{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T04:33:57Z","timestamp":1786682037482,"version":"3.56.0"},"reference-count":117,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2020,3,29]],"date-time":"2020-03-29T00:00:00Z","timestamp":1585440000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100005156","name":"Alexander von Humboldt-Stiftung","doi-asserted-by":"publisher","award":["Ref3.4-1164573-IRN-GFHERMES-P"],"award-info":[{"award-number":["Ref3.4-1164573-IRN-GFHERMES-P"]}],"id":[{"id":"10.13039\/100005156","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Understanding the spatial distribution of soil organic carbon (SOC) content over different climatic regions will enhance our knowledge of carbon gains and losses due to climatic change. However, little is known about the SOC content in the contrasting arid and sub-humid regions of Iran, whose complex SOC\u2013landscape relationships pose a challenge to spatial analysis. Machine learning (ML) models with a digital soil mapping framework can solve such complex relationships. Current research focusses on ensemble ML models to increase the accuracy of prediction. The usual ensemble method is boosting or weighted averaging. This study proposes a novel ensemble technique: the stacking of multiple ML models through a meta-learning model. In addition, we tested the ensemble through rescanning the covariate space to maximize the prediction accuracy. We first applied six state-of-the-art ML models (i.e., Cubist, random forests (RF), extreme gradient boosting (XGBoost), classical artificial neural network models (ANN), neural network ensemble based on model averaging (AvNNet), and deep learning neural networks (DNN)) to predict and map the spatial distribution of SOC content at six soil depth intervals for both regions. In addition, the stacking of multiple ML models through a meta-learning model with\/without rescanning the covariate space were tested and applied to maximize the prediction accuracy. Out of six ML models, the DNN resulted in the best modeling accuracies, followed by RF, XGBoost, AvNNet, ANN, and Cubist. Importantly, the stacking of models indicated a significant improvement in the prediction of SOC content, especially when combined with rescanning the covariate space. For instance, the RMSE values for SOC content prediction of the upper 0\u20135 cm of the soil profiles of the arid site and the sub-humid site by the proposed stacking approaches were 17% and 9% respectively, less than that obtained by the DNN models\u2014the best individual model. This indicates that rescanning the original covariate space by a meta-learning model can extract more information and improve the SOC content prediction accuracy. Overall, our results suggest that the stacking of diverse sets of models could be used to more accurately estimate the spatial distribution of SOC content in different climatic regions.<\/jats:p>","DOI":"10.3390\/rs12071095","type":"journal-article","created":{"date-parts":[[2020,4,1]],"date-time":"2020-04-01T03:44:13Z","timestamp":1585712653000},"page":"1095","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":206,"title":["Improving the Spatial Prediction of Soil Organic Carbon Content in Two Contrasting Climatic Regions by Stacking Machine Learning Models and Rescanning Covariate Space"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4620-6624","authenticated-orcid":false,"given":"Ruhollah","family":"Taghizadeh-Mehrjardi","sequence":"first","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"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0337-3024","authenticated-orcid":false,"given":"Karsten","family":"Schmidt","sequence":"additional","affiliation":[{"name":"eScience Center, University of T\u00fcbingen, 72070 T\u00fcbingen, Germany"},{"name":"DFG Cluster of Excellence \u201cMachine Learning\u201d, University of T\u00fcbingen, 72070 T\u00fcbingen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alireza","family":"Amirian-Chakan","sequence":"additional","affiliation":[{"name":"Department of Soil Science, Lorestan University, Khorramabad 6815144316, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3878-5539","authenticated-orcid":false,"given":"Tobias","family":"Rentschler","sequence":"additional","affiliation":[{"name":"Department of Geosciences, Soil Science and Geomorphology, University of T\u00fcbingen, 72070 T\u00fcbingen, Germany"},{"name":"CRC 1070 ResourceCultures, University of T\u00fcbingen, 72070 T\u00fcbingen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7209-0744","authenticated-orcid":false,"given":"Mojtaba","family":"Zeraatpisheh","sequence":"additional","affiliation":[{"name":"Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, College of Environment and Planning, Henan University, Kaifeng 475004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fereydoon","family":"Sarmadian","sequence":"additional","affiliation":[{"name":"Department of Soil Science, College of Agriculture, University of Tehran, Karaj 77871-31587, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2495-5277","authenticated-orcid":false,"given":"Roozbeh","family":"Valavi","sequence":"additional","affiliation":[{"name":"The