{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T10:51:07Z","timestamp":1784199067114,"version":"3.55.0"},"reference-count":78,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,1,19]],"date-time":"2022-01-19T00:00:00Z","timestamp":1642550400000},"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>This study tested and evaluated a suite of nine individual base learners and seven model averaging techniques for predicting the spatial distribution of soil properties in central Iran. Based on the nested-cross validation approach, the results showed that the artificial neural network and Random Forest base learners were the most effective in predicting soil organic matter and electrical conductivity, respectively. However, all seven model averaging techniques performed better than the base learners. For example, the Granger\u2013Ramanathan averaging approach resulted in the highest prediction accuracy for soil organic matter, while the Bayesian model averaging approach was most effective in predicting sand content. These results indicate that the model averaging approaches could improve the predictive accuracy for soil properties. The resulting maps, produced at a 30 m spatial resolution, can be used as valuable baseline information for managing environmental resources more effectively.<\/jats:p>","DOI":"10.3390\/rs14030472","type":"journal-article","created":{"date-parts":[[2022,1,19]],"date-time":"2022-01-19T21:01:51Z","timestamp":1642626111000},"page":"472","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["A Comparison of Model Averaging Techniques to Predict the Spatial Distribution of Soil Properties"],"prefix":"10.3390","volume":"14","author":[{"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":"CRC 1070 Resource Cultures, University of T\u00fcbingen, 72074 T\u00fcbingen, Germany"},{"name":"DFG Cluster of Excellence \u201cMachine Learning\u201d, University of T\u00fcbingen, 72070 T\u00fcbingen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2945-122X","authenticated-orcid":false,"given":"Hossein","family":"Khademi","sequence":"additional","affiliation":[{"name":"Department of Soil Science, College of Agriculture, Isfahan University of Technology, Isfahan 8415683111, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fatemeh","family":"Khayamim","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":"Henan Key Laboratory of Earth System Observation and Modeling, Henan University, Kaifeng 475004, China"},{"name":"College of Geography and Environmental Science, Henan University, Kaifeng 475004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brandon","family":"Heung","sequence":"additional","affiliation":[{"name":"Department of Plant, Food, and Environmental Sciences, Faculty of Agriculture, Dalhousie University, Truro, NS B2N 5E3, Canada"}],"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":"CRC 1070 Resource Cultures, University of T\u00fcbingen, 72074 T\u00fcbingen, Germany"},{"name":"DFG Cluster of Excellence \u201cMachine Learning\u201d, University of T\u00fcbingen, 72070 T\u00fcbingen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,19]]},"reference":[{"key":"ref_1","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_2","first-page":"55","article-title":"An investigation of spatial variation in soil erosion, soil properties, and crop production within an agricultural field in Devon, United Kingdom","volume":"57","author":"Quine","year":"2002","journal-title":"J. Soil Water Conserv."},{"key":"ref_3","first-page":"45","article-title":"Hazard assessment of desertification as a result of soil and water recourse degradation in Kashan Region, Iran","volume":"19","author":"Khosravi","year":"2014","journal-title":"Desert"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"114139","DOI":"10.1016\/j.geoderma.2019.114139","article-title":"Assessing the effects of deforestation and intensive agriculture on the soil quality through digital soil mapping","volume":"363","author":"Zeraatpisheh","year":"2020","journal-title":"Geoderma"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1016\/j.scitotenv.2017.01.062","article-title":"Spatial distribution of soil chemical properties in an organic farm in Croatia","volume":"584\u2013585","author":"Bogunovic","year":"2017","journal-title":"Sci. Total Environ."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Forkuor, G., Hounkpatin, O.K.L., Welp, G., and Thiel, M. (2017). High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: A Comparison of Machine Learning and Multiple Linear Regression Models. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0170478"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Taghizadeh-Mehrjardi, R., Schmidt, K., Amirian-Chakan, A., Rentschler, T., Zeraatpisheh, M., Sarmadian, F., Valavi, R., Davatgar, N., Behrens, T., and Scholten, T. (2020). Improving the Spatial Prediction of Soil Organic Carbon Content in Two Contrasting Climatic Regions by Stacking Machine Learning Models and Rescanning Covariate Space. Remote Sens., 12.","DOI":"10.3390\/rs12071095"},{"key":"ref_8","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_9","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_10","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_11","doi-asserted-by":"crossref","first-page":"103359","DOI":"10.1016\/j.earscirev.2020.103359","article-title":"Machine learning for digital soil mapping: Applications, challenges and suggested solutions","volume":"210","author":"Wadoux","year":"2020","journal-title":"Earth-Sci. Rev."