{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T20:49:53Z","timestamp":1781124593121,"version":"3.54.1"},"reference-count":115,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2021,6,27]],"date-time":"2021-06-27T00:00:00Z","timestamp":1624752000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100005825","name":"National Institute of Food and Agriculture","doi-asserted-by":"publisher","award":["2021-67019-34342"],"award-info":[{"award-number":["2021-67019-34342"]}],"id":[{"id":"10.13039\/100005825","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Remotely sensed data from both in situ and satellite platforms in visible, near-infrared, and shortwave infrared (VNIR\u2013SWIR, 400\u20132500 nm) regions have been widely used to characterize and model soil properties in a direct, cost-effective, and rapid manner at different scales. In this study, we assess the performance of machine-learning algorithms including random forest (RF), extreme gradient boosting machines (XGBoost), and support vector machines (SVM) to model salt marsh soil bulk density using multispectral remote-sensing data from the Landsat-7 Enhanced Thematic Mapper Plus (ETM+) platform. To our knowledge, use of remote-sensing data for estimating salt marsh soil bulk density at the vegetation rooting zone has not been investigated before. Our study reveals that blue (band 1; 450\u2013520 nm) and NIR (band 4; 770\u2013900 nm) bands of Landsat-7 ETM+ ranked as the most important spectral features for bulk density prediction by XGBoost and RF, respectively. According to XGBoost, band 1 and band 4 had relative importance of around 41% and 39%, respectively. We tested two soil bulk density classes in order to differentiate salt marshes in terms of their capability to support vegetation that grows in either low (0.032 to 0.752 g\/cm3) or high (0.752 g\/cm3 to 1.893 g\/cm3) bulk density areas. XGBoost produced a higher classification accuracy (88%) compared to RF (87%) and SVM (86%), although discrepancies in accuracy between these models were small (&lt;2%). XGBoost correctly classified 178 out of 186 soil samples labeled as low bulk density and 37 out of 62 soil samples labeled as high bulk density. We conclude that remote-sensing-based machine-learning models can be a valuable tool for ecologists and engineers to map the soil bulk density in wetlands to select suitable sites for effective restoration and successful re-establishment practices.<\/jats:p>","DOI":"10.3390\/s21134408","type":"journal-article","created":{"date-parts":[[2021,6,27]],"date-time":"2021-06-27T23:57:22Z","timestamp":1624838242000},"page":"4408","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Machine-Learning Classification of Soil Bulk Density in Salt Marsh Environments"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8267-2227","authenticated-orcid":false,"given":"Iman","family":"Salehi Hikouei","sequence":"first","affiliation":[{"name":"Appalachian Laboratory, University of Maryland Center for Environmental Science, Frostburg, MD 21532, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3468-0230","authenticated-orcid":false,"given":"S. Sonny","family":"Kim","sequence":"additional","affiliation":[{"name":"College of Engineering, University of Georgia, Athens, GA 30602, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8192-7681","authenticated-orcid":false,"given":"Deepak R.","family":"Mishra","sequence":"additional","affiliation":[{"name":"Department of Geography, University of Georgia, Athens, GA 30602, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.rse.2006.06.006","article-title":"Mapping salt-marsh vegetation by multispectral and hyperspectral remote sensing","volume":"105","author":"Belluco","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Reddy, K.R., and DeLaune, R.D. (2008). 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