{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T20:53:24Z","timestamp":1776286404600,"version":"3.50.1"},"reference-count":65,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2023,7,5]],"date-time":"2023-07-05T00:00:00Z","timestamp":1688515200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Natural Science Foundation of China","award":["42171424"],"award-info":[{"award-number":["42171424"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Ground subsidence is a significant safety concern in mining regions, making large-scale subsidence forecasting vital for mine site environmental management. This study proposes a deep learning-based prediction approach to address the challenges posed by the existing prediction methods, such as complicated model parameters or large data requirements. Small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technology was utilized to collect spatiotemporal ground subsidence data at the Pingshuo mining area from 2019 to 2022, which was then analyzed using the long-short term memory (LSTM) neural network algorithm. Additionally, an attention mechanism was introduced to incorporate temporal dependencies and improve prediction accuracy, leading to the development of the AT-LSTM model. The results demonstrate that the Pingshuo mine area had subsidence rates ranging from \u2212205.89 to \u221259.70 mm\/yr from 2019 to 2022, with subsidence areas mainly located around Jinggong-1 (JG-1) and the three open-pit mines, strongly linked to mining activities, and the subsidence range continuously expanding. The spatial distribution of the AT-LSTM prediction results is basically consistent with the real situation, and the correlation coefficient is more than 0.97. Compared with the LSTM, the AT-LSTM method better captured the fluctuation changes of the time series for fitting, while the model was more sensitive to the mining method of the mine, and had different expressiveness in open-pit and shaft mines. Furthermore, in comparison to existing time-series forecasting methods, the AT-LSTM is effective and practical.<\/jats:p>","DOI":"10.3390\/rs15133409","type":"journal-article","created":{"date-parts":[[2023,7,6]],"date-time":"2023-07-06T00:41:27Z","timestamp":1688604087000},"page":"3409","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Integrating SBAS-InSAR and AT-LSTM for Time-Series Analysis and Prediction Method of Ground Subsidence in Mining Areas"],"prefix":"10.3390","volume":"15","author":[{"given":"Yahong","family":"Liu","sequence":"first","affiliation":[{"name":"College of Mining Engineering, Taiyuan University of Technology, Taiyuan 030024, China"}]},{"given":"Jin","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Mining Engineering, Taiyuan University of Technology, Taiyuan 030024, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.erss.2019.01.008","article-title":"Energy Transitions or Additions?: Why a Transition from Fossil Fuels Requires More than the Growth of Renewable Energy","volume":"51","author":"York","year":"2019","journal-title":"Energy Res. Soc. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.resconrec.2019.04.021","article-title":"Life Cycle-Based Environmental Performance Indicator for the Coal-to-Energy Supply Chain: A Chinese Case Application","volume":"147","author":"Ghadimi","year":"2019","journal-title":"Resour. Conserv. Recycl."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.ijmst.2018.06.005","article-title":"Intelligent and Ecological Coal Mining as Well as Clean Utilization Technology in China: Review and Prospects","volume":"29","author":"Wang","year":"2019","journal-title":"Int. J. Min. Sci. Technol."},{"key":"ref_4","first-page":"42","article-title":"Current Status, Challenges and Policy Recommendations Regarding the Sustainable Development of Mining Areas in China","volume":"5","author":"Qian","year":"2014","journal-title":"J. Resour. Ecol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"15716","DOI":"10.1007\/s11356-020-08054-2","article-title":"Monitoring the Effects of Open-Pit Mining on the Eco-Environment Using a Moving Window-Based Remote Sensing Ecological Index","volume":"27","author":"Zhu","year":"2020","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"125061","DOI":"10.1016\/j.jclepro.2020.125061","article-title":"Spatial Estimate of Ecological and Environmental Damage in an Underground Coal Mining Area on the Loess Plateau: Implications for Planning Restoration Interventions","volume":"287","author":"Hou","year":"2021","journal-title":"J. Clean. Prod."