{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T14:20:11Z","timestamp":1776176411334,"version":"3.50.1"},"reference-count":43,"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":[{"name":"Science and Technology Project of Jiangxi Education Department","award":["GJJ191594"],"award-info":[{"award-number":["GJJ191594"]}]},{"name":"Mass entrepreneurship and innovation Project in Jiangxi Province","award":["202113434002"],"award-info":[{"award-number":["202113434002"]}]},{"name":"Ningxia Key R &amp; D Project","award":["2020BFG03009"],"award-info":[{"award-number":["2020BFG03009"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51604266"],"award-info":[{"award-number":["51604266"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004608","name":"Natural Science Foundation of Jiangsu Province","doi-asserted-by":"publisher","award":["BK20190642"],"award-info":[{"award-number":["BK20190642"]}],"id":[{"id":"10.13039\/501100004608","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>For the accurate and high-precision measurement of the deformation field in mining areas using different data sources, the probability integral model was used to process deformation data obtained from an Unmanned Aerial Vehicle (UAV), Differential InSAR (DInSAR), and Small Baseline Subset InSAR (SBAS-InSAR) to obtain the complete deformation field. The SBAS-InSAR, DInSAR, and UAV can be used to obtain small-scale, mesoscale, and large-scale deformations, respectively. The three types of data were all superimposed by the Kriging interpolation, and the deformation field was integrated using the probability integral model to obtain the complete high-precision deformation field with complete time series in the study area. The study area was in the WangJiata mine in Western China, where mining was carried out from 12 July 2018 to 25 October 2018, on the 2S201 working face. The first observation was made in June 2018, and steady-state observations were made in April 2019, totaling four UAV observations. During this period, the Canadian Earth Observation Satellite of Radarsat-2 (R2) was used to take 10 SAR images, the surface subsidence mapping was undertaken using DInSAR and SBAS-InSAR techniques, and the complete deformation field of the working face during the 106-day mining period was obtained by using the UAV technique. The results showed that the subsidence basin gradually expanded along the mining direction as the working face advanced. When the mining advance was greater than 1.2\u20131.4 times the coal seam burial depth, the supercritical conditions were reached, and the maximum subsidence stabilized at the value of 2.780 m. The subsidence rate was basically maintained at 0.25 m\/d. Finally, the accuracy of the method was tested by the Global Navigation Satellite System (GNSS) data, and the medium error of the strike was 0.103 m. A new method is reached by the fusion of active and passive remote sensing data to construct efficient, complete and high precision time-series subsidence basins with high precision.<\/jats:p>","DOI":"10.3390\/rs14020299","type":"journal-article","created":{"date-parts":[[2022,1,10]],"date-time":"2022-01-10T22:03:13Z","timestamp":1641852193000},"page":"299","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":45,"title":["A Novel Method of Monitoring Surface Subsidence Law Based on Probability Integral Model Combined with Active and Passive Remote Sensing Data"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1223-6160","authenticated-orcid":false,"given":"Rui","family":"Wang","sequence":"first","affiliation":[{"name":"School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China"},{"name":"College of Resources an Architecture, Gannan University of Science and Technology, Ganzhou 341000, China"}]},{"given":"Kan","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8262-1035","authenticated-orcid":false,"given":"Qimin","family":"He","sequence":"additional","affiliation":[{"name":"School of Geography Science and Geomatics Engineering, Suzhou University of Science and Technology, Suzhou 215009, China"}]},{"given":"Yibo","family":"He","sequence":"additional","affiliation":[{"name":"Institute of Land Reclamation and Ecological Restoration, China University of Mining and Technology, Beijing 100083, China"}]},{"given":"Yuanyuan","family":"Gu","sequence":"additional","affiliation":[{"name":"College of Surveying and Geoinformatics, Tongji University, Shanghai 200092, China"}]},{"given":"Shuang","family":"Wu","sequence":"additional","affiliation":[{"name":"Chinese Antarctic Center of Surveying and Mapping, Wuhan University, Wuhan 430079, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,10]]},"reference":[{"key":"ref_1","first-page":"867","article-title":"Spatial pattern and governance model of coal mining subsidence area in China","volume":"34","author":"Li","year":"2019","journal-title":"J. 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