Quantitative &amp; Applied Ecology Group, School of BioSciences, The University of Melbourne, Victoria 3010, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Naser","family":"Davatgar","sequence":"additional","affiliation":[{"name":"Soil &amp; Water Research Institute, Agricultural Research, Education and Extension Organization, Karaj 3177993545, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thorsten","family":"Behrens","sequence":"additional","affiliation":[{"name":"Department of Geosciences, Soil Science and Geomorphology, University of T\u00fcbingen, 72070 T\u00fcbingen, Germany"},{"name":"DFG Cluster of Excellence \u201cMachine Learning\u201d, University of T\u00fcbingen, 72070 T\u00fcbingen, Germany"},{"name":"CRC 1070 ResourceCultures, University of T\u00fcbingen, 72070 T\u00fcbingen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4875-2602","authenticated-orcid":false,"given":"Thomas","family":"Scholten","sequence":"additional","affiliation":[{"name":"Department of Geosciences, Soil Science and Geomorphology, University of T\u00fcbingen, 72070 T\u00fcbingen, Germany"},{"name":"DFG Cluster of Excellence \u201cMachine Learning\u201d, University of T\u00fcbingen, 72070 T\u00fcbingen, Germany"},{"name":"CRC 1070 ResourceCultures, University of T\u00fcbingen, 72070 T\u00fcbingen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,3,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1093\/jpe\/rtw065","article-title":"On the combined effect of soil fertility and topography on tree growth in subtropical forest ecosystems\u2014A study from SE China","volume":"10","author":"Scholten","year":"2017","journal-title":"J. Plant Ecol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1111\/ejss.12475","article-title":"Limited effect of organic matter on soil available water capacity","volume":"69","author":"Minasny","year":"2018","journal-title":"Eur. J. Soil Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1016\/j.geoderma.2007.06.003","article-title":"Spatial and vertical variation of soil carbon at two grassland sites\u2014implications for measuring soil carbon stocks","volume":"141","author":"Don","year":"2007","journal-title":"Geoderma"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1016\/j.scitotenv.2019.02.420","article-title":"Assessing soil organic carbon stock of Wisconsin, USA and its fate under future land use and climate change","volume":"667","author":"Adhikari","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/B978-0-12-405942-9.00001-3","article-title":"Digital soil mapping of carbon","volume":"118","author":"Minasny","year":"2013","journal-title":"Adv. Agron."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.geoderma.2016.06.017","article-title":"Environmental factors controlling soil organic carbon storage in loess soils of a subhumid region, northern Iran","volume":"281","author":"Ajami","year":"2016","journal-title":"Geoderma"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.geoderma.2015.12.003","article-title":"Digital mapping of soil organic carbon at multiple depths using different data mining techniques in Baneh region, Iran","volume":"266","author":"Nabiollahi","year":"2016","journal-title":"Geoderma"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1016\/j.catena.2018.06.018","article-title":"Pedogenic and microbial interrelations to regional climate and local topography: New insights from a climate gradient (arid to humid) along the Coastal Cordillera of Chile","volume":"170","author":"Bernhard","year":"2018","journal-title":"Catena"},{"key":"ref_9","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":"2020","journal-title":"Catena"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1111\/ejss.12790","article-title":"Pedology and digital soil mapping (DSM)","volume":"70","author":"Ma","year":"2019","journal-title":"Eur. J. Soil Sci."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"McBratney, A.B., Stockmann, U., Angers, D.A., Minasny, B., and Field, D.J. (2014). Challenges for soil organic carbon research. Soil Carbon, Springer.","DOI":"10.1007\/978-3-319-04084-4_1"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Minasny, B., McBratney, A.B., and Lark, R.M. (2008). Digital soil mapping technologies for countries with sparse data infrastructures. Digital Soil Mapping with Limited Data, Springer.","DOI":"10.1007\/978-1-4020-8592-5_2"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1002\/jpln.200521962","article-title":"Digital soil mapping in Germany\u2014A review","volume":"169","author":"Behrens","year":"2006","journal-title":"J. Plant Nutr. Soil Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/S0016-7061(03)00223-4","article-title":"On digital soil mapping","volume":"117","author":"McBratney","year":"2003","journal-title":"Geoderma"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.geoderma.2008.05.008","article-title":"Soil organic carbon concentrations and stocks on Barro Colorado Island\u2014Digital soil mapping using Random Forests