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It is necessary to identify strategies with reduced costs to obtain detailed information for soil mapping. We aimed to compare multispectral satellite image and relief parameters for the quantification and mapping of clay and sand contents. The Temporal Synthetic Spectral (TESS) reflectance and Synthetic Soil Image (SYSI) approaches were used to identify and characterize texture spectral signatures at the image level. Soil samples were collected (0\u201320 cm depth, 919 points) from an area of 14,614 km2 in Brazil for reference and model calibration. We compared different prediction approaches: (a) TESS and SYSI; (b) Relief-Derived Covariates (RDC); and (c) SYSI plus RDC. The TESS method produced highly similar behavior to the laboratory convolved data. The sandy textural class showed a greater increase in average spectral reflectance from Band 1 to 7 compared with the clayey class. The prediction using SYSI produced a better result for clay (R2 = 0.83; RMSE = 65.0 g kg\u22121) and sand (R2 = 0.86; RMSE = 79.9 g kg\u22121). Multispectral satellite images were more stable for the identification of soil properties than relief parameters.<\/jats:p>","DOI":"10.3390\/rs10101555","type":"journal-article","created":{"date-parts":[[2018,9,28]],"date-time":"2018-09-28T02:54:54Z","timestamp":1538103294000},"page":"1555","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":60,"title":["Improvement of Clay and Sand Quantification Based on a Novel Approach with a Focus on Multispectral Satellite Images"],"prefix":"10.3390","volume":"10","author":[{"given":"Caio T.","family":"Fongaro","sequence":"first","affiliation":[{"name":"Department of Soil Science, Luiz de Queiroz College of Agriculture, University of S\u00e3o Paulo, USP, Ave. P\u00e1dua Dias, 11, Cx Postal 09, Piracicaba 13416-900, S\u00e3o Paulo, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jos\u00e9 A. M.","family":"Dematt\u00ea","sequence":"additional","affiliation":[{"name":"Department of Soil Science, Luiz de Queiroz College of Agriculture, University of S\u00e3o Paulo, USP, Ave. P\u00e1dua Dias, 11, Cx Postal 09, Piracicaba 13416-900, S\u00e3o Paulo, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rodnei","family":"Rizzo","sequence":"additional","affiliation":[{"name":"Center of Nuclear Energy in Agriculture, University of S\u00e3o Paulo, Centen\u00e1rio Avenue, 303, Piracicaba 13416-000, S\u00e3o Paulo, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5410-5762","authenticated-orcid":false,"given":"Jos\u00e9","family":"Lucas Safanelli","sequence":"additional","affiliation":[{"name":"Department of Soil Science, Luiz de Queiroz College of Agriculture, University of S\u00e3o Paulo, USP, Ave. P\u00e1dua Dias, 11, Cx Postal 09, Piracicaba 13416-900, S\u00e3o Paulo, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1271-031X","authenticated-orcid":false,"given":"Wanderson de Sousa","family":"Mendes","sequence":"additional","affiliation":[{"name":"Department of Soil Science, Luiz de Queiroz College of Agriculture, University of S\u00e3o Paulo, USP, Ave. P\u00e1dua Dias, 11, Cx Postal 09, Piracicaba 13416-900, S\u00e3o Paulo, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andr\u00e9 Carnieletto","family":"Dotto","sequence":"additional","affiliation":[{"name":"Department of Soil Science, Luiz de Queiroz College of Agriculture, University of S\u00e3o Paulo, USP, Ave. P\u00e1dua Dias, 11, Cx Postal 09, Piracicaba 13416-900, S\u00e3o Paulo, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luiz Eduardo","family":"Vicente","sequence":"additional","affiliation":[{"name":"Embrapa Environment\/Low Carbon Agriculture Platform, Road SP-340, Km 127,5, PO Box 69, Tanquinho Velho, Jaguari\u00fana 13820-000, S\u00e3o Paulo, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1295-4798","authenticated-orcid":false,"given":"Marston H. D.","family":"Franceschini","sequence":"additional","affiliation":[{"name":"Laboratory of Geo-Information, Science and Remote Sensing, Wageningen University and Research, P.O. Box 47, 6700 AA Wageningen, the Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8551-0461","authenticated-orcid":false,"given":"Susan L.","family":"Ustin","sequence":"additional","affiliation":[{"name":"Department of Land, Air, and Water Resources, University of California-Davis, 1 Shields Avenue, Davis, CA 95616, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,9,27]]},"reference":[{"key":"ref_1","unstructured":"Mantel, S., Schulp, C.J.E., and van den Berg, M. (2014). Modelling of Soil Degradation and Its Impact on Ecosystem Services Globally, Part 1: A Study on the Adequacy of Models to Quantify Soil Water Erosion for Use within the IMAGE Modeling Framework, ISRIC\u2015World Soil Information. Report."