi Takashi , Zhong Chenxi
Published: October 2022

Quantifying vegetation responses after natural disasters helps clarify complex relationships between vegetation and surface processes such as soil erosion. The heterogenous post-disaster landscape offers a naturally stratified environment for this study. Existing research tends to be frequently monitored but small-scale or sporadically monitored but large-scale. The availability of high-quality and free satellite imagery bridges this gap by offering continuous, longer-term observations at the landscape scale. Here we take advantage of a dense Landsat time series to investigate landscape-scale vegetation response rates and factors at Unzen volcano, Japan. We do this by first investigating differences between two popular vegetation indices—The Normalized Difference Vegetation Index (NDVI) and the Normalized Burn Ratio (NBR), when applied to recovery studies. We then apply pixel-wise regressions to quantify spatio-temporal vegetation response and regression tree analyses to investigate drivers of recovery. Our findings showed that simple linear-log functions best model recovery rates reflecting primary succession trajectories caused by extreme disturbance and damage. Regression tree analyses showed that despite secondary disturbances, vegetation recovery in both the short and long-term is still dominated by eruption disturbance type and elevation. Finally, compared to NDVI, NBR is a better indicator of structural vegetation regrowth for the early years of revegetation.
A Review of Image Super-Resolution Approaches Based on Deep Learning and Applications in Remote Sensing
Authors: Wang Xuan , Yi Jinglei , Guo Jian , Song Yongchao , Lyu Jun , Xu Jindong , Yan Weiqing , Zhao Jindong , Cai Qing , Min Haigen
Published: October 2022

At present, with the advance of satellite image processing technology, remote sensing images are becoming more widely used in real scenes. However, due to the limitations of current remote sensing imaging technology and the influence of the external environment, the resolution of remote sensing images often struggles to meet application requirements. In order to obtain high-resolution remote sensing images, image super-resolution methods are gradually being applied to the recovery and reconstruction of remote sensing images. The use of image super-resolution methods can overcome the current limitations of remote sensing image acquisition systems and acquisition environments, solving the problems of poor-quality remote sensing images, blurred regions of interest, and the requirement for high-efficiency image reconstruction, a research topic that is of significant relevance to image processing. In recent years, there has been tremendous progress made in image super-resolution methods, driven by the continuous development of deep learning algorithms. In this paper, we provide a comprehensive overview and analysis of deep-learning-based image super-resolution methods. Specifically, we first introduce the research background and details of image super-resolution techniques. Second, we present some important works on remote sensing image super-resolution, such as training and testing datasets, image quality and model performance evaluation methods, model design principles, related applications, etc. Finally, we point out some existing problems and future directions in the field of remote sensing image super-resolution.
An Automatic Velocity Analysis Method for Seismic Data-Containing Multiples
Authors: Zhang Junming , Wang Deli , Hu Bin , Gong Xiangbo
Published: October 2022

Normal moveout (NMO)-based velocity analysis can provide macro velocity models for prestack data processing and seismic attribute inversion. Datasets with an increasing size require conventional velocity analysis to be transformed to a more automatic mode. The sensitivity to multiple reflections limits the wide application of automatic velocity analysis. Thus, we propose an automatic velocity analysis method for seismic data-containing multiples to overcome the limit of multiple interference. The core idea of the proposed algorithm is to utilize a multi-attribute analysis system to transform the multiple attenuation problem to a multiple identification problem. To solve the identification problem, we introduce the local similarity to attribute the predicted multiples and build a quantitative attribute called multiple similarity. Considering robustness and accuracy, we select two supplementary attributes based on velocity and amplitude difference, i.e., velocity variation with depth and amplitude level. Then we utilize the technique for order preference by similarity to ideal solution (TOPSIS) to balance weights for different attributes in automatic velocity analysis. An RGB system is adopted for multi-attributes fusion in velocity spectra for visualization and quality control. Using both synthetic and field examples to evaluate the effectiveness of the proposed method for data-containing multiples, the results demonstrate the excellent performance in the accuracy of the extracted velocity model.
Comparison of Weighted Mean Temperature in Greenland Calculated by Four Reanalysis Data
Authors: Luo Chengcheng , Xiao Feng , Gong Li , Lei Jintao , Li Wenhao , Zhang Shengkai
Published: October 2022

