Geospatial soil information is critical for agricultural policy formulation and decision making, land-use suitability analysis,
sustainable soil management, environmental assessment, and other research topics that are of vital importance to agriculture
and economy. Proximal and Remote sensing technologies enables us to collect, process, and analyze spectral data and to retrieve, synthesize, visualize valuable geospatial information for multidisciplinary uses. We obtained the soil class map provided in this article by processing and analyzing proximal and remote sensed data from soil samples collected in toposequences based on
pedomorphogeological relashionships. The soils were classified up to the second categorical level (suborder) of the Brazilian Soil
Classification System (SiBCS), as well as in the World Reference Base (WRB) and United States Soil Taxonomy (ST) systems. The raster map has 30 m resolution and its accuracy is 73% (Kappa coefficient of 0.73). The soil legend represents a soil class followed by its topsoil color

Demattê, José A. M.

Gallo, Bruna C.

Lacerda, Marilusa Pinto Coelho

Oliveira Jr., Manuel P.

Poppiel, Raúl R.

Safanelli, José L.

English

Geospatial soil information is critical for agricultural policy formulation and decision making, land-use suitability analysis,
sustainable soil management, environmental assessment, and other research topics that are of vital importance to agriculture
and economy. Proximal and Remote sensing technologies enables us to collect, process, and analyze spectral data and to retrieve, synthesize, visualize valuable geospatial information for multidisciplinary uses. We obtained the soil class map provided in this article by processing and analyzing proximal and remote sensed data from soil samples collected in toposequences based on
pedomorphogeological relashionships. The soils were classified up to the second categorical level (suborder) of the Brazilian Soil
Classification System (SiBCS), as well as in the World Reference Base (WRB) and United States Soil Taxonomy (ST) systems. The raster map has 30 m resolution and its accuracy is 73% (Kappa coefficient of 0.73). The soil legend represents a soil class followed by its topsoil color.Faculdade de Agronomia e Medicina Veterinária (FAV

