{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T23:38:28Z","timestamp":1783381108285,"version":"3.54.6"},"reference-count":97,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2020,4,8]],"date-time":"2020-04-08T00:00:00Z","timestamp":1586304000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001807","name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa do Estado de S\u00e3o Paulo","doi-asserted-by":"publisher","award":["2014\/22262-0, 2016\/26124-6 and 2016\/01597-9"],"award-info":[{"award-number":["2014\/22262-0, 2016\/26124-6 and 2016\/01597-9"]}],"id":[{"id":"10.13039\/501100001807","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005668","name":"Funda\u00e7\u00e3o de Apoio \u00e0 Pesquisa do Distrito Federal","doi-asserted-by":"publisher","award":["official notice 07\/2015"],"award-info":[{"award-number":["official notice 07\/2015"]}],"id":[{"id":"10.13039\/501100005668","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Soil color and mineralogy are used as diagnostic criteria to distinguish different soil types. In the literature, 350\u20132500 nm spectra were successfully used to predict soil color and mineralogy, but these attributes currently are not mapped for most Brazilian soils. In this paper, we provided the first large-extent maps with 30 m resolution of soil color and mineralogy at three depth intervals for 850,000 km2 of Midwest Brazil. We obtained soil 350\u20132500 nm spectra from 1397 sites of the Brazilian Soil Spectral Library at 0\u201320 cm, 20\u201360, and 60\u2013100 cm depths. Spectra was used to derive Munsell hue, value, and chroma, and also second derivative spectra of the Kubelka\u2013Munk function, where key spectral bands were identified and their amplitude measured for mineral quantification. Landsat composites of topsoil and vegetation reflectance, together with relief and climate data, were used as covariates to predict Munsell color and Fe\u2013Al oxides, and 1:1 and 2:1 clay minerals of topsoil and subsoil. We used random forest for soil modeling and 10-fold cross-validation. Soil spectra and remote sensing data accurately mapped color and mineralogy at topsoil and subsoil in Midwest Brazil. Hematite showed high prediction accuracy (R2 &gt; 0.71), followed by Munsell value and hue. Satellite topsoil reflectance at blue spectral region was the most relevant predictor (25% global importance) for soil color and mineralogy. Our maps were consistent with pedological expert knowledge, legacy soil observations, and legacy soil class map of the study region.<\/jats:p>","DOI":"10.3390\/rs12071197","type":"journal-article","created":{"date-parts":[[2020,4,9]],"date-time":"2020-04-09T03:40:19Z","timestamp":1586403619000},"page":"1197","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":47,"title":["Soil Color and Mineralogy Mapping Using Proximal and Remote Sensing in Midwest Brazil"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1628-4154","authenticated-orcid":false,"given":"Ra\u00fal Roberto","family":"Poppiel","sequence":"first","affiliation":[{"name":"Faculty of Agronomy and Veterinary Medicine, Darcy Ribeiro University Campus, University of Bras\u00edlia; ICC Sul, Asa Norte, Postal Box 4508, Bras\u00edlia 70910-960, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marilusa Pinto Coelho","family":"Lacerda","sequence":"additional","affiliation":[{"name":"Faculty of Agronomy and Veterinary Medicine, Darcy Ribeiro University Campus, University of Bras\u00edlia; ICC Sul, Asa Norte, Postal Box 4508, Bras\u00edlia 70910-960, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rodnei","family":"Rizzo","sequence":"additional","affiliation":[{"name":"Department of Soil Science, Luiz de Queiroz College of Agriculture, University of S\u00e3o Paulo; P\u00e1dua Dias Av., 11, Piracicaba, Postal Box 09, S\u00e3o Paulo 13416-900, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5410-5762","authenticated-orcid":false,"given":"Jos\u00e9 Lucas","family":"Safanelli","sequence":"additional","affiliation":[{"name":"Department of Soil Science, Luiz de Queiroz College of Agriculture, University of S\u00e3o Paulo; P\u00e1dua Dias Av., 11, Piracicaba, Postal Box 09, S\u00e3o Paulo 13416-900, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Benito Roberto","family":"Bonfatti","sequence":"additional","affiliation":[{"name":"Department of Soil Science, Luiz de Queiroz College of Agriculture, University of S\u00e3o Paulo; P\u00e1dua Dias Av., 11, Piracicaba, Postal Box 09, S\u00e3o Paulo 13416-900, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6021-2759","authenticated-orcid":false,"given":"N\u00e9lida Elizabet Qui\u00f1onez","family":"Silvero","sequence":"additional","affiliation":[{"name":"Department of Soil Science, Luiz de Queiroz College of Agriculture, University