analysis","volume":"146","author":"Grimm","year":"2008","journal-title":"Geoderma"},{"key":"ref_16","first-page":"179","article-title":"Application of artificial neural networks to estimate soil organic carbon in a high-organic-matter Mollisol","volume":"7","author":"Moreno","year":"2017","journal-title":"Span. J. Soil Sci. SJSS"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"e0220881","DOI":"10.1371\/journal.pone.0220881","article-title":"Comparison of catchment scale 3D and 2.5 D modeling of soil organic carbon stocks in Jiangxi Province, PR China","volume":"14","author":"Rentschler","year":"2019","journal-title":"PLoS ONE"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.agee.2018.02.012","article-title":"Spatio-temporal land use dynamics and soil organic carbon in Swiss agroecosystems","volume":"258","author":"Stumpf","year":"2018","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1016\/j.ecolind.2018.01.049","article-title":"Estimating soil organic carbon stocks using different modeling techniques in the semi-arid rangelands of eastern Australia","volume":"88","author":"Wang","year":"2018","journal-title":"Ecol. Indic."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1016\/j.ecolind.2014.12.028","article-title":"A comparative assessment of support vector regression, artificial neural networks, and random forests for predicting and mapping soil organic carbon stocks across an Afromontane landscape","volume":"52","author":"Were","year":"2015","journal-title":"Ecol. Indic."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1016\/j.geoderma.2018.09.006","article-title":"Digital mapping of soil properties using multiple machine learning in a semi-arid region, central Iran","volume":"338","author":"Zeraatpisheh","year":"2019","journal-title":"Geoderma"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1007\/s10661-017-5830-9","article-title":"Spatial 3D distribution of soil organic carbon under different land use types","volume":"189","author":"Kerry","year":"2017","journal-title":"Enviro. Monit. Assess."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"114008","DOI":"10.1016\/j.geoderma.2019.114008","article-title":"Spatial prediction of soil organic carbon stocks in Ghana using legacy data","volume":"360","author":"Owusu","year":"2020","journal-title":"Geoderma"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Adhikari, K., Hartemink, A.E., Minasny, B., Kheir, R.B., Greve, M.B., and Greve, M.H. (2014). Digital mapping of soil organic carbon contents and stocks in Denmark. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0105519"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1007\/s12665-018-7374-x","article-title":"Prediction of soil organic carbon stock using digital mapping approach in humid India","volume":"77","author":"Hinge","year":"2018","journal-title":"Environ. Earth Sci."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"e00250","DOI":"10.1016\/j.geodrs.2019.e00250","article-title":"Digital soil mapping of key GlobalSoilMap properties in Northern Karnataka Plateau","volume":"20","author":"Dharumarajan","year":"2020","journal-title":"Geoderma Reg."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"105172","DOI":"10.1016\/j.compag.2019.105172","article-title":"Extended model prediction of high-resolution soil organic matter over a large area using limited number of field samples","volume":"169","author":"Zhao","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"104477","DOI":"10.1016\/j.still.2019.104477","article-title":"Mapping field-scale soil organic carbon with unmanned aircraft system-acquired time series multispectral images","volume":"196","author":"Guo","year":"2020","journal-title":"Soil Tillage Res."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"104465","DOI":"10.1016\/j.still.2019.104465","article-title":"Improving prediction of soil organic carbon content in croplands using phenological parameters extracted from NDVI time series data","volume":"196","author":"Yang","year":"2020","journal-title":"Soil Tillage Res."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1080\/17583004.2018.1553434","article-title":"Assessing soil organic carbon stocks under land-use change scenarios using random forest models","volume":"10","author":"Nabiollahi","year":"2019","journal-title":"Carbon Manag."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"15244","DOI":"10.1038\/s41598-018-33516-6","article-title":"Multi-scale digital soil mapping with deep learning","volume":"8","author":"Behrens","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.geoderma.2019.06.016","article-title":"Convolutional neural network for simultaneous prediction of several soil properties using visible\/near-infrared, mid-infrared, and their combined spectra","volume":"352","author":"Ng","year":"2019","journal-title":"Geoderma"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"79","DOI":"10.5194\/soil-5-79-2019","article-title":"Using deep learning for digital soil mapping","volume":"5","author":"Padarian","year":"2019","journal-title":"Soil"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"107","DOI":"10.5194\/soil-5-107-2019","article-title":"Multi-source data integration for soil mapping using deep learning","volume":"5","author":"Wadoux","year":"2019","journal-title":"Soil"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1007\/BF00117832","article-title":"Stacked regressions","volume":"24","author":"Breiman","year":"1996","journal-title":"Mach. Learn."