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

{"title":"A Progressive Spectral Correction and Spatial Compensation Network for Pansharpening","authors":"Rixian Liu;Hangyuan Lu;Biwei Chi;Yong Yang;Shuying Huang","doi":"10.1109/JSTARS.2025.3559582","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3559582","url":null,"abstract":"Pansharpening aims to produce a high resolution multispectral image by fusing a panchromatic image with a low-resolution multispectral image. Current pansharpening methods often overlook the significant modality differences between source images and lack interaction between them, resulting in spatial-spectral distortions. To address these issues, we proposed a novel progressive spectral correction and spatial compensation network for pansharpening. The network comprises a spectral correction branch, a spatial compensation branch, and a spectral-spatial fusion (SSF) branch. In the spectral correction branch, we designed a local spectral reinforcement (LSR) module and a global spectral rectification (GSR) module to keep the spectral fidelity. The LSR module is designed to reinforce the unique local information from different kinds of spectral features, while the GSR module captures long-range dependency and rectifies the spectral features with a cross-attention mechanism. In the spatial compensation branch, we designed a multiscale dilated adaptive feature extraction module guided by spectral and spatial attention to extract useful spatial details, and the details are progressively compensated into the SSF branch to better keep spatial fidelity. The SSF branch is designed to interact with spectral correction branch and spatial compensation branch to mitigate the modal difference and progressively optimize the spectral-spatial information. Comprehensive experiments show that the proposed method outperforms current state-of-the-art pansharpening methods.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"10772-10785"},"PeriodicalIF":4.7,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10960710","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896281","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":"Dunes Identification Based on Attention Mechanism With Dual-Branch Codec","authors":"Zhaobin Wang;Yan Li;Yue Shi;Yaonan Zhang;Xuejun Guo","doi":"10.1109/JSTARS.2025.3557540","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3557540","url":null,"abstract":"The research on recognition of dune forms is of great significance for mastering desert landforms and controlling them. We first reviews the status quo of desertification monitoring and control in desert remote sensing image classification and dune form type recognition, and further expounds the advantages and significance of combining remote sensing image and deep learning with dune form type recognition. However, there are still some shortcomings in deep learning-based dune form type recognition, including lack of data set, poor network adaptation, and low segmentation accuracy. Thus, we takes Tengger Desert as the research area and extracts and identifies its dune form types based on deep learning. Specific work contents are as follows: A dual-branch codec dune morphological type segmentation model based on attention mechanism is proposed. In the dual-branch structure, the overcomplete and incomplete networks can take into account both small and large receptive fields, improving the situation of local detail loss caused by the incomplete network in the traditional semantic segmentation structure. The codec hybrid module makes the deep global information interact with the shallow detail information in the dual-branch network to obtain richer feature information. The multiscale mixed attention module is used to extract deep features, and lightweight upsampling operator is used to achieve feature recombination and reduce the number of network parameters. A series of ablation experiments, effectiveness analyses, and comparative studies across different algorithms on two datasets were conducted to evaluate the generalization ability of dual-branch codec network based on attention mechanism across diverse datasets. Using metrics such as Pixel Accuracy, F1-score, and mean intersection over union, its superior recognition performance among various algorithms was validated.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"11670-11685"},"PeriodicalIF":4.7,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10962321","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073113","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":"Large Area Crops Mapping by Phenological Horizon Attention Transformer (PHAT) Method Using MODIS Time-Series Imagery","authors":"Quanshan Gao;Taixia Wu;Hongzhao Tang;JingYu Yang;Shudong Wang","doi":"10.1109/JSTARS.2025.3559939","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3559939","url":null,"abstract":"Accurate collection of crop planting information at large area is essential for estimating agricultural productivity and ensuring food security. Different crops have varying growth cycles and phenological stages, and changes in factors such as topography, soil type, and moisture conditions can lead to diversity in crops growth status, which complicates uniform monitoring. Multiple crops mapping simultaneously with high precision presents a significant challenge due to the high spatial heterogeneity of crops distribution across vast regions. To address these challenges, this article