bluuebunny/crossref_metadata_2025 huggingface dataset:
Hakkō Ichiu: Religious Rhetoric in Imperial Japan
Authors: Wang Ziming
Published: December 2022

The wartime propaganda slogan Hakkō Ichiu 八紘一宇 (“Unify the whole world under one roof”) was loaded with historical meaning: Japan was glorifying the aggression and colonization of war by fostering a specific interpretation of the narrative about how Jimmu, the first emperor, founded the nation in State Shinto mythology. In this article, I consider this slogan as central to a religious rhetoric with nationalistic overtones and I analyze it in terms of etymology, connotation, and rhetorical devices. First, the expression Hakkō Ichiu originated in ancient East Asian cosmology, before becoming one of the rhetorical expressions of State Shinto, emphasizing the extent of the imperial reign. Second, the Nichirenist activist Tanaka Chigaku rediscovered it and gave it an expansionist connotation, fostering a syncretistic approach mixing Buddhist and Shinto features. Finally, during wartime, in official documents, lyrics, trademarks, etc., the slogan gave way to a number of graphic and monumental expressions, reinforcing its connections with militarism and ultranationalism. The most notable of these material expressions was the Hakkō Ichiu Tower, erected to commemorate the 2600th anniversary of the foundation of the nation and perpetuate the State Shinto rhetoric.
The Influence of FY-4A High-Frequency LST Data on Data Assimilation in a Climate Model
Authors: Nie Suping , Jia Xiaolong , Deng Weitao , Lu Yixiong , He Dongyan , Zhao Liang , Cao Weihua , Deng Xueliang
Published: December 2022

Based on the Beijing Climate Center’s land surface model BCC_AVIM2.0, an ensemble Kalman filter (EnKF) algorithm is developed to assimilate the land surface temperature (LST) product of the first satellite of Fengyun-4 series meteorological satellites of China to study the influence of LST data with different time frequencies on the surface temperature data assimilations. The MODIS daytime and nighttime LST products derived from Terra and Aqua satellites are used as independent validation data to test the assimilation results. The results show that diurnal variation information in the FY-4A LST data has significant effect on the assimilation results. When the time frequencies of the assimilated FY-4A LST data are sufficient, the assimilation scheme can effectively reduce the errors and the assimilation results reflect more reasonable spatial and temporal distributions. The assimilation experiments with a 3 h time frequency show less bias as well as RMSEs and higher temporal correlations than that of the model simulations at both daytime and nighttime periods. As the temporal frequency of assimilated LST observations decreases, the assimilation effects gradually deteriorate. When diurnal variation information is not considered at all in the assimilation, the assimilation with 24 h time frequency showed the largest errors and smallest time correlations in all experiments. The results demonstrate the potential of assimilating high-frequency FY-4A LST data to improve the performance of the BCC_AVIM2.0 land surface model. Furthermore, this study indicates that the diurnal variation information is a necessary factor needed to be considered when assimilating the FY-4A LST.
Atmospheric Delay Correction Utilization Method for Out-of-Network PPP-RTK Users
Authors: Wang Siyao , Tu Rui , Zhang Rui , Fan Lihong , Han Junqiang , Lu Xiaochun
Published: December 2022

Traditional PPP-RTK algorithm research has focused on users in a reference station network. However, if a proper PPP-RTK user algorithm can be designed for out-of-network users to achieve accurate and rapid positioning, the effective service area of existing reference station networks can be markedly expanded. To address the problem of extrapolating atmospheric corrections for out-of-network users, this study presents a single-reference-station (SRS) PPP-RTK model to address atmospheric delay corrections, by ensuring that only corrections from the closest reference station are used. Several numerical experiments were designed and conducted to compare the performance of the presented model and a widely used interpolation model. The extrapolation error distribution, mean and the maximum of absolute values of errors, and standard deviation of errors were selected as indicators. The results show that the SRS PPP-RTK model is significantly more accurate than the interpolation model. Thus, for out-of-network users, we suggest that the presented SRS PPP-RTK model should be used, and the residual atmospheric delays are needed to be estimated.
High-Resolution Remote Sensing Images Can Better Estimate Changes in Carbon Assimilation of an Urban Forest
Authors: Huang Qing , Lu Xuehe , Chen Fanxingyu , Zhang Qian , Zhang Haidong
Published: December 2022

Urban forests have the potential to sink atmospheric CO2. With the improvement of coverage of vegetation in urban environments, more attention has been paid to the carbon sequestration potential of the urban forest. However, the high fragmentation of urban forests makes it difficult to evaluate their carbon budget on a regional scale. In this study, the GPP-NIRv relationship model was employed to estimate GPP in Suzhou by MODIS, Landsat-8 and Sentinel-2 remote sensing data, and to further explore what kind of remote images can figure out the spatial-temporal pattern of GPP in urban forests. We found that the total GPP of the terrestrial ecosystem in Suzhou reached 8.43, 8.48, and 9.30 Tg C yr-1 for MODIS, Landsat-8, and Sentinel-2, respectively. Monthly changes of GPP were able to be derived by MODIS and Sentinel-2, with two peaks in April and July. According to Sentinel-2, urban forests accounted for the majority of total GPP, with an average of about 44.63%, which was larger than the results from GPP products with coarser resolutions. Additionally, it is clear from the high-resolution images that the decline of GPP in May was due to human activities such as the rotation of wheat and rice crops and the pruning of urban forests. Our results improve the understanding of the contribution of the urban forest to the carbon budget and highlight the importance of high-resolution remote sensing images for estimating urban carbon assimilation.
Calibration of MODIS-Derived Cropland Growing Season Using the Climotransfer Function and Ground Observations
Authors: Ye Liming , De Grave Johan , Van Ranst Eric , Xu Lijun
Published: December 2022