Repositório Institucional da Universidade de Brasília

Data in brief 25 (2019) 104070Contents lists available at ScienceDirectData in briefjournal homepage: www.elsevier .com/locate/dibData ArticleSoil class map of the Rio Jardim watershed inCentral Brazil at 30 meter spatial resolutionbased on proximal and remote sensed data andMESMA methodRaúl R. Poppiel a, *, Marilusa P.C. Lacerda a,Jose A.M. Dematte^ b, Manuel P. Oliveira Jr. a, Bruna C. Gallo b,Jose L. Safanelli ba Faculty of Agronomy and Veterinary Medicine, University of Brasília; ICC Sul, Darcy Ribeiro UniversityCampus, Asa Norte, Postal Box 4508, Brasília 70910-960, Brazilb Department of Soil Science, College of Agriculture Luiz de Queiroz, University of S~ao Paulo; Padua Dias Av.,11, Piracicaba, Postal Box 09, S~ao Paulo 13416-900, Brazila r t i c l e i n f oArticle history:Received 2 May 2019Received in revised form 17 May 2019Accepted 20 May 2019Available online 30 May 2019Keywords:Digital soil mappingSoil managementAgricultural planningSoil classification systemLandsatDOI of original article: https://doi.org/10.1016/* Corresponding author.E-mail address: raulpoppiel@gmail.com (R.R. Pohttps://doi.org/10.1016/j.dib.2019.1040702352-3409/© 2019 The Authors. Published by Elscreativecommons.org/licenses/by/4.0/).a b s t r a c tGeospatial soil information is critical for agricultural policyformulation and decision making, land-use suitability analysis,sustainable soil management, environmental assessment, andother research topics that are of vital importance to agricultureand economy. Proximal and Remote sensing technologies enablesus to collect, process, and analyze spectral data and to retrieve,synthesize, visualize valuable geospatial information for multi-disciplinary uses. We obtained the soil class map provided in thisarticle by processing and analyzing proximal and remote senseddata from soil samples collected in toposequences based onpedomorphogeological relashionships. The soils were classified upto the second categorical level (suborder) of the Brazilian SoilClassification System (SiBCS), as well as in the World ReferenceBase (WRB) and United States Soil Taxonomy (ST) systems. Theraster map has 30 m resolution and its accuracy is 73% (Kappaj.geoderma.2019.04.028.ppiel).evier Inc. This is an open access article under the CC BY license (http://R.R. Poppiel et al. / Data in brief 25 (2019) 1040702Specifications tableSubject area Environmental EaMore specific subject area Pedology, Soil GeType of data Digital soil map (How data was acquired Soils were samplemorphological anlaboratory usingUSA). We acquirebetween May andData format ProcessedExperimental factors Soils were classifiBrazilian Soil Classupport soil class(MESMA) to a barExperimental features The detailed digitlevel in the WRB,Central Brazil, froaccuracy is 73% (Kmap and the veriData source location The area is locatefrom 216,6 to 226SIRGAS 2000 datuData accessibility Data are availableRelated research article Poppiel, R.R.; LacePedology and soilpress).Value of the data The soil data of the map is up to date with ped The soil class map can be used to evaluate theareas and industry; to guide sustainable soil mto indicate areas with greater risk of soil degradprojects, land pricing and purchase, etc. Detailed soil map can be implemented from sm Map available here can be served as guidelinexperimental design in agronomy, forestry andused as reference for validation purpose of othcoefficient of 0.73). The soil legend represents a soil class followedby its topsoil color.© 2019 The Authors. Published by Elsevier Inc. This is an openaccess article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).rth and Soil Sciencesography, Soil Managementraster kmz file), movie (3D animation mp4 file)d according to pedomorphogeological relationships and were determinedd physico-chemical attributes. Topsoil reflectance was analyzed inthe FieldSpec Pro sensor (Analytical Spectral Devices Inc., Boulder, CO,d 10 Landsat 5 TM images corresponding to orbit 221 and point 71September (dry season) from 1984 to 2009.ed up field up to the second categorical level (suborder), according to thesification System e SiBCS, and topsoil patterns were determined tomapping by applying the Multiple Endmember Spectral Mixture Analysise soil composite (image).al mapping product (30 m) provides soil class information at suborderSiBCS and ST classification systems, performed for an area of 53,614 ha inm proximal and remoted sensed data by MESMA method. The mapappa coefficient of 0.73), indicating very good agreement between thefication sites.d in the Rio Jardim watershed, southeast of Federal District (DF), Brazil,,9 m E, and 8,261,507 to 8,244,566 m N, UTM projection, fuse 23 South,m.within the article.rda, P.C.L.; Dematte^, J.A.M.; Oliveira