},{"key":"ref_2","first-page":"1","article-title":"Methods for the Granulometric Analysis of Soil for Science and Practice","volume":"46","author":"Owczarzak","year":"2015","journal-title":"Pol. J. Soil Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"364","DOI":"10.2136\/sssaj1995.03615995005900020014x","article-title":"Near-Infrared Analysis as a Rapid Method to Simultaneously Evaluate Several Soil Properties","volume":"59","author":"Banin","year":"1995","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"510","DOI":"10.1016\/j.still.2015.07.021","article-title":"Estimating the soil clay content and organic matter by means of different calibration methods of vis-NIR diffuse reflectance spectroscopy","volume":"155","author":"Nawar","year":"2016","journal-title":"Soil Tillage Res."},{"key":"ref_5","first-page":"F04023","article-title":"Fine-resolution multiscale mapping of clay minerals in Australian soils measured with near infrared spectra","volume":"116","year":"2011","journal-title":"J. Geophys. Res."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1569","DOI":"10.1590\/S0100-204X2013001200006","article-title":"Abordagens semiquantitativa e quantitativa na avalia\u00e7\u00e3o da textura do solo por espectroscopia de reflect\u00e2ncia bidirecional no VIS-NIR-SWIR","volume":"48","author":"Franceschini","year":"2013","journal-title":"Pesqui. Agropecu\u00e1ria Bras."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1097\/00010694-199105000-00005","article-title":"Spectral band selection for quantifying selected properties in highly weathered soils","volume":"151","author":"Coleman","year":"1991","journal-title":"Soil Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.still.2006.03.009","article-title":"On-line measurement of some selected soil properties using a VIS\u2013NIR sensor","volume":"93","author":"Mouazen","year":"2007","journal-title":"Soil Tillage Res."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.biosystemseng.2016.04.018","article-title":"Machine learning based prediction of soil total nitrogen, organic carbon and moisture content by using VIS-NIR spectroscopy","volume":"152","author":"Morellos","year":"2016","journal-title":"Biosyst. Eng."},{"key":"ref_10","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_11","doi-asserted-by":"crossref","first-page":"767","DOI":"10.1111\/ejss.12255","article-title":"Integrating geospatial and multi-depth laboratory spectral data for mapping soil classes in a geologically complex area in southeastern Brazil","volume":"66","author":"Vasques","year":"2015","journal-title":"Eur. J. Soil Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1101","DOI":"10.1071\/SR02137","article-title":"Simultaneous estimation of several soil properties by ultra-violet, visible, and near-infrared reflectance spectroscopy","volume":"41","author":"Islam","year":"2003","journal-title":"Aust. J. Soil Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1071\/SR04182","article-title":"Ultra-violet, visible, near-infrared, and mid-infrared diffuse reflectance spectroscopic techniques to predict several soil properties","volume":"43","author":"Pirie","year":"2005","journal-title":"Aust. J. Soil Res."