International Journal of Applied Earth Observation and Geoinformation最新文献

{"title":"Three-dimensional time series building reconstruction framework in global south based on openly available satellite data","authors":"Yingying Pei, Qunshan Zhao, Junye Wang, Zhi Zheng, Xinyi Liu, Xuejun Huang, Yongjun Zhang, Yi Wan","doi":"10.1016/j.jag.2026.105160","DOIUrl":"https://doi.org/10.1016/j.jag.2026.105160","url":null,"abstract":"Rapid urbanization necessitates accurate 3D building data for effective urban planning and analysis. Building height provides critical vertical information reflecting urban morphology, land use intensity, and energy demand. However, high-resolution large-scale 3D datasets remain limited, particularly in the Global South, due to the high cost and complexity of traditional methods. Although machine learning approaches have been widely explored, they often underperform in dense urban areas and tend to underestimate tall buildings due to limited training data and generalizability. In this study, we propose a novel framework for large-scale building height estimation using only free remote sensing data. Leveraging 0.5 m resolution open-access satellite imagery and ICESat-2 photons, we construct an accurate parallel projection model for each image. It enables the generation of dense height points via triangulation across image pairs without additional geometric parameters. The height points are then integrated with 2D building footprints to reconstruct building height maps. Validation results of the full urban area in Nairobi, achieving a root mean square error (RMSE) of 3.338 m, demonstrated the feasibility of our framework. The method also exhibits strong temporal consistency, with a maximum mean deviation of only 1.93 m across multi-temporal height maps. Experiment results in the three additional Global South cities (Medellín, Salvador and Jakarta), achieving mean absolute errors (MAE) of 3.867 m, 3.642 m, and 2.484 m, respectively, further confirmed the transferability of our framework. These results highlight our framework’s capability to deliver low-cost, accurate, and high-resolution 3D urban reconstruction, particularly in resource-constrained cities, providing a scalable tool for urban analysis, planning, and policy support.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"95 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146209284","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":"Patch-based assessment of post-fire recovery after the 2003 Siberian wildfire","authors":"Taichang Liu, Fang Chen, Bo Yu, Lei Wang, Mengjie Gao","doi":"10.1016/j.jag.2026.105167","DOIUrl":"https://doi.org/10.1016/j.jag.2026.105167","url":null,"abstract":"Post-fire vegetation recovery in boreal forests is shaped by the complex interactions among burn severity, ecological conditions, and climatic factors. Traditional pixel-based assessments often overlook the spatial configuration and shape of burned areas, potentially underestimating heterogeneity and misrepresenting recovery dynamics. Here, we developed a patch-level framework to quantify recovery time for the 2003 Siberian wildfire, examining the combined effects of patch morphology, burn severity, climatic factors, and topography. By 2023, 72.7% of burned patches had recovered, with an average recovery time of 12.2 years. Recovery was fastest in smaller, more compact patches, whereas larger and elongated patches recovered more slowly. Within patches, recovery time increased from edges toward interiors, indicating consistent edge-to-interior gradients. Climatic factors, particularly higher temperature and radiation, slowed recovery, whereas greater precipitation and soil moisture accelerated it. Burn severity also delayed regeneration, while topographic influences were minor in this low-relief landscape. These results demonstrate that patch morphology and climate jointly shape post-fire recovery and emphasize the importance of incorporating spatial configuration into forest resilience assessments and restoration planning. The patch-based framework provides a scalable, ecologically realistic approach applicable to fire-prone ecosystems worldwide.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"59 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146209253","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":"Identification and characterization of estuarine submesoscale fronts based on multisource high-resolution remote sensing","authors":"Ruiyang Xu, Rui Yuan, Jing Chen","doi":"10.1016/j.jag.2026.105165","DOIUrl":"https://doi.org/10.1016/j.jag.2026.105165","url":null,"abstract":"Estuarine fronts represent critical submesoscale oceanographic phenomena that govern material transport, pollutant dispersion, and ecosystem dynamics in coastal environments. In this study, we developed a comprehensive remote sensing framework for monitoring suspended sediment fronts and plume fronts in the Yangtze River Estuary–Hangzhou Bay transition zone, a region of exceptional ecological and economic significance. Leveraging the Google Earth Engine cloud computing platform, we developed an integrated multi-algorithm approach that combines spectral index enhancement, automated thresholding, clustering analysis, and edge detection techniques for high-precision front feature extraction from Landsat-8 and Sentinel-2 imagery spanning 2013–2024. Validation against high-resolution unmanned aerial vehicle imagery yielded