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1007\/s11069-013-0837-1","article-title":"Mining Geohazards\u2014Land Subsidence Caused by the Dewatering of Opencast Coal Mines: The Case Study of the Amyntaio Coal Mine, Florina, Greece","volume":"70","author":"Loupasakis","year":"2014","journal-title":"Nat. Hazards"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1007\/s00254-005-0010-6","article-title":"Land Subsidence in China","volume":"48","author":"Xue","year":"2005","journal-title":"Environ. Geol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2969","DOI":"10.1080\/01431161.2012.756596","article-title":"InSAR Time-Series Analysis of Land Subsidence in Bangkok, Thailand","volume":"34","author":"Aobpaet","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/36.898661","article-title":"Permanent Scatterers in SAR Interferometry","volume":"39","author":"Ferretti","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1080\/17538947.2017.1336651","article-title":"Mapping Land Subsidence over the Eastern Beijing City Using Satellite Radar Interferometry","volume":"11","author":"Du","year":"2018","journal-title":"Int. J. Digit. Earth"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2375","DOI":"10.1109\/TGRS.2002.803792","article-title":"A New Algorithm for Surface Deformation Monitoring Based on Small Baseline Differential SAR Interferograms","volume":"40","author":"Berardino","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1007\/s00024-007-0192-9","article-title":"An Overview of the Small BAseline Subset Algorithm: A DInSAR Technique for Surface Deformation Analysis","volume":"164","author":"Lanari","year":"2007","journal-title":"Pure Appl. Geophys."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.geog.2021.09.007","article-title":"Review of the SBAS InSAR Time-Series Algorithms, Applications, and Challenges","volume":"13","author":"Li","year":"2022","journal-title":"Geod. Geodyn."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Dey, T.K., Biswas, K., Chakravarty, D., Misra, A., and Samanta, B. (August, January 28). Spatio-Temporal Subsidence Estimation of Jharia Coal Field, India Using SBAS-Dinsar with Cosmo-Skymed Data. Proceedings of the IGARSS 2019\u20142019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8898018"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Karamvasis, K., and Karathanassi, V. (2020). Performance Analysis of Open Source Time Series InSAR Methods for Deformation Monitoring over a Broader Mining Region. Remote Sens., 12.","DOI":"10.3390\/rs12091380"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"6077","DOI":"10.1109\/JSTARS.2020.3028083","article-title":"Coal Mining Deformation Monitoring Using SBAS-InSAR and Offset Tracking: A Case Study of Yu County, China","volume":"13","author":"Chen","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Chen, Y., Yu, S., Tao, Q., Liu, G., Wang, L., and Wang, F. (2021). Accuracy Verification and Correction of D-InSAR and SBAS-InSAR in Monitoring Mining Surface Subsidence. Remote Sens., 13.","DOI":"10.3390\/rs13214365"},{"key":"ref_19","first-page":"102527","article-title":"Monitoring Surface Deformation of Permafrost in Wudaoliang Region, Qinghai\u2013Tibet Plateau with ENVISAT ASAR Data","volume":"104","author":"Li","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1007\/s10346-019-01265-w","article-title":"Heifangtai Loess Landslide Type and Failure Mode Analysis with Ascending and Descending Spot-Mode TerraSAR-X Datasets","volume":"17","author":"Liu","year":"2020","journal-title":"Landslides"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"678","DOI":"10.1515\/geo-2018-0054","article-title":"Land Deformation Associated with Exploitation of Groundwater in Changzhou City Measured by COSMO-SkyMed and Sentinel-1A SAR Data","volume":"10","author":"Chen","year":"2018","journal-title":"Open Geosci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1007\/s00190-021-01519-3","article-title":"Estimation of Subcanopy Topography Based on Single-Baseline TanDEM-X InSAR Data","volume":"95","author":"Wang","year":"2021","journal-title":"J. Geod."},{"key":"ref_23","first-page":"291","article-title":"Time-Series Analysis of Subsidence in Nanning, China, Based on Sentinel-1A Data by the SBAS InSAR Method","volume":"88","author":"Li","year":"2020","journal-title":"PFG\u2014J. Photogramm. Remote Sens. Geoinf. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2739","DOI":"10.1007\/s10346-021-01678-6","article-title":"Deformation Monitoring and Failure Mode Research of Mining-Induced Jianshanying Landslide in Karst Mountain Area, China with ALOS\/PALSAR-2 Images","volume":"18","author":"Chen","year":"2021","journal-title":"Landslides"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"7762","DOI":"10.1109\/JSTARS.2021.3099105","article-title":"Observing Sea Surface Current by Gaofen-3 Satellite Along-Track Interferometric SAR Experimental Mode","volume":"14","author":"Yuan","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Potin, P., Rosich, B., Miranda, N., Grimont, P., Shurmer, I., O\u2019Connell, A., Krassenburg, M., and Gratadour, J.-B. (August, January 28). Copernicus Sentinel-1 Constellation Mission Operations Status. Proceedings of the IGARSS 2019\u20142019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8898949"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.undsp.2018.11.002","article-title":"Land Subsidence Prediction for a New Urban Mass Rapid Transit Line in Hanoi","volume":"5","author":"Giao","year":"2020","journal-title":"Undergr. Space"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1515","DOI":"10.1007\/s10040-018-01920-x","article-title":"A Three-Dimensional Fluid-Solid Model, Coupling High-Rise Building Load and Groundwater Abstraction, for Prediction of Regional Land Subsidence","volume":"27","author":"Li","year":"2019","journal-title":"Hydrogeol. J."