{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,15]],"date-time":"2026-03-15T06:12:15Z","timestamp":1773555135714,"version":"3.50.1"},"reference-count":90,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,5,9]],"date-time":"2023-05-09T00:00:00Z","timestamp":1683590400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"International Development Research Centre, Canada","award":["107644-001"],"award-info":[{"award-number":["107644-001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Soil organic carbon (SOC) is an essential component, which soil quality depends on. Thus, understanding the spatial distribution and controlling factors of SOC is paramount to achieving sustainable soil management. In this study, SOC prediction for the Ourika watershed in Morocco was done using four machine learning (ML) algorithms: Cubist, random forest (RF), support vector machine (SVM), and gradient boosting machine (GBM). A total of 420 soil samples were collected at three different depths (0\u201310 cm, 10\u201320 cm, and 20\u201330 cm) from which SOC concentration and bulk density (BD) were measured, and consequently SOC stock (SOCS) was determined. Modeling data included 88 variables incorporating environmental covariates, including soil properties, climate, topography, and remote sensing variables used as predictors. The results showed that RF (R2 = 0.79, RMSE = 1.2%) and Cubist (R2 = 0.77, RMSE = 1.2%) were the most accurate models for predicting SOC, while none of the models were satisfactory in predicting BD across the watershed. As with SOC, Cubist (R2 = 0.86, RMSE = 11.62 t\/ha) and RF (R2 = 0.79, RMSE = 13.26 t\/ha) exhibited the highest predictive power for SOCS. Land use\/land cover (LU\/LC) was the most critical factor in predicting SOC and SOCS, followed by soil properties and bioclimatic variables. Both combinations of bioclimatic\u2013topographic variables and soil properties\u2013remote sensing variables were shown to improve prediction performance. Our findings show that ML algorithms can be a viable tool for spatial modeling of SOC in mountainous Mediterranean regions, such as the study area.<\/jats:p>","DOI":"10.3390\/rs15102494","type":"journal-article","created":{"date-parts":[[2023,5,10]],"date-time":"2023-05-10T01:57:51Z","timestamp":1683683871000},"page":"2494","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Spatial Prediction of Soil Organic Carbon Stock in the Moroccan High Atlas Using Machine Learning"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6271-2730","authenticated-orcid":false,"given":"Modeste","family":"Meliho","sequence":"first","affiliation":[{"name":"AgroParisTech\u2014Centre de Nancy, 14 rue Girardet-CS 14216, 54042 Nancy CEDEX, France"}]},{"given":"Mohamed","family":"Boulmane","sequence":"additional","affiliation":[{"name":"Division d\u2019Am\u00e9nagement de Territoire et Conservation d\u2019Environnement et de Patrimoine au Conseil R\u00e9gional, B\u00e9ni Mellal-Khenifra 25000, Morocco"}]},{"given":"Abdellatif","family":"Khattabi","sequence":"additional","affiliation":[{"name":"Ecole Nationale Foresti\u00e8re d\u2019Ingenieurs (ENFI), Sal\u00e9 11000, Morocco"}]},{"given":"Caleb Efelic","family":"Dansou","sequence":"additional","affiliation":[{"name":"\u00c9cole des Sciences de L\u2019Information (ESI), Rabat 10100, Morocco"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0842-3097","authenticated-orcid":false,"given":"Collins Ashianga","family":"Orlando","sequence":"additional","affiliation":[{"name":"Independent Researcher, Rabat 10000, Morocco"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8722-9507","authenticated-orcid":false,"given":"Nadia","family":"Mhammdi","sequence":"additional","affiliation":[{"name":"Geophysics and Natural Hazards Laboratory, Institut Scientifique GEOPAC Research Center, Mohammed V University in Rabat, Av Ibn Batouta, B.P 703 Agdal, Rabat 10000, Morocco"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3305-174X","authenticated-orcid":false,"given":"Koffi Dodji","family":"Noumonvi","sequence":"additional","affiliation":[{"name":"Department of Forest Ecology and Management, Swedish University of Agricultural Sciences, Skogsmarksgr\u00e4nd 17, 90183 Ume\u00e5, Sweden"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.geoderma.2004.01.032","article-title":"Soil Carbon Sequestration to Mitigate Climate Change","volume":"123","author":"Lal","year":"2004","journal-title":"Geoderma"},{"key":"ref_2","first-page":"95","article-title":"Evaluating organic carbon storage capacity of forest soil. 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