The global environment experienced notable changes in the recent past of planet Earth. Satellite remote sensing has played an increasingly important role in monitoring and characterizing these changes. Being recognized as a sensitive indicator of global climate change, land surface phenology (LSP) observations by satellite remote sensing have received much attention in recent years; however, much less attention has been paid to the calibration of these observations using standardized procedures. Here, we propose a new approach to calibrating the satellite LSP products by developing a climotransfer function (CTF) based on a polynomial regression of the satellite-ground observation difference in key crop phenophases against climatic factors. We illustrate the model development and evaluation process with a case study of the cropland growing season in Northeast China (NEC) from 2001 to 2010 using the MODIS LSP product MCD12Q2 Collection 6 and the ground-observed crop phenology and climatic data from 98 agrometeorological stations across the region. Our results showed that the start of the cropland growing season (SOS) derived from MODIS data compared well to the ground-observed SOS, whereas the MODIS-derived season end (EOS) was delayed by 15.5 d, relative to ground observation. The MODIS-derived EOS was, therefore, spatiotemporally calibrated using a CTF model fitted to the satellite-ground difference in EOS (∆EOS) versus two climatic factors, namely, the growing degree-days on the base temperature of 10 °C (GDD10) and cloud cover (CL). The calibrated MODIS data revealed that the cropland growing season in NEC tended to shorten at 4.5 d decade−1 during 2001–2010, mainly driven by a significant delay in SOS at a similar rate, whereas no trend was detected for EOS. The calibrated data also revealed a significant shortening gradient of 1.7 d degree−1 of latitude northward. These spatiotemporal patterns would have been erroneously characterized if calibration had not been applied. More attention is therefore called to the proper calibration of satellite LSP products prior to any meaningful applications.
Multi-Level Dynamic Analysis of Landscape Patterns of Chinese Megacities during the Period of 2016–2021 Based on a Spatiotemporal Land-Cover Classification Model Using High-Resolution Satellite Imagery: A Case Study of Beijing, China
Authors: Li Zhi , Lu Yi , Yang Xiaomei
Published: December 2022

In today’s accelerating urbanization process, timely and effective monitoring of land-cover dynamics, landscape pattern analysis, and evaluation of built-up urban areas (BUAs) have important research significance and practical value for the sustainable development, planning and management, and ecological protection of cities. High-spatial-resolution remote sensing (HRRS) images have the advantages of high-accuracy Earth observations, covering a large area, and having a short playback period, and they can objectively and accurately provide fine dynamic spatial information about the land cover in urban built-up areas. However, the complexity and comprehensiveness of the urban structure have led to a single-scale analysis method, which makes it difficult to accurately and comprehensively reflect the characteristics of the BUA landscape pattern. Therefore, in this study, a joint evaluation method for an urban land-cover spatiotemporal-mapping chain and multi-scale landscape pattern using high-resolution remote sensing imagery was developed. First, a pixel–object–knowledge model with temporal and spatial classifications was proposed for the spatiotemporal mapping of urban land cover. Based on this, a multi-scale district–BUA–city block–land cover type map of the city was established and a joint multi-scale evaluation index was constructed for the multi-scale dynamic analysis of the urban landscape pattern. The accuracies of the land cover in 2016 and 2021 were 91.9% and 90.4%, respectively, and the kappa coefficients were 0.90 and 0.88, respectively, indicating that the method can provide effective and reliable information for spatial mapping and landscape pattern analysis. In addition, the multi-scale analysis of the urban landscape pattern revealed that, during the period of 2016–2021, Beijing maintained the same high urbanization rate in the inner part of the city, while the outer part of the city kept expanding, which also reflects the validity and comprehensiveness of the analysis method developed in this study.
A Multi-Scale Feasibility Study into Acid Mine Drainage (AMD) Monitoring Using Same-Day Observations
Authors: Chalkley Richard , Crane Rich Andrew , Eyre Matthew , Hicks Kathy , Jackson Kim-Marie , Hudson-Edwards Karen A.
Published: December 2022

