{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T20:56:27Z","timestamp":1765486587138,"version":"build-2065373602"},"reference-count":46,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2021,3,19]],"date-time":"2021-03-19T00:00:00Z","timestamp":1616112000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002322","name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior","doi-asserted-by":"publisher","award":["88882.383908\/2019-01"],"award-info":[{"award-number":["88882.383908\/2019-01"]}],"id":[{"id":"10.13039\/501100002322","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Geotechnologies allow natural resources to be surveyed more quickly and cheaply than traditional methods. This paper aimed to produce a digital soil map (DSM) based on Landsat time series data. The study area, located in the eastern part of the Brazilian Federal District (Rio Preto hydrographic basin), comprises a representative basin of the Central Brazil plateau in terms of pedodiversity. A spectral library was produced based on the soil spectroscopy (from the visible to shortwave infrared spectral range) of 42 soil samples from 0\u201315 cm depth using the Fieldspec Pro equipment in a laboratory. Pearson\u2019s correlation and principal component analysis of the soil attributes revealed that the dataset could be grouped based on the texture content. Hierarchical clustering analysis allowed for the extraction of 13 reference spectra. We interpreted the spectra morphologically and resampled them to the Landsat 5 Thematic Mapper satellite bands. Afterward, we elaborated a synthetic soil\/rock image (SySI) and a soil frequency image (number of times the bare soil was captured) from the Landsat time series (1984\u20132020) in the Google Earth Engine platform. Multiple Endmember Spectral Mixture Analysis (MESMA) was used to model the SySI, using the endmembers as the input and generating a DSM, which was validated by the Kappa index and the confusion matrix. MESMA successfully modeled 9 of the 13 endmembers: Dystric Rhodic Ferralsol (clayic); Dystric Rhodic Ferralsol (very clayic); Dystric Haplic Ferralsol (loam-clayic); Dystric Haplic Ferralsol (clayic); Dystric Petric Plinthosol (clayic); Dystric Petric Plinthosol (very clayic); Dystric Regosol (clayic); Dystric Regosol (very clayic); and Dystric, Haplic Cambisol (clayic). The root mean squared error (RMSE) varied from 0 to 1.3%. The accuracy of DSM achieved a Kappa index of 0.74, describing the methodology\u2019s effectiveness to differentiate the studied soils.<\/jats:p>","DOI":"10.3390\/rs13061181","type":"journal-article","created":{"date-parts":[[2021,3,21]],"date-time":"2021-03-21T23:47:41Z","timestamp":1616370461000},"page":"1181","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Digital Soil Mapping Using Multispectral Modeling with Landsat Time Series Cloud Computing Based"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6792-1208","authenticated-orcid":false,"given":"Jean J.","family":"Novais","sequence":"first","affiliation":[{"name":"Faculty of Agronomy and Veterinary Medicine, Darcy Ribeiro University Campus, University of Bras\u00edlia, ICC Sul, Asa Norte 70910-960, Brazil"}]},{"given":"Marilusa P. C.","family":"Lacerda","sequence":"additional","affiliation":[{"name":"Faculty of Agronomy and Veterinary Medicine, Darcy Ribeiro University Campus, University of Bras\u00edlia, ICC Sul, Asa Norte 70910-960, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5760-556X","authenticated-orcid":false,"given":"Edson E.","family":"Sano","sequence":"additional","affiliation":[{"name":"Empresa Brasileira de Pesquisa Agropecu\u00e1ria, Embrapa Cerrados, Brazilian Agricultural Research Corporation Planaltina, Bras\u00edlia 73310-970, Brazil"}]},{"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, Av. P\u00e1dua Dias, 11, Piracicaba 13416-900, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2315-7719","authenticated-orcid":false,"suffix":"Jr.","given":"Manuel P.","family":"Oliveira","sequence":"additional","affiliation":[{"name":"Faculty of Agronomy and Veterinary Medicine, Darcy Ribeiro University Campus, University of Bras\u00edlia, ICC Sul, Asa Norte 70910-960, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,19]]},"reference":[{"key":"ref_1","first-page":"377","article-title":"The use of multiple endmember spectral mixture analysis (MESMA) for the mapping of soil attributes using ASTER imagery","volume":"35","author":"Roberts","year":"2013","journal-title":"Acta Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1007\/s10712-019-09524-0","article-title":"Imaging spectroscopy for soil mapping and monitoring","volume":"40","author":"Chabrillat","year":"2019","journal-title":"Surv. Geophys."