root mean square errors (RMSE) of 7.3–13.4 m and discrete Fréchet distances of 23.5–29.6 m, confirming robust algorithmic performance with decameter-level accuracy suitable for submesoscale front detection. Our analysis revealed the occurrence of pronounced spatiotemporal variability in front distributions: suspended sediment fronts exhibit distinct seasonal patterns with nearshore concentration (0–2 km) and peak occurrence around Nanhui Shoal, while plume fronts demonstrate greater offshore extension (2–5 km). Mechanistic analysis revealed that tidal forcing is the primary factor controlling the front dynamics, with suspended sediment fronts favoring neap tide conditions and plume fronts peaking during spring tide floods. These findings advance the fundamental understanding of estuarine front dynamics and provide quantitative frameworks for coastal environmental management and sustainable development strategies.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"4 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146209254","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":"Accurate forest burned area detection by integrating active fire data with enhanced Sentinel-2 multi-temporal imagery","authors":"Huixin Ren, Chunying Ren, Lin Li, Yeqiao Wang, Mingming Jia, Zongming Wang, Yanbiao Xi, Pan Liu, Chenzhen Xia, Qinglin Hou, Xing Ruan","doi":"10.1016/j.jag.2026.105163","DOIUrl":"https://doi.org/10.1016/j.jag.2026.105163","url":null,"abstract":"Accurate delineation and mapping of forest burned areas (FBAs) are critical for assessing fire-induced ecological effects and informing post-fire management strategies. Although medium-resolution sensors (10–30 m) have advanced FBA detection, such detection at a finer spatial resolution remains challenging due to various confounding factors like cloud/terrain shadows and phenology-induced spectral confusions. To mitigate these false alarms, an accurate detection framework was developed, integrating active fire (AF) data with Sentinel-2 multi-temporal imagery. Potential candidates are identified using optimized spectral features across single- and dual-temporal scales. These candidates are subsequently refined by synergizing AF data and the novel smoothed Enhanced Burned Area Index (EBAI) imagery to enhance detection sensitivity. The EBAI leverages the temporal-spectral variations of FBAs, characterized by abrupt deviations from pre-fire reflectance and short-term post-fire anomalies, to highlight burned signals and suppress false alarms. Applied to the Amur River Basin, we generated the first 10-m resolution monthly FBA product (ARB_FBA) for 2020–2023. Validation with independent reference units demonstrated low commission errors (7.65–8.46%), low omission errors (9.43–13.02%), and high Dice coefficients (89.94–91.21%). ARB_FBA outperformed global products (GABAM, FireCCI51, and MCD64A1) by delivering more accurate, detailed, and extensive burned areas. Results indicated marked fluctuations in FBA, ranging from a minimum of 3.83 × 10<ce:sup loc=\"post\">5</ce:sup> ha in 2021 to a peak of 1.08 × 10<ce:sup loc=\"post\">6</ce:sup> ha in 2023. The 2023 surge was associated with record-breaking heatwaves and forest management deficiencies, while areal peaks confirmed spring (March to May) as the highest-risk period. These findings establish a robust foundation for post-fire impact assessments and offer actionable insights for forest management.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"321 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146209286","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":"Seasonal hazard-vulnerability patterns between drought and wildfire in New Caledonia derived from remote sensing products","authors":"Mathis Neuhauser, Alexandre Peltier, Thomas Ibanez, Marc Despinoy, Michel Le Page","doi":"10.1016/j.jag.2026.105159","DOIUrl":"https://doi.org/10.1016/j.jag.2026.105159","url":null,"abstract":"Climate change is escalating the frequency and intensity of droughts and wildfires globally. In New Caledonia, future intensification is projected, yet the seasonal and directional temporal relationships between vegetation drought and wildfire activity remain insufficiently characterized at the regional scale. This study presents a regional case study combining remote sensing and in-situ datasets spanning 2000–2024, aggregated at monthly and municipal scales, to analyse drought–fire temporal interactions in New Caledonia. The approach quantifies the temporal sequencing of interactions between vegetation condition and burned area occurrence, distinguishing periods when vegetation stress precedes fire activity (fire hazard) from periods when fire occurrence is followed by altered vegetation conditions (post-fire vulnerability and potential feedbacks). Lagged and seasonal correlation analyses were conducted using the Vegetation Health Index (VHI) as a proxy of vegetation drought and the Burned areas Anomaly Index (BAI) to characterize wildfire activity. Results reveal robust and spatially coherent correlations between vegetation drought and burned area extent, highlighting the strong association between VHI and subsequent wildfire activity. Distinct seasonal interaction patterns emerge, with vegetation condition preceding fire activity during the early dry season (August–September), while fire occurrence is followed by modified surface conditions over subsequent months (November–May), consistent with post-fire ecosystem vulnerability and feedback mechanism. Marked geographic contrasts are observed, particularly between the west and east coasts. These findings improve understanding of drought–fire temporal linkages in New Caledonia and provide actionable, region-specific insights for seasonally targeted and spatially explicit wildfire risk management.