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.geoderma.2018.09.007","article-title":"Merging country, continental and global predictions of soil texture: Lessons from ensemble modeling in france","volume":"337","author":"Caubet","year":"2019","journal-title":"Geoderma"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.geoderma.2014.04.033","article-title":"Using model averaging to combine soil property rasters from legacy soil maps and from point data","volume":"232","author":"Malone","year":"2014","journal-title":"Geoderma"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"e1249","DOI":"10.1002\/widm.1249","article-title":"Ensemble learning: A survey","volume":"8","author":"Sagi","year":"2018","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"105837","DOI":"10.1016\/j.asoc.2019.105837","article-title":"Ensemble approach based on bagging, boosting and stacking for short-term prediction in agribusiness time series","volume":"86","author":"Ribeiro","year":"2020","journal-title":"Appl. Soft Comput."},{"key":"ref_40","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_41","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Xue, J., Chen, S., Zhou, Y., Liang, Z., Wang, N., and Shi, Z. (2020). Fine-Resolution Mapping of Soil Total Nitrogen across China Based on Weighted Model Averaging. Remote Sens., 12.","DOI":"10.3390\/rs12010085"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"114237","DOI":"10.1016\/j.geoderma.2020.114237","article-title":"Model averaging for mapping topsoil organic carbon in France","volume":"366","author":"Chen","year":"2020","journal-title":"Geoderma"},{"key":"ref_43","unstructured":"Smith, D. (2014). Soil Survey Staff: Keys to Soil Taxonomy."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.geoderma.2013.07.020","article-title":"Digital mapping of soil salinity in Ardakan region, central Iran","volume":"213","author":"Minasny","year":"2014","journal-title":"Geoderma"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1378","DOI":"10.1016\/j.cageo.2005.12.009","article-title":"A conditioned Latin hypercube method for sampling in the presence of ancillary information","volume":"32","author":"Minasny","year":"2006","journal-title":"Comput. Geosci."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1002\/jpln.201500313","article-title":"Incorporating limited field operability and legacy soil samples in a hypercube sampling design for digital soil mapping","volume":"179","author":"Stumpf","year":"2016","journal-title":"J. Plant Nutr. Soil Sci."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Gholizadeh, A., Zizala, D., Saberioon, M., and Boruvka, L. (2018, January 26\u201329). Soil organic carbon content monitoring and mapping using airborne and Sentinel-2 spectral imaging. Proceedings of the Sixth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2018), Paphos, Cyprus.","DOI":"10.1117\/12.2323820"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.rse.2018.09.015","article-title":"Soil organic carbon and texture retrieving and mapping using proximal, airborne and Sentinel-2 spectral imaging","volume":"218","author":"Gholizadeh","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"2981","DOI":"10.1111\/j.1365-2486.2009.01940.x","article-title":"Driving forces of soil organic carbon evolution at the landscape and regional scale using data from a stratified soil monitoring","volume":"15","author":"Goidts","year":"2009","journal-title":"Glob. Chang. Biol."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Roozitalab, M.H., Toomanian, N., Dehkordi, V.R.G., and Khormali, F. (2018). Major soils, properties, and classification. The Soils of Iran, Springer.","DOI":"10.1007\/978-3-319-69048-3_7"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1080\/17583004.2017.1330593","article-title":"Artificial bee colony feature selection algorithm combined with machine learning algorithms to predict vertical and lateral distribution of soil organic matter in South Dakota, USA","volume":"8","author":"Neupane","year":"2017","journal-title":"Carbon Manag."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.geoderma.2018.07.026","article-title":"Soil organic carbon storage as a key function of soils-a review of drivers and indicators at various scales","volume":"333","author":"Wiesmeier","year":"2019","journal-title":"Geoderma"},{"key":"ref_53","first-page":"961","article-title":"Total carbon, organic carbon, and organic matter","volume":"5","author":"Nelson","year":"1996","journal-title":"Methods Soil Anal. Part 3 Chem. Methods"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.geoderma.2009.10.007","article-title":"Mapping continuous depth functions of soil carbon storage and available water capacity","volume":"154","author":"Malone","year":"2009","journal-title":"Geoderma"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/B978-0-12-800137-0.00003-0","article-title":"GlobalSoilMap: Toward a fine-resolution global grid of soil properties","volume":"Volume 125","author":"Arrouays","year":"2014","journal-title":"Advances in Agronomy"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"974","DOI":"10.1016\/j.scitotenv.2014.06.088","article-title":"Interaction effects of climate and land use\/land cover change on soil organic carbon sequestration","volume":"493","author":"Xiong","year":"2014","journal-title":"Sci. Total Environ."