Mapping the Soil Frontiers with Legacy Soil Data: An Approach for Covering the Lack of Updated Reference Maps of Minas Gerais, Brazil

Authors

DOI:

https://doi.org/10.11137/1982-3908_2023_46_49327

Keywords:

Digital soil mapping, Unmapped geographic regions, Soil profile descriptions

Abstract

Digital Soil Mapping (DSM) of large areas is a time-consuming and  expensive process, where soil scientists take as many as possible observations to predict soil classes and their attributes. Sometimes, the DSM is made in geographic regions with no updated geographic information, leading the soil scientist to depend on Legacy Soil Data (LSD). However, LSD is not always available at an adequate scale or resolution, forcing soil scientists to find creative solutions. Here we present a method for mapping soil frontiers with no updated reference data. We demonstrate that by combining different LSD sources with adequate predictive environmental covariables, the results could be consistent enough for mapping the soil frontiers of a large geographic region without updated reference data. For doing that, we have adopted the full geographic extension of Minas Gerais  state – Brazil – as a study area. Within its extension, Minas Gerais has heterogeneity in soil classes and soil formation processes, phenomena triggered by such a divergent universe of environmental variables. Minas Gerais has no updated soil maps, making it a relevant study case for this research. Thus, we conclude that the Digital Soil Mapping process could be  enriched by using different sources of Legacy Soil Data, even when there is no updated reference data.

References

Alvares, C.A., Stape, J.L., Sentelhas, P.C., Gonçalves, J.L.M. & Sparovek, G. 2013, 'Köppen’s climate classification map for Brazil', Meteorologische Zeitschrift, vol. 22, no. 6, pp. 711-28, DOI:10.1127/0941-2948/2013/0507.

Arrouays, D., Lagacherie, P. & Hartemink, A. E. 2017, 'Digital soil mapping across the globe', Geoderma Regional, vol. 9, pp. 1-4, DOI:10.1016/j.geodrs.2017.03.002.

Barthold, F. K., Wiesmeier, M., Breuer, L., Frede, H.G., Wu, J. & Blank, F.B. 2013, 'Land use and climate control the spatial distribution of soil types in the grasslands of Inner Mongolia', Journal of Arid Environments, vol. 88, pp. 194–205.

Breiman, L. 2001, 'Random Forests', Machine learning, vol. 45.1, pp. 5–32.

Camboim, S.P., Bravo, J.V.M. & Sluter, C.R. 2015, 'An investigation into the completeness of, and the updates to, OpenStreetMap data in a heterogeneous area in Brazil', ISPRS International Journal of Geo-Information, vol. 4, no. 3, pp. 1366–88, DOI:10.3390/ijgi4031366.

Carneiro, A.F.T. & Miranda, C.R. 2020, 'Evolution and trends in research on land administration and cadastre', Revista Brasileira de Cartografia, vol. 72, pp. 880–97, DOI:10.14393/rbcv72nespecial50anos-56586.

Carré, F., Mcbratney, A. B. & Minasny, B. 2007, 'Estimation and potential improvement of the quality of legacy soil samples for digital soil mapping', Geoderma, vol. 141, no. 1-2, pp. 1–14, DOI:10.1016/j.geoderma.2007.01.018.

CODEMIG – Companhia Desenvolvimento Econômico de Minas Gerais 2014, Mapa geológico. Retrieved May 26, 2016. <http://www.portaldageologia.com.br>.

Congalton, R.G. & Green, K. 2019, Assessing the Accuracy of Remotely Sensed Data, Assessing the Accuracy of Remotely Sensed Data, 3rd edn, CRC Press, Boca Raton.

CPRM – Companhia de Pesquisa de Recursos Minerais 2016. Mapa Geodversidade. Retrieved May 26, 2019. <http://www.cprm.gov.br/geobank>.

de Sá Júnior, A., Carvalho, L.G., Silva, F.F. & Alves, M.C. (2012) 'Application of the Köppen classification for climatic zoning in the state of Minas Gerais, Brazil', Theoretical and Applied Climatology, vol. 108, no. 1, pp. 1-7, DOI:10.1007/s00704-011-0507-8.

de Souza, J.J.L.L., Abrahão, W.A., da Silva, J., da Costa, L.M. & de Oliveira, T.S. 2015, 'Geochemistry and spatial variability of metal(loid) concentrations in soils of the state of Minas Gerais, Brazil', Science of the Total Environment, vol. 505, pp. 338–49, DOI:10.1016/j.scitotenv.2014.09.098.

EMPRABA - Empresa Brasileira de Pesquisa Agropecuária 2016. Sistema de Informação de Solos Brasileiros. <https://www.sisolos.cnptia.embrapa.br/>.

Estes, J.E. & Mooneyhan, D.W. 1994, 'Of maps and myths', Photogrammetric Engineering & Remote Sensing, vol. 60, no. 5, pp. 517–24.