of S\u00e3o Paulo; P\u00e1dua Dias Av., 11, Piracicaba, Postal Box 09, S\u00e3o Paulo 13416-900, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jos\u00e9 Alexandre Melo","family":"Dematt\u00ea","sequence":"additional","affiliation":[{"name":"Department of Soil Science, Luiz de Queiroz College of Agriculture, University of S\u00e3o Paulo; P\u00e1dua Dias Av., 11, Piracicaba, Postal Box 09, S\u00e3o Paulo 13416-900, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,4,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Schwertmann, U., and Taylor, R.M. (1989). Iron Oxides. Minerals in Soil Environments, Soil Science Society of America.","DOI":"10.2136\/sssabookser1.2ed.c8"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.geoderma.2013.02.013","article-title":"Prediction of soil characteristics and colour using data from the National Soils Inventory of Scotland","volume":"200\u2013201","author":"Aitkenhead","year":"2013","journal-title":"Geoderma"},{"key":"ref_3","unstructured":"Ciolkosz, E.J., and Bigham, U. (1993). Relations Between Iron Oxides, Soil Color, and Soil Formation. Soil Color, Soil Science Society of America."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Bigham, J.M., and CiolKosz, E.J. (1993). Significance of Organic Matter in Determining Soil Colors. Soil Color, Soil Science Society of America.","DOI":"10.2136\/sssaspecpub31"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"341","DOI":"10.2136\/sssaj1984.03615995004800020024x","article-title":"Toposequence of Oxisols from the Central Plateau of Brazil","volume":"48","author":"Curi","year":"1984","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_6","unstructured":"Moraes, J.M. (2014). Geodiversidade do Estado de Goi\u00e1s e do Distrito Federal, CPRM."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1180\/claymin.2008.043.1.11","article-title":"Minerals in the clay fraction of Brazilian Latosols (Oxisols): A review","volume":"43","author":"Schaefer","year":"2008","journal-title":"Clay Miner."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Torrent, J., and Barr\u00f3n, V. (1993). Laboratory Measurement of Soil Color: Theory and Practice. Soil Color, Soil Science Society of America.","DOI":"10.2136\/sssaspecpub31.c2"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1111\/j.1365-2389.1986.tb00382.x","article-title":"Use of the Kubelka\u2014Munk Theory to Study the Influence of Iron Oxides on Soil Colour","volume":"37","author":"Torrent","year":"1986","journal-title":"J. Soil Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"104258","DOI":"10.1016\/j.catena.2019.104258","article-title":"Spatial variability of iron oxides in soils from Brazilian sandstone and basalt","volume":"185","author":"Silva","year":"2020","journal-title":"Catena"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.catena.2005.11.001","article-title":"Symbolism, knowledge and management of soil and land resources in indigenous communities: Ethnopedology at global, regional and local scales","volume":"65","year":"2006","journal-title":"Catena"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/bs.agron.2017.01.003","article-title":"Delineation of Soil Management Zones for Variable-Rate Fertilization: A Review","volume":"Volume 143","author":"Sparks","year":"2017","journal-title":"Advances in Agronomy"},{"key":"ref_13","unstructured":"Embrapa\u2014Brazilian Agricultural Research Corporation, and National Soils Research Center (2018). Brazilian Soil Classification System, Embrapa-Cnps. [5th ed.]. Available online: https:\/\/www.embrapa.br\/busca-de-publicacoes\/-\/publicacao\/1094001\/brazilian-soil-classification-system."},{"key":"ref_14","unstructured":"IUSS Working Group WRB (2015). World Reference Base for Soil Resources 2014: International Soil Classification System for NAming Soils and Creating Legends for Soil Maps, Food and Agriculture Organization. Available online: http:\/\/www.fao.org\/3\/i3794en\/I3794EN.pdf."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1130\/0016-7606(1977)88<174:VEOIIS>2.0.CO;2","article-title":"Visual estimation of iron in saprolite","volume":"88","author":"Hurst","year":"1977","journal-title":"GSA Bull."