{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T17:41:56Z","timestamp":1770226916509,"version":"3.49.0"},"reference-count":69,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2023,6,17]],"date-time":"2023-06-17T00:00:00Z","timestamp":1686960000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Science and Higher Education of the Russian Federation","award":["FMRS-2023-0006"],"award-info":[{"award-number":["FMRS-2023-0006"]}]},{"name":"Ministry of Science and Higher Education of the Russian Federation","award":["FMEN 2022-0012"],"award-info":[{"award-number":["FMEN 2022-0012"]}]},{"name":"Ministry of Science and Higher Education of the Russian Federation","award":["FMEN 2022-0014"],"award-info":[{"award-number":["FMEN 2022-0014"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This article aims to explore the use of machine learning (ML) methods for mapping the distribution of mercury (Hg) content in topsoil, using the city of Ufa (Russia) and adjacent areas as an example. For this purpose, a soil dataset of 250 points sampled from a 0\u201320 cm depth on different land uses, including residential, industrial and undisturbed (forests and parks), was used. Random Forest (RF), Extreme Gradient Boosting (XGboost), Cubist and k-Nearest Neighbor (kNN) ML techniques were employed to model and map the Hg concentrations. We used remote sensing data (RSD) and topographic attributes as explanatory variables. ML models were calibrated and validated using the leave-one-out cross-validation approach. The Hg content varied from 0.005 to 0.58 mg\/kg and was characterized by very high variability. According to the MAE and RMSE metrics, the RF method resulted in the most accurate spatial prediction for the Hg content (0.029 and 0.065 mg\/kg, respectively), while the XGBoost approach showed the lowest prediction efficiency (0.032 and 0.073 mg\/kg, respectively). The results showed that the slope map, spectral index MSI and Sentinel-2A band B11 were the key variables in explaining the variability of Hg content. We found that higher uncertainty values of soil Hg were found in croplands, urban residential and industrial areas, which supports the view that spatial modelling of HM in urban landscapes is challenging. The present study provides insights into the potential of digital soil mapping techniques in combination with RSD and terrain variables for identifying areas at risk of Hg contamination in urban areas, which can inform land-use planning and management strategies to protect human health and the environment.<\/jats:p>","DOI":"10.3390\/rs15123158","type":"journal-article","created":{"date-parts":[[2023,6,19]],"date-time":"2023-06-19T01:59:51Z","timestamp":1687139991000},"page":"3158","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Mercury Prediction in Urban Soils by Remote Sensing and Relief Data Using Machine Learning Techniques"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7974-4931","authenticated-orcid":false,"given":"Azamat","family":"Suleymanov","sequence":"first","affiliation":[{"name":"Laboratory of Soil Science, Ufa Institute of Biology, Ufa Federal Research Centre, Russian Academy of Sciences, 450054 Ufa, Russia"},{"name":"Department of Environmental Protection and Prudent Exploitation of Natural Resources, Ufa State Petroleum Technological University, 450064 Ufa, Russia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7754-0406","authenticated-orcid":false,"given":"Ruslan","family":"Suleymanov","sequence":"additional","affiliation":[{"name":"Department of Geodesy, Cartography and Geographic Information Systems, Ufa University of Science and Technology, 32, Zaki Validi St., 450076 Ufa, Russia"},{"name":"Laboratory for Ecological Monitoring and Modeling, Department of Multidisciplinary Scientific Research of the Karelian Research Centre, Russian Academy of Sciences, 185910 Petrozavodsk, Russia"}]},{"given":"Andrey","family":"Kulagin","sequence":"additional","affiliation":[{"name":"Department of Ecology, Faculty of Ecology and Engineering, Nizhnevartovsk State University, 628600 Nizhnevartovsk, Russia"},{"name":"Department of Environmental Engineering, Institute of Civil Protection, Udmurt State University, Universitetskaya Street, 426034 Izhevsk, Russia"}]},{"given":"Marija","family":"Yurkevich","sequence":"additional","affiliation":[{"name":"Laboratory for Soil Ecology and Soil Geography, Institute of Biology of the Karelian Research Centre, Russian Academy of Sciences, 185910 Petrozavodsk, Russia"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"643972","DOI":"10.3389\/fphar.2021.643972","article-title":"Toxic Mechanisms of Five Heavy Metals: Mercury, Lead, Chromium, Cadmium, and Arsenic","volume":"12","author":"Naseri","year":"2021","journal-title":"Front. 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