developed an advanced deep learning crop mapping method, i.e., phenological horizon attention mechanism-transformer model (PHAT) to achieve rapid and accurate multiple crops extraction over large areas. Initially, time-series data were constructed using the normalized differential vegetation index (NDVI) dataset based on moderate resolution imaging spectroradiometer (MODIS) product. Subsequently, in the mixed pixel decomposition phase, orthogonal subspace projection and vertex component analysis were employed to identify crop types and extract endmembers. While the regular changes in the time-series NDVI reflect the phenological evolution trend among multiple crops, but the phenological characteristics difference between the same crop is extremely difficult to find. The PHAT model was therefore trained using the phenological features of endmembers to obtain the spatial distribution of crops, and to resolve the issue of varying time-series curves for the same crop across large areas. This study selected the North China Plain in 2021 as the research area, utilizing Google Earth data and Landsat 8 images to verify the approach's accuracy. Based on the MODIS NDVI data with a coarse spatial resolution of 250 m, our method achieved an OA of 90.1% for the synchronous extraction of soybean, spring peanut-summer sesame, winter wheat-summer maize, paddy rice, and rapeseed-cotton, with a RMSE of approximately 12% in 16.6 million mu of cultivated land.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"10995-11013"},"PeriodicalIF":4.7,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10962301","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896283","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 Multiscale Convolution SAR Image Target Recognition Method Based on Complex-Valued Neural Networks","authors":"Guangyu Hou;Zhihui Xin;Guisheng Liao;Penghui Huang;Yuhao Huang;Rui Zou","doi":"10.1109/JSTARS.2025.3559656","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3559656","url":null,"abstract":"Recent advances in deep learning have driven significant success in synthetic aperture radar (SAR) automatic target recognition, particularly through convolutional neural network (CNN) based classification algorithms. However, SAR images possess distinctive physical scattering properties, owing to their unique imaging mechanism. Many deep learning algorithms rely solely on amplitude information, ignoring phase information, which may result in the loss of information in the original complex-valued SAR image and suboptimal performance. To tackle these problems, this article introduces a SAR target recognition approach based on complex-valued operations, designated as complex-valued residual mish activation and convolution block attention module (CBAM) net (CRMC-Net). The CRMC-Net effectively utilizes the amplitude and phase information in complex-valued SAR data. Specifically, first, the elements of CNN, including the input and output layers, the convolution layers, the activation functions, and the pooling layers, are extended to the complex-valued domain. Second, in order to further enhance the representation ability of the model, multiscale information of the target is extracted through different convolution kernel sizes. Finally, the network constructs many complex-valued operation blocks to enhance the robustness of the designed network, including the complex-valued residual block, complex-valued Mish activation function, and complex-valued CBAM. The experimental results obtained from the moving and stationary target capture and recognition dataset and OpenSARShip2.0 dataset demonstrate that the proposed network model outperforms the traditional real-valued models. It can further reduce the classification error and enhance performance.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"10657-10673"},"PeriodicalIF":4.7,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10960712","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896518","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":"RDSF-Net: Residual Wavelet Mamba-Based Differential Completion and Spatio-Frequency Extraction Remote Sensing Change Detection Network","authors":"Shuo Wang;Dapeng Cheng;Genji Yuan;Jinjiang Li","doi":"10.1109/JSTARS.2025.3559708","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3559708","url":null,"abstract":"Remote sensing change detection is a task of identifying and analyzing the area of surface change by comparing remote sensing images from different periods. It is widely used in many fields such as environmental monitoring, urban planning, and agricultural management. Although the remote sensing change detection technology has made great progress in recent years, it still faces many thorny problems: first, the complex heterogeneity of ground objects leads to imperfect processing of the change structure information; second, the influence of nonstationary changes due to seasonal factors. To address these problems, we innovatively propose the residual wavelet mamba-based differential completion and spatio-frequency extraction remote sensing change detection network (RDSF) network. The network is designed with residual wavelet transform as the downsampler, which effectively integrates the key directional information and the overall structural information in the original features, and uses convolutional neural network and Mamba as the backbone network for both long-range and short-range feature extraction. Meanwhile, in order to better capture and compare the differences between time points, we innovatively developed a difference completion sensor to ensure the capture of subtle changes by adjusting the selection, comparison, and dynamic weight assignment between features. In addition, we design a multiscale frequency domain approach that uses a combination of spatial and frequency domain enhancement strategies to reveal the deep structure and boundary changes of the features while reducing the noise interference. RDSF-Net has been extensively experimentally validated on three datasets: the LEVIR-CD, the WHU-CD, and the GZ-CD datasets, and achieved better results than the other state-of-the-art datasets in terms of effect metrics and achieved better results than other state-of-the-art methods.