},{"key":"ref_3","unstructured":"Reatto, A., Martins, E.S., Farias, M.F.R., Silva, A.V., and Carvalho, O.A. (2004). Mapa Pedol\u00f3gico Digital: SIG Atualizado do Distrito Federal Escala 1:100.000 e uma S\u00edntese do Texto Explicativo, Embrapa Cerrados. (Documentos, 120)."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"38","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_5","doi-asserted-by":"crossref","unstructured":"Diek, S., Schaepman, M.E., and Jong, R. (2016). Creating multi-temporal composites of airborne imaging spectroscopy data in support of digital soil mapping. Remote Sens., 8.","DOI":"10.3390\/rs8110906"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Lacerda, M.P.C., Dematt\u00ea, J.A.M., Sato, M.V., Fongaro, C.T., Gallo, B.C., and Souza, A.B. (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_7","doi-asserted-by":"crossref","unstructured":"Fongaro, C.T., Dematt\u00ea, J.A.M., Rizzo, R., Safanelli, J.L., Mendes, W.S., Dotto, A.C., Vicente, L.E., Franceschini, M.H.D., and Ustin, S.L. (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_8","doi-asserted-by":"crossref","unstructured":"Gallo, B.C., Dematt\u00ea, J.A.M., Rizzo, R., Safanelli, J.L., Mendes, W.S., Lepsch, I.F., Sato, M.V., Romero, D.J., and Lacerda, M.P.C. (2018). Multi-temporal satellite images on topsoil attribute quantification and the relationship with soil classes and geology. Remote Sens., 10.","DOI":"10.3390\/rs10101571"},{"key":"ref_9","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_10","doi-asserted-by":"crossref","first-page":"104070","DOI":"10.1016\/j.dib.2019.104070","article-title":"Soil class map of the Rio Jardim watershed in Central Brazil at 30 meter spatial resolution based on proximal and remote sensed data and MESMA method","volume":"25","author":"Poppiel","year":"2019","journal-title":"Data Brief."},{"key":"ref_11","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":"348","author":"Poppiel","year":"2019","journal-title":"Geoderma"},{"key":"ref_12","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_13","doi-asserted-by":"crossref","first-page":"2749","DOI":"10.1007\/s11368-020-02623-1","article-title":"Visible and near-infrared spectroscopy with chemometrics are able to predict soil physical and chemical properties","volume":"20","author":"Liu","year":"2020","journal-title":"J. Soil Sediment."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"104485","DOI":"10.1016\/j.catena.2020.104485","article-title":"Prediction of soil texture classes through different wavelength regions of reflectance spectroscopy at various soil depths","volume":"189","author":"Coblinski","year":"2020","journal-title":"Catena"},{"key":"ref_15","unstructured":"USGS (2019). Landsat Data Users Handbook."},{"key":"ref_16","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_17","doi-asserted-by":"crossref","unstructured":"Poppiel, R.R., Lacerda, M.P.C., Safanelli, J.L., Rizzo, R., Oliveira, M.P., Novais, J.J., and Dematt\u00ea, J.A.M. (2019). Mapping at 30 m Resolution of Soil Attributes at Multiple Depths in Midwest Brazil. Remote Sens., 11.","DOI":"10.3390\/rs11242905"},{"key":"ref_18","first-page":"193","article-title":"Klassifikation der Klimate nach Temperatur, Niederschlag und Jahresablauf (Classification of climates according to temperature, precipitation and seasonal cycle)","volume":"64","year":"1918","journal-title":"Petermanns Geogr. Mitt."},{"key":"ref_19","unstructured":"Castro, K.B., and Lima, L.A.S. (2020). Atlas do Distrito Federal, CODEPLAN."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"709","DOI":"10.1590\/S0100-06832012000300003","article-title":"Rela\u00e7\u00f5es pedomorfol\u00f3gicas e distribui\u00e7\u00e3o de pedoformas na esta\u00e7\u00e3o ecol\u00f3gica \u00c1guas Emendadas, Distrito Federal","volume":"36","author":"Lacerda","year":"2012","journal-title":"Rev. Bras. Cienc. Solo"},{"key":"ref_21","unstructured":"Pinto, M.N. (1994). Caracteriza\u00e7\u00e3o geomorfol\u00f3gica do Distrito Federal. Cerrado: Caracteriza\u00e7\u00e3o, Ocupa\u00e7\u00e3o e Perspectivas, UnB\u2014SEMATEC."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"818","DOI":"10.1016\/j.jenvman.2018.11.108","article-title":"Cerrado ecoregions: A spatial framework to assess and prioritize Brazilian savanna environmental diversity for conservation","volume":"232","author":"Sano","year":"2019","journal-title":"J. Environ. Manag."},{"key":"ref_23","unstructured":"Barros, J.C.B. (1987). Geologia do Distrito Federal. Invent\u00e1rio Hidrogeol\u00f3gico e dos Recursos H\u00eddricos Superficiais do Distrito Federal, CAESB."},{"key":"ref_24","unstructured":"Schad, P., van Huysteen, C., and Mich\u00e9li, E. (2014). World Reference Base for Soil Resources 2014: International Soil Classification System for Naming Soils and Creating Legends for Soil Maps, FAO."