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

{"title":"Spatial Downscaling of Gridded Soil Moisture Products Using Optical and Thermal Satellite Data: Effect of Using Different Vegetation Indices","authors":"Tómas Halldórsson Alexander;Haijun Luan;Hongxiao Jin;Zheng Duan","doi":"10.1109/JSTARS.2025.3543012","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3543012","url":null,"abstract":"Satellite remote sensing offers global-scale soil moisture (SM) estimation to assess water and energy cycles. However, the coarse resolution of SM products from microwave remote sensing is unsuitable for fine-scale analysis. This study explored spatial downscaling methods to refine the 0.25° ESA CCI SM product to a 1-km resolution, utilizing optical and thermal remote sensing data, including the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), kernel NDVI (kNDVI), and plant phenology index (PPI), together with land surface temperature from MODIS products over two study areas in Europe. The vegetation temperature condition index based approach was used for downscaling, in which the wet and dry edges of the triangular feature space were determined by fitting a line to the maximum and minimum temperatures, respectively, for each vegetation index. The PPI-based downscaling showed consistent results between the two study areas, having a good correlation coefficient and unbiased root-mean-square deviation (ubRMSD) against the in-situ measurements. The NDVI-based downscaling had poor performance overall in terms of ubRMSD and correlation. Results from the EVI- and kNDVI-based methods varied in the two study areas. Compared with the original coarse SM product, spatially downscaled SM products exhibited inferior performance against in-situ SM measurements in terms of evaluation metrics.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"7728-7741"},"PeriodicalIF":4.7,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10908228","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143667390","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 Multilevel Point-Matching Algorithm Based on Hierarchical Feature Detection and Description for SAR-to-Optical Image Registration","authors":"Zhixin Lian;Shiyang Tang;Jiahao Han;Yue Wu;Mingjin Zhang;Zhanye Chen;Linrang Zhang","doi":"10.1109/JSTARS.2025.3546224","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3546224","url":null,"abstract":"High-precision registration of synthetic aperture radar (SAR) and optical images based on point features remains a particularly challenging task, as the detection and description of feature points are susceptible to nonlinear radiometric distortions and SAR speckle noise. For this purpose, a multilevel point-matching algorithm based on hierarchical feature detection and description is proposed in this letter to improve the accuracy of SAR-to-optical (S-O) image registration. First, a FAST feature detector (OIPC-Fast) is constructed by combining overlapping chunking, image stratification, and phase congruency (PC). The OIPC-Fast detector performs hierarchical feature detection on SAR and optical images based on image properties by two-dimensional discrete wavelet transform and multimoment of PC map, respectively. Feature points with high consistency are screened out by voting criteria. The repeatability of keypoints is effectively improved. Then, a multilevel matching strategy is proposed. The SAR feature descriptor is constructed in this strategy by capturing more layers of image information rather than using a single denoised SAR image information after preprocessing, thus enhancing the robustness of SAR feature descriptors. Ten sets of real image data are used for experimental validation. Compared with some of the most advanced algorithms, the results indicate that the registration accuracy can be improved by applying the proposed point-matching algorithm to S-O image registration.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"7318-7333"},"PeriodicalIF":4.7,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10906637","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143655069","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 New Flexible Approach for Reconstructing Satellite-Based Land Surface Temperature Images: A Case Study With MODIS Data","authors":"Seyedkarim Afsharipour;Li Jia;Massimo Menenti","doi":"10.1109/JSTARS.2025.3545404","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3545404","url":null,"abstract":"Time series of spatially continuous satellite data are increasingly used for environmental studies. Among these, land surface temperature (LST), retrieved from data such as the MODerate resolution Imaging Spectroradiometer (MODIS), plays a vital role in numerous applications. However, cloud cover significantly reduces the number of usable pixelwise LST observations. Despite various documented methods for reconstructing missing LST pixels, challenges remain regarding their flexibility to handle varying gap percentages and reliance on multiple ancillary datasets. This study presents a flexible and automated technique to reconstruct missing LST pixels without relying on ancillary data. The approach combines three innovative techniques: global regression analysis, local regression analysis, and geospatial analysis. The missing pixels percentage of each day determines the appropriate technique