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1029\/2005RG000183","article-title":"The shuttle radar topography mission","volume":"45","author":"Farr","year":"2007","journal-title":"Rev. Geophys."},{"key":"ref_58","unstructured":"Conrad, O., and Olaya, V. (2019, January 22). SAGA-GIS module library documentation (v2. 2.3). Available online: http:\/\/www.saga-gis.org\/saga_tool_doc\/2.2.3\/index.html."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/j.rse.2015.11.032","article-title":"The global Landsat archive: Status, consolidation, and direction","volume":"185","author":"Wulder","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.rse.2011.11.026","article-title":"Sentinel-2: ESA\u2019s optical high-resolution mission for GMES operational services","volume":"120","author":"Drusch","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Fongaro, C., Dematt\u00ea, J., Rizzo, R., Lucas Safanelli, J., Mendes, W., Dotto, A., Vicente, L., Franceschini, M., and Ustin, S. (2018). Improvement of clay and sand quantification based on a novel approach with a focus on multispectral satellite images. Remote Sens., 10.","DOI":"10.3390\/rs10101555"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.geoderma.2019.03.017","article-title":"Modeling the extent of northern peat soil and its uncertainty with Sentinel: Scotland as example of highly cloudy region","volume":"346","author":"Poggio","year":"2019","journal-title":"Geoderma"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/0034-4257(88)90106-X","article-title":"A soil-adjusted vegetation index (SAVI)","volume":"25","author":"Huete","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v036.i11","article-title":"Feature selection with the Boruta package","volume":"36","author":"Kursa","year":"2010","journal-title":"J. Stat. Softw."},{"key":"ref_66","first-page":"18","article-title":"Classification and regression by randomForest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News"},{"key":"ref_67","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":"2019","journal-title":"Geoderma"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"8868","DOI":"10.1021\/acs.jpcc.8b03405","article-title":"Significantly improving the prediction of molecular atomization energies by an ensemble of machine learning algorithms and rescanning input space: A stacked generalization approach","volume":"122","author":"Wang","year":"2018","journal-title":"J. Phys. Chem. C"},{"key":"ref_69","unstructured":"Quinlan, J.R. (1992, January 16\u201318). Learning with continuous classes. Proceedings of the 5th Australian Joint Conference on Artificial Intelligence, Tasmania, Australia."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Kuhn, M., and Johnson, K. (2013). Applied Predictive Modeling, Springer.","DOI":"10.1007\/978-1-4614-6849-3"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.chemolab.2008.06.003","article-title":"Regression rules as a tool for predicting soil properties from infrared reflectance spectroscopy","volume":"94","author":"Minasny","year":"2008","journal-title":"Chem. Intell. Lab. Syst."},{"key":"ref_72","unstructured":"Kuhn, M., Weston, S., Keefer, C., and Kuhn, M.M. (2019, January 22). Package \u2018Cubist\u2019. Available online: https:\/\/cran.r-project.org\/web\/packages\/Cubist\/index.html."},{"key":"ref_73","first-page":"237","article-title":"Classification and regression trees","volume":"37","author":"Breiman","year":"1984","journal-title":"Wadsworth Int. Group"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1111\/j.1365-2389.2009.01205.x","article-title":"The ConMap approach for terrain-based digital soil mapping","volume":"61","author":"Behrens","year":"2010","journal-title":"Eur. J. Soil Sci."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"9959","DOI":"10.1038\/s41598-018-28244-w","article-title":"Predicting reference soil groups using legacy data: A data pruning and Random Forest approach for tropical environment (Dano catchment, Burkina Faso)","volume":"8","author":"Hounkpatin","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1007\/s11104-010-0425-z","article-title":"Digital mapping of soil organic matter stocks using Random Forest modeling in a semi-arid steppe ecosystem","volume":"340","author":"Wiesmeier","year":"2011","journal-title":"Plant Soil"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1016\/j.ecolmodel.2007.05.011","article-title":"Random forests as a tool for ecohydrological distribution modeling","volume":"207","author":"Peters","year":"2007","journal-title":"Ecol. Model."