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"637","DOI":"10.2136\/sssaj2014.09.0390","article-title":"Estimating a Soil Quality Index with VNIR Reflectance Spectroscopy","volume":"79","author":"Veum","year":"2015","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1393","DOI":"10.2136\/sssaj2016.05.0136","article-title":"Estimation of Clay and Soil Organic Carbon Using Visible and Near-Infrared Spectroscopy and Unground Samples","volume":"80","author":"Wang","year":"2016","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"O\u2019Rourke, S.M., Minasny, B., Holden, N.M., and McBratney, A.B. (2016). Synergistic Use of Vis-NIR, MIR, and XRF Spectroscopy for the Determination of Soil Geochemistry. Soil Sci. Soc. Am. J., 80.","DOI":"10.2136\/sssaj2015.10.0361"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Conforti, M., Matteucci, G., and Buttafuoco, G. (2017). Using laboratory Vis-NIR spectroscopy for monitoring some forest soil properties. J. Soils Sediments.","DOI":"10.1007\/s11368-017-1766-5"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1359","DOI":"10.1590\/s0100-204x2016000900035","article-title":"Mapeamento digital de areia, argila e carbono org\u00e2nico por modelos Random Forest sob diferentes resolu\u00e7\u00f5es espaciais","volume":"51","author":"Bhering","year":"2016","journal-title":"Pesqui. Agropecu\u00e1ria Bras."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.geoderma.2016.11.010","article-title":"Predictive ability of soil properties to spectral degradation from laboratory Vis-NIR spectroscopy data","volume":"288","author":"Adeline","year":"2017","journal-title":"Geoderma"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Dotto, A.C., Dalmolin, R.S.D., ten Caten, A., and Grunwald, S. (2018). A systematic study on the application of scatter-corrective and spectral-derivative preprocessing for multivariate prediction of soil organic carbon by Vis-NIR spectra. Geoderma, 314.","DOI":"10.1016\/j.geoderma.2017.11.006"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1016\/j.geoderma.2004.06.007","article-title":"Australia-wide predictions of soil properties using decision trees","volume":"124","author":"Henderson","year":"2005","journal-title":"Geoderma"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1031","DOI":"10.1590\/S0100-06832006000600012","article-title":"Comportamento da linha do solo obtida por espectrorradiometria laboratorial para diferentes classes de solo","volume":"30","author":"Nanni","year":"2006","journal-title":"Rev. Bras. Ci\u00eancia do Solo"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1590\/S0006-87052010000200025","article-title":"Diferencia\u00e7\u00e3o espectral de solos utilizando dados obtidos em laborat\u00f3rio e por sensor orbital","volume":"69","author":"Fiorio","year":"2010","journal-title":"Bragantia"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"6059","DOI":"10.3390\/rs70506059","article-title":"Soil clay content mapping using a time series of Landsat TM data in semi-arid lands","volume":"7","author":"Shabou","year":"2015","journal-title":"Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1016\/j.catena.2016.01.001","article-title":"Spatial prediction of soil surface texture in a semiarid region using random forest and multiple linear regressions","volume":"139","author":"Chagas","year":"2016","journal-title":"Catena"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Diek, S., Fornallaz, F., Schaepman, M.E., and de Jong, R. (2017). Barest Pixel Composite for agricultural areas using landsat time series. Remote Sens., 9.","DOI":"10.3390\/rs9121245"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Forkuor, G., Hounkpatin, O.K.L., Welp, G., Thiel, M., Zhu, A.-X., Scholten, T., Koch, B., and Shepherd, K. (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_28","doi-asserted-by":"crossref","first-page":"073587","DOI":"10.1117\/1.JRS.7.073587","article-title":"Estimation of agricultural soil properties with imaging and laboratory spectroscopy","volume":"7","author":"Zhang","year":"2013","journal-title":"J. Appl. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.rse.2016.03.025","article-title":"Evaluation of the potential of the current and forthcoming multispectral and hyperspectral imagers to estimate soil texture and organic carbon","volume":"179","author":"Castaldi","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"422","DOI":"10.1590\/S0100-06832013000200013","article-title":"Building predictive models of soil particle-size distribution","volume":"37","author":"Dalmolin","year":"2013","journal-title":"Rev. Bras. Ci\u00eancia do Solo"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1016\/j.ecolind.2007.05.005","article-title":"Prediction of soil property distribution in paddy soil landscapes using terrain data and satellite information as indicators","volume":"8","author":"Sumfleth","year":"2008","journal-title":"Ecol. Indic."