to fill the gaps. The method was applied to daily Terra MODIS LST datasets (MOD11A1) at 1 km spatial resolution from 2002 to 2022. Two evaluation methods were conducted: comparing with in-situ measurements and introducing artificial gaps. The validation was demonstrated over the Heihe River basin in China and in four experimental areas worldwide with available ground measurements from FLUXNET. Validation with artificial gaps produced average root-mean-square error (RMSE) and mean absolute error (MAE) of 2.33 K and 1.76 K, respectively. In-situ measurements indicated superior performance with <italic>R</i>², RMSE, and MAE of 0.85, 4 K, and 3.4 K, outperforming two existing methods. The study demonstrates that the model accurately reconstructs missing pixels on heterogeneous surfaces under diverse conditions, effectively handling large datasets and complex gaps.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"7451-7467"},"PeriodicalIF":4.7,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10906521","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143655097","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":"Computation of Irradiance Distribution Near the Lunar South Pole and Data Validation","authors":"Menghao Li;Jiapu Yan;Zhihai Xu;Qi Li;Haoyang Mao;Yueting Chen","doi":"10.1109/JSTARS.2025.3546230","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3546230","url":null,"abstract":"The lunar south pole has emerged as a focal point in deep space research in recent years and irradiance computation in this region, which elucidates spatial and temporal irradiance distribution, is vital for future exploration. The irradiance of both the directly illuminated areas and permanently shadowed regions (PSRs) fluctuates rapidly with changes in the solar vector. In addition to scattered solar radiation, earthshine also contributes to the illumination of the lunar surface and its effect warrants further investigation. In this article, we propose a model to calculate the spatial and temporal distribution of irradiance near the lunar south pole, incorporating topographic data and ephemeris, which accounts for direct sunlight, scattered solar illumination, and earthshine. The accuracy of irradiance distribution contributed by solar light is validated by the calibrated images captured by narrow-angle cameras equipped on the Lunar Reconnaissance Orbiter. Moreover, our algorithm shows that the earthshine illuminates certain regions within the PSRs, although the intensity is comparatively low relative to that of scattered solar radiation. Combined with the simulation and real-captured images, the abnormal reflectance of the Shackleton crater is confirmed. The irradiance distribution from 88<inline-formula><tex-math>$^{circ }$</tex-math></inline-formula>s to 90<inline-formula><tex-math>$^{circ }$</tex-math></inline-formula>s on the lunar surface, spanning the period from 30 June 2026 to 31 December 2026 at 4-h intervals is calculated, providing a valuable reference for future lunar exploration.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"8203-8214"},"PeriodicalIF":4.7,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10906455","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706574","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":"Frequency-Dependent Scaling Factors to Estimate Multiple Time-Period Global Mass Changes Observed by GRACE/GRACE-FO","authors":"Zhenran Peng;Linsong Wang;Qing Liang;Chao Chen","doi":"10.1109/JSTARS.2025.3546524","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3546524","url":null,"abstract":"The scaling factor method is commonly used to restore near-true time-variable gravity from gravity recovery and climate experiment/follow-on (GRACE/GRACE-FO). This study presents a novel method for computing frequency-dependent scaling factors (FDSFs) using spherical harmonic decomposition of GRACE/GRACE-FO Level-2 data at full frequency. Two applications based on a global hydrological model (land excluding Greenland and Antarctica) and a combined model (Greenland) demonstrate that FDSFs reduce the theoretical recovery residuals versus using a single scaling factor by ∼11.0% across short-term, seasonal, inter-annual, and long-term scales. Given the ability to capture more model details, the FDSFs improved the estimates at the basin scale and in glacier regions such as High Mountain Asia. In Greenland, the FDSF-scaled results revealed an enhanced amplitude with an averaged relative increase of 38% and improved resolution to 0.5 degrees below 1500 m, compared with the GRACE Level-3 mascon solution. Our results also imply that using FDSFs would cause uncertainties, particularly in scaled short-term mass change, which could be attributed to the large discrepancy between the hydrological model and GRACE/GRACE-FO. Our study provides insights into estimating mass changes using a downscaled GRACE/GRACE-FO solution and suggests that users select FDSFs for regions of interest based on a reliable model.