Globally, many mines emit acid mine drainage (AMD) during and after their operational life cycle. AMD can affect large and often inaccessible areas. This leads to expensive monitoring via conventional ground-based sampling. Recent advances in remote sensing which are both non-intrusive and less time-consuming hold the potential to unlock a new paradigm of automated AMD analysis. Herein, we test the feasibility of remote sensing as a standalone tool to map AMD at various spatial resolutions and altitudes in water-impacted mining environments. This was achieved through the same-day collection of satellite-based simulated Sentinel-2 (S2) and PlanetScope (PS2.SD) imagery and drone-based UAV Nano-Hyperspec (UAV) imagery, in tandem with ground-based visible and short-wave infrared analysis. The study site was a historic tin and copper mine in Cornwall, UK. The ground-based data collection took place on the 30 July 2020. Ferric (Fe(III) iron) band ratio (665/560 nm wavelength) was used as an AMD proxy to map AMD pixel distribution. The relationship between remote-sensed Fe(III) iron reflectance values and ground-based Fe(III) iron reflectance values deteriorated as sensor spatial resolution decreased from high-resolution UAV imagery (<50 mm2 per pixel; r2 = 0.78) to medium-resolution PlanetScope Dove-R (3 m2 per pixel; r2 = 0.51) and low-resolution simulated Sentinel-2 (10 m2 per pixel; r2 = 0.23). A fractioned water pixel (FWP) analysis was used to identify mixed pixels between land and the nearby waterbody, which lowered spectral reflectance. Increases in total mixed pixels were observed as the spatial resolution of sensors decreased (UAV: 2.4%, PS: 3.7%, S2: 8.5%). This study demonstrates that remote sensing is a non-intrusive AMD surveying tool with varying degrees of effectiveness relative to sensor spatial resolution. This was achieved by identifying and successfully mapping a cross-sensor Fe(III) iron band ratio whilst recognizing water bodies as reflectance inhibitors for passive sensors.
An Improved Retrieval of Snow and Ice Properties Using Spaceborne OLCI/S-3 Spectral Reflectance Measurements: Updated Atmospheric Correction and Snow Impurity Load Estimation
Authors: Kokhanovsky Alexander , Vandecrux Baptiste , Wehrlé Adrien , Danne Olaf , Brockmann Carsten , Box Jason E.
Published: December 2022

We present an update of the Snow and Ice (SICE) property retrieval algorithm based on the spectral measurements of Ocean and Land Color Instrument (OLCI) onboard Sentinel-3 satellites combined with the asymptotic radiative transfer theory valid for weakly absorbing turbid media. The main improvements include the introduction of a new atmospheric correction, retrieval of snow impurity load and properties, retrievals for partially snow-covered ground and also accounting for various thresholds to be used to assess the retrieval quality. The technique can be applied to various optical sensors (satellite and ground-based) operated in the visible and near infrared regions of electromagnetic spectra.
Non-Uniform MIMO Array Design for Radar Systems Using Multi-Channel Transceivers
Authors: Kim Eunhee , Kim Ilkyu , Kim Wansik
Published: December 2022

Multiple-input multiple-output (MIMO) technology has recently attracted attention with regard to improving the angular resolution of small antennas such as automotive radars. If appropriately placed, the co-located transmit and receive arrays can make a large virtual aperture. This paper proposes a new method for designing arrays by adopting a structure with minimum redundancy. The proposed structure can significantly increase the virtual array aperture while keeping the transmit and receive antennas at the same size. We describe the application of the proposed method to subarray-type antennas using multi-channel transceivers, which is essential for arranging RF hardware in a small antenna operating at high frequency. Further, we present an analysis of the final beam pattern and discuss its benefits and limitations.
Feature Extraction for Moving Targets Based on the Statistical Characteristics of Echo Amplitude with the L-Band Fully Polarimetric Radar
Authors: Zhang Chuang , Mu Yaxin , Xia Zhenghuan , Jin Shichao , Yue Fuzhan , Liu Xin , Zhang Lanqing , Tian Zhixin , Liu Zongqiang , Zhang Yao , Gao Wenning , Zhang Tao , Zhao Zhilong , Zhang Ying
Published: December 2022

Non-imaging radar can achieve the detection and localization of moving targets, but it faces difficulties in the classification of targets. Aiming for the application of moving target classification by low/medium resolution non-imaging radar, this paper proposes a novel feature extraction method, based on the statistical characteristics of echo amplitude with the L-band fully polarimetric radar. A feature plane, composed of the 3rd-order central moment (skewness) and 4th-order central moment (kurtosis) as the statistical characteristics of the echo envelope, is established. In addition, two types of moving targets, pedestrians and non-motorized vehicles, are able to be effectively distinguished according to whether the echo signals of different polarizations have overlapping areas in the feature plane. The L-band fully polarimetric radar has been developed and field experiments have been conducted. The experimental results verify that the kurtosis and skewness of the fully polarimetric echo amplitude of the pedestrians are very close, and there is an overlapping area in the feature plane, while the statistical characteristics of the fully polarimetric echo signal of non-motor vehicles are different, and there is no overlapping area in the feature plane. This proposed feature extraction method has the advantage of being simple and robust, compared to the traditional imaging approach.
Evaluation of Three Long-Term Remotely Sensed Precipitation Estimates for Meteorological Drought Monitoring over China
Authors: Li Yanzhong , Zhuang Jiacheng , Bai Peng , Yu Wenjun , Zhao Lin , Huang Manjie , Xing Yincong
Published: December 2022