},{"key":"ref_16","unstructured":"Munsell, A.H. (1907). A Color Notation, G. H. Ellis Company. Available online: http:\/\/books.google.com.br\/books?id=PgcCAAAAYAAJ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1111\/ejss.12699","article-title":"Digital mapping of a soil profile","volume":"70","author":"Zhang","year":"2019","journal-title":"Eur. J. Soil Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"114039","DOI":"10.1016\/j.geoderma.2019.114039","article-title":"Predicting the color of sandy soils from Wisconsin, USA","volume":"361","author":"Simon","year":"2019","journal-title":"Geoderma"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Marques, K.P., Rizzo, R., Dotto, A.C., Souza, A.B., Mello, F.A., Neto, L.G., Anjos, L.H.C., and Dematt\u00ea, J.A. (2019). How qualitative spectral information can improve soil profile classification?. J. Near Infrared Spectrosc.","DOI":"10.1177\/0967033518821965"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.geoderma.2016.03.019","article-title":"Digital soil mapping at local scale using a multi-depth Vis\u2013NIR spectral library and terrain attributes","volume":"274","author":"Rizzo","year":"2016","journal-title":"Geoderma"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/S0034-4257(96)00075-2","article-title":"Soil color modeling for the visible and near-infrared bands of Landsat sensors using laboratory spectral measurements","volume":"59","author":"Mattikalli","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1277","DOI":"10.2136\/sssaj1987.03615995005100050033x","article-title":"Calculation of Soil Color from Reflectance Spectra","volume":"51","author":"Fernandez","year":"1987","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_23","first-page":"249","article-title":"Modeling the relationships between Munsell soil color and soil spectral properties","volume":"4","author":"Escadafal","year":"1988","journal-title":"Int. Agrophysics"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"528","DOI":"10.1346\/CCMN.1998.0460506","article-title":"Use and limitations of second-derivative diffuse reflectance spectroscopy in the visible to near-infrared range to identify and quantify Fe oxide minerals in soils","volume":"46","author":"Scheinost","year":"1998","journal-title":"Clays Clay Miner."},{"key":"ref_25","first-page":"F04031","article-title":"Mapping iron oxides and the color of Australian soil using visible\u2013near-infrared reflectance spectra","volume":"115","author":"Bui","year":"2010","journal-title":"J. Geophys. Res."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1443","DOI":"10.1016\/j.rse.2011.02.004","article-title":"Digitally mapping the information content of visible\u2013near infrared spectra of surficial Australian soils","volume":"115","author":"Rossel","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Viscarra Rossel, R.A. (2011). Fine-resolution multiscale mapping of clay minerals in Australian soils measured with near infrared spectra. J. Geophys. Res. Earth Surf., 116.","DOI":"10.1029\/2011JF001977"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.geodrs.2014.08.001","article-title":"A model for the identification of terrons in the Lower Hunter Valley, Australia","volume":"1","author":"Malone","year":"2014","journal-title":"Geoderma Reg."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1016\/j.rse.2013.08.018","article-title":"Characterizing regional soil mineral composition using spectroscopy and geostatistics","volume":"139","author":"Mulder","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"5297","DOI":"10.1038\/s41467-019-13276-1","article-title":"Exposed soil and mineral map of the Australian continent revealing the land at its barest","volume":"10","author":"Roberts","year":"2019","journal-title":"Nat. Commun."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2835","DOI":"10.1080\/014311697217369","article-title":"Visible spectrometric indices of hematite (Hm) and goethite (Gt) content in lateritic soils: The application of a Thematic Mapper (TM) image for soil-mapping in Brasilia, Brazil","volume":"18","author":"Bedidi","year":"1997","journal-title":"Int. J. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1590\/2317-4889201620160023","article-title":"de Mapping iron oxides with Landsat-8\/OLI and EO-1\/Hyperion imagery from the Serra Norte iron deposits in the Caraj\u00e1s Mineral Province, Brazil","volume":"46","author":"Ducart","year":"2016","journal-title":"Braz. J. Geol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"e20170430","DOI":"10.1590\/1678-992x-2017-0430","article-title":"Open legacy soil survey data in Brazil: Geospatial data quality and how to improve it","volume":"77","author":"Dalmolin","year":"2020","journal-title":"Sci. Agric."