IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing最新文献

{"title":"Kriging-Based Atmospheric Phase Screen Compensation Incorporating Time-Series Similarity in Ground-Based Radar Interferometry","authors":"Yuta Izumi;Giovanni Nico;Othmar Frey;Simone Baffelli;Irena Hajnsek;Motoyuki Sato","doi":"10.1109/JSTARS.2024.3469158","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3469158","url":null,"abstract":"Accuracy of radar interferometry is often hindered by the atmospheric phase screen (APS). To address this limitation, the geostatistical approach known as Kriging has been employed to predict APS from sparse observations for compensation purposes. In this article, we propose an enhanced Kriging approach to achieve more accurate APS predictions in ground-based (GB) radar interferometry applications. Specifically, the Kriging system is augmented with a time-series measure through correlation analysis, effectively leveraging spatiotemporal information for APS prediction. The validity of the introduced Kriging method in the APS compensation framework was tested with Ku-band GB radar datasets collected over two different mountainous sites. A comparison of this method with simple Kriging reveals a noticeable improvement in APS prediction accuracy and temporal phase stability.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10702498","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142447208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Profile Data Reconstruction for Deep Chl$a$ Maxima in Mediterranean Sea via Improved-MLP Networks","authors":"Yongjun Yu;Wanchuan Kan;He Gao;Jie Yang;Baoxiang Huang","doi":"10.1109/JSTARS.2024.3468330","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3468330","url":null,"abstract":"Deep chlorophyll maximum (DCM) is a common oceanographic phenomenon characterized by a significant peak in chlorophyll concentration at a specific depth below the ocean surface. DCM formation is closely related to factors, such as light availability, nutrient distribution, and ocean circulation, making it an important indicator for studying marine ecosystems and their changes. This study aims to estimate subsurface chlorophyll concentrations in the Mediterranean region using an improved multilayer perceptron model, bridging the gap between sparse observation data and dense sea surface data. We utilize Biogeochemical Argo and satellite data, including longitude, latitude, sea surface temperature, surface chlorophyll concentration, and month, as inputs to the model to estimate subsurface chlorophyll concentrations from 1 to 300 m depth. Through fitting and analyzing chlorophyll concentration data in the Mediterranean region, we explore DCM characteristics and their variations across different regions and seasons. The results indicate that the IMLP model performs excellently in estimating subsurface chlorophyll concentrations and effectively captures DCM features in various regions and seasons. By comparing the model estimations with observation data, we reveal patterns in DCM characteristics in the Mediterranean region, providing valuable data support for further research into marine ecosystems.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10700938","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142443023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning for Mesoscale Eddy Detection With Feature Fusion of Multisatellite Observations","authors":"Huarong Xie;Qing Xu;Changming Dong","doi":"10.1109/JSTARS.2024.3468457","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3468457","url":null,"abstract":"Accurate oceanic eddy detection is crucial for understanding their dynamic behavior. In this study, we apply attention dual-U-net, a specialized deep learning model, to simultaneously detect the location and contours of mesoscale eddies in the South China Sea (SCS). This model integrates various features from satellite-observed absolute dynamic topography and sea surface temperature anomaly (SSTA), and is established separately for anticyclonic eddies (AEs) and cyclonic eddies (CEs) detection. Eddy contours from the delayed-time altimetric mesoscale eddy trajectories atlas are used for model training and evaluation. Results indicate that the model excels in detecting the shape and location of mesoscale eddies in the SCS, achieving success detection rates (SDRs) of 95.2% for AEs and 94.7% for CEs. Incorporating SSTA as an additional input enhances the accuracy of eddy shape and aids in further distinguishing normal from abnormal eddies. Abnormal eddies, characterized by cold AEs and warm CEs, constitute 16.8% and 29.8% of total AEs and CEs, respectively, with SDRs of 95.3% and 94.7%, underscoring the model robustness to abnormal eddies. Moreover, the mean absolute errors of AEs (CEs) are notably smaller than those estimated by the pyramid scene parsing network and EddyNet, with reductions of 49.1% (45.1%) and 67.6% (70.8%), respectively. These reductions are particularly pronounced in coastal areas and deep waters exceeding 200 m in depth. The amalgamation of the accurate eddy detection model and high-resolution multisatellite observations presents an effective approach to capturing eddy occurrences, contributing to a comprehensive understanding of eddy dynamics in marginal seas and open oceans.