Jr, M.P.; Gallo, B.C.; Safanelli, J.L.class mapping from proximal and remote sensed data. Geoderma, 2019 (inologic knowledge and current demands.locations most suitable for agriculture and expansion of urban/settlementanagement and planning; to identify areas for protection or conservation;ation; to optimize sampling in precision agriculture; to support irrigationall-farm to basin scale for making land use decisions.e for other studies highlighting in the field of hydrological modelling,biology to define the study area and to allocate the factors. As well can beer studies on digital soil mapping.1. DataThe dataset contains spatial variability of soil classes at detailed scale (30 m spatial resolution),classified according to the Brazilian Soil Classification System e SiBCS [1], the World Reference Base eWRB [2] and the United States Soil Taxonomy e ST [3].The sampling points were distributed over the Rio Jardim Watershed, in Central Brazil (Fig. 1),where we collected soil samples of Ferralsols, Plinthosols, Regosols and Cambisols (Fig. 2). Afteranalyzing and processing all the data, we mapped the soils and quantified the results (Fig. 3). The soilmap was saved in a KMZ file and provided in a 3D movie overlapped on the digital elevation model(Supplementary materials).Fig. 1. Dataset location, soil sampling points and aerial photography with natural color composite.R.R. Poppiel et al. / Data in brief 25 (2019) 104070 32. Experimental design, materials and methods2.1. Soil samplingThe soil sampling design based on the soil-geoform-geology (pedomorphogeological) relationshipsallowed to identify representative areas wherewe defined six toposequences and 34 sites to visit in thefield. In each site, we performed morphological description according to Santos et al. [4], and wecollected soil samples from surface A horizon and diagnostic subsurface horizons (B or C), as well as at0e0.2, 0.2e0.4 and 0.8e1.0 m layers. The soils were classified up to the second categorical level(suborder), according to the Brazilian Soil Classification System e SiBCS [1] (Fig. 2). For the validationstep, we used an external and independent dataset of 231 sites distributed on a regular grid of1400  1400 m.2.2. Soil attributes analysisThe soil samples were air-dried, ground and sieved (2 mm mesh) and analyzed for physical andchemical determination according to Embrapa [5]. The spectroscopic analysis was conducted using theFig. 2. Soil observations according to the Brazilian Soil Classification System e SiBCS [1] and corresponding World Reference Base eWRB [2] and Soil Taxonomy e ST [3] classifications.Fig. 3. Summary quantifications of the soil map. LV: Rhodic Ferralsol; LVA: Haplic Ferralsol; RR: Dystric Regosol; CX: DystricCambisol; FX: Haplic Plinthosol; FF: Endopetric Plinthosol.R.R. Poppiel et al. / Data in brief 25 (2019) 1040704FieldSpec Pro sensor (Analytical Spectral Devices Inc., Boulder, CO, USA), which has 1 nm of spectralresolution from 350 nm to 2500 nm (VIS-NIR-SWIR).2.3. Satellite images acquisitionWe acquire a Landsat 5 TM Level-1 time series (VIS-NIR-SWIR range) with 10 images from theUnited States Geological Survey (USGS), corresponding to orbit 221 and point 71 between May andSeptember (dry season) from 1984 to 2009, with up to 10% maximum cloud cover.2.4. Data processingLaboratory topsoil reflectance spectra were processed by removing the continuous spectrum (CR)[6] and by applying the second derivative (SD) of the KubelkaeMunk (K-M) function [7] from 350 to2500nm for mineralogical assessment. We clustered soils with similar attributes based on topsoilreflectance to obtain spectral patterns, which were convolved using a Gaussian function of Landsat 5TM spectral bands to obtain endmembers. Then, we added to each cluster (a soil class) the main topsoilattribute for creating soil legend for the map. Poppiel et al. [8] described further information about themethod.The raw digital numbers in the images were converted to radiance and then to reflectance-at-surface values [9] by applying the module Fast Line-of-sight Atmospheric Analysis of Hyperspectralcubes (FLAASH) using the ENVI software. We obtained bare soil images by applying the NormalizedR.R. Poppiel et al. / Data in brief 25 (2019) 104070 5Difference Vegetation IndexeNDVI [10] to suppress vegetated areas (dense, moderate and sparse), andthe Middle Infrared Index-MIDII [11] to mask areas with straw or burned. Then, the bare soil reflec-tancewas retrieved in a bare soil composite (single image), denominated Synthetic Soil Image (SYSI) byselecting the lowest value of the MIDII which is correlated with low moisture content using the Rprograming language [12], according to [8]. We applied the Multiple Endmember Spectral MixtureAnalysis (MESMA) [13] to address the inter-class and