International Journal of Applied Earth Observation and Geoinformation最新文献

{"title":"Three distinct types of heavy snowfall clouds Revealed by spaceborne Dual-Frequency Precipitation Radar observations (2014–2023)","authors":"Jihoon Ryu","doi":"10.1016/j.jag.2026.105142","DOIUrl":"https://doi.org/10.1016/j.jag.2026.105142","url":null,"abstract":"This study examines global heavy snowfall (snow rate &gt; 2.5 mm h<ce:sup loc=\"post\">−1</ce:sup>) using 10 years (2014–2023) of observations from the Global Precipitation Measurement (GPM) Dual-frequency Precipitation Radar (DPR). Heavy snowfall cases are categorized into three distinct types using an Expectation-Maximization (EM) approach based on liquid-equivalent mass-weighted diameter (D<ce:inf loc=\"post\">m</ce:inf>) and normalized intercept parameter (logN<ce:inf loc=\"post\">w</ce:inf>).","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"92 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146146855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A knowledge-informed fusion network for process-aware crop yield prediction","authors":"Ruixin Fang, Lei Yu, Nan Xie, Kexin Wang, Keyi Rao, Liheng Zhong, Jingdong Chen, Zhaocong Wu","doi":"10.1016/j.jag.2026.105154","DOIUrl":"https://doi.org/10.1016/j.jag.2026.105154","url":null,"abstract":"Crop yield prediction is crucial for food security and agricultural decision-making. Conventional approaches struggle to reconcile the heavy parameter reliance of process-based models with the extensive label dependencies of data-driven models. To address this, we propose a process-aware crop yield prediction framework that integrates the mechanistic interpretability of crop growth models with the flexibility of deep learning. A Knowledge-Informed Fusion Network (KIFN) is introduced to transcend conventional shallow fusion by transferring mechanistic knowledge of biomass accumulation dynamics from process-based models into data-driven architectures. KIFN incorporates a three-stage design. (1) In the knowledge-informed pre-training stage, an expert network is trained on synthetic daily growth trajectories generated through crop growth model simulation. (2) In the process-aware fine-tuning stage, the expert network is aligned with historical yield data. (3) In the dual-stream fusion stage, the expert network output is calibrated using remote sensing observations via a cross-attention mechanism, thereby dynamically refining in-season predictions. Experimental results from the USDA’s multi-year corn yield dataset show that our model achieves high accuracy (<mml:math altimg=\"si94.svg\" display=\"inline\"><mml:msup><mml:mrow><mml:mi>R</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:math> = 0.793, RMSE = 1.039 t/ha). The model’s predictions improve progressively throughout the growing season as satellite observations are used for calibration, demonstrating its effectiveness for dynamic in-season yield prediction. Additionally, interpretability analysis indicates that the cross-attention mechanism in KIFN assists the network in identifying more informative temporal windows throughout the crop growth process. This work provides a principled and interpretable pathway for crop yield prediction, offering new insights into integrating domain knowledge with deep learning in agricultural applications.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"30 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146146854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the impacts of dual-polarized vegetation indices and U-shaped deep learning architectures on SAR-based burned area mapping","authors":"S.M. Sohel Rana, Jaese Lee, Yoojin Kang, Jungho Im","doi":"10.1016/j.jag.2026.105141","DOIUrl":"https://doi.org/10.1016/j.jag.2026.105141","url":null,"abstract":"Frequent and severe wildfires driven by climate change intensify the need for accurate burned area (BA) mapping, which can be effectively addressed using Synthetic Aperture Radar (SAR) due to its cloud-penetration capability and sensitivity to vegetation and moisture changes. However, BA mapping based on SAR-only approaches relies on U-Net with ResNet50 backbone or fully convolutional neural network, while the potential of advanced architectural components remains underexplored. Moreover, prior research primarily emphasizes log-ratio features, with limited focus on standalone capacity of dual polarized vegetation indices (VIs). This study addresses these gaps by evaluating the performance of five U-Net variants (U-Net, Attention U-Net, Residual Attention U-Net, U-Net++, and U-Net 3 +) using four input schemes: log-ratio, log-ratio without cross-ratio, VIs, and a combined feature set of all. Three combinations of loss function such as binary cross entropy (BCE), dice, and focal are also applied to the best model of all scheme. Experimental results show that U-Net++ with log-ratio inputs under BCE loss function achieves the highest performance, yielding an F1 score of 0.8218 and an Intersection of Union (IoU) of 0.6795. Further analysis reveals that VIs alone can effectively delineate burned areas (F1: 0.8244; IoU: 0.7013) with focal loss, and combining them with log-ratio features delivers the best performance (F1: 0.8364; IoU: 0.7188), when dice and focal loss functions are applied. Overall, this study offers a quantitative evaluation of how dual-polarized VIs and deep learning architectures affect SAR-based BA mapping performance and suggests promising directions for future enhancement through advanced feature extraction techniques.