Remotely sensed precipitation estimates (RSPEs) play an essential role in monitoring drought, especially in ungauged or sparsely gauged areas. In this study, we evaluated the ability of three popular long-term RSPEs (PERSIANN, CHIRPS, and MSWEP) in capturing the meteorological drought variations over the 10 first-level water resource basins of China, based on the standardized precipitation index (SPI). Drought events were identified by run theory, and the drought characteristics (i.e., duration, severity, and intensity) were also evaluated and compared with a gridded in situ observational precipitation dataset (CMA). The results showed that the three RSPEs could generally capture the spatial patterns and trends of the CMA and showed better performance in the wetter basins. MSWEP had the best performance for the categorical skill of POD, followed by CHIRPS and PERSIANN for the four timescales. SPI6 was the optimal timescale for identifying meteorological drought events. There were large skill divergences in the 10 first-level basins for capturing the drought characteristics. CHIRPS can efficiently reproduce the spatial distribution of drought characteristics, with similar metrics of MDS, MDI, and MDP, followed by MSWEP and PERSIANN. Overall, no single product always outperformed the other products in capturing drought characteristics, underscoring the necessity of multiproduct ensemble applications. Our study’s findings may provide useful information for drought monitoring in areas with complex terrain and sparse rain-gauge networks.
Simulation and Prediction of Urban Land Use Change Considering Multiple Classes and Transitions by Means of Random Change Allocation Algorithms
Authors: Marques-Carvalho Rômulo , Almeida Cláudia Maria de , Escobar-Silva Elton Vicente , Oliveira Alves Rayanna Barroso de , Anjos Lacerda Camila Souza dos
Published: December 2022

The great majority of the world population resides nowadays in urban areas. Understanding their physical and social structure, and especially their urban land use pattern dynamics throughout time, becomes crucial for successful, effective management of such areas. This study is committed to simulate and predict urban land use change in a pilot city belonging to the São Paulo Metropolitan Region, southeast of Brazil, by means of a cellular automata model associated with the Markov chain. This model is driven by data derived from orbital and airborne remotely sensed images and is parameterized by the Bayesian weights of evidence method. Several layers related to infrastructure and biophysical aspects of the pilot city, São Caetano do Sul, were used as evidence in the simulation process. Alternative non-stationary scenarios were generated for the short-run, and the results obtained from past simulations were statistically validated using a multiresolution “goodness-of-fit” metric relying on fuzzy logic. The best simulations reached fuzzy similarity indices around 0.25–0.58 for small neighborhood windows when an exponential decay approach was employed for the analysis, and approximately 0.65–0.95 when a constant decay and larger windows were considered. The adopted Bayesian inference method proved to be a good parameterization approach for simulating processes of urban land use change involving multiple classes and transitions.
Using Various Analysis Center Products to Assess the Time-Frequency Transfer Performance of GPS/Galileo/BDS PPPAR Methods
Authors: Ouyang Mingjun , Zhu Xiangwei , Yi Ruite , Lyu Daqian
Published: December 2022

Numerous organizations and Analysis Centers (AC) currently offer various Ambiguity Resolution (AR) products using various methodologies. However, there are no associated studies on their use for time-frequency transfer. This paper examines 16 Multi-GNSS Experiment (MGEX) stations with external high-precision atomic clocks to constitute 15 international time comparison links, and uses AR products data from CNES, SGG, CODE, and PRIDE laboratories, using three ambiguity-fixed strategies, to thoroughly evaluate the effects of various strategies and AR products for high-precision time-frequency transfer. We reach the following results by using the IGS final clock product as a reference and comparing it to ambiguity-float. With various ambiguity-fixed procedures, the time stability Standard Deviation (STD) of time transfer is increased for a single GPS, and the improvement ranges from 10 to 40%. The frequency stability has barely improved; up to 40%, the most notable improvement comes from FCB with GRM products. The time stability STD of combinations has improved after the addition of the Galileo system compared to the single GPS, and the improvement ranges from 2 to 9%. Most strategies have been improved, while a few techniques have been weakened with the GEC (GPS + Galileo + BDS) combination. We feel that the stability has not significantly increased with the systems’ increase in terms of short-term stability after comparing multiple groups of linkages.
Quick Quality Assessment and Radiometric Calibration of C-SAR/01 Satellite Using Flexible Automatic Corner Reflector
Authors: Huang Qiqi , Zhang Fengli , Li Lu , Liu Xiaochen , Jiao Yanan , Yuan Xinzhe , Li Huirong
Published: December 2022

C-SAR/01, the successor of China’s Gaofen-3 Satellite, which launched on 23 November 2021, is the first synthetic aperture radar (SAR) satellite to be launched in China’s civil space infrastructure plan and has served as an invaluable data resource. Radiometric calibration and validation are prerequisites for the quantitative application of SAR data. In this study, the radiometric calibration experiments of C-SAR/01 data of the ultra-fine strip (UFS) and fine strip I (FSI) modes were conducted applying flexible automatic triangular trihedral corner reflectors deployed in Xilinhot SAR satellite calibration and validation site. Accordingly, the image quality and radiometric calibration accuracy were evaluated. The results show that the spatial resolution, peak sidelobe ratio, and integrated sidelobe ratio of UFS and FSI mode data of C-SAR/01 are better than those of the design indexes, and the calibration results from the integral method are more stable than those from the peak method. Furthermore, the standard deviation of the calibration constant for UFS mode data is 0.234 dB, with the relative and absolute calibration accuracies obtained as 0.233 and 0.532 dB, respectively, whereas the standard deviation calibration constant for FSI mode data is 0.198 dB, with its relative and absolute calibration accuracies evaluated as 0.199 and 0.333 dB, respectively.
Combining Object-Oriented and Deep Learning Methods to Estimate Photosynthetic and Non-Photosynthetic Vegetation Cover in the Desert from Unmanned Aerial Vehicle Images with Consideration of Shadows
Authors: He Jie , Lyu Du , He Liang , Zhang Yujie , Xu Xiaoming , Yi Haijie , Tian Qilong , Liu Baoyuan , Zhang Xiaoping
Published: December 2022