intra-class soil spectral variability using as inputsdata the SYSI and the endmembers to select the mixture model (a soil class) that best fits each pixel. Toimprove spatial coherence, we smoothed the resulting image by applying a median filter, using a 7  7kernel.For assessment of the map accuracy, we calculated the Kappa coefficient [14] based on a confusionmatrix, and its values were classified as proposed by [15]. Poppiel et al. [8] presented a flowchart toillustrate the complete methodology. We showed summary quantifications of the mapped soil classesin Fig. 3.AcknowledgementsThe authors gratefully acknowledge the University of Brasilia e UnB, the Brazilian Federal Agencyfor Support and Evaluation of Graduate Education e CAPES for the first author master scholarship(finance code 001), the S~ao Paulo Research Foundation e FAPESP (grant numbers: 2014-22262-0 and2016/01597e9) for providing the equipment used in the VIS-NIR-SWIR reflectance analysis, the Geo-processing and Pedomorphology Laboratory e Geoped at the FAV/UnB for provided data, the Geo-technologies in Soil Science group e GeoSS (http://esalqgeocis.wixsite.com/geocis/equipe) for thesupport.Appendix A. Supplementary dataSupplementary data associated with this article can be found, in the online version, at https://doi.org/10.1016/j.dib.2019.104070.References[1] Embrapa - Brazilian Agricultural Research Corporation - National Soils Research Center, Brazilian System of Soil Classi-fication, fourth ed., Embrapa-Cnps, Brasilia, Brazil, 2014.[2] IUSS Working Group WRB, World Reference Base for Soil Resources 2014: International Soil Classification System forNaming Soils and Creating Legends for Soil Maps, Food and Agriculture Organization, Rome, 2015. http://www.fao.org/3/i3794en/I3794EN.pdf.[3] Soil Survey Staff, Keys to Soil Taxonomy, United States Department of Agriculture, Washington, 2014. http://www.nrcs.usda.gov/Internet/FSE_DOCUMENTS/nrcs142p2_051546.pdf.[4] R.D. Santos, R.C. Lemos, H.G. Santos, J.C. Ker, L.H.C. Anjos, Manual of Field Soil Description and Collection, sixth ed., Sbcs/Embrapa-Cnps, Viçosa, Brazil, 2013.[5] Embrapa - Brazilian Agricultural Research Corporation - National Soils Research Center, Manual of Soil Analysis Methods,third ed., Embrapa Solos, Brasilia, DF, 2017. http://ainfo.cnptia.embrapa.br/digital/bitstream/item/171907/1/Manual-de-Metodos-de-Analise-de-Solo-2017.pdf.[6] R.N. Clark, T.L. Roush, Reflectance spectroscopy: quantitative analysis techniques for remote sensing applications, J.Geophys. Res. 89 (1984) 6329e6340, https://doi.org/10.1029/JB089iB07p06329.[7] A.C. Scheinost, A. Chavernas, V. Barron, J. Torrent, Use and limitations of second-derivative diffuse reflectance spectroscopyin the visible to near-infrared range to identify and quantify Fe oxide minerals in soils, Clay Clay Miner. 46 (1998)528e536, https://doi.org/10.1346/CCMN.1998.0460506.[8] R.R. Poppiel, M.P.C. Lacerda, J.A.M. Dematte^, M.P. Oliveira Jr., B.C. Gallo, J.L. Safanelli, Pedology and soil class mapping fromproximal and remote sensed data, Geoderma 348 (2019) 189e206, https://doi.org/10.1016/j.geoderma.2019.04.028.[9] T. Cooley, G.P. Anderson, G.W. Felde, M.L. Hoke, A.J. Ratkowski, J.H. Chetwynd, J.A. Gardner, S.M. Adler-Golden, M.W.Matthew, A. Berk, L.S. Bernstein, P.K. Acharya, D. Miller, P. Lewis, FLAASH, a MODTRAN4-based atmospheric correctionalgorithm, its application and validation, Proc. Int. Geosci. Remote Sens. Symp. - IGARSS. 3 (2002) 1414e1418, https://doi.org/10.1109/IGARSS.2002.1026134.[10] J.W. Rouse, R.H. Haas, J.A. Schell, D.W. Deering, Monitoring Vegetation Systems in the Great Plains with ERTS, ProceedingERTS Symp, 1973, pp. 309e317, citeulike-article-id:12009708.[11] J. Madeira Netto, Etude quantitative des relations constituants mineralogiques - reflectance diffuse des latosols bresiliens:Application a l’utilisation pedologique des donnees satellitaires TM (region de Brasilia), Doctorat thesis (sciences des sols),Universite Paris-VI, 1991.[12] R Core Team, R: A Language and Environment for Statistical Computing, 2018. https://www.r-project.org/.R.R. Poppiel et al. / Data in brief 25 (2019) 1040706[13] D.A. Roberts, K. Halligan, P. Dennison, VIPER Tools User Manual, University of California, Santa Barbara, 2007. http://www.vipertools.org/.[14] J. Cohen, A coefficient of agreement of nominal scales, Educ. Psychol. Meas. 20 (1960) 37e46, https://doi.org/10.1177/001316446002000104.[15] J.R. Landis, G.G. Koch, The measurement of observer agreement for categorical data, Biometrics 33 (1977) 159, https://doi.org/10.2307/2529310.

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

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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

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