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"11573-11587"},"PeriodicalIF":4.7,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10960633","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144072951","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":"Foreword to the Special Issue “Exploring the Potential of Urban Remote Sensing”","authors":"Nektarios Chrysoulakis;Giorgos Somarakis;Monika Kuffer;Clement Mallet;Hannes Taubenböck","doi":"10.1109/JSTARS.2025.3559018","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3559018","url":null,"abstract":"Urban growth and decline, changing urban patterns, densification, conversion, or deconstruction of the built landscape, gain, loss or alteration of natural space, socioeconomic inequalities, variabilities of structural types within and across cities, efficiency of land consumption, causes and effects of the urban heat island, environmental burdens of air pollution, impacts of natural hazards on urban assets and people, or projected effects of climate change; these manifold and crucial topics in the urban domain are all issues in a long list that could be continued almost indefinitely. All these and many other issues are omnipresent in today's cities on our planet.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"11323-11329"},"PeriodicalIF":4.7,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10960404","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143938010","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 Difference Wavelet Feature Index for Estimating Aerial N Uptake of Winter Wheat from In Situ Hyperspectral Remote Sensing","authors":"Bin-Bin Guo;Wen-Hui Wang;Chao Ma;Jun Zhang;Fei Yin;Wei Feng","doi":"10.1109/JSTARS.2025.3559100","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3559100","url":null,"abstract":"The real-time and accurate assessment of crop aerial nitrogen (N) uptake is of significant importance for optimizing N fertilization. To develop a robust method for determining aerial N uptake in winter wheat, a field experiment with different N fertilizer levels was conducted over three successive years at two ecological sites in Henan, China. This research systematically compared the correlation between aerial N uptake and spectral parameters derived from various spectral transform methods: continuum removal (CR), standard normal variate transform method, first derivative reflectance (FDR), and continuous wavelet transforms (CWT). The findings revealed that CWT exhibited the highest efficacy among all the spectral transform methods, followed by FDR, with <italic>R</i>² values of 0.777 for WF(4,770) and 0.764 for FDR₇₄₈. A new index, termed the difference wavelet feature index (DWF), is defined as DWF(4 560 770) = WF(4560) − WF(4770). This simple yet effective index significantly enhances the assessment of aerial N uptake, achieving an <italic>R</i>² of 0.815. Validation with independent data showed that the RMSE for the DIDA, FDR₇₄₈, WF(4770), and DWF(4 560 770) under different cultivation factors were 3.578–4.361 g m^-2, 3.501–4.219 g m^-2, 3.472–4.309 g m^-2, 3.262–4.030 g m^-2, respectively. It was further verified that the newly DWF(4 560 770) index has excellent universality and stability. Therefore, the aforementioned studies indicated that the novel DWF(4 560 770) is more suitable for evaluating aerial N uptake at the heterogeneous field scale and also has significant potential for precise prediction of aerial N uptake using UAV remote sensing.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"11213-11224"},"PeriodicalIF":4.7,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10959048","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900571","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 Composite Network for CS ISAR Integrating Deep Adaptive Sampling and Imaging","authors":"Lianzi Wang;Ling Wang;Miguel Heredia Conde;DaiYin Zhu","doi":"10.1109/JSTARS.2025.3559569","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3559569","url":null,"abstract":"Compressive sensing (CS) actively contributes to inverse synthetic aperture radar (ISAR) imaging with less raw data. The design of the measurement matrix and the development of reconstruction methods are critical processes in CS ISAR imaging. However, the existing CS ISAR imaging methods based on deep learning (DL) mainly focus on improving the performance of the reconstruction algorithm while ignoring the potential room for improvement given by the design of the measurement matrix. To take full advantage of the compression potential of the measurement matrix, we propose a CS ISAR imaging technique based on adaptive sampling, utilizing DL to learn a priori information about the target