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/0016-7061(94)90063-9","article-title":"Spatial prediction of soil properties from landform attributes derived from a digital elevation model","volume":"63","author":"Odeh","year":"1994","journal-title":"Geoderma"},{"key":"ref_33","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_34","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1127\/0941-2948\/2013\/0507","article-title":"K\u00f6ppen\u2019s climate classification map for Brazil","volume":"22","author":"Alvares","year":"2013","journal-title":"Meteorol. Zeitschrift"},{"key":"ref_35","unstructured":"Working Group WRB (2015). World Reference Base for Soil Resources 2014, update 2015. International Soil Classification System for Naming Soils and Creating Legends for Soil Maps, FAO. World Soil Resources Reports No. 106."},{"key":"ref_36","unstructured":"Donagemma, G.K., Campos, D.V.B. de, Calderano, S.B., Teixeira, W.G., and Viana, J.H.M. (2011). Manual de M\u00e9todos de An\u00e1lise de Solo, Embrapa Solos. [2nd ed.]."},{"key":"ref_37","unstructured":"Lehnert, L.W., Meyer, H., and Bendix, J. (2016, May 09). Hsdar: Manage, Analyse and Simulate Hyperspectral Data in R. R Packag. Version 0.4 2016. Available online: https:\/\/cran.r-project.org\/web\/packages\/hsdar\/index.html."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1109\/LGRS.2005.857030","article-title":"A Landsat Surface Reflectance Dataset for North America, 1990\u20132000","volume":"3","author":"Masek","year":"2006","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"17131","DOI":"10.1029\/97JD00201","article-title":"Atmospheric correction of visible to middle-infrared EOS-MODIS data over land surfaces: Background, operational algorithm and validation","volume":"102","author":"Vermote","year":"1997","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1590\/0103-9016-2013-0365","article-title":"Morphological Interpretation of Reflectance Spectrum (MIRS) using libraries looking towards soil classification","volume":"71","author":"Bellinaso","year":"2014","journal-title":"Sci. Agric."},{"key":"ref_41","unstructured":"Escadafal, R., Mulders, M., and Thiombiano, L. (1996). Evaluation of several soil properties using convolved TM spectra. Monitoring in the Environment with Remote Sensing and GIS, ORSTOM e\u0301ditions."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Moura-Bueno, J.M.J.M., Dalmolin, R.S.D.R.S.D., ten Caten, A., Ruiz, L.F.C.L.F.C., Ramos, P.V.P.V., and Dotto, A.C.A.C. (2016). Assessment of Digital Elevation Model for Digital Soil Mapping in a Watershed with Gently Undulating Topography. Rev. Bras. Ci\u00eancia do Solo, 40.","DOI":"10.1590\/18069657rbcs20150022"},{"key":"ref_43","unstructured":"Santos, H.G., Jacomine, P.K.T., Anjos, L.H.C., Oliveira, V.A., Lumbreras, J.F., Coelho, M.R., Almeida, J.A., Cunha, T.J.F., and Oliveira, J.B. (2013). Sistema Brasileiro de Classifica\u00e7\u00e3o de Solos, Embrapa. 3 rev. amp."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1991","DOI":"10.5194\/gmd-8-1991-2015","article-title":"System for Automated Geoscientific Analyses (SAGA) v. 2.1.4","volume":"8","author":"Conrad","year":"2015","journal-title":"Geosci. Model Dev."},{"key":"ref_45","unstructured":"Kuhn, M., Weston, S., Keefer, C., Coulter, N., and Quinlan, C. (2016, May 08). Code for C. by R. Cubist: Rule- and Instance-Based Regression Modeling. R Package Version 0.0.19. Available online: https:\/\/CRAN.R-project.org\/package=Cubist."},{"key":"ref_46","first-page":"18","article-title":"Classification and Regression by randomForest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News"},{"key":"ref_47","unstructured":"R Core Team R: A language and environment for statistical computing 2018."},{"key":"ref_48","unstructured":"Hijmans, R.J. (2016, August 22). Raster: Geographic Data Analysis and Modeling. R package version 2.5-8. Available online: https:\/\/CRAN.R-project.org\/package=raster."