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10694783","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142443022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Seasonal Dynamics in Land Surface Temperature in Response to Land Use Land Cover Changes Using Google Earth Engine","authors":"Lei Feng;Sajjad Hussain;Narcisa G. Pricope;Sana Arshad;Aqil Tariq;Li Feng;Muhammad Mubeen;Rana Waqar Aslam;Mohammed S. Fnais;Wenzhao Li;Hesham El-Askary","doi":"10.1109/JSTARS.2024.3466191","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3466191","url":null,"abstract":"Changes in land use and land cover (LULC) are critical for evaluating global spatiotemporal trends, especially regarding climate change and urbanization. This study investigates the dynamics of Landsat surface temperature (LST) in response to LULC changes and their effects on the seasonal microclimate in Kasur District, Pakistan. Using the Google Earth Engine platform, we employed a random forest algorithm to detect LULC changes (cropland, forest, built-up, fallow, barren, and water) and analyze seasonal spectral indices from Landsat imagery for 1988, 2002, and 2022. Significant LULC changes were observed, including a 9.8% increase in built-up areas, a 4.2% decrease in cropland, and a 1.4% decrease in forested areas, linked to urban heat island effects and population growth. Additionally, there was a 2.7% increase in fallow and open land, contributing to the district's impervious surface area. Significant correlations (\u0000<italic>p</i>\u0000 < 0.001) were found between LST and spectral indices—normalized difference vegetation index, enhanced vegetation index, and normalized difference built index (NDBI)—ranging from 0.7 to 0.8 in both winter and summer. In summer, the maximum LST increased from 43 °C in 1988 to 44 °C in 2002, with a linear correlation (\u0000<italic>R</i>\u0000²) increase from 0.57 to 0.75 and a polynomial correlation (\u0000<italic>R</i>\u0000²) increase from 0.63 to 0.76 with NDBI from 1988 to 2022. Understanding these dynamics is crucial as LULC changes and the resulting temperature variations have significant implications for local climate, agriculture, and human health. This study underscores the need for effective LULC policies to mitigate impacts, protect vegetation cover, and ensure sustainable land management. These findings provide valuable insights for policymakers and urban planners aiming to balance development with environmental sustainability.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10694779","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142434629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DEPDet: A Cross-Spatial Multiscale Lightweight Network for Ship Detection of SAR Images in Complex Scenes","authors":"Jing Zhang;Fan Deng;Yonghua Wang;Jie Gong;Ziyang Liu;Wenjun Liu;Yinmei Zeng;Zeqiang Chen","doi":"10.1109/JSTARS.2024.3469209","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3469209","url":null,"abstract":"Nowadays, the intricate nature of synthetic aperture radar (SAR) ship scenes, coupled with the presence of multiscale targets, poses a significant challenge in detection accuracy. Furthermore, to reduce the financial outlay on hardware, there is also a considerable challenge in lightweighting the model. In order to resolve the aforementioned concerns, we propose a cross-spatial multiscale lightweight network, designated as DEPDet. First, a new efficient multiscale detection backbone network DEMNet is redesigned. To improve the feature extraction capability of the network, a cross-spatial multiscale convolution (CSMSConv) is designed and a new CSMSConv module CSMSC2F is constructed. Meanwhile, we introduce an efficient multiscale attention module. DEMNet is capable of more effectively extracting information pertaining to multiscale ships. Moreover, to enhance the fusion of features at diverse scales, we design a new path aggregation feature pyramid network DEPAFPN, which combines deformable convolution and CSMSC2F. Finally, we introduce partial convolution to construct a lightweight detection head module PCHead, which can be employed to extract spatial features with greater efficiency. The publicly available SAR ship datasets, SAR Ship Detection Dataset and High-Resolution SAR Image Dataset, are employed for the purpose of conducting experiments. The mean average precision (mAP) obtained was 98.2% (+1.4%) and 91.6% (+1.6%), respectively. The F1 obtained 0.950 (+1.7%) and 0.871 (+1.4%), respectively. Concurrently, the Params decreased from 3.2M to 2.1M, a decrease of approximately 34%. The floating-point operations (FLOPs) decreased from 8.7G to 4.5G, a decrease of approximately 48%. The experimental results indicate that the network achieves an effective balance between detection accuracy and lightweight effect with good generalization and extensibility.