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.catena.2013.07.001","article-title":"Estimating wet soil aggregate stability from easily available properties in a highly mountainous watershed","volume":"111","author":"Besalatpour","year":"2013","journal-title":"Catena"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.geomorph.2017.02.015","article-title":"Comparing the efficiency of digital and conventional soil mapping to predict soil types in a semi-arid region in Iran","volume":"285","author":"Zeraatpisheh","year":"2017","journal-title":"Geomorphology"},{"key":"ref_14","first-page":"141","article-title":"Legacy soil maps as a covariate in digital soil mapping: A case study from Northern Iran","volume":"276","author":"Khormali","year":"2016","journal-title":"Geoderma"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Mohammed, S., Al-Ebraheem, A., Holb, I.J., Alsafadi, K., Dikkeh, M., Pham, Q.B., Linh, N.T.T., and Szabo, S. (2020). Soil management effects on soil water erosion and runoff in central Syria\u2014A comparative evaluation of general linear model and random forest regression. Water, 12.","DOI":"10.3390\/w12092529"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zeraatpisheh, M., Rahman, M.M., Wang, S., and Xu, M. (2021). Texture Is Important in Improving the Accuracy of Mapping Photovoltaic Power Plants: A Case Study of Ningxia Autonomous Region, China. Remote Sens., 13.","DOI":"10.3390\/rs13193909"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"105723","DOI":"10.1016\/j.catena.2021.105723","article-title":"Improving the spatial prediction of soil organic carbon using environmental covariates selection: A comparison of a group of environmental covariates","volume":"208","author":"Zeraatpisheh","year":"2022","journal-title":"Catena"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.geoderma.2013.09.023","article-title":"Constructing a soil class map of Denmark based on the FAO legend using digital techniques","volume":"214\u2013215","author":"Adhikari","year":"2014","journal-title":"Geoderma"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1016\/j.geoderma.2009.11.005","article-title":"Soil type classification and estimation of soil properties using support vector machines","volume":"154","author":"Bajat","year":"2010","journal-title":"Geoderma"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.geomorph.2013.06.010","article-title":"Selection of a taxonomic level for soil mapping using diversity and map purity indices: A case study from an Iranian arid region","volume":"201","author":"Jafari","year":"2014","journal-title":"Geomorphology"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Patel, H., and Upla, K.P. (2021). A shallow network for hyperspectral image classification using an autoencoder with convolutional neural network. Multimed. Tools Appl., 1\u201320.","DOI":"10.1007\/s11042-021-11422-w"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"269","DOI":"10.14358\/PERS.85.4.269","article-title":"Machine learning-based ensemble prediction of water-quality variables using feature-level and decision-level fusion with proximal remote sensing","volume":"85","author":"Peterson","year":"2019","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"108224","DOI":"10.1016\/j.patcog.2021.108224","article-title":"Deep neural networks-based relevant latent representation learning for hyperspectral image classification","volume":"121","author":"Sellami","year":"2022","journal-title":"Pattern Recognit."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"115412","DOI":"10.1016\/j.envpol.2020.115412","article-title":"Ensemble machine-learning-based framework for estimating total nitrogen concentration in water using drone-borne hyperspectral imagery of emergent plants: A case study in an arid oasis, NW China","volume":"266","author":"Wang","year":"2020","journal-title":"Environ. Pollut."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"809","DOI":"10.1007\/s00477-010-0378-z","article-title":"Comparison of point forecast accuracy of model averaging methods in hydrologic applications","volume":"24","author":"Diks","year":"2010","journal-title":"Stoch. Environ. Res. Risk Assess."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.geoderma.2016.05.005","article-title":"An assessment of model averaging to improve predictive power of portable vis-NIR and XRF for the determination of agronomic soil properties","volume":"279","author":"Stockmann","year":"2016","journal-title":"Geoderma"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Taghizadeh-Mehrjardi, R., Minasny, B., Toomanian, N., Zeraatpisheh, M., Amirian-Chakan, A., and Triantafilis, J. (2019). Digital Mapping of Soil Classes Using Ensemble of Models in Isfahan Region, Iran. Soil Syst., 3.","DOI":"10.3390\/soilsystems3020037"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1255\/jnirs.1157","article-title":"Using Visible and near Infrared Spectroscopy to Estimate Carbonates and Gypsum in Soils in Arid and Subhumid Regions of Isfahan, Iran","volume":"23","author":"Khayamim","year":"2015","journal-title":"J. Near Infrared Spectrosc."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"199","DOI":"10.2134\/agronmonogr9.2.2ed.c12","article-title":"Chemical and microbiological properties","volume":"Volume 2","author":"McLean","year":"1982","journal-title":"Methods of Soil Analysis Part 2"},{"key":"ref_30","first-page":"643","article-title":"Nitrogen\u2014Inorganic Forms","volume":"Volume 2","author":"Page","year":"1982","journal-title":"Methods of Soil Analysis Part 2"},{"key":"ref_31","unstructured":"Page, A.L. (1983). Total Carbon, Organic Carbon, and Organic Matter. Methods of Soil Analysis, American Society of Agronomy, Soil Science Society of America."