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"802","DOI":"10.1111\/j.1365-2656.2008.01390.x","article-title":"A working guide to boosted regression trees","volume":"77","author":"Elith","year":"2008","journal-title":"J. Anim. Ecol."},{"key":"ref_79","unstructured":"Chen, T., He, T., Benesty, M., Khotilovich, V., and Tang, Y. (2019, January 22). Xgboost: Extreme Gradient Boosting. R Package Version 0.4-2. Available online: https:\/\/github.com\/dmlc\/xgboost."},{"key":"ref_80","unstructured":"Venables, B., and Ripley, B. (2019, January 22). VR: Bundle of MASS, class, nnet, spatial. R package version 7.2-42. Available online: http:\/\/CRAN.R-project.org\/package=VR."},{"key":"ref_81","unstructured":"Ripley, B.D. (2007). Pattern Recognition and Neural Networks, Cambridge University Press."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"989","DOI":"10.1109\/72.329697","article-title":"Training feedforward networks with the Marquardt algorithm","volume":"5","author":"Hagan","year":"1994","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"424","DOI":"10.1016\/j.atmosres.2015.09.021","article-title":"Comparison of four machine learning algorithms for their applicability in satellite-based optical rainfall retrievals","volume":"169","author":"Meyer","year":"2016","journal-title":"Atmos. Res."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/j.geoderma.2007.11.016","article-title":"Optimisation of pedotransfer functions using an artificial neural network ensemble method","volume":"144","author":"Baker","year":"2008","journal-title":"Geoderma"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v028.i05","article-title":"Building Predictive Models in R Using the caret Package","volume":"28","author":"Kuhn","year":"2008","journal-title":"J. Stat. Softw."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","article-title":"A fast learning algorithm for deep belief nets","volume":"18","author":"Hinton","year":"2006","journal-title":"Neural Comput."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"8558","DOI":"10.1029\/2018WR022643","article-title":"A transdisciplinary review of deep learning research and its relevance for water resources scientists","volume":"54","author":"Shen","year":"2018","journal-title":"Water Resour. Res."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"e1283","DOI":"10.1002\/widm.1283","article-title":"Big data analytics: Machine learning and Bayesian learning perspectives\u2014What is done? What is not?","volume":"9","author":"Suthaharan","year":"2019","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"ref_89","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"1043","DOI":"10.1016\/j.neucom.2017.09.047","article-title":"Ensemble dropout extreme learning machine via fuzzy integral for data classification","volume":"275","author":"Zhai","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_91","unstructured":"Candel, A., Parmar, V., LeDell, E., and Arora, A. (2016). Deep Learning with H2O, H2O. AI Inc."},{"key":"ref_92","first-page":"1","article-title":"Regularization Paths for Generalized Linear Models via Coordinate Descent","volume":"33","author":"Friedman","year":"2008","journal-title":"J. Stat. Sotw."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_94","first-page":"779","article-title":"Support vector regression machines","volume":"28","author":"Drucker","year":"1997","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_95","unstructured":"Dimitriadou, E., Hornik, K., Leisch, F., Meyer, D., Weingessel, A., and Leisch, M.F. (2019, January 22). The e1071 Package. Misc Functions of Department of Statistics (e1071), TU Wien, Vienna, Austria. Available online: https:\/\/CRAN.R-project.org\/package=e1071."},{"key":"ref_96","first-page":"5938","article-title":"mlr: Machine learning in R","volume":"17","author":"Bischl","year":"2016","journal-title":"J. Mach. Learn. Res."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1111\/2041-210X.13107","article-title":"blockCV: An R package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution models","volume":"10","author":"Valavi","year":"2019","journal-title":"Methods Ecol. Evol."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1016\/j.apm.2019.12.016","article-title":"Selecting appropriate machine learning methods for digital soil mapping","volume":"81","author":"Khaledian","year":"2019","journal-title":"Appl. Math. Model."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.catena.2016.12.014","article-title":"Predictive performance of mobile vis-near infrared spectroscopy for key soil properties at different geographical scales by using spiking and data mining techniques","volume":"151","author":"Nawar","year":"2017","journal-title":"Catena"},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"627","DOI":"10.1016\/S0140-1963(03)00077-6","article-title":"Effective environmental factors in the distribution of vegetation types in Poshtkouh rangelands of Yazd Province (Iran)","volume":"56","author":"Jafari","year":"2004","journal-title":"J. Arid Environ."