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"1 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146209288","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":"Do-it-yourself built-up mapping tool: A practical cloud-based solution using Sentinel imagery for mapping urban expansion in Africa","authors":"Marta Sapena, Johannes Mast, Elisabeth Schoepfer, Hannes Taubenböck","doi":"10.1016/j.jag.2026.105153","DOIUrl":"https://doi.org/10.1016/j.jag.2026.105153","url":null,"abstract":"Urban areas across Africa have undergone unprecedented growth, posing significant challenges for sustainable development, infrastructure planning, and climate resilience. Existing mapping products often struggle to capture the dynamic and heterogeneous nature of these evolving urban landscapes, highlighting the need for maps that are both up-to-date and locally relevant. This study introduces a practical, cloud-based solution: an online tool for site-specific mapping (i.e., tailored maps for a defined area of interest) that leverages the capabilities of Google Earth Engine. The tool uses Sentinel-1 and Sentinel-2 imagery to derive a wide range of spectral and texture metrics, supplemented by terrain data, and is trained using open building footprint datasets available for 2022. In an experimental setup, nine model configurations were tested under varying data availability conditions across 100 urban sites in Africa. The best-performing model achieved a mean F1-score of 0.59 (recall 0.63; precision 0.51) when validated against building footprints, with higher accuracy observed in dense urban areas. This configuration was integrated into the freely available ‘Do-it-yourself built-up mapping tool’ (DIY-BU). A quantitative analysis across the 100 test sites showed that the maps generated by our tool for 2022 were substantially more accurate (with an increase of F1-score by 0.18–0.30) than global multi-temporal products analysed for the same period (i.e., Dynamic World, ESRI land cover, GISA, GLC_FCS30D and GISD30). While the quantitative assessment was limited to the 2022 reference year, and the multi-temporal maps rely on a monotonic growth assumption (preventing the detection of demolition), a qualitative analysis highlighted the tool’s advantages in capturing detailed urban expansion and small-scale structures. The DIY-BU-mapping tool offers a valuable resource for a variety of applications, including urban planning, infrastructure monitoring, disaster preparedness and climate adaptation. Beyond presenting the tool’s functionality, the paper discusses its limitations and potential applications across diverse geographic and data availability contexts.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"96 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146209291","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":"High accuracy water level estimation using super-resolution Sentinel-1 data","authors":"Yan Jia, Quan Liu, Hongjie He, Shuanggen Jin, Chunqiao Song, Kyle Gao, Zebiao Wu","doi":"10.1016/j.jag.2026.105156","DOIUrl":"https://doi.org/10.1016/j.jag.2026.105156","url":null,"abstract":"Water level can be effectively monitored through satellite remote sensing. However, a persistent challenge lies in balancing spatial coverage and resolution. High-resolution data provide detailed information but suffer from limited spatial coverage, while low-resolution data are inadequate for accurate large-scale monitoring. In this paper, a super-resolution approach with machine learning is proposed to enhance the accuracy of water level monitoring as a solution to this problem. By leveraging Real-world Enhanced Super-Resolution Generative Adversarial Network (Real-ESRGAN) and a high-quality pretrained model, this study enhances the spatial resolution of Sentinel-1 SAR imagery, whose features are then used to develop a water level estimation model, enabling dynamic monitoring of river water levels. Results show that after super-resolution, the Root Mean Squared Error (RMSE) is reduced to 0.322 m (∼30.6% decrease), the Nash-Sutcliffe Efficiency (NSE) increases to 0.957, and the Pearson Correlation Coefficient (R) improves to 0.987. The proposed super-resolution approach improves both spatial resolution (to 2.5 m/pixel) and water level estimation accuracy. Moreover, the results from representative stations, including densely urbanized regions, agricultural irrigation zones, and areas near sluices, show that our super-resolution approach provides accurate water level estimates and reliable trend predictions. Our experiments confirm its usefulness across a diverse spectrum of high accuracy hydrological monitoring scenarios.