Combining multiple methods for automated soil delineation: from traditional to digital
Fellipe A. O. Mello
A , José A. M. Demattê A * , André C. Dotto A , Karina P. P. Marques A , Danilo C. Mello B , Michele D. Menezes C , Sérgio H. G. Silva C and Nilton Curi C
+ Author Affiliations
- Author Affiliations
A Department of Soil Science, Luiz de Queiroz College of Agriculture, University of São Paulo, Pádua Dias Avenue, 11, Postal Box 09, Piracicaba, São Paulo 13416-900, Brazil.
B Department of Soil Science, Federal University of Viçosa, Peter Henry Rolfs Avenue, Viçosa, Minas Gerais 36570-900, Brazil.
C Department of Soil Science, Federal University of Lavras, Lavras, Minas Gerais 37200-900, Brazil.
Soil Research 61(1) 55-69 https://doi.org/10.1071/SR21067
Submitted: 22 October 2021 Accepted: 7 June 2022 Published: 11 July 2022
© 2023 The Author(s) (or their employer(s)). Published by CSIRO Publishing
Abstract
Context: Soil maps are a fundamental tool for agriculture development and for land management planning. Digital soil mapping (DSM) consists of a group of techniques based on geotechnologies and statistics/geostatistics that helps soil specialists to map soil types and properties.
Aims: Four DSM strategies were applied in south-east Brazil. The goal was to visually delineate soil polygons with support of different strategies.
Methods: The delineation started with aerial photographs, followed by a bare soil image composition. Afterwards, it was added layers with landscape characterisation derived from digital terrain covariates and clustering analysis. Finally, digital clay content map from A and B horizons were used to produce a soil texture gradient raster (clay content increasing in depth).
Key results: The increasing number of polygons proved that the addition of covariates increased the detail level of the soil map, enhancing visualisation of the landscape variation, resulting on a map that substantially improved both national and state soil inventories.
Conclusions: We concluded that combining simple geotechnological tools might be of great utility for increasing detailed soil information proper for farmers and decision making.
Implications: Therefore, new soil information will be available for end users, supporting land management, food production sustainability, and soil conservation.
Keywords: aerial images, digital soil mapping, landscape compartments, landscape modelling, pedology, remote sensing, soil geography, soil mapping.
References
Abbaszadeh Afshar F, Ayoubi S, Jafari A (2018) The extrapolation of soil great groups using multinomial logistic regression at regional scale in arid regions of Iran. Geoderma 315, 36–48.| The extrapolation of soil great groups using multinomial logistic regression at regional scale in arid regions of Iran.Crossref | GoogleScholarGoogle Scholar |
Alvares CA, Stape JL, Sentelhas PC, de Moraes Gonçalves JL, Sparovek G (2013) Köppen’s climate classification map for Brazil. Meteorologische Zeitschrift 22, 711–728.
| Köppen’s climate classification map for Brazil.Crossref | GoogleScholarGoogle Scholar |
Arruda GPd, Demattê JAM, Chagas CdS, Fiorio PR, Souza ABe, Fongaro CT (2016) Digital soil mapping using reference area and artificial neural networks. Scientia Agricola 73, 266–273.
| Digital soil mapping using reference area and artificial neural networks.Crossref | GoogleScholarGoogle Scholar |
Ball GH, Hall DJ (1965) ‘ISODATA, a novel method of data analysis and pattern classification.’ (Stanford Research Institute: Menlo Park, CA)
Bazaglia Filho O, Rizzo R, Lepsch IF, Prado Hd, Gomes FH, Mazza JA, Demattê JAM (2013) Comparison between detailed digital and conventional soil maps of an area with complex geology. Revista Brasileira de Ciência do Solo 37, 1136–1148.