},{"key":"ref_32","first-page":"1201","article-title":"Cation exchange capacity and exchange coefficients","volume":"5","author":"Sumner","year":"1996","journal-title":"Methods Soil Anal: Part 3 Chemical Methods"},{"key":"ref_33","unstructured":"Klute, A. (1986). Particle size analysis. Methods Soil Anal, Part 1, American Society of Agronomy, Soil Science Society of America. Agron. Monogr. No. 9."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Richards, L.A. (1954). Diagnosis and Improvement of Saline and Alkali Soils.","DOI":"10.1097\/00010694-195408000-00012"},{"key":"ref_35","unstructured":"Olaya, V. (2004). A Gentle Introduction to SAGA GIS, The SAGA User Group eV."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1347","DOI":"10.1029\/2002WR001426","article-title":"A multiresolution index of valley bottom flatness for mapping depositional areas","volume":"39","author":"Gallant","year":"2003","journal-title":"Water Resour. Res."},{"key":"ref_37","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_38","doi-asserted-by":"crossref","first-page":"1309","DOI":"10.1016\/j.geoderma.2018.08.006","article-title":"Estimating soil salinity from remote sensing and terrain data in southern Xinjiang Province, China","volume":"337","author":"Peng","year":"2019","journal-title":"Geoderma"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.geoderma.2014.03.025","article-title":"Assessing soil salinity using soil salinity and vegetation indices derived from IKONOS high-spatial resolution imageries: Applications in a date palm dominated region","volume":"230","author":"Allbed","year":"2014","journal-title":"Geoderma"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0034-4257(02)00188-8","article-title":"Remote sensing of soil salinity: Potentials and constraints","volume":"85","author":"Metternicht","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Boettinger, J., Ramsey, R., Bodily, J., Cole, N., Kienast-Brown, S., Nield, S., Saunders, A., and Stum, A. (2008). Landsat spectral data for digital soil mapping. Digital Soil Mapping with Limited Data, Springer.","DOI":"10.1007\/978-1-4020-8592-5_16"},{"key":"ref_42","first-page":"309","article-title":"Monitoring vegetation systems in the Great Plains with ERTS","volume":"351","author":"Rouse","year":"1974","journal-title":"NASA Spec. Publ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"4302","DOI":"10.1002\/joc.5086","article-title":"WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas","volume":"37","author":"Fick","year":"2017","journal-title":"Int. J. Climatol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.geoderma.2014.09.019","article-title":"Machine learning for predicting soil classes in three semi-arid landscapes","volume":"239\u2013240","author":"Brungard","year":"2015","journal-title":"Geoderma"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1016\/j.geoderma.2019.07.005","article-title":"Digital mapping of soil invertebrates using environmental attributes in a deciduous forest ecosystem","volume":"353","author":"Tajik","year":"2019","journal-title":"Geoderma"},{"key":"ref_46","unstructured":"R Development Core Team (2015). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing."},{"key":"ref_47","unstructured":"Kuhn, M., Weston, S., Keefer, C., Coulter, N., and Quinlan, R.K. (2021, November 09). Cubist: Rule-and Instance-Based Regression Modeling; CRAN; R package version 0.0, 13; 2013. Available online: https:\/\/cran.r-project.org\/web\/packages\/Cubist\/vignettes\/cubist.html."},{"key":"ref_48","unstructured":"(2015). RStudio: Integrated Development for R, RStudio, Inc.. Computer Software v0.98.1074."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1057\/jors.1969.103","article-title":"The Combination of Forecasts","volume":"20","author":"Bates","year":"1969","journal-title":"J. Oper. Res. Soc."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"603","DOI":"10.2307\/2533961","article-title":"Model Selection: An Integral Part of Inference","volume":"53","author":"Buckland","year":"1997","journal-title":"Biometrics"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1214\/ss\/1009212519","article-title":"Bayesian model averaging: A tutorial (with comments by M. Clyde, David Draper and E. I. George, and a rejoinder by the authors","volume":"14","author":"Hoeting","year":"1999","journal-title":"Stat. Sci."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1155","DOI":"10.1175\/MWR2906.1","article-title":"Using Bayesian model averaging to calibrate forecast ensembles","volume":"133","author":"Raftery","year":"2005","journal-title":"Mon. Weather Rev."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"900","DOI":"10.1198\/016214503000000819","article-title":"The Focused Information Criterion","volume":"98","author":"Claeskens","year":"2003","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"879","DOI":"10.1198\/016214503000000828","article-title":"Frequentist Model Average Estimators","volume":"98","author":"Hjort","year":"2003","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1002\/for.3980030207","article-title":"Improved methods of combining forecasts","volume":"3","author":"Granger","year":"1984","journal-title":"J. Forecast."