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"59 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146209290","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":"Analysis of the 4 December 2021 lahar on Mount Semeru using optical and SAR remote sensing","authors":"Samuel McGowan, Jonathan Procter, Gabor Kereszturi, Fabien Albino, Indranova Suhendro","doi":"10.1016/j.jag.2026.105162","DOIUrl":"https://doi.org/10.1016/j.jag.2026.105162","url":null,"abstract":"This study characterizes the amplitude changes associated with the 4 December 2021 eruption of Mount Semeru and maps the resulting deposits using Sentinel-1 SAR backscatter and PlanetScope optical imagery. Results show that lahar deposits caused surface smoothening which reduced SAR backscatter, whereas pyroclastic density currents (PDCs) increased backscatter due to higher moisture content. We have also shown the potential of differential polarimetric responses between surface cover types to identify areas of channel widening and vegetation destruction, providing a rapid means of identifying impacted areas in the context of volcanic crisis management. The supervised classification of SAR and high-resolution optical images enabled the production of an accurate geomorphological map able to separate different pyroclastic flow and lahar flow deposits both sedimentologically and spatially. Classified channelized lahar deposits were also used to quantify channel widening associated with the eruption, which significantly impacted the Supiturang Village.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"106 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146209287","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":"Forest canopy cover loss dynamics in Germany between 2017 and 2024: Revealing regional differences","authors":"Frank Thonfeld, Patrick Kacic, Stefanie Holzwarth, Marco Wegler, Sarah Asam, Claudia Kuenzer","doi":"10.1016/j.jag.2026.105157","DOIUrl":"https://doi.org/10.1016/j.jag.2026.105157","url":null,"abstract":"From 2018 onwards, multiple droughts and heatwaves hit Central Europe including Germany. These triggered, together with other disturbance drivers, unprecedented damages to forests. While annual field-based crown defoliation surveys provide valuable insights into overall forest condition in Germany, continuous spatio-temporal dynamics are not explicitly addressed in current monitoring practices. Here, we present a forest canopy cover loss (FCCL) assessment in Germany at monthly resolution between September 2017 and September 2024 based on Sentinel-2 and Landsat satellite time series. This approach enables near-continuous monitoring of forest disturbance dynamics across spatial and temporal scales. Our results reveal that a strip in central Germany was most affected. Storm events in late 2017 and early 2018 caused the first large-scale canopy losses, while drought- and heat-related disturbances, including bark beetle outbreaks in spruce forests, dominated in 2019 and 2020. FCCL rates generally declined after 2020. We recorded a total of 924,000 ha FCCL over the seven-year period (8.4% of Germany’s forest area), 84.5% of which were found in coniferous forests. At district and municipality level, we detected FCCL of up to more than 50%. Two-thirds of FCCL were recorded during winter (November-April), underscoring the importance of off-season monitoring. This study demonstrates the value of dense, multi-sensor satellite time series for operational forest monitoring. It highlights regions with high future disturbance potential, particularly in southern and southeastern Germany due to their high share of spruce forests. Future monitoring should integrate seasonal data and early-warning systems to improve adaptive forest management at national scale.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"124 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146209289","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":"Earth observation and machine-learning–based mapping of 0–1 m soil moisture at 10-cm intervals in a permafrost-affected basin","authors":"Yao Xiao, Guojie Hu, Lin Zhao, Erji Du, Zanpin Xing, Ren Li, Tonghua Wu, Xiaodong Wu, Guangyue Liu, Defu Zou, Yonghua Zhao, Nan Zhou, Yifan Wu","doi":"10.1016/j.jag.2026.105147","DOIUrl":"https://doi.org/10.1016/j.jag.2026.105147","url":null,"abstract":"Depth-resolved soil moisture is hard to obtain at basin scale because satellites sense only the upper few centimeters and field profiles are sparse. We use Earth-observation (EO) predictors with limited profile data to map volumetric water content (VWC) at 10-cm intervals from 0–1 m in a permafrost-affected basin. The pipeline benchmarks tree ensembles and then applies Shapley Additive Explanations (SHAP)-guided selection to build a compact 12-predictor Extra Trees model, followed by block-wise mapping on 30-m grids. On an external 10% hold-out, the model achieves R<ce:sup loc=\"post\">2</ce:sup> = 0.85 and RMSE = 0.08 L/L, with &gt; 80% of residuals within ± 0.10 L/L. Skill is stable through most layers but degrades at 90–100 cm, reflecting limited test support and weaker surface constraint at depth. SHAP attribution identifies permafrost presence, wetland extent, vegetation greenness, and terrain metrics as leading controls, with surface influences weaken below 50 cm. Residual diagnostics show no pronounced distance-to-river shift in residual centroids and no significant spatial clustering in site-mean residuals (Moran’s I). The resulting GIS-ready GeoTIFFs provide depth-layered, climatology-consistent thaw-season VWC patterns that complement coarse satellite soil-moisture products by resolving subsurface heterogeneity.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"42 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146209370","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}