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"315 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146146856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Self-supervised global − local collaborative network for real SAR despeckling","authors":"Yang Yang, Jiangong Xu, Yuchuan Bai, Liangyu Chen, Junli Li, Jun Pan, Mi Wang","doi":"10.1016/j.jag.2026.105135","DOIUrl":"https://doi.org/10.1016/j.jag.2026.105135","url":null,"abstract":"Synthetic Aperture Radar (SAR) is crucial for Earth observation because it can acquire high-resolution images in all weather conditions. However, the presence of speckles—an inherent multiplicative noise caused by the coherent imaging process—severely degrades image quality and impairs the performance of subsequent interpretation tasks. To effectively capture both global contextual cues and fine-grained structural details in SAR image despeckling, we design a dual-branch Global-Local Collaborative Network (GLCNet) based on blind-spot convolution. GLCNet is trained in a self-supervised manner, requiring only original images for learning, making it well-suited for SAR data without ground truth. In the global branch, the SAR image is first decomposed into multiple frequency sub-bands through a Wavelet-Shuffle Downsampling (WSD), which decorrelates speckle components across scales and frequencies. A multi-scale blind-spot convolution is then applied to each sub-band in parallel, enabling the extraction of global textures without introducing speckle bias. In contrast, the local branch focuses on structure-aware restoration by jointly modeling frequency and spatial priors. By leveraging neighboring-pixel dependencies, this branch enhances local detail recovery and edge sharpness. Finally, an adaptive Detail-Guided Module (DGM) dynamically integrates complementary features from both branches, ensuring a harmonious balance between texture smoothness and structural fidelity. The proposed method is validated using various SAR sensors, including Sentinel-1, GF-3, TerraSAR-X, and Capella-X, demonstrating its superiority over traditional and deep learning approaches. Additionally, the application analysis confirms that the method enhances both the visual quality and analytical reliability of SAR images, making it a valuable preprocessing step for real-world scenarios. For reproducibility, our code and data are available at https://github.com/yangyang12318/LGCN.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"302 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146129286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The yield strikes back: Enhancing the transferability of field scale wheat and barley yield models by leveraging Sentinel-1/2","authors":"Belen Franch, Italo Moletto-Lobos, Javier Tarín-Mestre, Lucio Mascolo, Eric Vermote, Natacha Kalecinski, Inbal Becker-Reshef, Alberto San-Bautista, Constanza Rubio, Sara San Francisco, Miguel Ángel Naranjo, Vanessa Paredes, David Nafria, Carlos Cantero-Martinez","doi":"10.1016/j.jag.2026.105140","DOIUrl":"https://doi.org/10.1016/j.jag.2026.105140","url":null,"abstract":"Accurate and transferable crop monitoring from remote sensing remains challenging because vegetation signals are strongly affected by phenological asynchrony, climatic variability, and sensor-specific responses. Existing approaches rely on local calibrated relationships , limiting their effectiveness in data-sparse regions. This study investigates whether models calibrated on high-quality localized reference data can generalize to other regions by stabilizing sensor–biophysical relationships.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"26 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146129287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mapping Swiss soil bulk density at 30 m Resolution: Insights from Machine Learning, environmental Covariates, and national data","authors":"Surya Gupta, Simon Scheper, Christine Alewell","doi":"10.1016/j.jag.2026.105112","DOIUrl":"https://doi.org/10.1016/j.jag.2026.105112","url":null,"abstract":"Soil bulk density (BD) is a key indicator of soil health and quality, influencing air and water fluxes in the soil, soil biology, plant growth, nutrient availability, and water retention. While BD is typically measured through field and lab methods, these are time-consuming and resource-intensive. Alternatively, researchers use pedotransfer functions and machine learning algorithms for BD prediction. Although several BD maps exist for Europe, Switzerland is often excluded due to its non-European Union member status, creating a data gap known as a “blank spot”. Additionally, existing Swiss BD maps have coarse spatial resolution (∼250 m). To address this, we used the national Swiss Soil Information System NABODAT dataset to produce high-resolution (30 m) BD maps at multiple depths (0, 30, 60, 100 cm) using a Quantile Random Forest algorithm. Using five-fold cross-validation, we obtained a concordance correlation coefficient (CCC) of 0.57 and an R<ce:sup loc=\"post\">2</ce:sup> of 0.42, while external validation resulted in a CCC of 0.39 and an R<ce:sup loc=\"post\">2</ce:sup> of 0.36. The maps revealed that croplands had the highest BD, followed by grasslands and forests. Regionally, the Central Plateau and Jura exhibited higher BD compared to the Alps. BD increased with depth, and key predictors were depth, elevation, and temperature. Although we initially expected surface reflectance to be a relevant predictor due to its link with organic carbon, it showed low importance in our model. These maps provide valuable insights for national-scale applications such as soil carbon stock estimation and compaction assessment.