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10695810","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142443021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Visible-to-Infrared Image Translation for Matching Tasks","authors":"Decao Ma;Shaopeng Li;Juan Su;Yong Xian;Tao Zhang","doi":"10.1109/JSTARS.2024.3468456","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3468456","url":null,"abstract":"Visible-to-infrared image translation is an important way to enrich infrared data. However, the reliability of the data generated by image translation in downstream tasks has always been controversial. This article proposes a method that integrates visible-to-infrared image translation tasks and multimodal template matching tasks. The image generation network is based on a generative adversarial networks (GANs), and network training is supervised by L1 loss, GANs loss, and match loss, where the matching loss includes normalized cross-correlation (NCC) loss and match patch (MP) loss. NCC loss is constructed based on the NCC matching algorithm. MP loss is calculated by modeling template matching as a contrastive learning problem. In experiments on the KAIST, VEDAI, and AVIID datasets, this method outperforms state-of-the-art methods in terms of image generation quality and template matching accuracy. Our method incorporates the image matching process into image-to-image translation, demonstrating the usability of GANs-based image generation for critical downstream tasks. This research resolves the practical controversy of generating images based on GANs and provides a theoretical reference for image generation for tasks, such as multisource image object detection and data association.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10694789","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Urban Morphologic Structures Retrieved by Satellite Imagery Correlate With Socioeconomic Household Data—Insights From the City of Kigali, Rwanda","authors":"Andreas Braun;Christian Khouri;Oliver Assmann;Gebhard Warth;Michael Schultz;Volker Hochschild","doi":"10.1109/JSTARS.2024.3466298","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3466298","url":null,"abstract":"A substantial body of research exists on the use of remote sensing in urban contexts. However, only a limited number of studies have contributed to our understanding of the socioeconomic conditions of different urban areas. This research aims to demonstrate the potential of very high-resolution images and geospatial data by examining the interrelations between socioeconomic data retrieved from household surveys in the city of Kigali and spatial data on urban morphology retrieved by satellite imagery. As the surveys yielded large amounts of data of varying levels of measurement (categorical and numeric), we present different methods of statistical correlation, data mining, and machine learning to highlight socioeconomic patterns within the spatial data. The results demonstrate a significant correlation between the share of different building types, building density, average building heights, and distances to public infrastructure with a range of surveyed data, including building properties, household members, financial resources, and overall lifestyle habits. This highlights the potential of remote sensing and geospatial data to provide valuable insights into the socioeconomic conditions of urban areas. It also underscores the importance of using advanced statistical methods, data mining, and machine learning to enhance our understanding of urban morphology and its socioeconomic implications. However, it is important to acknowledge the limitations of such approaches, including the lack of information on ownership, potential for false inference and the direction of causation, which require further investigation.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10690174","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142434605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Surface Depth Estimation From Multiview Stereo Satellite Images With Distribution Contrast Network","authors":"Ziyang Chen;Wenting Li;Zhongwei Cui;Yongjun Zhang","doi":"10.1109/JSTARS.2024.3457616","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3457616","url":null,"abstract":"The calculation of surface depth based on multiview \u0000<bold>s</b>\u0000tereo (MVS) satellite imagery is of significant importance in fields such as military and surveying. The challenge in extracting depth information from satellite imagery lies in the fact that these images often exhibit similar colors, necessitating the development of algorithms that can integrate shape and texture information. Moreover, the application of classical convolutional neural network (CNN) MVS is limited by its inability to capture long-range terrain relationships, which presents a bottleneck in existing surface depth estimation algorithms. To address the above problems, we propose the Distribution Contrast Network for Surface Depth Estimation from Satellite Multi\u0000<bold>V</b>\u0000iew \u0000<bold>S</b>\u0000tereo Images (DC-SatMVS), a novel satellite MVS network. In order to learn short-range and long-range features, we designed separate CNN and ViT branches. To emphasize the importance of shape and texture, we propose the Distribution Contrast Loss mechanism. This mechanism supervises the model training based on the similarity between the predicted depth and the ground truth depth distribution. Experimental results demonstrate that our method achieves state-of-the-art (SOTA) performance. We produce a remarkable 18.14% reduction in root mean square error compared to the Sat-MVSF on the WHU-TLC dataset. To validate the generalization performance of our framework, we trained and tested it on the DTU dataset, a common MVS dataset, and achieve SOTA results in this dataset as well.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10689488","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142397057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fusing Transformers in a Tuning Fork Structure for Hyperspectral Image Classification Across Disjoint Samples","authors":"Muhammad Ahmad;Muhammad Usama;Manuel Mazzara;Salvatore Distefano;Hamad Ahmed Altuwaijri;Silvia Liberata Ullo","doi":"10.1109/JSTARS.2024.3465831","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3465831","url":null,"abstract":"The 3-D swin transformer (3DST) and spatial–spectral transformer (SST) each excel in capturing distinct aspects of image information: the 3DST with hierarchical attention and window-based processing, and the SST with self-attention mechanisms for long-range dependencies. However, applying them independently reveals the following limitations: the 3DST struggles with spectral information, while the SST lacks in capturing fine spatial details. In this article, we propose a novel tuning fork fusion approach to overcome these shortcomings, integrating the 3DST and SST to enhance the hyperspectral image (HSI) classification (HSIC). Our method integrates the hierarchical attention mechanism from the 3DST with the long-range dependence modeling of the SST. This combination refines spatial and spectral information representation and merges insights from both transformers at a fine-grained level. By emphasizing the fusion of attention mechanisms from both architectures, our approach significantly enhances the model's ability to capture complex spatial–spectral relationships, resulting in improved HSIC accuracy. In addition, we highlight the importance of disjoint training, validation, and test samples to enhance model generalization. Experimentation on benchmark HSI datasets demonstrates the superiority of our fusion approach over other state-of-the-art methods and standalone transformers. The source code has been developed from scratch and will be made public upon acceptance.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10685113","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Novel PRI Variation Strategy for Range Ambiguity Suppression in Quad-Pol Staggered Spaceborne SAR Based on Azimuth Phase Coding","authors":"Ruizhen Song;Wei Wang;Yongwei Zhang;Yuwei Wu;Weidong Yu","doi":"10.1109/JSTARS.2024.3466135","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3466135","url":null,"abstract":"Staggered synthetic aperture radar (SAR) is compatible with quadrature-polarimetric (quad-pol) SAR, enabling ultrawide continuous swath quad-pol SAR imaging with fine azimuth resolution. However, the quad-pol staggered spaceborne SAR is constrained by severe range ambiguity in the cross-pol channel. Moreover, azimuth phase coding (APC), as an excellent technique for range ambiguity suppression, is no longer exactly effective in the staggered imaging mode because the range ambiguities for different azimuth samples are located at different slant ranges. Given the above challenges presented in quad-pol staggered SAR, in this article, a novel pulse repetition interval (PRI) variation strategy based on APC is proposed to suppress range ambiguity in quad-pol staggered spaceborne SAR. First, the range ambiguity signal for quad-pol staggered SAR is modeled and analyzed. Then, the dedicated PRI variation strategy for quad-pol staggered SAR is developed, by which the near or far first-order range ambiguity is coherent in azimuth and can be subsequently suppressed using APC technique. Especially, a scale factor is developed in the novel PRI variation strategy, which can be flexibly designed to optimize the time interval of alternately transmitted polarized pulses. By this, most of the incoherent far or near first-order range ambiguity energy can be significantly reduced. Finally, simulations are conducted to verify the advancement of the proposed approach for range ambiguity suppression in quad-pol staggered SAR. The work in this article can be viewed as an important candidate for range ambiguity suppression in future quad-pol staggered spaceborne SAR.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10689276","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142408746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}