Soil erosion is a global environmental problem. The rapid monitoring of the coverage changes in and spatial patterns of photosynthetic vegetation (PV) and non-photosynthetic vegetation (NPV) at regional scales can help improve the accuracy of soil erosion evaluations. Three deep learning semantic segmentation models, DeepLabV3+, PSPNet, and U-Net, are often used to extract features from unmanned aerial vehicle (UAV) images; however, their extraction processes are highly dependent on the assignment of massive data labels, which greatly limits their applicability. At the same time, numerous shadows are present in UAV images. It is not clear whether the shaded features can be further classified, nor how much accuracy can be achieved. This study took the Mu Us Desert in northern China as an example with which to explore the feasibility and efficiency of shadow-sensitive PV/NPV classification using the three models. Using the object-oriented classification technique alongside manual correction, 728 labels were produced for deep learning PV/NVP semantic segmentation. ResNet 50 was selected as the backbone network with which to train the sample data. Three models were used in the study; the overall accuracy (OA), the kappa coefficient, and the orthogonal statistic were applied to evaluate their accuracy and efficiency. The results showed that, for six characteristics, the three models achieved OAs of 88.3–91.9% and kappa coefficients of 0.81–0.87. The DeepLabV3+ model was superior, and its accuracy for PV and bare soil (BS) under light conditions exceeded 95%; for the three categories of PV/NPV/BS, it achieved an OA of 94.3% and a kappa coefficient of 0.90, performing slightly better (by ~2.6% (OA) and ~0.05 (kappa coefficient)) than the other two models. The DeepLabV3+ model and corresponding labels were tested in other sites for the same types of features: it achieved OAs of 93.9–95.9% and kappa coefficients of 0.88–0.92. Compared with traditional machine learning methods, such as random forest, the proposed method not only offers a marked improvement in classification accuracy but also realizes the semiautomatic extraction of PV/NPV areas. The results will be useful for land-use planning and land resource management in the areas.
Enhancing Solar Energy Forecast Using Multi-Column Convolutional Neural Network and Multipoint Time Series Approach
Authors: Kumar Anil , Kashyap Yashwant , Kosmopoulos Panagiotis
Published: December 2022

The rapid expansion of solar industries presents unknown technological challenges. A dedicated and suitable energy forecast is an effective solution for the daily dispatching and production of the electricity grid. The traditional forecast technique uses weather and plant parameters as the model information. Nevertheless, these are insufficient to consider problematic weather variability and the various plant characteristics in the actual field. Considering the above facts and inspired by the excellent implementation of the multi-column convolutional neural network (MCNN) in image processing, we developed a novel approach for forecasting solar energy by transforming multipoint time series (MT) into images for the MCNN to examine. We first processed the data to convert the time series solar energy into image matrices. We observed that the MCNN showed a preeminent response under a ground-based high-resolution spatial–temporal image matrix with a 0.2826% and 0.5826% RMSE for 15 min-ahead forecast under clear (CR) and cloudy (CD) conditions, respectively. Our process was performed on the MATLAB deep learning platform and tested on CR and CD solar energy conditions. The excellent execution of the suggested technique was compared with state-of-the-art deep neural network solar forecasting techniques.
NRN-RSSEG: A Deep Neural Network Model for Combating Label Noise in Semantic Segmentation of Remote Sensing Images
Authors: Xi Mengfei , Li Jie , He Zhilin , Yu Minmin , Qin Fen
Published: December 2022

The performance of deep neural networks depends on the accuracy of labeled samples, as they usually contain label noise. This study examines the semantic segmentation of remote sensing images that include label noise and proposes an anti-label-noise network framework, termed Labeled Noise Robust Network in Remote Sensing Image Semantic Segmentation (NRN-RSSEG), to combat label noise. The algorithm combines three main components: network, attention mechanism, and a noise-robust loss function. Three different noise rates (containing both symmetric and asymmetric noise) were simulated to test the noise resistance of the network. Validation was performed in the Vaihingen region of the ISPRS Vaihingen 2D semantic labeling dataset, and the performance of the network was evaluated by comparing the NRN-RSSEG with the original U-Net model. The results show that NRN-RSSEG maintains a high accuracy on both clean and noisy datasets. Specifically, NRN-RSSEG outperforms UNET in terms of PA, MPA, Kappa, Mean_F1, and FWIoU in the presence of noisy datasets, and as the noise rate increases, each performance of UNET shows a decreasing trend while the performance of NRN-RSSEG decreases slowly and some performances show an increasing trend. At a noise rate of 0.5, the PA (−6.14%), MPA (−4.27%) Kappa (−8.55%), Mean_F1 (−5.11%), and FWIOU (−9.75%) of UNET degrade faster; while the PA (−2.51%), Kappa (−3.33%), and FWIoU of NRN-RSSEG (−3.26) degraded more slowly, MPA (+1.41) and Mean_F1 (+2.69%) showed an increasing trend. Furthermore, comparing the proposed model with the baseline method, the results demonstrate that the proposed NRN-RSSEG anti-noise framework can effectively help the current segmentation model to overcome the adverse effects of noisy label training.
Improved Surface Soil Organic Carbon Mapping of SoilGrids250m Using Sentinel-2 Spectral Images in the Qinghai–Tibetan Plateau
Authors: Yang Jiayi , Fan Junjian , Lan Zefan , Mu Xingmin , Wu Yiping , Xin Zhongbao , Miping Puqiong , Zhao Guangju
Published: December 2022