FEAM - Fundação Estadual do Meio Ambiente 2016. Banco de Solos de Minas Gerais. <http://www.feam.br/>.

Griffin, A.L., Griffin, A.L., White, T.M., Fish, C.S., Tomio, B., Huang, H., Sluter, C.R., Bravo, J.V., Fabrikant, S.I., Bleisch, S., Yamada, M.M. & Picanço, P.L. 2017, 'Designing across map use contexts: a research agenda', International Journal of Cartography. Taylor & Francis, vol. 3, sup. 1, pp. 90–114, DOI:10.1080/23729333.2017.1315988.

Griffin, A.L., Robinson, A.C. & Roth, R.E. 2017, 'Envisioning the future of cartographic research', International Journal of Cartography, vol. 3, sup. 1, pp. 1–8, DOI:10.1080/23729333.2017.1316466.

Grunwald, S. 2009, 'Multi-criteria characterization of recent digital soil mapping and modeling approaches', Geoderma, vol. 152, no. 3-4, pp. 195-207, DOI:10.1016/j.geoderma.2009.06.003.

Grunwald, S., Thompson, J.A. & Boettinger, J.L. 2011, 'Digital Soil Mapping and Modeling at Continental Scales: Finding Solutions for Global Issues', Soil Science Society of America Journal, vol. 75, no. 4, pp. 1201-13, DOI:10.2136/sssaj2011.0025.

Hendriks, C.M.J., Stoorvogel, JJ., Lutz, F. & Claessens, L.2019, 'When can legacy soil data be used, and when should new data be collected instead?', Geoderma, vol. 348, no. 2, pp. 181–8, DOI:10.1016/j.geoderma.2019.04.026.

Hengl, T., Toomanian, N., Reuter, H.I. & Malakouti, M.J. 2007, 'Methods to interpolate soil categorical variables from profile observations: Lessons from Iran', Geoderma, vol. 140, no. 4, pp. 417–27.

Heung, B., Hodúl, M. & Schmidt, M.G. 2017, 'Comparing the use of training data derived from legacy soil pits and soil survey polygons for mapping soil classes', Geoderma, vol. 290, pp. 51-68, DOI:10.1016/j.geoderma.2016.12.001.

Hounkpatin, K.O.L., Schmidt, K., Stumpf, F., Forkuor, G., Behrens, T., Scholten, T., Ameleung, W. & Welp, G. 2018, 'Predicting reference soil groups using legacy data: A data pruning and Random Forest approach for tropical environment (Dano catchment, Burkina Faso)', Scientific Reports, vol. 8, no. 1, e9959, DOI:10.1038/s41598-018-28244-w.

ISO 2013, Geographic information — Data quality ISO/FDIS 19157, Iso/Tc 211.

Kempen, B., Brus, D.J., Heuvelink, G.B.M. & Stoorvogel, J.J. 2009, 'Updating the 1:50,000 Dutch soil map using legacy soil data: A multinomial logistic regression approach', Geoderma, vol. 151, no. 3-4, pp. 311-26, DOI:10.1016/j.geoderma.2009.04.023.

Ker, J.C. 1997, 'Latossolos Do Brasil: Uma Revisão', Geonomos, vol. 5, no. 1, pp. 17–40, DOI:10.18285/geonomos.v5i1.187.

Kim, J., Grunwald, S., Rivero, R.G. & Robbins, R. 2012, 'Multi-scale Modeling of Soil Series Using Remote Sensing in a Wetland Ecosystem', Soil Science Society of America Journal, vol. 76, no. 6, pp. 2327-41, DOI:10.2136/sssaj2012.0043.

Lagacherie, P. 2008, 'Digital soil mapping: A state of the art', in A.E. Hartemink, A. McBratney & M. Mendonça-Santos, M. (eds), Digital Soil Mapping with Limited Data, Springer, Dordrecht, pp. 3-14.

Lamichhane, S., Kumar, L. & Adhikari, K. 2021, 'Updating the national soil map of Nepal through digital soil mapping', Geoderma, vol. 139, e108951, DOI:10.1016/j.geoderma.2021.115041.

Lillesand, T.M., Kiefer, R.W. & Chipman, J.W. 2004, Remote sensing and image interpretation, New York.

McBratney, A.B., Mendonça Santos, M.L. & Minasny, B. 2003, 'On digital soil mapping', Geoderma, vol. 117, no. 1-2, pp.3-52, DOI:10.1016/S0016-7061(03)00223-4.

Odgers, N.P., McBratney, A.B. & Minasny, B. 2015, 'Digital soil property mapping and uncertainty estimation using soil class probability rasters', Geoderma, vol. 237-238, pp. 190-8, DOI:10.1016/j.geoderma.2014.09.009.

Pereira, G., Cardozo, F.S., Negreiros, A.B., Zanin, G.D., Costa, J.C., Lima, T.E.R., Rufino, P.R. & Ramos, R.C. 2018, 'Análise Da Variabilidade Da Precipitação Para O Estado De Minas Gerais (1981-2017)', Revista Brasileira de Climatologia, vol. 1, pp. 213–29, DOI:10.5380/abclima.v1i0.61028.

