{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,3,16]],"date-time":"2024-03-16T00:19:29Z","timestamp":1710548369483},"reference-count":44,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2024,3,15]],"date-time":"2024-03-15T00:00:00Z","timestamp":1710460800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R & D Program of China","award":["2023YFD1901203"]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42271329"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Sichuan Province Science and Technology Program","award":["2022YFQ0066"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"Ground-penetrating radar (GPR) is a rapid and non-destructive geophysical technique widely employed to detect and quantify subsurface structures and characteristics. Its capability for time lapse (TL) detection provides essential insights into subsurface hydrological dynamics, including lateral flow and soil water distribution. However, during TL-GPR surveys, field conditions often create discrepancies in surface geometry, which introduces mismatches across sequential TL-GPR images. These discrepancies may generate spurious signal variations that impede the accurate interpretation of TL-GPR data when assessing subsurface hydrological processes. In responding to this issue, this study introduces a TL-GPR image alignment method by employing the dynamic time warping (DTW) algorithm. The purpose of the proposed method, namely TLIAM\u2013DTW, is to correct for geometric mismatch in TL-GPR images collected from the identical survey line in the field. We validated the efficacy of the TLIAM\u2013DTW method using both synthetic data from gprMax V3.0 simulations and actual field data collected from a hilly, forested area post-infiltration experiment. Analyses of the aligned TL-GPR images revealed that the TLIAM\u2013DTW method effectively eliminates the influence of geometric mismatch while preserving the integrity of signal variations due to actual subsurface hydrological processes. Quantitative assessments of the proposed methods, measured by mean absolute error (MAE) and root mean square error (RMSE), showed significant improvements. After performing the TLIAM\u2013DTW method, the MAE and RMSE between processed TL-GPR images and background images were reduced by 96% and 78%, respectively, in simple simulation scenarios; in more complex simulations, MAE declined by 27\u201331% and RMSE by 17\u201343%. Field data yielded reductions in MAE and RMSE of >82% and 69%, respectively. With these substantial improvements, the processed TL-GPR images successfully depict the spatial and temporal transitions associated with subsurface lateral flows, thereby enhancing the accuracy of monitoring subsurface hydrological processes under field conditions.<\/jats:p>","DOI":"10.3390\/rs16061040","type":"journal-article","created":{"date-parts":[[2024,3,15]],"date-time":"2024-03-15T13:32:30Z","timestamp":1710509550000},"page":"1040","source":"Crossref","is-referenced-by-count":0,"title":["Enhancing Image Alignment in Time-Lapse-Ground-Penetrating Radar through Dynamic Time Warping"],"prefix":"10.3390","volume":"16","author":[{"given":"Jiahao","family":"Wen","sequence":"first","affiliation":[{"name":"State Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, China"}]},{"given":"Tianbao","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Water Conservancy & Architectural Engineering, Shihezi University, Shihezi 832000, China"}]},{"given":"Xihong","family":"Cui","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]},{"given":"Yaling","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, China"}]},{"given":"Jinfeng","family":"Shi","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, China"}]},{"given":"Yanjia","family":"Jiang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, China"}]},{"given":"Xiangjie","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-3821-4058","authenticated-orcid":false,"given":"Li","family":"Guo","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1269","DOI":"10.1093\/treephys\/21.17.1269","article-title":"Use of ground-penetrating radar to study tree roots in the southeastern United States","volume":"21","author":"Butnor","year":"2001","journal-title":"Tree Physiol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1111\/j.1365-2478.1989.tb02221.x","article-title":"Ground-penetrating radar for high-resolution mapping of soil and rock stratigraphy","volume":"37","author":"Davis","year":"1989","journal-title":"Geophys. 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