{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T11:57:39Z","timestamp":1780487859990,"version":"3.54.1"},"reference-count":50,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,1,10]],"date-time":"2022-01-10T00:00:00Z","timestamp":1641772800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42004011"],"award-info":[{"award-number":["42004011"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41874044"],"award-info":[{"award-number":["41874044"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2020M671646"],"award-info":[{"award-number":["2020M671646"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"name":"First-class research projects of Yellow River Engineering Consulting Co.,Ltd.","award":["2021KY055"],"award-info":[{"award-number":["2021KY055"]}]},{"name":"Priority Academic Program Development (PAPD) of Jiangsu Higher Education Institutions","award":["Science and Technology of Surveying and Mapping"],"award-info":[{"award-number":["Science and Technology of Surveying and Mapping"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>With the launch of the Sentinel-1 satellites, it becomes easy to obtain long time-series dual-pol (i.e., VV and VH channels) SAR images over most areas of the world. By combining the information from both VV and VH channels, the polarimetric persistent scatterer interferometry (PolPSI) techniques is supposed to achieve better ground deformation monitoring results than conventional PSI techniques (using only VV channel) with Sentinel-1 data. According to the quality metric used for polarimetric optimizations, the most commonly used PolPSI techniques can be categorized into three main categories. They are PolPSI-ADI (amplitude dispersion index as the phase quality metric), PolPSI-COH (coherence as the phase quality metric), and PolPSI-AOS (taking adaptive optimization strategies). Different categories of PolPSI techniques are suitable for different study areas and with different performances. However, the study that simultaneously applies all the three types of PolPSI techniques on Sentinel-1 PolSAR images is rare. Moreover, there has been little discussion about different characteristics of the three types of PolPSI techniques and how to use them with Sentinel-1 data. To this end, in this study, three data sets in China have been used to evaluate the three types of PolPSI techniques\u2019 performances. Based on results obtained, the different characteristics of PolPSI techniques have been discussed. The results show that all three PolPSI techniques can improve the phase quality of interferograms. Thus, more qualified pixels can be used for ground deformation estimation by PolPSI methods with respect to the PSI technique. Specifically, this pixel density improvement is 50%, 12%, and 348% for the PolPSI-ADI, PolPSI-COH, and POlPSI-AOS, respectively. PolPSI-ADI is the most efficient method, and it is the first choice for the area with abundant deterministic scatterers (e.g., urban areas). Benefitting from its adaptive optimization strategy, PolPSI-AOS has the best performances at the price of highest computation cost, which is suitable for rural area applications. On the other hand, limited by the medium resolution of Sentinel-1 PolSAR images, PolPSI-COH\u2019s improvement with respect to conventional PSI is relatively insignificant.<\/jats:p>","DOI":"10.3390\/rs14020309","type":"journal-article","created":{"date-parts":[[2022,1,10]],"date-time":"2022-01-10T22:03:13Z","timestamp":1641852193000},"page":"309","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Polarimetric Persistent Scatterer Interferometry for Ground Deformation Monitoring with VV-VH Sentinel-1 Data"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8750-5340","authenticated-orcid":false,"given":"Feng","family":"Zhao","sequence":"first","affiliation":[{"name":"Key Laboratory of Land Environment and Disaster Monitoring, MNR, China University of Mining and Technology, Xuzhou 221116, China"},{"name":"School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7322-2822","authenticated-orcid":false,"given":"Teng","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Land Environment and Disaster Monitoring, MNR, China University of Mining and Technology, Xuzhou 221116, China"},{"name":"School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9167-2966","authenticated-orcid":false,"given":"Leixin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Land Environment and Disaster Monitoring, MNR, China University of Mining and Technology, Xuzhou 221116, China"},{"name":"School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Han","family":"Feng","sequence":"additional","affiliation":[{"name":"Guizhou Provincial First