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"8025-8039"},"PeriodicalIF":4.7,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10906428","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706714","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":"Assessing the Freeze–Thaw Dynamics With the Diurnal Amplitude Variations Algorithm Utilizing NEON Soil Temperature Profiles","authors":"Shaoning Lv;Tianjie Zhao;Yin Hu;Jun Wen","doi":"10.1109/JSTARS.2025.3546014","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3546014","url":null,"abstract":"Accurately determining the freeze/thaw state (FT) is crucial for understanding land–atmosphere interactions, with significant implications for climate change, ecological systems, agriculture, and water resource management. This article introduces a novel approach to assess FT dynamics by comparing the new diurnal amplitude variations (DAV) algorithm with the traditional seasonal threshold algorithm (STA) based on the soil moisture active passive (SMAP) brightness temperature data. Utilizing soil temperature profiles from 44 sites recorded by the National Ecological Observatory Network between July 2019 and June 2022. The results reveal that the DAV algorithm demonstrates a remarkable potential for capturing FT signals, achieving an average accuracy of 0.82 (0.89 for the SMAP–FT product) across all sites and a median accuracy of 0.94 (0.92 for the SMAP–FT product) referring to soil temperature at 0.02 m. Notably, the DAV algorithm outperforms the SMAP-adopted STA in 25 out of 44 sites. The accuracy of the DAV algorithm is affected by daily temperature fluctuations and geographical latitudes, while the STA exhibits limitations in certain regions, particularly those with complex terrains or variable climatic patterns. This article's innovative contribution lies in systematically comparing the performance of the DAV and STA algorithms, providing valuable insights into their respective strengths and weaknesses.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"7904-7916"},"PeriodicalIF":4.7,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10904289","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706585","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":"Spatial Patterns and Key Drivers of Soil Organic Carbon in Northeastern China's Discontinuous Permafrost Zone","authors":"Haoran Man;Xingfeng Dong;Chao Liu;Xiaodong Wu;Miao Li;Zhichao Zheng;Qi Jiang;Shuying Zang","doi":"10.1109/JSTARS.2025.3541197","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3541197","url":null,"abstract":"Based on field survey data of soil organic carbon (SOC) content and remote sensing datasets including climate, vegetation and terrain, this article analyzed the controlling factors of SOC and its spatial distribution in the 0–20 cm layer of the Huma River Basin in Northeastern China using the geographically weighted regression (GWR) model and the ordinary least squares (OLS) regression model. The results show that the SOC content in this area ranged from 13.2 to 167.2 g/kg, the GWR model outperformed the OLS model, and the GWR model is a useful tool for mapping SOC in discontinuous permafrost regions. The results further show that SOC was negatively correlated with air temperature and slope, but positively correlated with precipitation and elevation. The spatial consistency of SOC with the topography in this basin indicated an important role of the latter in controlling the SOC distribution. In addition, climate warming likely promotes SOC mineralization, while wetting favors the preservation of SOC in the southern boundary of high-latitudinal permafrost regions.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"6738-6745"},"PeriodicalIF":4.7,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10904459","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143594354","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 Multiplatform Approach for Chlorophyll Level Estimation for Irish Lakes","authors":"Minyan Zhao;Fiachra O'Loughlin","doi":"10.1109/JSTARS.2025.3546060","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3546060","url":null,"abstract":"To overcome the obstacles from discontinuous detection by single satellites, we introduce a new approach for the derivation of lake chlorophyll levels via multisensor remote sensing reflectance. In this study, we used lakes throughout the Republic of Ireland as a test bed. In the first stage, three machine learning models (random forest, extreme gradient boosting, and support vector machine) were built directly between chlorophyll levels and remote sensing reflectance from Sentinel-2, Landsat-8, Moderate Resolution Imaging Spectroradiometer (MODIS) Terra, and MODIS Aqua. The results of these 12 single-sensor algorithms (3 machine learning methods × 4 remote sensing platforms) indicate that MODIS Aqua achieved the highest average performance metric, likely due to its design, which is specifically optimized for the derived water color. Then, a multiplatform model was built using the best individual model for each satellite combined using individual performance of each model. Our multiplatform model performs well with accuracies of 78% and 70% in the training and testing datasets, respectively. The model can also capture the spatial and temporal variations observed in the in situ observations . Our results also highlight that our multiplatform approach can provide an increase of 550% in the number of chlorophyll observations compared to the in situ measurements. These findings underscore the potential of both our approach and optical remote sensing for water quality monitoring, even in locations with small water bodies.