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"6 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146129289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A downscaling algorithm for obtaining hourly gross primary productivity maps at the global scale","authors":"Yong Wang, Jiyan Wang, Wei Zhao, Yanqing Yang, Jiujiang Wu, Xiaobin Guan, Xinyao Xie","doi":"10.1016/j.jag.2025.105059","DOIUrl":"https://doi.org/10.1016/j.jag.2025.105059","url":null,"abstract":"Monitoring global vegetation gross primary productivity (GPP) at an hourly scale is critical for understanding terrestrial carbon dynamics, while recent global GPP products often suffer from limitations in their temporal resolutions. Here, a light use efficiency (LUE) model, integrated with FLUXNET and reanalysis datasets as meteorological inputs, was employed to obtain GPP at both 1-hourly and 6-hourly resolutions. We developed a downscaling algorithm that partitions 6-hourly GPP into 1-hourly estimates by weighting the 6-hourly values according to the hourly cosine of the solar zenith angle and applying linear regression. The algorithm was then applied to global 6-hourly reanalysis-driven GPP maps during 2001–2020. Using GPP simulated from 1-hourly meteorological inputs and eddy covariance (EC) GPP as references, the 6-hourly resolution GPP before and after downscaling were evaluated by mean-absolute-deviation (<ce:italic>MAD</ce:italic>) and nash–sutcliffe-efficiency (<ce:italic>NSE</ce:italic>). At 150 sites, results showed that the 6-hourly FLUXNET-driven and reanalysis-driven GPP after downscaling exhibited a significantly stronger correlation (<ce:italic>MAD</ce:italic> = 0.03 gCm<ce:sup loc=\"post\">−2</ce:sup>h<ce:sup loc=\"post\">−1</ce:sup>, <ce:italic>NSE</ce:italic> = 0.95) with corresponding 1-hourly estimates than the 6-hourly GPP estimates before downscaling (<ce:italic>MAD</ce:italic> = 0.06 gCm<ce:sup loc=\"post\">−2</ce:sup>h<ce:sup loc=\"post\">−1</ce:sup>, <ce:italic>NSE</ce:italic> = 0.83). Compared to EC GPP, the 6-hourly GPP estimates after downscaling also achieved notable improvements, with a <ce:italic>MAD</ce:italic> lower by 0.02 gCm<ce:sup loc=\"post\">−2</ce:sup>h<ce:sup loc=\"post\">−1</ce:sup> and an <ce:italic>NSE</ce:italic> higher by 0.09. At the global scale, the mean annual bias in total GPP summed from 6-hourly reanalysis-driven maps, decreased from 4.14 gCyr<ce:sup loc=\"post\">−1</ce:sup> before downscaling to 0.53 gCyr<ce:sup loc=\"post\">−1</ce:sup> after downscaling over the period 2001–2020, as compared with corresponding 1-hourly GPP maps. At the hourly scale, the mean relative error between the 6-hourly and corresponding 1-hourly GPP maps decreased from 32.40% before downscaling to 18.49% after downscaling. This downscaling algorithm effectively reduces biases in global GPP estimates, which could offer valuable insights into carbon modeling at finer temporal resolutions.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"38 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146129290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multimodal large language models meet self-supervised diffusion for real-world aerial image super-resolution","authors":"Lijing Lu, Zhou Huang, Yi Bao, Lin Wan, Zhihang Li","doi":"10.1016/j.jag.2026.105136","DOIUrl":"https://doi.org/10.1016/j.jag.2026.105136","url":null,"abstract":"Real-world aerial image super-resolution (SR) remains particularly challenging because degradations in remote-sensing imagery involve random combinations of anisotropic blur, signal-dependent noise, and unknown downsampling kernels. Most existing SR methods either rely on simplified degradation assumptions or lack semantic perception of degradation, resulting in limited generalization to real-world conditions. To address these gaps, we propose a novel diffusion-based SR framework that integrates Multi-modal Large Language Models (MLLMs) and self-supervised contrastive learning for extracting degradation-insensitive representation. Specifically, we introduce a contrastive learning strategy into a ControlNet module, where the HR and LR counterparts of the same image are regarded as positive pairs, while representations from different images serve as negative pairs, enabling the network to learn degradation-insensitive structural features. To further enhance semantic awareness of degradation, an MLLM-generated change caption is incorporated into the diffusion process as textual guidance, allowing the model to explicitly perceive and reconstruct different degradation types. Moreover, a classifier-free guidance (CFG) distillation strategy compresses the original dual-branch diffusion model into a single lightweight network, substantially improving inference efficiency while maintaining high reconstruction fidelity. Extensive experiments conducted on various datasets have showcased the superior performance of our proposed model compared to existing state-of-the-art methods. Furthermore, our distillation algorithm achieves a twofold reduction in inference time compared to its non-distilled counterpart, making it more feasible for real-time and resource-limited applications.