Soil organic carbon (SOC) is a critical indicator for the global carbon cycle and the overall carbon pool balance. Obtaining soil maps of surface SOC is fundamental to evaluating soil quality, regulating climate change, and global carbon cycle modeling. However, efficient approaches for obtaining accurate SOC information remain challenging, especially in remote or inaccessible regions of the Qinghai–Tibet Plateau (QTP), which is influenced by complex terrains, climate change, and human activities. This study employed field measurements, SoilGrids250m (SOC_250m, a spatial resolution of 250 m × 250 m), and Sentinel-2 images with different machine learning methods to map SOC content in the QTP. Four machine learning methods including partial least squares regression (PLSR), support vector machines (SVM), random forest (RF), and artificial neural network (ANN) were used to construct spatial prediction models based on 396 field-collected sampling points and various covariates from remote sensing images. Our results revealed that the RF model outperformed the PLSR, SVM, and ANN models, with a higher determination coefficient (R2 of 0.82 is from the training datasets) and the ratio of performance to deviation (RPD = 2.54). The selected covariates according to the variable importance in projection (VIP) were: SOC_250m, B2, B11, Soil-Adjusted Vegetation Index (SAVI), Normalized Difference Vegetation Index (NDVI), B5, and Soil-Adjusted Total Vegetation Index (SATVI). The predicted SOC map showed an overall decrease in SOC content ranging from 69.30 g·kg−1 in the southeast to 1.47 g·kg−1 in the northwest. Our prediction showed spatial heterogeneity of SOC content, indicating that Sentinel-2 images were acceptable for characterizing the variability of SOC. The findings provide a scientific basis for carbon neutrality in the QTP and a reference for the digital mapping of SOC in the alpine region.
Ecosystem Service Function Supply–Demand Evaluation of Urban Functional Green Space Based on Multi-Source Data Fusion
Authors: Wang Yingqi , Huang Huiping , Yang Guang , Chen Wei
Published: December 2022

With the rapid development of urbanization, it is an important goal for urban green space (UGS) to meet the needs of residents’ production and life using a supply–demand evaluation method of ecosystem service (ES). However, most studies have considered all functional types of UGS as the supply side, or rely solely on a single supply or demand side to conduct ecosystem service function (ESF) evaluation, resulting in less accurate and targeted research findings. As a result, a novel methodological framework for matching each ESF with corresponding functional types of UGS, and considering both supply and demand sides have been required. Firstly, the object-oriented approach combining support vector machine (SVM) and normalized difference vegetation index (NDVI) was used to automatically identify UGS, and integrated Point of Interest (POI), urban built-up area, road land, parcel, and socio-economic data to classify six functional types of UGS using the near-convex-hull. Next, matching the functional types of UGS with five ESFs, both supply and demand status were evaluated using the carbon sequestration and release analysis, Gaussian two-step floating catchment area, and spatial equilibrium degree methods. This method was demonstrated in Beijing, China. The results show: (1) the ES supply–demand situation provided by each functional type of UGS is different in five ESFs; (2) considering both supply and demand is more intuitive to see whether the city’s demand for UGS has been met. Our results provide a new perspective for evaluating the contribution of UGS and have practical implications for UGS planning.
Advanced Machine Learning Methods for Major Hurricane Forecasting
Authors: Martinez-Amaya Javier , Radin Cristina , Nieves Veronica
Published: December 2022

Hurricanes, rapidly increasing in complexity and strength in a warmer world, are one of the worst natural disasters in the 21st century. Further studies integrating the changing hurricane features are thus crucial to aid in the prediction of major hurricanes. With this in mind, we present a new framework based on automated decision tree analysis, which has the capability to identify the most important cloud structural parameters from GOES imagery as predictors for hurricane intensification potential in the Atlantic and Pacific oceans. The proposed framework has been proved effective for predicting major hurricanes with an overall accuracy of 73% from 6 to 54 h in advance (both regions combined).
Identification of Supercooled Cloud Water by FY-4A Satellite and Validation by CALIPSO and Airborne Detection
Authors: Xu Xiaohong , Zeng Yi , Yu Xing , Liu Guihua , Yue Zhiguo , Dai Jin , Feng Qiujuan , Liu Pu , Wang Jin , Zhu Yannian
Published: December 2022