Robinson, A.C., Demsar, U. , Moore, A.B., Buckley, A., Jiang, B., Field, K., Kraak, M.-J., Camboim, S.P. & Sluter, C.R. 2017, 'Geospatial big data and cartography: research challenges and opportunities for making maps that matter', International Journal of Cartography, vol. 3, sup. 1, pp. 32–60, DOI:10.1080/23729333.2016.1278151.

Rossiter, D.G., Liu, J., Carlisle, S. & Zhu, A.-X. 2015, 'Can citizen science assist digital soil mapping?', Geoderma, vol. 259-260, pp. 71-80, DOI:10.1016/j.geoderma.2015.05.006.

Samuel-Rosa, A., Dalmolin, R.S.D., Moura-Bueno, J.M., Teixeira, W.G. & Alba, J.M.F. 2020, 'Open legacy soil survey data in Brazil: Geospatial data quality and how to improve it', Scientia Agricola, vol. 77, no. 1, e20170430, DOI:10.1590/1678-992x-2017-0430.

Schaefer, C.E.G.R. 2013, 'Bases físicas da paisagem brasileira: estrutura geológica, relevo e solos. Tópicos em ciência do solo', Sociedade Brasileira de Ciência do Solo, vol. 8, no. 1, pp. 221–78.

Scull, P. 2003, 'Predictive soil mapping: A review', Progress in Physical Geography, vol. 27, no. 2, pp. 171-97, DOI:10.1191/0309133303pp366ra.

Silva, E.B, Giasson, E., Dotto, A.C., ten Caten, A.,Demattê, J.A.M., Bacic, I.L.Z. & Veiga, M. 2019, 'A regional legacy soil dataset for prediction of sand and clay content with VIS-NIR-SWIR, in southern Brazil', Revista Brasileira de Ciencia do Solo, vol. 43, e0180174, DOI:10.1590/18069657rbcs20180174.

Sluter, C.R., Carneiro, A.F.T., Iescheck, A.L., Pontes, D.R. & Gediel, J.A.P. 2020, 'Cartografia e Direito na Formação Territorial e na Configuração da Propriedade no Brasil', Revista Brasileira de Cartografia. EDUFU - Editora da Universidade Federal de Uberlandia, vol. 72, pp. 916–39, DOI:10.14393/rbcv72nespecial50anos-56599.

Souza, C.M., Shimbo, J., Rosa, M.R., Parente, L.L., Alencar, A., Rudorff, B.F.T., Hasenack, H., Matsumoto, M., Ferreira, L.G., Souza-Filho, P.W.M., de Oliveira, S.W., Rocha, W.F., Fonseca, A.V., Marques, C.B., Diniz, C.G., Costa, D., Monteiro, D., Rosa, E.R., Vélez-Martin, E., Weber, E.J., Lenti, F.E.B., Paternost, F.F., Pareyn, F.G.C., Siqueira, J.V., Viera, J.L., Ferreira Neto, L.C., Saraiva, M.M., Sales, M.H., Salgado, M.P.G., Vasconcelos, R., Galano, S., Mesquita, V.V., Azevedo, T. 2020, 'Reconstructing three decades of land use and land cover changes in brazilian biomes with landsat archive and earth engine', Remote Sensing, vol. 12, no. 17, e2735, DOI:10.3390/RS12172735.

Sulaeman, Y., Minasny, B., McBratney, A.B., Sarwani, M. & Sutandi, A. 2013, 'Harmonizing legacy soil data for digital soil mapping in Indonesia', Geoderma, vol. 192, pp. 77-85, DOI:10.1016/j.geoderma.2012.08.005.

ten Caten, A., Dalmolin, R.S.D., Mendonça-Santos, M.L. & Giasson, E. 2012, 'Mapeamento digital de classes de solos: Características da abordagem brasileira', Ciencia Rural, vol. 42, no. 11, pp. 1989-97, DOI:10.1590/S0103-84782012001100013.

Teske, R., Giasson, E. & Bagatini, T. 2015, 'Comparação De Esquemas De Amostragem Para Treinamento De Modelos Preditores No Mapeamento Digital De Classes De Solos', Revista Brasileira de Ciência do Solo, vol. 39, no. 1, pp. 14-20.

UFV - Universidade Federal de Viçosa; Fundação Centro Tecnológico de Minas Gerais (CETEC-MG); Universidade Federal de Lavras (UFLA); Fundação Estadual do Meio Ambiente (FEAM) 2010. Mapa de Solos Do Estado de Minas Gerais: legenda expandida. Fundação Estadual do Meio Ambiente.

WORLDCLIM 2015. Global Climate Data. <http://www.worldclim.com.br>.

Anuário do Instituto de Geociências vol. 46. Year 2023

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Published

2023-05-03

Issue

Vol. 46 (2023)

Section

Geology

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