The weighted mean temperature ( Tm) is a critical parameter for precipitable water vapor (PWV) retrieval in global navigation satellite system (GNSS) meteorology. Reanalysis data are an important data source for Tm calculation and Tm empirical model establishment. This study uses radiosonde data to evaluate the accuracy and the spatiotemporal variation of Tm that is derived from four reanalysis data, namely, the release of the fifth-generation accurate global atmospheric reanalysis (ERA5), the modern-era retrospective analysis for research and applications version 2 (MERRA-2), the NCEP/DOE, and the NCEP/NCAR, from 2005 to 2019 in Greenland, due to the paucity of research on the performance of Tm in the polar region that is derived from reanalysis data, particularly on a long temporal scale. The results were as follows: (1) The 15-year mean bias errors (MBEs) and root mean square errors (RMSEs) of Tm that were obtained from the four reanalysis data are 0.267 and 0.691 K for the ERA5, −0.247 and 0.962 K for the MERRA-2, 0.192 and 1.148 K for the NCEP/DOE, and −0.069 and 1.37 K for the NCEP/NCAR. The Tm that was derived from the ERA5 (ERA5 Tm) has the highest accuracy, followed by the MERRA-2 Tm, the NCEP/DOE Tm, and the NCEP/NCAR Tm. (2) In the inter-annual stability of the Tm precision compared with the radiosonde data, the results of the ERA5 are the most stable, followed by the NCEP/DOE Tm, the NCEP/NCAR Tm, and the MERRA-2 Tm. The ERA5 Tm have improved from 2005 to 2019. (3) The Tm accuracy that was computed by the four reanalysis data exhibits significant seasonal variation characteristics in Greenland, as follows: the summer and the autumn accuracy is higher than that in the winter and the spring, which may be related to the variation of the surface temperature (Ts) accuracy. (4) The Tm that was estimated from the four reanalysis data exhibits a consistent spatial distribution, as follows: the Tm is smaller in the middle region of Greenland and is greater at the island’s edge. The comparative study of Tm that is obtained from the four reanalysis data can serve as a reference for future research on Tm model development and water vapor retrieval in polar regions by utilizing reanalysis data.
Satellite-Observed Time and Length Scales of Global Sea Surface Salinity Variability: A Comparison of Three Satellite Missions
Authors: Yi Daling Li , Melnichenko Oleg , Hacker Peter , Fan Ke
Published: October 2022

Sea surface salinity (SSS) observations from Aquarius, Soil Moisture and Ocean Salinity (SMOS), and Soil Moisture Active Passive (SMAP) satellite missions are compared to characterize the time and length scales of SSS variability globally. Overall, there is general agreement between the global patterns of the time and length scales of SSS variability estimated from the three satellite missions. The temporal scales of SSS variability vary from more than 90 days in the tropics to ~15 days in the Southern Ocean. The very short temporal scales (close to the Nyquist period) in some parts of the ocean are probably due to the high level of noise in the satellite data or the high noise-to-signal ratio. The longest temporal scales are observed along the South Pacific Convergence Zone (SPCZ) and in the central and western tropical Pacific. These areas are also related to the strongest ENSO-related signal in SSS. The processes governing the SSS variability and distribution are also non-stationary, such that the scales determined over different observation periods may differ. Dominant spatial scales of SSS variability are generally the longest (up to 150 km) in the tropics and the shortest (<60 km) in the subpolar regions. The distribution of the dominant spatial scales is not simply latitudinal but exhibits a more complex spatial pattern. In the tropics, there is slight east-west and inter-hemispheric asymmetry observed in the Pacific but absent in the other two oceans. The analysis also reveals that the length scales of SSS variability are highly anisotropic in the tropics (the zonal scales are generally shorter than the meridional ones) and become more isotropic towards higher latitudes. Regional differences in the estimates of the scales from the three satellite SSS datasets may arise due to differences in the observation duration, spatial resolution and/or different level of noise.
Dynamic Modelling of Water and Wind Erosion in Australia over the Past Two Decades
Authors: Zhang Mingxi , Viscarra Rossel Raphael A. , Zhu Qinggaozi , Leys John , Gray Jonathan M. , Yu Qiang , Yang Xihua
Published: October 2022

Soil erosion caused by water and wind is a complicated natural process that has been accelerated by human activity. It results in increasing areas of land degradation, which further threaten the productive potential of landscapes. Consistent and continuous erosion monitoring will help identify the location, magnitude, and trends of soil erosion. This information can then be used to evaluate the impact of land management practices and inform programs that aim to improve soil conditions. In this study, we applied the Revised Universal Soil Loss Equation (RUSLE) and the Revised Wind Erosion Equation (RWEQ) to simulate water and wind erosion dynamics. With the emerging earth observation big data, we estimated the monthly and annual water erosion (with a resolution of 90 m) and wind erosion (at 1 km) from 2001 to 2020. We evaluated the performance of three gridded precipitation products (SILO, GPM, and TRMM) for monthly rainfall erosivity estimation using ground-based rainfall. For model validation, water erosion products were compared with existing products and wind erosion results were verified with observations. The datasets we developed are particularly useful for identifying finer-scale erosion dynamics, where more sustainable land management practices should be encouraged.
Attitude Determination with GPS L1/Galileo E1 Observations from Common-Clock Receiver: A Comparison of Four Different Models
Authors: Wu Mingkui , Li Jiahang , Luo Shuai , Liu Wanke
Published: October 2022

