{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T05:40:25Z","timestamp":1773898825370,"version":"3.50.1"},"reference-count":49,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2021,6,12]],"date-time":"2021-06-12T00:00:00Z","timestamp":1623456000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100014716","name":"CCDC Ground Vehicle Systems Center","doi-asserted-by":"publisher","award":["w56hzv-14-2-0001"],"award-info":[{"award-number":["w56hzv-14-2-0001"]}],"id":[{"id":"10.13039\/100014716","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Soil strength characterization is essential for any problem that deals with geomechanics, including terramechanics\/terrain mobility. Presently, the primary method of collecting soil strength parameters through in situ measurements but sending a team of people out to a site to collect data this has significant cost implications and accessing the location with the necessary equipment can be difficult. Remote sensing provides an alternate approach to in situ measurements. In this lab study, we compare the use of Apparent Thermal Inertia (ATI) against a GeoGauge for the direct testing of soil stiffness. ATI correlates with stiffness, so it allows one to predict the soil strength remotely using machine-learning algorithms. The best performing regression algorithm among the ones tested with different predictor variable combinations was found to be KNN with an R2 of 0.824 and a RMSE of 0.141. This study demonstrates the potential for using remote sensing to acquire thermal images that characterize terrain strength for mobility utilizing different machine-learning algorithms.<\/jats:p>","DOI":"10.3390\/rs13122306","type":"journal-article","created":{"date-parts":[[2021,6,14]],"date-time":"2021-06-14T22:25:46Z","timestamp":1623709546000},"page":"2306","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Characterizing Soil Stiffness Using Thermal Remote Sensing and Machine Learning"],"prefix":"10.3390","volume":"13","author":[{"given":"Jordan","family":"Ewing","sequence":"first","affiliation":[{"name":"Department of Computational Science and Engineering, Michigan Technological University, Houghton 49931, MI, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1024-3474","authenticated-orcid":false,"given":"Thomas","family":"Oommen","sequence":"additional","affiliation":[{"name":"Department of Geological Engineering, Michigan Technological University, Houghton 49931, MI, USA"}]},{"given":"Paramsothy","family":"Jayakumar","sequence":"additional","affiliation":[{"name":"U.S. Army DEVCOM Ground Vehicle Systems Center, Warren 48092, MI, USA"}]},{"given":"Russell","family":"Alger","sequence":"additional","affiliation":[{"name":"Director of the Institute of Snow Research, Keweenaw Research Center, Calumet 49913, MI, USA"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,12]]},"reference":[{"key":"ref_1","unstructured":"McCullough, D.M., Jayakumar, D.P., Dasch, D.J., and Gorsich, D.D. 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