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1016\/j.geoderma.2009.01.025","article-title":"In situ measurements of soil colour, mineral composition and clay content by vis\u2013NIR spectroscopy","volume":"150","author":"Cattle","year":"2009","journal-title":"Geoderma"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.geoderma.2019.04.028","article-title":"Pedology and soil class mapping from proximal and remote sensed data","volume":"328","author":"Poppiel","year":"2019","journal-title":"Geoderma"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.geoderma.2019.01.025","article-title":"Is it possible to map subsurface soil attributes by satellite spectral transfer models?","volume":"343","author":"Gallo","year":"2019","journal-title":"Geoderma"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"e0160519","DOI":"10.1590\/18069657rbcs20160519","article-title":"Surface Spectroscopy of Oxisols, Entisols and Inceptisol and Relationships with Selected Soil Properties","volume":"42","author":"Poppiel","year":"2018","journal-title":"Revista Brasileira de Ci\u00eancia do Solo"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2017.11.004","article-title":"Building an exposed soil composite processor (SCMaP) for mapping spatial and temporal characteristics of soils with Landsat imagery (1984\u20132014)","volume":"205","author":"Rogge","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.rse.2018.04.047","article-title":"Geospatial Soil Sensing System (GEOS3): A powerful data mining procedure to retrieve soil spectral reflectance from satellite images","volume":"212","author":"Fongaro","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Poppiel, R.R., Lacerda, P.C.M., Safanelli, L.J., Rizzo, R., Oliveira, P.M., Novais, J.J., and Dematt\u00ea, A.M.J. (2019). Mapping at 30 m Resolution of Soil Attributes at Multiple Depths in Midwest Brazil. Remote Sens., 11.","DOI":"10.3390\/rs11242905"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Hartemink, A., McBratney, A., and Mendon\u00e7a-Santos, M.L. (2008). Digital Soil Mapping: A State of the Art. Digital Soil Mapping with Limited Data, Springer.","DOI":"10.1007\/978-1-4020-8592-5"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1716","DOI":"10.1214\/15-AOS1321","article-title":"Consistency of random forests","volume":"43","author":"Scornet","year":"2015","journal-title":"Ann. Stat."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Vieira, B.C., Salgado, A.A.R., and Santos, L.J.C. (2015). Landscapes and Landforms of Brazil, Springer.","DOI":"10.1007\/978-94-017-8023-0"},{"key":"ref_45","unstructured":"(2019, September 30). IBGE\u2014Instituto Brasileiro de Geografia e Estat\u00edstica Pedologia, Available online: https:\/\/www.ibge.gov.br\/geociencias\/informacoes-ambientais\/pedologia\/10871-pedologia.html?=&t=downloads."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"113793","DOI":"10.1016\/j.geoderma.2019.05.043","article-title":"The Brazilian Soil Spectral Library (BSSL): A general view, application and challenges","volume":"354","author":"Dotto","year":"2019","journal-title":"Geoderma"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1007\/BF00329030","article-title":"Maximum rooting depth of vegetation types at the global scale","volume":"108","author":"Canadell","year":"1996","journal-title":"Oecologia"},{"key":"ref_48","unstructured":"Stevens, A., and Ramirez-Lopez, L. (2019, December 18). Prospectr: Processing and Sample Selection for Vis-NIR Spectral Data. Available online: https:\/\/cran.r-project.org\/package=prospectr."},{"key":"ref_49","unstructured":"R Core Team (2018). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing."},{"key":"ref_50","unstructured":"Wyszecki, G., and Stiles, W.S. (1982). Color Science: Concepts and Methods, Quantitative Data and Formulae, John Wiley & Sons. [2nd ed.]."},{"key":"ref_51","unstructured":"Centore, P. (2014). The Munsell and Kubelka-Munk Toolbox, GitHub. Available online: http:\/\/centore.isletech.net\/~centore\/MunsellAndKubelkaMunkToolbox\/MunsellAndKubelkaMunkToolbox.html."},{"key":"ref_52","unstructured":"Borchers, H.W. (2019, December 20). Pracma: Practical Numerical Math Functions. Available online: https:\/\/cran.r-project.org\/package=pracma."},{"key":"ref_53","unstructured":"Agostinelli, C. (2019, December 20). CircStats: Circular Statistics, from \u201cTopics in Circular Statistics\u201d. Available online: https:\/\/cran.r-project.org\/package=CircStats."},{"key":"ref_54","first-page":"1438","article-title":"Diffuse Reflectance Spectroscopy of Iron Oxides","volume":"1","author":"Torrent","year":"2002","journal-title":"Encycl. Surf. Colloid Sci."},{"key":"ref_55","unstructured":"CAMO Software Inc (2007). The Unscrambler Version 9.7, CAMO Software AS."