The development of the commercial multi global navigation satellite system (GNSS) dual (multi)-antenna common-clock receiver that uses time-synchronization technology has brought new opportunities for high-precision GNSS-based attitude determination. In this article, for the first time, we present a performance comparison of global positioning system (GPS) L1/Galileo navigation satellite system (Galileo) E1 attitude determination with a common-clock receiver using four different models, i.e., the loosely combined single-differenced (SD-LC) model, the tightly combined single-differenced (SD-TC) model, the loosely combined double-differenced (DD-LC) model, and the tightly combined double-differenced (DD-TC) model. We first introduce the SD-LC, SD-TC, DD-LC, and DD-TC relative positioning models with GPS L1/Galileo E1 observations from a common-clock receiver. Then, we present a performance comparison of the four models in both single-epoch and multi-epoch modes using static data collected with a Trimble BD992 common-clock receiver in terms of the ambiguity dilution of precision (ADOP), the ambiguity resolution (AR) success and failure rates, and the positioning and attitude determination accuracy. In the case of the single-epoch mode, the experimental results revealed that the results of the single-differenced (SD) models were identical to those of double-differenced (DD) models, i.e., the results of SD-LC and SD-TC models were identical to DD-LC and DD-TC models, respectively. Moreover, compared with the loosely combined model (SD-LC/DD-LC), the tightly combined model (SD-TC/DD-TC) delivered a much higher AR success rate and a lower AR failure rate, especially under a high elevation cutoff angle. The AR success rate increased by approximately 35.1% under a 40° elevation cutoff angle, while the AR failure rate decreased by approximately 4.3%. In the case of the multi-epoch mode, the experimental results confirmed the advantages of the tightly combined model over the loosely combined model as well as the SD model over the DD model. Compared with the DD-LC and SD-LC models, the AR success rates of the DD-TC and SD-TC models were improved by approximately 16.7% and 0.6% under a 45° elevation cutoff angle, respectively. The AR failure rates were reduced by approximately 12.4% and 0.3%, respectively. Moreover, compared with the DD-LC and DD-TC models, the AR success rates of the SD-LC and SD-TC models under a 45° elevation cutoff angle were improved by approximately 24.0% and 7.9%, respectively, and the AR failure rates were reduced by approximately 19.9% and 7.8%, respectively. Meanwhile, compared with the DD model, the SD model delivered comparable yaw accuracy and remarkably better pitch accuracy. The pitch accuracy was improved by approximately 65.2–75.0%.
Temporal Feature Learning and Pulse Prediction for Radars with Variable Parameters
Authors: Yuan Shuo , Liu Zhang-Meng
Published: October 2022

Many modern radars use variable pulse repetition intervals (PRI) to improve anti-reconnaissance and anti-jamming performance. Their PRI features are probably software-defined, but the PRI values at different time instants are variable. Previous statistical pattern analyzing methods are unable to extract such undetermined PRI values and features, which greatly increases the difficulty of Electronic Support Measures (ESM) against such radars. In this communication, we first establish a model to describe the temporal patterns of software-defined radar pulse trains, then introduce the recurrent neural network (RNN) to mine high-order relationships between successive pulses, and finally exploit the temporal features to predict the time of arrival of upcoming pulses. In the simulation part, we compare different time series prediction models to verify the RNN’s adaptability for pulse sequences of variable parameter radars. Moreover, behaviors of different RNN units in this task are compared, and the results show that the proposed method can learn complex PRI features in pulse trains even in the presence of significant data noises and agile PRIs.
High-Resolution Drone Images Show That the Distribution of Mussels Depends on Microhabitat Features of Intertidal Rocky Shores
Authors: Barbosa Romina Vanessa , Jaud Marion , Bacher Cédric , Kerjean Yann , Jean Fred , Ammann Jérôme , Thomas Yoann
Published: October 2022

In this study, we used orthomosaics and a digital surface model (DSM) generated from drone surveys to (1) characterize the distribution of mussel (Mytilus galloprovincialis) aggregations at high resolution (centimeters), and (2) evaluate the role of topographic features, intertidal height, slope, and orientation angle in determining mussel distribution on two rocky shores oriented differently on both sides of a beach on the French Brittany coast. We first developed and tested a mussel visualization index (MVI) for mapping mussel aggregations from drone images. Then, we analyzed mussel distribution on the two shores. The results showed a contrasted total mussel-occupied area between the two rocky shores, with a higher occupation rate and a clear pattern of distribution depending on topographic features on the rocky shore oriented to the west. Intertidal height, and its associated immersion time, was the main factor determining mussel distribution. An optimum intertidal height was found in the center of the distribution height range, at c.a. 4.5 m above the lowest astronomical tide (LAT), where individuals are under immersion phase on average 43% of the time. Within this optimum, the occupation rate of the mussels was significantly higher in microhabitats facing south and west, particularly at intermediate slope angles. These results demonstrate the role of microhabitat topographic features on the development of intertidal mussels and their final distribution. Furthermore, the results highlight the importance of mesoscale structures of habitats (e.g., 100 m), which seem to be responsible for the differences we observed between the two shores. Our methodological approach highlights the main advantage of using high-resolution drone images to address ecological processes in intertidal ecosystems. Indeed, drone imagery offers the possibility to assess small-scale interactions between individuals and habitat conditions over a wide area, which is technically infeasible from fieldwork approaches or by using satellite remote sensing due to their lower resolution. Scale integration and methodological complementarity are powerful approaches to correctly represent the processes governing the ecology of intertidal ecosystems. We suggest using this methodology to monitor long-term changes of sentinel sessile species.