},{"key":"ref_49","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_50","unstructured":"Moeys, J. (2016, October 05). Soiltexture: Functions for Soil Texture Plot, Classification and Transformation. Available online: https:\/\/CRAN.R-project.org\/package=soiltexture."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.geoderma.2003.09.012","article-title":"Visible\u2013NIR reflectance: A new approach on soil evaluation","volume":"121","author":"Campos","year":"2004","journal-title":"Geoderma"},{"key":"ref_52","first-page":"190","article-title":"Spectral pedology: A new perspective on evaluation of soils along pedogenetic alterations","volume":"217\u2013218","author":"Terra","year":"2014","journal-title":"Geoderma"},{"key":"ref_53","unstructured":"Musick, H.B., and Pelletier, R.E. (1986). Response of Some Thematic Mapper Band Ratios to Variation in Soil Water Content. Photogram. Eng. Remote Sens., 1166\u20131661."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"12","DOI":"10.2174\/187541390100201012","article-title":"Estimation of soil properties by orbital and laboratory reflectance means and its relation with soil classification","volume":"2","author":"Fiorio","year":"2009","journal-title":"Open Remote Sens. J."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Lacerda, M., Dematt\u00ea, J., Sato, M., Fongaro, C., Gallo, B., and Souza, A. (2016). Tropical Texture Determination by Proximal Sensing Using a Regional Spectral Library and Its Relationship with Soil Classification. Remote Sens., 8.","DOI":"10.3390\/rs8090701"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1016\/j.geoderma.2015.07.016","article-title":"Digital mapping of soil carbon in a viticultural region of Southern Brazil","volume":"261","author":"Bonfatti","year":"2016","journal-title":"Geoderma"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Noi, P., Degener, J., and Kappas, M. (2017). Comparison of Multiple Linear Regression, Cubist Regression, and Random Forest Algorithms to Estimate Daily Air Surface Temperature from Dynamic Combinations of MODIS LST Data. Remote Sens., 9.","DOI":"10.3390\/rs9050398"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.earscirev.2016.01.012","article-title":"A global spectral library to characterize the world\u2019s soil","volume":"155","author":"Behrens","year":"2016","journal-title":"Earth-Sci. Rev."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1002\/jpln.201400570","article-title":"Assessment of soil texture class on agricultural fields using ECa, Amber NDVI, and topographic properties","volume":"178","author":"Samborski","year":"2015","journal-title":"J. Plant Nutr. Soil Sci."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.agwat.2014.07.013","article-title":"An approach for delineating homogeneous within-field zones using proximal sensing and multivariate geostatistics","volume":"147","author":"Landrum","year":"2015","journal-title":"Agric. Water Manag."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/S1364-8152(01)00067-6","article-title":"Prediction of soil properties by digital terrain modelling","volume":"17","author":"Florinsky","year":"2002","journal-title":"Environ. Model. Softw."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"S38","DOI":"10.1016\/j.rse.2008.09.019","article-title":"Using Imaging Spectroscopy to study soil properties","volume":"113","author":"Chabrillat","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"100","DOI":"10.3923\/ja.2014.100.109","article-title":"Soil Mapping by Laboratory and Orbital Spectral Sensing Compared with a Traditional Method in a Detailed Level","volume":"13","author":"Nanni","year":"2014","journal-title":"J. Agron."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.geoderma.2017.04.019","article-title":"Soil class and attribute dynamics and their relationship with natural vegetation based on satellite remote sensing","volume":"302","author":"Rizzo","year":"2017","journal-title":"Geoderma"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Nouri, M., Gomez, C., Gorretta, N., and Roger, J.M. (2017). Clay content mapping from airborne hyperspectral Vis-NIR data by transferring a laboratory regression model. Geoderma, 298.","DOI":"10.1016\/j.geoderma.2017.03.011"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.compag.2004.11.006","article-title":"Mapping clay content variation using electromagnetic induction techniques","volume":"46","author":"Triantafilis","year":"2005","journal-title":"Comput. Electron. Agric."