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1180\/0009855023740112","article-title":"Origin and distribution of clay minerals in calcareous arid and semi-arid soils of Fars Province, southern Iran","volume":"38","author":"Khormali","year":"2003","journal-title":"Clay Miner."},{"key":"ref_57","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_58","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.still.2012.01.011","article-title":"Soil aggregation and organic carbon as affected by topography and land use change in western Iran","volume":"121","author":"Ayoubi","year":"2012","journal-title":"Soil Tillage Res."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"500","DOI":"10.1007\/s10661-017-6197-7","article-title":"Digital soil mapping using remote sensing indices, terrain attributes, and vegetation features in the rangelands of northeastern Iran","volume":"189","author":"Mahmoudabadi","year":"2017","journal-title":"Environ. Monit. Assess."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.compag.2019.03.007","article-title":"Determining the best ISUM (Improved stock unearthing Method) sampling point number to model long-term soil transport and micro-topographical changes in vineyards","volume":"159","author":"Keshavarzi","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1111\/j.1475-4762.2006.00671.x","article-title":"Landscape connectivity: The geographic basis of geomorphic applications","volume":"38","author":"Brierley","year":"2006","journal-title":"Area"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1007\/s10661-016-5204-8","article-title":"The effectiveness of digital soil mapping to predict soil properties over low-relief areas","volume":"188","author":"Mosleh","year":"2016","journal-title":"Environ. Monit. Assess."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"860","DOI":"10.2136\/sssaj2012.0275","article-title":"High-Resolution 3-D Mapping of Soil Texture in Denmark","volume":"77","author":"Adhikari","year":"2013","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_64","unstructured":"Nath, D.A. (2016). Soil Landscape Modeling in the Northwest Iowa Plains Region of O\u2019Brien County, Iowa, Iowa State University."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1953","DOI":"10.2136\/sssaj2014.05.0202","article-title":"Digital Mapping of Soil Particle-Size Fractions for Nigeria","volume":"78","author":"Akpa","year":"2014","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.catena.2016.05.026","article-title":"Comparing Soil Taxonomy (2014) and updated WRB (2015) for describing calcareous and gypsiferous soils, Central Iran","volume":"145","author":"Sarmast","year":"2016","journal-title":"Catena"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1590\/18069657rbcs20170421","article-title":"Digital Soil Mapping Using Machine Learning Algorithms in a Tropical Mountainous Area","volume":"42","author":"Meier","year":"2018","journal-title":"Rev. Bras. Cienc. Solo"},{"key":"ref_68","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":"2020","journal-title":"Appl. Math. Model."},{"key":"ref_69","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_70","doi-asserted-by":"crossref","first-page":"707","DOI":"10.1111\/ejss.12382","article-title":"Predicting and mapping of soil particle-size fractions with adaptive neuro-fuzzy inference and ant colony optimization in central Iran","volume":"67","author":"Toomanian","year":"2016","journal-title":"Eur. J. Soil Sci."},{"key":"ref_71","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":"232-234","author":"Malone","year":"2014","journal-title":"Geoderma"},{"key":"ref_72","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_73","doi-asserted-by":"crossref","first-page":"115108","DOI":"10.1016\/j.geoderma.2021.115108","article-title":"Enhancing the accuracy of machine learning models using the super learner technique in digital soil mapping","volume":"399","author":"Hamzehpour","year":"2021","journal-title":"Geoderma"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1016\/S0341-8162(03)00136-X","article-title":"Micromorphology and classification of Argids and associated gypsiferous Aridisols from central Iran","volume":"54","author":"Khademi","year":"2003","journal-title":"Catena"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"115399","DOI":"10.1016\/j.geoderma.2021.115399","article-title":"Assessing toxic metal chromium in the soil in coal mining areas via proximal sensing: Prerequisites for land rehabilitation and sustainable development","volume":"405","author":"Wang","year":"2022","journal-title":"Geoderma"},{"key":"ref_76","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_77","doi-asserted-by":"crossref","first-page":"136092","DOI":"10.1016\/j.scitotenv.2019.136092","article-title":"Machine learning-based detection of soil salinity in an arid desert region, Northwest China: A comparison between Landsat-8 OLI and Sentinel-2 MSI","volume":"707","author":"Wang","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_78","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"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/3\/472\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:04:12Z","timestamp":1760133852000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/3\/472"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,19]]},"references-count":78,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["rs14030472"],"URL":"https:\/\/doi.org\/10.3390\/rs14030472","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,19]]}}}