Cold clouds are the main operation target of artificial precipitation enhancement, and its key is to find a supercooled cloud water area where the catalyst can be seeded to promote the formation of precipitation particles and increase precipitation to the ground. Based on the multi-spectral characteristics of the Fengyun-4A (FY-4A) satellite, a methodology for identifying supercooled cloud water is developed. Superimposed by a cloud top brightness temperature of 10.8 µm, a combination of 0.46 µm, 1.6 µm, and 2.2 µm red–green–blue (RGB) composites are used to identify the cloud phase and to obtain the real-time supercooled cloud water distribution every 5 min and in a 2 km resolution for the whole coverage of China. Based on the RGB composition, the supervised machine learning method K-mean clustering was applied to classify the cloud top phase. The results were validated extensively with Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP). It is worthwhile to highlight that the corresponding hit rate reached 87% over the full disk domain for both the summer and winter seasons. Furthermore, on 29 November 2019, microphysical properties were measured, and the data of supercooled cloud droplets and ice crystals were obtained using YUN-12 transport aircraft in Taiyuan. After simultaneously matching the satellite with an airborne track, the cloud particle image data were obtained near the cloud top and within the clouds during the climb and descending stages of the flight. The phase obtained from the microphysical properties of supercooled cloud droplets and ice crystals was compared with cloud phase results identified by FY-4A and Moderate Resolution Imaging Spectroradiometer (MODIS) cloud phase products. The case study and comparison show that (1) the supercooled water clouds and ice particles identified by FY-4A are in good agreement with those from the airborne measurement at the cloud top and within the cloud and (2) the positions and shapes of water clouds and ice clouds identified by FY-4A correspond well with MODIS cloud phase products. However, there is a small deviation in the extent of ice clouds, which is mainly located in the transition area between ice clouds and water clouds. The extent of ice clouds identified by FY-4A is slightly larger than that of MODIS products. Combined with airborne detection, the comparison shows that the ice clouds identified by the FY-4A satellite are consistent with aircraft detection. The supercooled cloud water identified by FY-4A can meet the needs of the operational precipitation enhancement of cold clouds, improve operational effectiveness, and promote the application of satellite technology for weather modification.
An Epipolar HS-NCC Flow Algorithm for DSM Generation Using GaoFen-3 Stereo SAR Images
Authors: Wang Jian , Lv Xiaolei , Huang Zenghui , Fu Xikai
Published: December 2022

Radargrammetry is a widely used methodology to generate the large-scale Digital Surface Model (DSM). Stereo matching is the most challenging step in radargrammetry due to the significant geometric differences and the inherent speckle noise. The speckle noise results in significant grayscale differences of the same feature points, which makes the traditional Horn–Schunck (HS) flow or multi-window zero-mean normalized cross-correlation (ZNCC) methods degrade. Therefore, this paper proposes an algorithm named Epipolar HS-NCC Flow (EHNF) for dense stereo matching, which is an improved HS flow method with normalized cross-correction constraint based on epipolar stereo images. First, the epipolar geometry is applied to resample the image to realize the coarse stereo matching. Subsequently, the EHNF method forms a global energy function to achieve fine stereo matching. The EHNF method constructs a local normalized cross-correlation constraint term to compensate for the grayscale invariance constraint, especially for the SAR stereo images. Additionally, two assessment methods are proposed to calculate the optimal cross-correlation parameter and smoothness parameter according to the refined matched point pairs. Two GaoFen-3 (GF-3) image pairs from ascending and descending orbits and the open Light Detection and Ranging (LiDAR) data are utilized to fully evaluate the proposed method. The results demonstrate that the EHNF algorithm improves the DSM elevation accuracy by 9.6% and 27.0% compared with the HS flow and multi-window ZNCC methods, respectively.
A Light Field Full-Focus Image Feature Point Matching Method with an Improved ORB Algorithm
Authors: Zuo Ying , Guan Hongliang , Duan Fuzhou , Wu Tingsong
Published: December 2022

Most of the traditional image feature point extraction and matching methods are based on a series of light properties of images. These light properties easily conflict with the distinguishability of the image features. The traditional light imaging methods focus only on a fixed depth of the target scene, and subjects at other depths are often easily blurred. This makes the traditional image feature point extraction and matching methods suffer from a low accuracy and a poor robustness. Therefore, in this paper, a light field camera is used as a sensor to acquire image data and to generate a full-focus image with the help of the rich depth information inherent in the original image of the light field. The traditional ORB feature point extraction and matching algorithm is enhanced with the goal of improving the number and accuracy of the feature point extraction for the light field full-focus images. The results show that the improved ORB algorithm extracts not only most of the features in the target scene but also covers the edge part of the image to a greater extent and produces extracted feature points which are evenly distributed for the light field full-focus image. Moreover, the extracted feature points are not repeated in a large number in a certain part of the image, eliminating the aggregation phenomenon that exists in traditional ORB algorithms.
Proposal: Apparatus for Sensing the Effect of Surface Roughness on the Surface Resistance of Metals
Authors: Torokhtii Kostiantyn , Alimenti Andrea , Vidal García Pablo , Pompeo Nicola , Silva Enrico
Published: December 2022

The root mean square surface roughness Rq of metals is detrimental in several microwave applications. Rq characterization methods are thus largely used and of great interest. In this work, a new dielectric loaded resonator (DR) design is proposed to evaluate the surface resistance variations of samples with different Rq. The new design is thought to make the measurement accuracy, usually strongly affected by the measurement repeatability, suitable for this study. We analyze the measurement method’s sensitivity and accuracy in order to assess the possibility of using this new DR design for highly accurate surface resistance measurements sensitive to Rq variations.
Constant Tension Control System of High-Voltage Coil Winding Machine Based on Smith Predictor-Optimized Active Disturbance Rejection Control Algorithm
Authors: Ai Yuming , Yu Baocheng , Zhang Yanduo , Xu Wenxia , Lu Tao
Published: December 2022

