{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T05:06:08Z","timestamp":1787029568888,"version":"3.56.0"},"reference-count":53,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2022,9,13]],"date-time":"2022-09-13T00:00:00Z","timestamp":1663027200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This study was conducted to examine the capability of topographic features and remote sensing data in combination with other auxiliary environmental variables (geology and geomorphology) to predict CEC by using different machine learning models ((random forest (RF), k-nearest neighbors (kNNs), Cubist model (Cu), and support vector machines (SVMs)) in the west of Iran. Accordingly, the collection of ninety-seven soil samples was performed from the surface layer (0\u201320 cm), and a number of soil properties and X-ray analyses, as well as CEC, were determined in the laboratory. The X-ray analysis showed that the clay types as the main dominant factor on CEC varied from illite to smectite. The results of modeling also displayed that in the training dataset based on 10-fold cross-validation, RF was identified as the best model for predicting CEC (R2 = 0.86; root mean square error: RMSE = 2.76; ratio of performance to deviation: RPD = 2.67), whereas the Cu model outperformed in the validation dataset (R2 = 0.49; RMSE = 4.51; RPD = 1.43)). RF, the best and most accurate model, was thus used to prepare the CEC map. The results confirm higher CEC in the early Quaternary deposits along with higher soil development and enrichment with smectite and vermiculite. On the other hand, lower CEC was observed in mountainous and coarse-textured soils (silt loam and sandy loam). The important variable analysis also showed that some topographic attributes (valley depth, elevation, slope, terrain ruggedness index\u2014TRI) and remotely sensed data (ferric oxides, normalized difference moisture index\u2014NDMI, and salinity index) could be considered as the most imperative variables explaining the variability of CEC by the best model in the study area.<\/jats:p>","DOI":"10.3390\/s22186890","type":"journal-article","created":{"date-parts":[[2022,9,13]],"date-time":"2022-09-13T04:05:41Z","timestamp":1663041941000},"page":"6890","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Comparison of Different Machine Learning Methods for Predicting Cation Exchange Capacity Using Environmental and Remote Sensing Data"],"prefix":"10.3390","volume":"22","author":[{"given":"Sanaz","family":"Saidi","sequence":"first","affiliation":[{"name":"Department of Soil Science, College of Agriculture, Isfahan University of Technology, Isfahan 8415683111, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4455-3139","authenticated-orcid":false,"given":"Shamsollah","family":"Ayoubi","sequence":"additional","affiliation":[{"name":"Department of Soil Science, College of Agriculture, Isfahan University of Technology, Isfahan 8415683111, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mehran","family":"Shirvani","sequence":"additional","affiliation":[{"name":"Department of Soil Science, College of Agriculture, Isfahan University of Technology, Isfahan 8415683111, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kamran","family":"Azizi","sequence":"additional","affiliation":[{"name":"Department of Soil Science, College of Agriculture, Isfahan University of Technology, Isfahan 8415683111, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7209-0744","authenticated-orcid":false,"given":"Mojtaba","family":"Zeraatpisheh","sequence":"additional","affiliation":[{"name":"Rubenstein School of Environment and Natural Resources, University of Vermont, 81 Carrigan Drive, Burlington, VT 05405, USA"},{"name":"Gund Institute for Environment, University of Vermont, 210 Colchester Ave, Burlington, VT 05401, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1717","DOI":"10.2136\/sssaj2015.05.0187","article-title":"An Error Budget for Mapping Field-Scale Soil Salinity at Various Depths using Different Sources of Ancillary Data","volume":"79","author":"Huang","year":"2015","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"105046","DOI":"10.1016\/j.envint.2019.105046","article-title":"Soil amendments for immobilization of potentially toxic elements in contaminated soils: A critical review","volume":"134","author":"Palansooriya","year":"2020","journal-title":"Environ. Int."