scene and designing an optimal sampling strategy that uses less data to achieve high-quality imaging. Furthermore, we integrate CS ISAR imaging into a composite network, in which the sampling and reconstruction stage is optimized globally, realizing deep adaptive sampling imaging with a high compression ratio. The CS ISAR imaging with adaptive sampling consists of sampling and reconstruction networks, where the sampling network compresses the radar data by a convolutional neural network, and the reconstruction network mainly performs the image reconstruction by convolutional dictionary learning. In addition, we adopt the block-based CS method in the sampling network to alleviate the computational burden caused by vectorizing and stacking the data and introduce a nonlocal self-similarity model into the reconstruction network to improve the imaging quality. The qualitative and quantitative analysis of the experiments on real data demonstrates that the novel method can achieve higher quality ISAR imaging than other nonadaptive sampling methods at a low sampling ratio, demonstrating its superiority.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"11599-11609"},"PeriodicalIF":4.7,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10960535","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143937916","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":"Adaptive Gaussian-PSO XGBoost Model for Alpine Forests Aboveground Biomass Estimation Using Spaceborne PolSAR and LiDAR Data","authors":"Fu-Gen Jiang;Ming-Dian Li;Si-Wei Chen","doi":"10.1109/JSTARS.2025.3559233","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3559233","url":null,"abstract":"Accurate estimation of forest aboveground biomass (AGB) is fundamental to forest management and ecosystem monitoring. Natural forest ecosystems are an important guarantee to maintain the global ecological balance and carbon cycle, but the complex climate, dramatic topographic relief, and saturation effects make it difficult to achieve reasonable AGB estimation of alpine forests with commonly used optical data. In this study, spaceborne dual-polarimetric synthetic aperture radar and light detection and ranging data were combined to break through the limitation of optical data, and the information on the vertical structure inside the forests was extracted, to achieve high-precision forest AGB estimation and reveal the distribution pattern of forest AGB. An adaptive Gaussian-particle swarm algorithm XGBoost model (AGP-XGBOOST) was proposed to improve the forest AGB estimation, which adjusted the PSO through the built-in adaptive parameter of the Gaussian function to achieve the hyperparameter optimization for the XGBoost model. The proposed method was validated with the forest survey data, and classic machine-learning models were constructed for comparison. The comparative analysis was carried out using natural forests in the eastern Tibetan Plateau as an example, and the results showed that the proposed AGP-XGBOOST model consistently maintained the best performance across all models, and the AGB estimation errors caused by the combined data source decreased by 30.8%, 24.4%, and 10.1% compared to the independent data sources. In addition, the forest AGB mapping showed that the distribution pattern of forest AGB on the eastern Tibetan Plateau was significantly affected by terrain fluctuations.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"10157-10171"},"PeriodicalIF":4.7,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10959714","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143883495","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":"HTC-HAD: A Hybrid Transformer-CNN Approach for Hyperspectral Anomaly Detection","authors":"Minghua Zhao;Wen Zheng;Jing Hu","doi":"10.1109/JSTARS.2025.3559079","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3559079","url":null,"abstract":"Hyperspectral anomaly detection (HAD) identifies anomalies by analyzing differences between anomalies and background pixels without prior information, presenting a significant challenge. Most existing studies leverage the high correlation in spectral and spatial dimensions, primarily focusing on local spectral and spatial information for background reconstruction while neglecting long-range dependencies. This local perception constrains models from fully capturing intrinsic spatial–spectral connections. To address this, we propose a novel hybrid transformer-CNN network for HAD (HTC-HAD). Specifically, HTC-HAD combines CNNs with transformers, where the CNN focuses on local modeling, and the transformer addresses long-range modeling. This dual approach ensures the accurate reconstruction of backgrounds by capturing both local and long-range dependencies. Meanwhile, to reduce model complexity and redundancy among neighboring bands, we incorporate a simplified and effective band selection strategy as preprocessing. In addition, to prevent anomalies from being reconstructed during background estimation, we employ an adaptive weight loss function to suppress them. Experimental results on several real datasets, both qualitatively and quantitatively, demonstrate that our HTC-HAD achieves satisfying detection performance.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"10144-10156"},"PeriodicalIF":4.7,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10959089","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143883496","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}