{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T16:04:56Z","timestamp":1787241896386,"version":"3.56.0"},"reference-count":115,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2020,7,12]],"date-time":"2020-07-12T00:00:00Z","timestamp":1594512000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Estimation of the soil organic carbon (SOC) content is of utmost importance in understanding the chemical, physical, and biological functions of the soil. This study proposes machine learning algorithms of support vector machines (SVM), artificial neural networks (ANN), regression tree, random forest (RF), extreme gradient boosting (XGBoost), and conventional deep neural network (DNN) for advancing prediction models of SOC. Models are trained with 1879 composite surface soil samples, and 105 auxiliary data as predictors. The genetic algorithm is used as a feature selection approach to identify effective variables. The results indicate that precipitation is the most important predictor driving 14.9% of SOC spatial variability followed by the normalized difference vegetation index (12.5%), day temperature index of moderate resolution imaging spectroradiometer (10.6%), multiresolution valley bottom flatness (8.7%) and land use (8.2%), respectively. Based on 10-fold cross-validation, the DNN model reported as a superior algorithm with the lowest prediction error and uncertainty. In terms of accuracy, DNN yielded a mean absolute error of 0.59%, a root mean squared error of 0.75%, a coefficient of determination of 0.65, and Lin\u2019s concordance correlation coefficient of 0.83. The SOC content was the highest in udic soil moisture regime class with mean values of 3.71%, followed by the aquic (2.45%) and xeric (2.10%) classes, respectively. Soils in dense forestlands had the highest SOC contents, whereas soils of younger geological age and alluvial fans had lower SOC. The proposed DNN (hidden layers = 7, and size = 50) is a promising algorithm for handling large numbers of auxiliary data at a province-scale, and due to its flexible structure and the ability to extract more information from the auxiliary data surrounding the sampled observations, it had high accuracy for the prediction of the SOC base-line map and minimal uncertainty.<\/jats:p>","DOI":"10.3390\/rs12142234","type":"journal-article","created":{"date-parts":[[2020,7,14]],"date-time":"2020-07-14T09:30:49Z","timestamp":1594719049000},"page":"2234","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":273,"title":["Predicting and Mapping of Soil Organic Carbon Using Machine Learning Algorithms in Northern Iran"],"prefix":"10.3390","volume":"12","author":[{"given":"Mostafa","family":"Emadi","sequence":"first","affiliation":[{"name":"Department of Soil Science, College of Crop Sciences, Sari Agricultural Sciences and Natural Resources University, Sari 4818168984, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4620-6624","authenticated-orcid":false,"given":"Ruhollah","family":"Taghizadeh-Mehrjardi","sequence":"additional","affiliation":[{"name":"Department of Geosciences, Soil Science and Geomorphology, University of T\u00fcbingen, 72070 T\u00fcbingen, Germany"},{"name":"Faculty of Agriculture and Natural Resources, Ardakan University, Ardakan 8951656767, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali","family":"Cherati","sequence":"additional","affiliation":[{"name":"Soil and Water Research Department, Mazandaran Agricultural and Natural Resources Research and Education Center, AREEO, Sari 4849155356, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Majid","family":"Danesh","sequence":"additional","affiliation":[{"name":"Department of Soil Science, College of Crop Sciences, Sari Agricultural Sciences and Natural Resources University, Sari 4818168984, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4842-0613","authenticated-orcid":false,"given":"Amir","family":"Mosavi","sequence":"additional","affiliation":[{"name":"Faculty of Civil Engineering, Technische Universit\u00e4t Dresden, 01069 Dresden, Germany"},{"name":"Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam"},{"name":"Department of Informatics, J. 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