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"114918","DOI":"10.1016\/j.geoderma.2020.114918","article-title":"Carbon sequestration of forest soils is reflected by changes in physicochemical soil indicators\u2014A comprehensive discussion of a long-term experiment on a detritus manipulation","volume":"385","author":"Juhos","year":"2021","journal-title":"Geoderma"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"841","DOI":"10.2134\/agronj2008.0112","article-title":"Digital Soil-Class Mapping from Proximal and Remotely Sensed Data at the Field Level","volume":"101","author":"Triantafilis","year":"2009","journal-title":"Agron. J."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1080\/00380768.2021.1883998","article-title":"Control of climate on soil charge characteristics through organic matter and clay mineral distributions in volcanic soils of Mt. Kilimanjaro, Tanzania","volume":"67","author":"Lyu","year":"2021","journal-title":"Soil Sci. Plant Nutr."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Datta, R., and Meena, R.S. (2021). Adsorption: An Important Phenomenon in Controlling Soil Properties and Carbon Stabilization. Soil Carbon Stabilization to Mitigate Climate Change, Springer.","DOI":"10.1007\/978-981-33-6765-4"},{"key":"ref_7","first-page":"167","article-title":"Cation Exchange Capacity","volume":"Volume 9","author":"Page","year":"1983","journal-title":"Methods of Soil Analysis: Part 2 Chemical and Microbiological Properties"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/S0016-7061(00)00058-6","article-title":"Genesis of gypsum enriched soils in north-west Isfahan, Iran","volume":"99","author":"Toomanian","year":"2001","journal-title":"Geoderma"},{"key":"ref_9","first-page":"225","article-title":"From pedotransfer function to soil interference systems","volume":"93","author":"McBratney","year":"2002","journal-title":"Geoderma"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"795","DOI":"10.2136\/vzj2010.0045","article-title":"Using pedotransfer functions to estimate the van Genuchten-Mualem soil hydualic peopoeties: A review","volume":"9","author":"Vereecken","year":"2010","journal-title":"Vdose Zone J."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1106","DOI":"10.1080\/00103624.2019.1604728","article-title":"Predicting Cationic Exchange Capacity in Calcareous Soils of East-Azerbaijan Province, Northwest Iran","volume":"50","author":"Asadzadeh","year":"2019","journal-title":"Commun. Soil Sci. Plant Anal."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/S0016-7061(00)00025-2","article-title":"Study of spatial relationships among some soil physico-chemical properties of a field in central Italy using multivariate geostatistics","volume":"97","author":"Giugliarini","year":"2000","journal-title":"Geoderma"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1547","DOI":"10.2136\/sssaj2001.6551547x","article-title":"Map Quality for Site-Specific Fertility Management","volume":"65","author":"Mueller","year":"2001","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1387","DOI":"10.2136\/sssaj2005.0273","article-title":"Spatial Characteristics of Claypan Soil Properties in an Agricultural Field","volume":"70","author":"Jung","year":"2006","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1016\/j.compag.2004.11.012","article-title":"Delineating productivity zones on claypan soil fields using apparent soil electrical conductivity","volume":"46","author":"Kitchen","year":"2005","journal-title":"Comput. Electron. Agric."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1007\/s40333-019-0059-9","article-title":"Determining the spatial distribution of soil properties using the environmental covariates and multivariate statistical analysis: A case study in semi-arid regions of Iran","volume":"11","author":"Zeraatpisheh","year":"2019","journal-title":"J. Arid Land"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1071\/SR08240","article-title":"Field level digital soil mapping of cation exchange capacity using electromagnetic induction and a hierarchical spatial regression model","volume":"47","author":"Triantafilis","year":"2009","journal-title":"Aust. J. Soil Res."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1080\/03650340.2015.1038253","article-title":"Digital mapping of cation exchange capacity using genetic programming and soil depth functions in Baneh region. Iran","volume":"62","author":"Taghizadeh","year":"2016","journal-title":"Arch. Agron. Soil Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.5194\/soil-4-1-2018","article-title":"Evaluation of digital soil mapping approaches with large sets of environmental covariates","volume":"4","author":"Nussbaum","year":"2018","journal-title":"Soil"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"115316","DOI":"10.1016\/j.geoderma.2021.115316","article-title":"Predictive soil mapping using historic bare soil composite imagery and legacy soil survey data","volume":"401","author":"Sorenson","year":"2021","journal-title":"Geoderma"},{"key":"ref_21","unstructured":"Soil Survey Staff (2014). Keys to Soil Taxonomy, USDA-Natural Resources Conservation Service. [12th ed.]