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"557","DOI":"10.5721\/EuJRS20144731","article-title":"Evaluation of Landsat TM5 Multispectral Data for Automated Mapping of Surface Soil Texture and Organic Matter in GIS","volume":"47","author":"Ahmed","year":"2014","journal-title":"Eur. J. Remote Sens."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1443","DOI":"10.1016\/j.rse.2011.02.004","article-title":"Digitally mapping the information content of visible-near infrared spectra of surficial Australian soils","volume":"115","author":"Chen","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1080\/05704928.2013.811081","article-title":"The Performance of Visible, Near-, and Mid-Infrared Reflectance Spectroscopy for Prediction of Soil Physical, Chemical, and Biological Properties","volume":"49","author":"Janik","year":"2014","journal-title":"Appl. Spectrosc. Rev."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Dematt\u00ea, J., Ramirez-Lopez, L., Rizzo, R., Nanni, M., Fiorio, P., Fongaro, C., Medeiros Neto, L., Safanelli, J., and da S. Barros, P. (2016). Remote Sensing from Ground to Space Platforms Associated with Terrain Attributes as a Hybrid Strategy on the Development of a Pedological Map. Remote Sens., 8.","DOI":"10.3390\/rs8100826"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Khalil, R.Z., Khalid, W., and Akram, M. (2016, January 10\u201315). Estimating of soil texture using landsat imagery: A case study of Thatta Tehsil, Sindh. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.","DOI":"10.1109\/IGARSS.2016.7729804"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.geoderma.2015.06.002","article-title":"Predicting clay content on field-moist intact tropical soils using a dried, ground VisNIR library with external parameter orthogonalization","volume":"259\u2013260","author":"Ackerson","year":"2015","journal-title":"Geoderma"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2011\/358193","article-title":"Use of Airborne Hyperspectral Imagery to Map Soil Properties in Tilled Agricultural Fields","volume":"2011","author":"Hively","year":"2011","journal-title":"Appl. Environ. Soil Sci."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.geoderma.2009.11.032","article-title":"Measuring soil organic carbon in croplands at regional scale using airborne imaging spectroscopy","volume":"158","author":"Stevens","year":"2010","journal-title":"Geoderma"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.geoderma.2012.05.023","article-title":"Regional predictions of eight common soil properties and their spatial structures from hyperspectral Vis\u2013NIR data","volume":"189\u2013190","author":"Gomez","year":"2012","journal-title":"Geoderma"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.10.047","article-title":"Sensitivity of clay content prediction to spectral configuration of VNIR\/SWIR imaging data, from multispectral to hyperspectral scenarios","volume":"204","author":"Gomez","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.geodrs.2014.09.005","article-title":"Predicting and mapping the soil available water capacity of Australian wheatbelt","volume":"2\u20133","author":"Padarian","year":"2014","journal-title":"Geoderma Reg."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"1371","DOI":"10.1590\/s0100-204x2016000900036","article-title":"Mapping soil carbon, particle-size fractions, and water retention in tropical dry forest in Brazil","volume":"51","author":"Vasques","year":"2016","journal-title":"Pesqui. Agropecu\u00e1ria Bras."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/10\/1555\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:22:46Z","timestamp":1760196166000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/10\/1555"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,9,27]]},"references-count":78,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2018,10]]}},"alternative-id":["rs10101555"],"URL":"https:\/\/doi.org\/10.3390\/rs10101555","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,9,27]]}}}