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"401","DOI":"10.2136\/sssaj1984.03615995004800020036x","article-title":"Characterization of Iron Oxide Minerals by Second-Derivative Visible Spectroscopy","volume":"48","author":"Kosmas","year":"1984","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"690","DOI":"10.2136\/sssaj1987.03615995005100030025x","article-title":"Morphology, Mineralogy, and Genesis of a Hydrosequence of Oxisols in Brazil","volume":"51","author":"Macedo","year":"1987","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1590\/S0100-06832004000400010","article-title":"Mineralogia, morfologia e an\u00e1lise microsc\u00f3pica de solos do bioma cerrado","volume":"28","author":"Gomes","year":"2004","journal-title":"Revista Brasileira de Ci\u00eancia do Solo"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1204","DOI":"10.2136\/sssaj2006.0014","article-title":"Edaphic Controls on Soil Organic Carbon Retention in the Brazilian Cerrado: Texture and Mineralogy","volume":"71","author":"Zinn","year":"2007","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.geoderma.2017.10.053","article-title":"Proximal spectral sensing in pedological assessments: Vis\u2013NIR spectra for soil classification based on weathering and pedogenesis","volume":"318","author":"Terra","year":"2018","journal-title":"Geoderma"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v087.c03","article-title":"ggtern: An Extension to \u201cggplot2\u201d, for the Creation of Ternary Diagrams","volume":"87","author":"Hamilton","year":"2018","journal-title":"J. Stat. Softw."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"12653","DOI":"10.1029\/JB095iB08p12653","article-title":"High spectral resolution reflectance spectroscopy of minerals","volume":"95","author":"Clark","year":"1990","journal-title":"J. Geophys. Res. Solid Earth"},{"key":"ref_63","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_64","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_65","unstructured":"CPRM\u2014Companhia de Pesquisa de Recursos Minerais (2020, January 15). Carta Geol\u00f3gica do Brasil ao Milion\u00e9simo: Sistema de Informa\u00e7\u00f5es Geogr\u00e1ficas-SIG, Available online: http:\/\/www.cprm.gov.br\/publique\/Geologia\/Geologia-Basica\/Carta-Geologica-do-Brasil-ao-Milionesimo-298.html."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1965","DOI":"10.1002\/joc.1276","article-title":"Very high resolution interpolated climate surfaces for global land areas","volume":"25","author":"Hijmans","year":"2005","journal-title":"Int. J. Climatol."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"71","DOI":"10.5194\/isprsannals-II-4-71-2014","article-title":"Precise global DEM generation by ALOS PRISM","volume":"2","author":"Tadono","year":"2014","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Hengl, T., de Jesus, J.M., MacMillan, R.A., Batjes, N.H., Heuvelink, G.B.M., Ribeiro, E., Samuel-Rosa, A., Kempen, B., Leenaars, J.G.B., and Walsh, M.G. (2014). SoilGrids1km\u2014Global Soil Information Based on Automated Mapping. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0105992"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Hengl, T., Heuvelink, G.B.M., Kempen, B., Leenaars, J.G.B., 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_70","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/j.geoderma.2019.01.007","article-title":"Modelling and mapping soil organic carbon stocks in Brazil","volume":"340","author":"Gomes","year":"2019","journal-title":"Geoderma"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Hengl, T., Mendes de Jesus, J., Heuvelink, G.B.M., Ruiperez Gonzalez, M., 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_72","first-page":"101905","article-title":"Satellite data integration for soil clay content modelling at a national scale","volume":"82","author":"Loiseau","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_73","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_74","doi-asserted-by":"crossref","first-page":"114067","DOI":"10.1016\/j.geoderma.2019.114067","article-title":"Mapping the major soil-landscape resources of the Ethiopian Highlands using random forest","volume":"361","author":"Leenaars","year":"2019","journal-title":"Geoderma"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"13763","DOI":"10.1038\/s41598-019-50376-w","article-title":"Digital soil mapping including additional point sampling in Posses ecosystem services pilot watershed, southeastern Brazil","volume":"9","author":"Silva","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"e5518","DOI":"10.7717\/peerj.5518","article-title":"Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables","volume":"6","author":"Hengl","year":"2018","journal-title":"PeerJ"},{"key":"ref_77","unstructured":"Wright, M.N., and Ziegler, A. (2015). Ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R. arXiv."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"e1301","DOI":"10.1002\/widm.1301","article-title":"Hyperparameters and tuning strategies for random forest","volume":"9","author":"Probst","year":"2019","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"ref_79","unstructured":"Kuhn, M. (2019, December 15). Caret: Classification and Regression Training. Available online: https:\/\/cran.r-project.org\/web\/packages\/caret\/index.html."},{"key":"ref_80","first-page":"1","article-title":"Machine learning and soil sciences: A review aided by machine learning tools","volume":"2019","author":"Padarian","year":"2019","journal-title":"SOIL Discuss."