."},{"key":"ref_22","unstructured":"Zahedi, M., and Hajian, J. (1985). The Geological Map, Cartographic Department of Geological Survey of Iran."},{"key":"ref_23","unstructured":"Klute, A. (1986). Particle-size analysis. Methods of Soil Analysis, Part 1. Agronomy Monograph 9, American Statistical Association and Soil Science Society of America."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Nelson, D.W., and Sommers, L.E. (1982). Total carbon, organic carbon, and organic matter. Methods of Soil Analysis: Part 2, Chemical and Microbiological Properties, The American Society of Agronomy.","DOI":"10.2134\/agronmonogr9.2.2ed.c29"},{"key":"ref_25","unstructured":"Moore, D.M., and Reynolds, R.C. (1997). X-ray Diffraction and the Identification and Analysis of Clay Minerals, Oxford University Press. [2nd ed.]."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"873","DOI":"10.1021\/es0492642","article-title":"Feasibility of reflectance spectroscopy for the assessment of soil mercury contamination. Environ","volume":"39","author":"Wu","year":"2005","journal-title":"Sci. Technol."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Lie\u00df, M., Schmidt, J., and Glaser, B. (2016). Improving the spatial prediction of soil organic carbon stocks in a complex tropical mountain landscape by methodological specifications in machine learning approaches. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0153673"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1016\/j.catena.2017.05.034","article-title":"Performance evaluation of GIS-based new ensemble data mining techniques of adaptive neuro-fuzzy inference system (ANFIS) with genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO) for landslide spatial modelling","volume":"157","author":"Chen","year":"2017","journal-title":"Catena"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.geoderma.2018.12.037","article-title":"Digital mapping of soil carbon fractions with machine learning","volume":"339","author":"Keskin","year":"2018","journal-title":"Geoderma"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"271","DOI":"10.3233\/FI-2010-288","article-title":"Boruta\u2014A system for feature selection","volume":"101","author":"Kursa","year":"2010","journal-title":"Fundam. Inform."},{"key":"ref_31","unstructured":"R Development Core Team (2015). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing. Available online: http:\/\/www.R-project.org."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10115-007-0114-2","article-title":"Top 10 algorithms in data mining","volume":"14","author":"Wu","year":"2008","journal-title":"Knowl. Inf. Syst."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Vapnik, V. (1995). The Nature of Statistical Learning Theory, Wiley Press.","DOI":"10.1007\/978-1-4757-2440-0"},{"key":"ref_34","first-page":"18","article-title":"Classification and regression by random Forest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News"},{"key":"ref_35","unstructured":"Quinlan, J.R. (1992, January 16\u201318). Learning with continuous classes. Proceedings of the 5th Australian joint conference on artificial intelligence, Hobart, Tasmania."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"McBratney, A.B., Minasny, B., and Stockmann, U. (2018). Digital mapping of soil classes and continuous soil properties. Pedometrics, Springer International Publishing.","DOI":"10.1007\/978-3-319-63439-5"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Hengl, T., Heuvelink, G.B., Kempen, B., Leenaars, J.G., Walsh, M.G., Shepherd, K.D., Sila, A., MacMillan, R.A., Mendes de Jesus, J., and Tamene, L. (2015). Mapping soil properties of Africa at 250 m resolution: Random forests significantly improve current predictions. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0125814"},{"key":"ref_38","first-page":"101906","article-title":"Modelling of soil organic carbon and bulk density in invaded coastal wetlands using Sentinel-1 imagery","volume":"82","author":"Yang","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf. ITC J."