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1057","DOI":"10.1016\/S1365-1609(01)00061-2","article-title":"Prediction of Progressive Surface Subsidence above Longwall Coal Mining Using a Time Function","volume":"38","author":"Cui","year":"2001","journal-title":"Int. J. Rock Mech. Min. Sci."},{"key":"ref_30","first-page":"1","article-title":"Predictable Condition Analysis and Prediction Method of SBAS-InSAR Coal Mining Subsidence","volume":"60","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1016\/j.jenvman.2019.02.020","article-title":"Land Subsidence Hazard Modeling: Machine Learning to Identify Predictors and the Role of Human Activities","volume":"236","author":"Rahmati","year":"2019","journal-title":"J. Environ. Manag."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Cie\u015blik, K., and Milczarek, W. (2022). Application of Machine Learning in Forecasting the Impact of Mining Deformation: A Case Study of Underground Copper Mines in Poland. Remote Sens., 14.","DOI":"10.3390\/rs14194755"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"4095","DOI":"10.1007\/s11440-022-01496-7","article-title":"Characteristics of Ground Settlement Due to Combined Actions of Groundwater Drawdown and Enclosure Wall Movement","volume":"17","author":"Zeng","year":"2022","journal-title":"Acta Geotech."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"129400","DOI":"10.1016\/j.jhydrol.2023.129400","article-title":"Behaviours of Groundwater and Strata during Dewatering of Large-Scale Excavations with a Nearby Underground Barrier","volume":"620","author":"Zeng","year":"2023","journal-title":"J. Hydrol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"W07417","DOI":"10.1029\/2007WR006442","article-title":"Physics-Based Continuous Simulation of Long-Term near-Surface Hydrologic Response for the Coos Bay Experimental Catchment: SIMULATION OF LONG-TERM HYDROLOGIC RESPONSE","volume":"44","author":"Ebel","year":"2008","journal-title":"Water Resour. Res."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2851","DOI":"10.1007\/s10064-018-1278-6","article-title":"A Review on Land Subsidence Caused by Groundwater Withdrawal in Xi\u2019an, China","volume":"78","author":"Wang","year":"2019","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.enggeo.2015.01.003","article-title":"Time-Dependent Subsidence Prediction Model and Influence Factor Analysis for Underground Gas Storages in Bedded Salt Formations","volume":"187","author":"Zhang","year":"2015","journal-title":"Eng. Geol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1080\/19475705.2020.1716860","article-title":"An Improved GM(1,3) Model Combining Terrain Factors and Neural Network Error Correction for Urban Land Subsidence Prediction","volume":"11","author":"Zhou","year":"2020","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1080\/07038992.2017.1291335","article-title":"Assessing the Impact of Building Volume on Land Subsidence in the Central Business District of Beijing with SAR Tomography","volume":"43","author":"Jiao","year":"2017","journal-title":"Can. J. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1059","DOI":"10.1007\/s00477-020-01810-3","article-title":"A GIS-Based Comparative Study of Hybrid Fuzzy-Gene Expression Programming and Hybrid Fuzzy-Artificial Neural Network for Land Subsidence Susceptibility Modeling","volume":"34","author":"Nakhaei","year":"2020","journal-title":"Stoch. Environ. Res. Risk Assess."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"947","DOI":"10.1080\/1064119X.2019.1644406","article-title":"Multi-Scale Geotechnical Features of Dredger Fills and Subsidence Risk Evaluation in Reclaimed Land Using BN","volume":"38","author":"Wu","year":"2020","journal-title":"Mar. Georesources Geotechnol."},{"key":"ref_42","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017). Attention Is All You Need. arXiv."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.neucom.2021.03.091","article-title":"A Review on the Attention Mechanism of Deep Learning","volume":"452","author":"Niu","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"940","DOI":"10.1016\/j.jclepro.2019.05.334","article-title":"Tempo-Spatial Changes and Main Anthropogenic Influence Factors of Vegetation Fractional Coverage in a Large-Scale Opencast Coal Mine Area from 1992 to 2015","volume":"232","author":"Zhang","year":"2019","journal-title":"J. Clean. Prod."},{"key":"ref_45","first-page":"87","article-title":"Investigation on current situation of geological environment in Pinshuo coal mining area","volume":"48","author":"Yang","year":"2016","journal-title":"Coal Eng."