},{"key":"ref_81","unstructured":"FAO (2018). Soil Organic Carbon Mapping Cookbook, FAO. [2nd ed.]."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"1073","DOI":"10.1016\/j.trac.2010.05.006","article-title":"Critical review of chemometric indicators commonly used for assessing the quality of the prediction of soil attributes by NIR spectroscopy","volume":"29","author":"Palagos","year":"2010","journal-title":"TrAC Trends Anal. Chem."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1002\/jpln.19921550520","article-title":"Munsell Colors of Soils Simulated by Mixtures of Goethite and Hematite with Kaolinite","volume":"155","author":"Fernandez","year":"1992","journal-title":"Zeitschrift f\u00fcr Pflanzenern\u00e4hrung und Bodenkunde"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1071\/SR15142","article-title":"Pedogenic and lithogenic gravels as indicators of soil polygenesis in the Brazilian Cerrado","volume":"54","author":"Zinn","year":"2016","journal-title":"Soil Res."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"1373","DOI":"10.1590\/S0100-06832009000500029","article-title":"Pedomorphogeological relations in the chapadas elevadas of the Distrito Federal, Brazil","volume":"33","author":"Barbosa","year":"2009","journal-title":"Revista Brasileira de Ci\u00eancia do Solo"},{"key":"ref_86","unstructured":"Rodrigues, T.E. (1977). Mineralogy and Genesis of a Sequence of Cerrados Soils in the Federal District. [Master\u2019s Thesis, University of Rio Grande do Sul]. Available online: https:\/\/www.ufrgs.br\/agronomia\/materiais\/19777dt.pdf."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"1324","DOI":"10.2136\/sssaj2001.6541324x","article-title":"Chemical and Mineralogical Properties of Kaolinite-Rich Brazilian Soils","volume":"65","author":"Melo","year":"2001","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"114061","DOI":"10.1016\/j.geoderma.2019.114061","article-title":"High-resolution and three-dimensional mapping of soil texture of China","volume":"361","author":"Liu","year":"2020","journal-title":"Geoderma"},{"key":"ref_89","unstructured":"Hengl, T., and MacMillan, R.A. (2019). Predictive Soil Mapping with R, OpenGeoHub Foundation."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"2173","DOI":"10.2136\/sssaj2013.02.0057","article-title":"Developing predictive soil C models for soils using quantitative color measurements","volume":"77","author":"Liles","year":"2013","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.geoderma.2014.09.018","article-title":"Impact of multi-scale predictor selection for modeling soil properties","volume":"239\u2013240","author":"Miller","year":"2015","journal-title":"Geoderma"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"1809","DOI":"10.2136\/sssaj1994.03615995005800060033x","article-title":"Relations between Soil Color and Landsat Reflectance on Semiarid Rangelands","volume":"58","author":"Post","year":"1994","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.geoderma.2011.05.007","article-title":"Soil texture mapping over low relief areas using land surface feedback dynamic patterns extracted from MODIS","volume":"171\u2013172","author":"Liu","year":"2012","journal-title":"Geoderma"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.geoderma.2016.09.024","article-title":"Hyper-temporal remote sensing for digital soil mapping: Characterizing soil-vegetation response to climatic variability","volume":"285","author":"Maynard","year":"2017","journal-title":"Geoderma"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"617","DOI":"10.1016\/j.gsd.2019.03.003","article-title":"Comparison among influencing factor, frequency ratio, and analytical hierarchy process techniques for groundwater potential zonation in Vaitarna basin, Maharashtra, India","volume":"8","author":"Das","year":"2019","journal-title":"Groundw. Sustain. Dev."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"741","DOI":"10.1016\/j.crte.2008.07.006","article-title":"Variation of the kaolinite and gibbsite content at regional and local scale in Latosols of the Brazilian Central Plateau","volume":"340","author":"Reatto","year":"2008","journal-title":"C. R. Geosci."},{"key":"ref_97","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."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/7\/1197\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:16:34Z","timestamp":1760174194000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/7\/1197"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4,8]]},"references-count":97,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2020,4]]}},"alternative-id":["rs12071197"],"URL":"https:\/\/doi.org\/10.3390\/rs12071197","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,4,8]]}}}