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"8635","DOI":"10.1038\/s41598-019-45156-5","article-title":"The strength of soil-plant interactions under forest is related to a Critical Soil Depth","volume":"9","author":"Goebes","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/j.scitotenv.2018.02.204","article-title":"High resolution mapping of soil organic carbon stocks using remote sensing variables in the semi-arid rangelands of eastern Australia","volume":"630","author":"Wang","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.rse.2019.01.006","article-title":"Sentinel-2 image capacities to predict common topsoil properties of temperate and Mediterranean agroecosystems","volume":"223","author":"Vaudour","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1016\/j.catena.2018.11.010","article-title":"Assessment of spatial hybrid methods for predicting soil organic matter using DEM derivatives and soil parameters","volume":"174","author":"Tziachris","year":"2019","journal-title":"Catena"},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1007\/s10705-017-9870-x","article-title":"Soil nutrient maps of Sub-Saharan Africa: Assessment of soil nutrient content at 250 m spatial resolution using machine learning","volume":"109","author":"Hengl","year":"2017","journal-title":"Nutr. Cycl. Agroecosyst."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"186","DOI":"10.2136\/sssaj2017.04.0122","article-title":"Soil property and class maps of the conterminous United States at 100-meter spatial resolution","volume":"82","author":"Ramcharan","year":"2018","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/j.scitotenv.2018.11.230","article-title":"A high-resolution map of soil pH in China made by hybrid modeling of sparse soil data and environmental covariates and its implications for pollution","volume":"655","author":"Chen","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_108","doi-asserted-by":"crossref","unstructured":"Hengl, T., de Jesus, J.M., Heuvelink, G.B., Gonzalez, M.R., Kilibarda, M., Blagoti\u0107, A., Shangguan, W., Wright, M.N., Geng, X., and Bauer-Marschallinger, B. (2017). SoilGrids250m: Global gridded soil information based on machine learning. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0169748"},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1002\/2016MS000686","article-title":"Mapping the global depth to bedrock for land surface modeling","volume":"9","author":"Shangguan","year":"2017","journal-title":"J. Adv. Model. Earth Syst."},{"key":"ref_110","doi-asserted-by":"crossref","unstructured":"Saeedimoghaddam, M., and Stepinski, T.F. (2019). Automatic extraction of road intersection points from USGS historical map series using deep convolutional neural networks. Int. J. Geogr. Inf. Sci., 1\u201322.","DOI":"10.1080\/13658816.2019.1696968"},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"e00198","DOI":"10.1016\/j.geodrs.2018.e00198","article-title":"Using deep learning to predict soil properties from regional spectral data","volume":"16","author":"Padarian","year":"2019","journal-title":"Geoderma Regional"},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"1413","DOI":"10.2136\/sssaj2016.11.0376","article-title":"More data or a better model? Figuring out what matters most for the spatial prediction of soil carbon","volume":"81","author":"Somarathna","year":"2017","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_113","unstructured":"Molnar, C. (2018). Interpretable Machine Learning: A Guide for Making Black Box Models Explainable, Lean Publishing."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1016\/j.ecolmodel.2004.12.007","article-title":"The evaluation strip: A new and robust method for plotting predicted responses from species distribution models","volume":"186","author":"Elith","year":"2005","journal-title":"Ecol. Model."},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/j.geoderma.2018.05.020","article-title":"A mixed model for landscape soil organic carbon prediction across continuous profile depth in the mountainous subtropics","volume":"330","author":"Laub","year":"2018","journal-title":"Geoderma"},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"614","DOI":"10.2136\/sssaj2007.0410","article-title":"Predicting soil organic carbon stock using profile depth distribution functions and ordinary kriging","volume":"73","author":"Mishra","year":"2009","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1016\/j.geoderma.2010.03.002","article-title":"Regional modeling of soil carbon at multiple depths within a subtropical watershed","volume":"156","author":"Vasques","year":"2010","journal-title":"Geoderma"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/7\/1095\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:13:07Z","timestamp":1760173987000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/7\/1095"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,29]]},"references-count":117,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2020,4]]}},"alternative-id":["rs12071095"],"URL":"https:\/\/doi.org\/10.3390\/rs12071095","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,3,29]]}}}