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"8261-8274"},"PeriodicalIF":4.7,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10904453","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740280","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":"DSF2-NAS: Dual-Stage Feature Fusion via Network Architecture Search for Classification of Multimodal Remote Sensing Images","authors":"Shiyang Feng;Zhaowei Li;Bo Zhang;Tao Chen;Bin Wang","doi":"10.1109/JSTARS.2025.3545831","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3545831","url":null,"abstract":"Recently, the classification of multimodal remote sensing images (RSIs) has garnered significant attention due to its ability to provide rich information for various scenes on Earth. Compared to traditional feature fusion methods used for the classification of multimodal RSIs, neural architecture search (NAS) is capable of identifying the optimal network structure for multimodal RSIs and downstream tasks. However, due to the diverse spatial resolutions, complex channel dimensions, and drastic foreground scale variations of multimodal RSIs, challenges arise when employing NAS methods for precise classification: 1) Due to the complementary and redundant nature between different modalities in RSIs, determining the features within each modality for fusion becomes quite challenging; 2) the design of fusion operators does not take into account the spatial positions and channel relationships between different modalities of RSIs, making it difficult for the fused features to match downstream tasks. To address these issues, we propose a dual-stage feature fusion framework based on NAS, termed DSF2-NAS, for the classification of multimodal RSIs. It primarily consists of two components: the feature candidate search (FCS) module and the fusion operator search (FOS) module, which execute sequentially. In the FCS module, a feature distance-based regularization approach is proposed to ensure fusion using multimodal features with the highest complementarity. Meanwhile, in the FOS module, a series of fusion operators are designed, which are based on spatial positions, channel relationships, and self-attention mechanisms, aiming to better integrate multimodal features with complex spatial and channel information. The proposed method has been evaluated on various datasets of multimodal RSIs, and experimental results consistently show that this method achieves state-of-the-art performance across multiple classification metrics.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"7207-7220"},"PeriodicalIF":4.7,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10904332","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143645226","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":"FRORS: An Effective Fine-Grained Retrieval Framework for Optical Remote Sensing Images","authors":"Yong-Qiang Mao;Zhizhuo Jiang;Yu Liu;Yiming Zhang;Kehan Qi;Hanbo Bi;You He","doi":"10.1109/JSTARS.2025.3545828","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3545828","url":null,"abstract":"Fine-grained retrieval of remote sensing images is an image interpretation task that is still in its infancy. With the rapid development of convolutional neural networks (CNN) in the field of remote sensing, it has become possible for remote sensing image retrieval tasks to move toward more fine-grained classes. However, since current methods focus on how to construct similarity metrics between sample pairs, the model ignores the learning of fine-grained intraclass heterogeneity and interclass commonality features, which poses a huge challenge to fine-grained retrieval. To solve this problem, we propose a novel fine-grained retrieval framework of optical remote sensing (FRORS) images, which aims to improve fine-grained retrieval capabilities by constructing interaction and matching between intraclass heterogeneity features, interclass commonality features, and image features. Specifically, we first construct a fine-grained prototype memory (FPM) module, and continuously update the local prototype storage unit through a lightweight CNN to achieve a refined representation of fine-grained heterogeneity features. Furthermore, to learn interclass commonality, we propose a gram learning (GraL) strategy, which is achieved by learning the correlation between feature dimensions. On this basis, we introduce a gram-based metric match (GMM) mechanism, which fuses the prototype features representing intraclass heterogeneity and the gram vector representing interclass commonality through an embedding manner, thereby achieving the purpose of fully interactive matching between image features and fine-grained class features. With FPM, GraL, and GMM, our FRORS can better learn deep features representing fine-grained classes and promote the improvement of the network's fine-grained retrieval ability. Extensive experiments conducted on a self-constructed THUFG-OPT dataset prove that the proposed FRORS achieves state-of-the-art fine-grained retrieval performance, which is 5.75% higher than the baseline method on <inline-formula><tex-math>$mathrm{mAP@10}$</tex-math></inline-formula>.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"7406-7419"},"PeriodicalIF":4.7,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10904305","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143645345","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}