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1017","DOI":"10.5194\/isprs-archives-XLII-2-W7-1017-2017","article-title":"The Analysis of Object-Based Change Detection in Mining Area: A Case Study with Pingshuo Coal Mine","volume":"XLII-2\/W7","author":"Zhang","year":"2017","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.ecoleng.2016.12.028","article-title":"The Development of Topsoil Properties under Different Reclaimed Land Uses in the Pingshuo Opencast Coalmine of Loess Plateau of China","volume":"100","author":"Zhou","year":"2017","journal-title":"Ecol. Eng."},{"key":"ref_48","first-page":"32","article-title":"A case study on pingsshuo mining area: Land rehabilitation and reutilization in mining districts","volume":"10","author":"Bai","year":"2008","journal-title":"Resour. Ind."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"126371","DOI":"10.1016\/j.jclepro.2021.126371","article-title":"Ecological Risk Assessment of Coal Mine Area Based on \u201cSource-Sink\u201d Landscape Theory\u2014A Case Study of Pingshuo Mining Area","volume":"295","author":"Wu","year":"2021","journal-title":"J. Clean. Prod."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1007\/s10346-021-01785-4","article-title":"Deformation Responses of Landslides to Seasonal Rainfall Based on InSAR and Wavelet Analysis","volume":"19","author":"Liu","year":"2022","journal-title":"Landslides"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.neuroimage.2019.05.048","article-title":"A Better Way to Define and Describe Morlet Wavelets for Time-Frequency Analysis","volume":"199","author":"Cohen","year":"2019","journal-title":"NeuroImage"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/j.engappai.2010.09.007","article-title":"A Review on Time Series Data Mining","volume":"24","author":"Fu","year":"2011","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2379776.2379788","article-title":"Time-Series Data Mining","volume":"45","author":"Esling","year":"2012","journal-title":"ACM Comput. Surv."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long Short-Term Memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"2451","DOI":"10.1162\/089976600300015015","article-title":"Learning to Forget: Continual Prediction with LSTM","volume":"12","author":"Gers","year":"2000","journal-title":"Neural Comput."},{"key":"ref_56","unstructured":"Kingma, D.P., and Ba, J. (2023, April 02). Adam: A Method for Stochastic Optimization. Available online: https:\/\/arxiv.org\/abs\/1412.6980v9."},{"key":"ref_57","unstructured":"Liu, Q., and Zhou, W. (2019, January 12). Remote sensing monitoring and research on land subsidence in coal mining areas\u2014A case study of Pingshuo mining area. Proceedings of the 8th National Academic Conference on Land Reclamation and Ecological Restoration in Mining Areas, Henan, China."},{"key":"ref_58","first-page":"162","article-title":"Ground deformation monitoring of open-pit coal mine based on SBAS-InSAR and offset tracking techniques","volume":"53","author":"Sun","year":"2022","journal-title":"Saf. Coal Mines"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1007\/s00603-012-0232-3","article-title":"Monitoring and Analysis of the Mining-Induced Ground Movement in the Longshou Mine, China","volume":"46","author":"Zhao","year":"2013","journal-title":"Rock Mech. Rock Eng."},{"key":"ref_60","first-page":"102217","article-title":"Monitoring Active Open-Pit Mine Stability in the Rhenish Coalfields of Germany Using a Coherence-Based SBAS Method","volume":"93","author":"Tang","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"8679","DOI":"10.1109\/JSTARS.2021.3106666","article-title":"HLSTM: Heterogeneous Long Short-Term Memory Network for Large-Scale InSAR Ground Subsidence Prediction","volume":"14","author":"Liu","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"20200209","DOI":"10.1098\/rsta.2020.0209","article-title":"Time-Series Forecasting with Deep Learning: A Survey","volume":"379","author":"Lim","year":"2021","journal-title":"Philos. Trans. R. Soc. A"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"388","DOI":"10.1016\/j.ijforecast.2020.06.008","article-title":"Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions","volume":"37","author":"Hewamalage","year":"2021","journal-title":"Int. J. Forecast."},{"key":"ref_64","first-page":"396","article-title":"Time series prediction method of large-scale surface subsidence based on deep learning","volume":"50","author":"Liu","year":"2021","journal-title":"Acta Geod. Et Cartogr. Sin."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1109\/MCOM.2019.1800155","article-title":"Deep Learning with Long Short-Term Memory for Time Series Prediction","volume":"57","author":"Hua","year":"2019","journal-title":"IEEE Commun. Mag."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/13\/3409\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:06:41Z","timestamp":1760126801000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/13\/3409"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,5]]},"references-count":65,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["rs15133409"],"URL":"https:\/\/doi.org\/10.3390\/rs15133409","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,5]]}}}