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"47 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146129288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Big data-driven spectral index construction for fine-scale salt pond mapping","authors":"Jin Zhang, Chong Huang, He Li, Shuxuan Wang, Qingsheng Liu, Xiaoya Tang, Chenchen Zhang, Fenzhen Su","doi":"10.1016/j.jag.2026.105139","DOIUrl":"https://doi.org/10.1016/j.jag.2026.105139","url":null,"abstract":"Accurate mapping of salt pond systems is crucial for coastal resource management and ecological conservation. However, existing spectral indices for salt pond detection are mostly derived empirically, limiting their ability to differentiate pond subtypes or generalize across diverse coastal environments. This study proposes a big data-driven framework for constructing novel spectral indices that enhance the fine-scale classification of salt ponds in the Bohai Rim region of China. By systematically generating and screening over 390,000 candidate indices derived from Sentinel-2 bands, four new indices were identified to effectively discriminate salt pond subtypes: the Non-Water Body Detection Index (NWBDI), Crystallization Pond Index (CPI), Red-Green Concentration Pond Index (RGCPI), and Evaporation Pond Discrimination Index (EPDI). To rigorously evaluate their performance, three Random Forest configurations were compared, including a baseline model with original spectral bands, an expert-index-enhanced model, and a model incorporating the proposed novel indices. Across the Bohai Rim region, the model with proposed new indices achieved an overall accuracy (OA) of 81.14%, demonstrating clear advantages over both original spectral bands (OA of 72.93%) and expert-index approaches (OA of 76.64%). The resulting fine-scale map revealed a total salt pond area of 2,214.33 km<ce:sup loc=\"post\">2</ce:sup> in 2020 across the Bohai Rim, consistent with national statistics. Beyond improving classification accuracy, the data-driven index discovery revealed physically meaningful spectral relationships linked to salinity gradients and hydro-biogeochemical properties, marking a methodological shift from expert-driven hypothesis testing to automated, data-driven feature discovery. This study demonstrates that the data-driven approach can provide a transferable solution for constructing task-specific spectral indices and advancing large-scale environmental monitoring.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"18 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146129291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Human-in-the-loop based framework for solid waste dumps detection in remote sensing images","authors":"Luhan Wang, Pengfeng Xiao, Xueliang Zhang, Yina Song, Lei Guo, Di Wang, Yao Zhang","doi":"10.1016/j.jag.2026.105133","DOIUrl":"https://doi.org/10.1016/j.jag.2026.105133","url":null,"abstract":"Detecting solid waste dumps (SWDs) is crucial for a sustainable environment and public health. Deep learning methods have demonstrated significant potential in detecting SWDs from high-spatial resolution remote sensing images (RSIs). However, training a SWD detection model necessitates annotated data that encompasses diverse geographical distributions and styles. Following the conventional approach of manually labeling data prior to model training is both costly and time-consuming. In this study, we propose a human-in-the-loop framework for the detection of SWDs. An initial model is trained using public datasets of Dumpsites, solid waste aerial detection (SWAD), and AerialWaste to identify potential samples from extensive unlabeled RSIs, which are then validated by experts for data expansion and model reinforcement. Specifically, multi-view inference is introduced to enhance the applicability of the model for real-world SWD detection tasks by integrating inference results from multiple style-transformed images. Moreover, we utilize adaptive thresholds that are dynamically calculated from inference results in each round to select potential SWDs, all while maintaining a low computational cost. With the proposed framework, we construct the LHRS-SWD dataset for SWD detection, derived from a random sampling of over 70 countries and encompassing 3,377 SWDs. The effectiveness of our framework is validated through experiments on LHRS-SWD, with a 20.26% improvement in AP50 versus initial iteration.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"89 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146129292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}