In the production process of high-voltage coils, a constant tension control system is designed to improve the quality of the transformer. The system is composed of a controller, execution structure, detection structure, etc. The active disturbance rejection control (ADRC), optimized by the Smith predictor (SP), is adopted to achieve constant tension control. The experiment results show that the tension control system based on the SP-ADRC has higher control accuracy, shorter stabilization time and stronger anti-interference ability compared with the traditional PID algorithm. The actual experiment shows that the constant tension control system of the high-voltage coil winding machine based on SP-ADRC has a superior control effect and high practical value.
Survey on Multi-Objective Task Allocation Algorithms for IoT Networks
Authors: Weikert Dominik , Steup Christoph , Mostaghim Sanaz
Published: December 2022

The Internet of Things (IoT) has been an area of growing research interest for decades. Task allocation is an important problem for the optimized operation of Internet-of-Things networks. This paper provides an overview of recent research in the field of Internet-of-Things task allocation optimization. First, the task allocation problem for the IoT itself is analyzed and divided into distinct sub-problem categories, such as deployment optimization, static or dynamic optimization as well as single- or multi-objective optimization. Following that, the commonly used optimization objectives are explained. Various recent works in the field of task allocation optimization are then summarized and catalogued according to the problem categories. Finally, the paper concludes with a qualitative analysis of the categorized approaches and a description of open problems and highlights promising directions for future research.
Design of a Hybrid Refractive/Diffractive Lens System for Broadband UV
Authors: Hu Yuan , Huo Jiaqi , Cheng Binpeng
Published: December 2022

Traditional broadband UV optical designs often have complex structural problems and cannot meet the current requirements of light and miniaturization. In this study, we first design the substrate material of double-layer diffractive optical elements (DOEs) in the 0.23–0.8 μm band, optimize the diffraction efficiency and analyze the effect of the angle of incidence on the diffraction efficiency of double-layer DOEs. Second, we design a refractive lens system and a refractive/diffractive hybrid lens system with double-layer DOEs designed for a wide UV wavelength range of 0.23–0.8 μm, a half field of view of 15 mm, an objective aperture of 0.1 and a magnification of 1. The refractive/diffractive hybrid lens system designed with seven lenses produces a higher image quality than the refractive lens system. The novel design is an effective solution to the problem of the low transmission rate of traditional UV refractive lens systems.
DTTrans: PV Power Forecasting Using Delaunay Triangulation and TransGRU
Authors: Song Keunju , Jeong Jaeik , Moon Jong-Hee , Kwon Seong-Chul , Kim Hongseok
Published: December 2022

In an era of high penetration of renewable energy, accurate photovoltaic (PV) power forecasting is crucial for balancing and scheduling power systems. However, PV power output has uncertainty since it depends on stochastic weather conditions. In this paper, we propose a novel short-term PV forecasting technique using Delaunay triangulation, of which the vertices are three weather stations that enclose a target PV site. By leveraging a Transformer encoder and gated recurrent unit (GRU), the proposed TransGRU model is robust against weather forecast error as it learns feature representation from weather data. We construct a framework based on Delaunay triangulation and TransGRU and verify that the proposed framework shows a 7–15% improvement compared to other state-of-the-art methods in terms of the normalized mean absolute error. Moreover, we investigate the effect of PV aggregation for virtual power plants where errors can be compensated across PV sites. Our framework demonstrates 41–60% improvement when PV sites are aggregated and achieves as low as 3–4% of forecasting error on average.
Resistive-Based Micro-Kelvin Temperature Resolution for Ultra-Stable Space Experiments
Authors: Roma-Dollase David , Gualani Vivek , Gohlke Martin , Abich Klaus , Morales Jordan , Gonzalvez Alba , Martín Victor , Ramos-Castro Juan , Sanjuan Josep , Nofrarias Miquel
Published: December 2022

High precision temperature measurements are a transversal need in a wide area of physical experiments. Space-borne gravitational wave detectors are a particularly challenging case, requiring both high precision and high stability in temperature measurement. In this contribution, we present a design able to reach 1 μK/Hz in most of the measuring band down to 1 mHz, and reaching 20 μK/Hz at 0.1 mHz. The scheme is based on resistive sensors in a Wheatstone bridge configuration which is AC modulated to minimize the 1/f noise. As a part of our study, we include the design of a test bench able to guarantee the high stability environment required for measurements. We show experimental results characterising both the test bench and the read-out, and discuss potential noise sources that may limit our measurement.
Highly Selective Gas Sensor Based on Litchi-like g-C3N4/In2O3 for Rapid Detection of H2
Authors: Zhang Ji , Li Xu , Pan Qinhe , Liu Tong , Wang Qingji
Published: December 2022

Hydrogen (H2) has gradually become a substitute for traditional energy, but its potential danger cannot be ignored. In this study, litchi-like g-C3N4/In2O3 composites were synthesized by a hydrothermal method and used to develop H2 sensors. The morphology characteristics and chemical composition of the samples were characterized to analyze the gas-sensing properties. Meanwhile, a series of sensors were tested to evaluate the gas-sensing performance. Among these sensors, the sensor based on the 3 wt% g-C3N4/In2O3 (the mass ratio of g-C3N4 to In2O3 is 3:100) showeds good response properties to H2, exhibiting fast response/recovery time and excellent selectivity to H2. The improvement in the gas-sensing performance may be related to the special morphology, the oxygen state and the g-C3N4/In2O3 heterojunction. To sum up, a sensor based on 3 wt% g-C3N4/In2O3 exhibits preeminent performance for H2 with high sensitivity, fast response, and excellent selectivity.