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"84","DOI":"10.2134\/jae1985.0084","article-title":"Expressing cation exchange capacity in milliequivalents per 100 grams and in SI units","volume":"14","author":"Reganold","year":"1985","journal-title":"J. Agron. Educ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1110","DOI":"10.1016\/j.scitotenv.2018.06.394","article-title":"Developing global pedotransfer functions to estimate available soil phosphorus","volume":"644","author":"Khaledian","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.geoderma.2019.06.040","article-title":"Capability of Sentinel-2 MSI data for monitoring and mapping of soil salinity in dry and wet seasons in the Ebinur Lake region, Xinjiang, China","volume":"353","author":"Wang","year":"2019","journal-title":"Geoderma"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"e00256","DOI":"10.1016\/j.geodrs.2020.e00256","article-title":"Digital mapping of soil organic carbon using ensemble learning model in Mollisols of Hyrcanian forests, northern Iran","volume":"20","author":"Tajik","year":"2020","journal-title":"Geoderma Reg."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"113913","DOI":"10.1016\/j.geoderma.2019.113913","article-title":"Sampling design optimization for soil mapping with random forest","volume":"355","author":"Wadoux","year":"2019","journal-title":"Geoderma"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"104424","DOI":"10.1016\/j.catena.2019.104424","article-title":"Conventional and digital soil mapping in Iran: Past, present, and future","volume":"188","author":"Zeraatpisheh","year":"2019","journal-title":"Catena"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"106439","DOI":"10.1016\/j.catena.2022.106439","article-title":"Clay mineralogy of soils developed from Miocene marls of Medvednica Mt., NW Croatia: Origin and transformation in temperate climate","volume":"216","author":"Vrbanec","year":"2022","journal-title":"Catena"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"105874","DOI":"10.1016\/j.catena.2021.105874","article-title":"Clay mineralogy fingerprinting of loess-mantled soils on different underlying substrates in the south-western Poland","volume":"210","author":"Kowalska","year":"2021","journal-title":"Catena"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1991","DOI":"10.5194\/gmd-8-1991-2015","article-title":"System for Automated Geoscientific analyses (SAGA) v. 2.1.4","volume":"8","author":"Conrad","year":"2015","journal-title":"Geosci. Model Dev."},{"key":"ref_48","first-page":"264","article-title":"Spatial heterogeneity of soil detachment capacity by overland flow at a hillslope with ephemeral gullies on the Loess Plateau","volume":"248","author":"Li","year":"2015","journal-title":"Catena"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.ecohyd.2019.05.006","article-title":"Is the hillslope position relevant for runoff and soil loss activation under high rainfall conditions in vineyards?","volume":"20","author":"Cerda","year":"2020","journal-title":"Ecohydrol. Hydrobiol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1016\/j.still.2004.09.013","article-title":"Crop management effects on soil carbon sequestration on selected farmers\u2019 fields in northeastern Ohio","volume":"81","author":"Jarecki","year":"2005","journal-title":"Soil Tillage Res."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"S38","DOI":"10.1016\/j.rse.2008.09.019","article-title":"Using Imaging Spectroscopy to study soil properties","volume":"113","author":"Chabrillat","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.geoderma.2012.07.020","article-title":"Prediction of soil organic carbon for different levels of soil moisture using Vis-NIR spectroscopy","volume":"199","author":"Nocita","year":"2013","journal-title":"Geoderma"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"3184","DOI":"10.3390\/rs70303184","article-title":"Improvement of Soil Moisture Retrieval from Hyperspectral VNIR-SWIR Data Using Clay Content Information: From Laboratory to Field Experiments","volume":"7","author":"Baup","year":"2015","journal-title":"Remote Sens."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/18\/6890\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:30:06Z","timestamp":1760142606000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/18\/6890"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,13]]},"references-count":53,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["s22186890"],"URL":"https:\/\/doi.org\/10.3390\/s22186890","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,13]]}}}