},{"key":"ref_25","unstructured":"Ditzler, C., and Scheffe, K. (2017). Soil Survey Manual Agriculture, USDA Handbook 18."},{"key":"ref_26","unstructured":"Santos, H.G., Jacomine, P.K.T., Anjos, L.H.C., Oliveira, V.A., Lumbreras, J.F., Coelho, M.R., Almeida, J.A., Ara\u00fajo Filho, J.C., Oliveira, J.B., and Cunha, T.J.F. (2018). Sistema Brasileiro de Classifica\u00e7\u00e3o de Solos, Embrapa Solos."},{"key":"ref_27","unstructured":"USDA (2015). Illustrated Guide to Soil Taxonomy (Version 2)."},{"key":"ref_28","unstructured":"Schoeneberger, P.J., Wysocki, D.A., and Benham, E.C. (2012). Field Book for Describing and Sampling Soils, Version 3.0."},{"key":"ref_29","unstructured":"Soil Survey Staff (2014). Soil Survey Field and Laboratory Methods Manual."},{"key":"ref_30","unstructured":"Teixeira, P.C., Donagemma, G.K., Fontana, A., and Teixeira, W.G. (2017). Manual de M\u00e9todos de An\u00e1lise de Solo, Embrapa Solos."},{"key":"ref_31","unstructured":"Munsell Color (2015). Munsell Soil Color Book, Munsell Color."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"464","DOI":"10.2134\/agronj1962.00021962005400050028x","article-title":"Hydrometer method improved for making particle size analyses of soils 1","volume":"54","author":"Bouyoucos","year":"1962","journal-title":"Agron. J."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"480","DOI":"10.2136\/sssaj2001.652480x","article-title":"Near-infrared reflectance spectroscopy\u2014Principal components regression analyses of soil properties","volume":"65","author":"Chang","year":"2001","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/j.geoderma.2019.01.022","article-title":"Cluster-based spectral models for a robust assessment of soil properties","volume":"340","author":"Ogen","year":"2019","journal-title":"Geoderma"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"ASD Inc. (2019). ASD Fieldspec\u00ae 4: The Industry-Leading Portable Device for Field Spectroscopy, ASD Inc.. [6th ed.].","DOI":"10.24108\/asd"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.geoderma.2017.09.014","article-title":"Internal soil standard method for the Brazilian soil spectral library: Performance and proximate analysis","volume":"312","author":"Romero","year":"2018","journal-title":"Geoderma"},{"key":"ref_37","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_38","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_39","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/S0034-4257(98)00037-6","article-title":"Mapping chaparral in the Santa Monica Mountains using multiple endmember spectral mixture models","volume":"65","author":"Roberts","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_40","unstructured":"Crabb\u00e9, A.H., Jakimow, B., Somers, B., Roberts, D.A., Halligan, K., Dennison, P., and Dudley, K. (2021, January 31). Viper Tools Software. Available online: http:\/\/tools2019.innopolis.ru\/."},{"key":"ref_41","unstructured":"Congalton, R.G., and Green, K. (2013). Assessing the Accuracy of Remotely Sensed Data Principles and Practices, CRC Press. [2nd ed.]."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"159","DOI":"10.2307\/2529310","article-title":"The measurement of observer agreement for categorical data","volume":"33","author":"Landis","year":"2011","journal-title":"Biometrics"},{"key":"ref_43","unstructured":"Sousa, D.M.G., and Lobato, E. (2004). Cerrado: Corre\u00e7\u00e3o do Solo e Aduba\u00e7\u00e3o, Embrapa."},{"key":"ref_44","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-NIR spectral library and terrain attributes","volume":"274","author":"Rizzo","year":"2016","journal-title":"Geoderma"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1192","DOI":"10.1590\/s0100-204x2017001200008","article-title":"WorldView-2 sensor for the detection of hematite and goethite in tropical soils","volume":"52","author":"Baptista","year":"2017","journal-title":"Pesq. Agrop. Brasileira"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"4461","DOI":"10.1038\/s41598-020-61408-1","article-title":"Bare Earth\u2019s surface spectra as a proxy for soil resource monitoring","volume":"10","author":"Safanelli","year":"2020","journal-title":"Sci. Rep."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/6\/1181\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:38:19Z","timestamp":1760161099000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/6\/1181"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,19]]},"references-count":46,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2021,3]]}},"alternative-id":["rs13061181"],"URL":"https:\/\/doi.org\/10.3390\/rs13061181","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2021,3,19]]}}}