Institute of Surveying and Mapping, Guiyang 550025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shiyong","family":"Yan","sequence":"additional","affiliation":[{"name":"Key Laboratory of Land Environment and Disaster Monitoring, MNR, China University of Mining and Technology, Xuzhou 221116, China"},{"name":"School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongdong","family":"Fan","sequence":"additional","affiliation":[{"name":"Key Laboratory of Land Environment and Disaster Monitoring, MNR, China University of Mining and Technology, Xuzhou 221116, China"},{"name":"School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongbiao","family":"Xu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Land Environment and Disaster Monitoring, MNR, China University of Mining and Technology, Xuzhou 221116, China"},{"name":"School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China"},{"name":"Yellow River Engineering Consulting Co., Ltd., Zhengzhou 450000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunjia","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Land Environment and Disaster Monitoring, MNR, China University of Mining and Technology, Xuzhou 221116, China"},{"name":"School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2202","DOI":"10.1109\/36.868878","article-title":"Nonlinear subsidence rate estimation using permanent scatterers in differential SAR interferometry","volume":"38","author":"Ferretti","year":"2000","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","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_3","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_4","doi-asserted-by":"crossref","first-page":"2243","DOI":"10.1109\/TGRS.2003.814657","article-title":"Linear and nonlinear terrain deformation maps from a reduced set of interferometric SAR images","volume":"41","author":"Mora","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"L23611","DOI":"10.1029\/2004GL021737","article-title":"A new method for measuring deformation on volcanoes and other natural terrains using InSAR persistent scatterers","volume":"31","author":"Hooper","year":"2004","journal-title":"Geophys. Res. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1167","DOI":"10.1007\/s00024-008-0352-6","article-title":"The coherent pixels technique (CPT): An advanced DInSAR technique for nonlinear deformation monitoring","volume":"165","author":"Duque","year":"2008","journal-title":"Pure Appl. Geophys."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"530","DOI":"10.3390\/rs70100530","article-title":"PSI deformation map retrieval by means of temporal sublook coherence on reduced sets of SAR images","volume":"7","author":"Iglesias","year":"2015","journal-title":"Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"8350","DOI":"10.1109\/TGRS.2019.2920536","article-title":"A Temporal Phase Coherence Estimation Algorithm and Its Application on DInSAR Pixel Selection","volume":"57","author":"Zhao","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1142","DOI":"10.1109\/TGRS.2007.894440","article-title":"Submillimeter accuracy of InSAR time series: Experimental validation","volume":"45","author":"Ferretti","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.rse.2006.01.023","article-title":"A quantitative assessment of the SBAS algorithm performance for surface deformation retrieval from DInSAR data","volume":"102","author":"Casu","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1080\/07038992.2016.1171136","article-title":"Reconstructing the vertical component of ground deformation from ascending ALOS and descending ENVISAT datasets\u2014A case study in the Cangzhou area of China","volume":"42","author":"Zhao","year":"2016","journal-title":"Can. J. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"112161","DOI":"10.1016\/j.rse.2020.112161","article-title":"Present-day land subsidence rates, surface faulting hazard and risk in Mexico City with 2014\u20132020 Sentinel-1 IW InSAR","volume":"253","author":"Cigna","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Fan, H., Lu, L., and Yao, Y. (2018). Method combining probability integration model and a small baseline subset for time series monitoring of mining subsidence. Remote Sens., 10.","DOI":"10.3390\/rs10091444"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Du, S., Mallorqui, J.J., Fan, H., and Zheng, M. (2020). Improving PSI Processing of Mining Induced Large Deformations with External Models. Remote Sens., 12.","DOI":"10.3390\/rs12193145"},{"key":"ref_15","first-page":"100049","article-title":"Investigation of deformation patterns by DS-InSAR in a coal resource-exhausted region with Spaceborne SAR imagery","volume":"5","author":"Du","year":"2021","journal-title":"J. Asian Earth Sci. X"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"e2021GL093043","DOI":"10.1029\/2021GL093043","article-title":"Improving the Resolving Power of InSAR for Earthquakes Using Time Series: A Case Study in Iran","volume":"48","author":"Liu","year":"2021","journal-title":"Geophys. Res. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2540","DOI":"10.1038\/s41598-021-82292-3","article-title":"Detection of volcanic unrest onset in La Palma, Canary Islands, evolution and implications","volume":"11","author":"Escayo","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhao, F., Mallorqui, J.J., Iglesias, R., Gili, J., and Corominas, J. (2018). Landslide Monitoring Using Multi-Temporal SAR Interferometry with Advanced Persistent Scatterers Identification Methods and Super High-Spatial Resolution TerraSAR-X Images. Remote Sens., 10.","DOI":"10.3390\/rs10060921"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Liu, J., Wang, Y., Yan, S., Zhao, F., Li, Y., Dang, L., Liu, X., Shao, Y., and Peng, B. (2021). Underground Coal Fire Detection and Monitoring Based on Landsat-8 and Sentinel-1 Data Sets in Miquan Fire Area, XinJiang. Remote Sens., 13.","DOI":"10.3390\/rs13061141"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3460","DOI":"10.1109\/TGRS.2011.2124465","article-title":"A new algorithm for processing interferometric data-stacks: SqueeSAR","volume":"49","author":"Ferretti","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2050","DOI":"10.1109\/TGRS.2014.2352853","article-title":"CAESAR: An approach based on covariance matrix decomposition to improve multibaseline\u2013multitemporal interferometric SAR processing","volume":"53","author":"Fornaro","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1074","DOI":"10.1109\/TGRS.2015.2473818","article-title":"A phase-decomposition-based PSInSAR processing method","volume":"54","author":"Cao","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.isprsjprs.2012.06.007","article-title":"Retrieval of Phase History Parameters from Distributed Scatterers in Urban Areas Using Very High Resolution SAR Data","volume":"73","author":"Wang","year":"2012","journal-title":"ISPRS-J. Photogramm. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1213","DOI":"10.1109\/TGRS.2014.2336237","article-title":"Fast Statistically Homogeneous Pixel Selection for Covariance Matrix Estimation for Multitemporal InSAR","volume":"53","author":"Jiang","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1109\/LGRS.2008.2009007","article-title":"Polarimetric differential SAR interferometry: First results with ground-based measurements","volume":"6","author":"Pipia","year":"2009","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1109\/LGRS.2009.2033013","article-title":"A contribution of polarimetry to satellite differential SAR interferometry: Increasing the number of pixel candidates","volume":"7","year":"2010","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_27","unstructured":"Lee, J.S., and Pottier, E. (2009). Polarimetric Radar Imaging: From Basics to Applications, CRC Press."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"609","DOI":"10.1109\/LGRS.2011.2176715","article-title":"Improvement of persistent-scatterer interferometry performance by means of a polarimetric optimization","volume":"9","year":"2012","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2875","DOI":"10.1109\/TGRS.2013.2267095","article-title":"Phase quality optimization in polarimetric differential SAR interferometry","volume":"52","author":"Iglesias","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1667","DOI":"10.1109\/TGRS.2013.2253111","article-title":"Polarimetric approaches for persistent scatterers interferometry","volume":"52","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"4548","DOI":"10.1109\/TGRS.2013.2282406","article-title":"Spatial adaptive speckle filtering driven by temporal polarimetric statistics and its application to PSI","volume":"52","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1109\/LGRS.2014.2326684","article-title":"Polarimetric optimization of temporal sublook coherence for DInSAR applications","volume":"12","author":"Iglesias","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.isprsjprs.2016.03.018","article-title":"Improved persistent scatterer analysis using amplitude dispersion index optimization of dual polarimetry data","volume":"117","author":"Esmaeili","year":"2016","journal-title":"ISPRS-J. Photogramm. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"3371","DOI":"10.1109\/TGRS.2018.2798705","article-title":"Polarimetry-based distributed scatterer processing method for PSI applications","volume":"56","author":"Mullissa","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"6547","DOI":"10.1109\/TGRS.2018.2840423","article-title":"A New Polarimetric Persistent Scatterer Interferometry Method Using Temporal Coherence Optimization","volume":"56","author":"Sadeghi","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"7819","DOI":"10.1109\/TGRS.2019.2916649","article-title":"Coherency Matrix Decomposition-Based Polarimetric Persistent Scatterer Interferometry","volume":"57","author":"Zhao","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"7135","DOI":"10.1109\/TGRS.2019.2911670","article-title":"SMF-POLOPT: An Adaptive Multitemporal Pol(DIn)SAR Filtering and Phase Optimization Algorithm for PSI Applications","volume":"57","author":"Zhao","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Wang, G., Xu, B., Li, Z., Fu, H., Gao, H., Wan, J., and Wang, C. (2021). A Phase Optimization Method for DS-InSAR Based on SKP Decomposition From Quad-Polarized Data. IEEE Geosci. Remote Sens. Lett., 19.","DOI":"10.1109\/LGRS.2021.3050675"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Shen, P., Wang, C., Lu, L., Luo, X., Hu, J., Fu, H., and Zhu, J. (2021). A Novel Polarimetric PSI Method Using Trace Moment-Based Statistical Properties and Total Power Interferogram Construction. IEEE Trans. Geosci. Remote Sens., 60.","DOI":"10.1109\/TGRS.2021.3093050"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Zhao, F., Mallorqui, J.J., and Lopez-Sanchez, J.M. (2021). Impact of SAR Image Resolution on Polarimetric Persistent Scatterer Interferometry With Amplitude Dispersion Optimization. IEEE Trans. Geosci. Remote Sens., 60.","DOI":"10.1109\/TGRS.2021.3059247"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"3105","DOI":"10.1109\/JSTARS.2018.2848111","article-title":"Persistent scatterer analysis using dual-polarization sentinel-1 data: Contribution from VH channel","volume":"11","author":"Shamshiri","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_42","first-page":"101950","article-title":"Evaluation of polarimetric capabilities of dual polarized Sentinel-1 and TerraSAR-X data to improve the PSInSAR algorithm using amplitude dispersion index optimization","volume":"84","author":"Azadnejad","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Luo, X., Wang, C., and Shen, P. (2020). Polarimetric Stationarity Omnibus Test (PSOT) for Selecting Persistent Scatterer Candidates with Quad-Polarimetric SAR Datasets. Sensors, 20.","DOI":"10.3390\/s20061555"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"B07407","DOI":"10.1029\/2006JB004763","article-title":"Persistent scatterer interferometric synthetic aperture radar for crustal deformation analysis, with application to Volc\u00e1n Alcedo, Gal\u00e1pagos","volume":"112","author":"Hooper","year":"2007","journal-title":"J. Geophys. Res.-Solid Earth"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1551","DOI":"10.1109\/36.718859","article-title":"Polarimetric SAR interferometry","volume":"36","author":"Cloude","year":"1998","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1109\/LGRS.2007.908885","article-title":"Multibaseline Polarimetric SAR Interferometry Coherence Optimization","volume":"5","author":"Neumann","year":"2008","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"2363","DOI":"10.1109\/36.789635","article-title":"Polarimetric SAR speckle filtering and its implication for classification","volume":"37","author":"Lee","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"139111","DOI":"10.1016\/j.scitotenv.2020.139111","article-title":"Land subsidence and its relation with groundwater aquifers in Beijing Plain of China","volume":"735","author":"Chen","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Lei, K., Ma, F., Chen, B., Luo, Y., Cui, W., Zhou, Y., Liu, H., and Sha, T. (2021). Three-Dimensional Surface Deformation Characteristics Based on Time Series InSAR and GPS Technologies in Beijing, China. Remote Sens., 13.","DOI":"10.3390\/rs13193964"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"3456","DOI":"10.1109\/TGRS.2018.2800087","article-title":"InSAR-BM3D: A nonlocal filter for SAR interferometric phase restoration","volume":"56","author":"Sica","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/2\/309\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:27:17Z","timestamp":1760362037000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/2\/309"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,10]]},"references-count":50,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2022,1]]}},"alternative-id":["rs14020309"],"URL":"https:\/\/doi.org\/10.3390\/rs14020309","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,10]]}}}