| Comparison between detailed digital and conventional soil maps of an area with complex geology.Crossref | GoogleScholarGoogle Scholar |
Bellinaso H, Silvero NEQ, Ruiz LFC, Accorsi Amorim MT, Rosin NA, Mendes WdS, Sousa GPBd, Sepulveda LMA, Queiroz LGd, Nanni MR, Demattê JAM (2021) Clay content prediction using spectra data collected from the ground to space platforms in a smallholder tropical area. Geoderma 399, 115116
| Clay content prediction using spectra data collected from the ground to space platforms in a smallholder tropical area.Crossref | GoogleScholarGoogle Scholar |
Bock M, Köthe R (2008) Predicting the depth of hydromorphic soil characteristics influenced by ground water. In ‘Hamburg contributions to physical geography and landscape ecology’ Issue 19, SAGA - seconds out. (Eds J Böhner, T Blaschke, L Montanarella) pp.13–23 (University of Hamburg: Germany) Available at https://fiona.uni-hamburg.de/e2bfe5e6/boehner-et-al–saga-seconds-out.pdf
Bonfatti BR, Demattê JAM, Marques KPP, Poppiel RR, Rizzo R, Mendes WdS, Silvero NEQ, Safanelli JL (2020) Digital mapping of soil parent material in a heterogeneous tropical area. Geomorphology 367, 107305
| Digital mapping of soil parent material in a heterogeneous tropical area.Crossref | GoogleScholarGoogle Scholar |
Bordonal R de O, Carvalho JLN, Lal R, de Figueiredo EB, de Oliveira BG, La Scala N (2018) Sustainability of sugarcane production in Brazil. A review. Agronomy for Sustainable Development 38, 13
| Sustainability of sugarcane production in Brazil. A review.Crossref | GoogleScholarGoogle Scholar |
Brown LR (1981) World population growth, soil erosion, and food security. Science 214, 995–1002.
| World population growth, soil erosion, and food security.Crossref | GoogleScholarGoogle Scholar | 7302578PubMed |
Buol SW, Southard RJ, Graham RC, McDaniel PA (2011) Soil-forming processes. In ‘Soil genesis and classification’. (Eds SW Buol, RJ Southard, RC Graham, PA McDaniel) pp. 163–179. (Wiley Online Books)
| Crossref |
Buringh P (1954) The analysis and interpretation of aerial photographs in soil survey and land classification. NJAS: Wageningen Journal of Life Sciences 2, 16–26. Available at https://library.wur.nl/ojs/index.php/njas/article/view/17853/17267
Carvalho Filho Ad, Inda AV, Fink JR, Curi N (2015) Iron oxides in soils of different lithological origins in Ferriferous Quadrilateral (Minas Gerais, Brazil). Applied Clay Science 118, 1–7.
| Iron oxides in soils of different lithological origins in Ferriferous Quadrilateral (Minas Gerais, Brazil).Crossref | GoogleScholarGoogle Scholar |
Congalton RG, Green K (2019) ‘Assessing the accuracy of remotely sensed data principles and practices.’ (Taylor & Francis Group: Boca Raton, FL)
| Crossref |
Conrad O, Bechtel B, Bock M, Dietrich H, Fischer E, Gerlitz L, Wehberg J, Wichmann V, Böhner J (2015) System for Automated Geoscientific Analyses (SAGA) v. 2.1.4. Geoscientific Model Development 8, 1991–2007.
| System for Automated Geoscientific Analyses (SAGA) v. 2.1.4.Crossref | GoogleScholarGoogle Scholar |
CPRM (2014) Mapa Da Geodiversidade. (Serviço Geológico do Brasil - CPRM) Available at https://rigeo.cprm.gov.br/bitstream/doc/10169/2/Leg_expand_Geodiv_Brasil.pdf
Demattê JAM, Demètrio VA (1995) Fotointerpretação de padrões de drenagem de bacias hidrográficas na caracterização de solos desenvolvidos de rochas eruptivas básicas no estado do Paraná. Scientia Agricola 52, 569–577.
| Fotointerpretação de padrões de drenagem de bacias hidrográficas na caracterização de solos desenvolvidos de rochas eruptivas básicas no estado do Paraná.Crossref | GoogleScholarGoogle Scholar |
Demattê JAM, Dotto AC, Paiva AFS, Sato MV, Dalmolin RSD, de Araújo MdSB, da Silva EB, Nanni MR, ten Caten A, Noronha NC, Lacerda MPC, de Araújo Filho JC, Rizzo R, Bellinaso H, Francelino MR, Schaefer CEGR, Vicente LE, dos Santos UJ, de Sá Barretto Sampaio EV, Menezes RSC, de Souza JJLL, Abrahão WAP, Coelho RM, Grego CR, Lani JL, Fernandes AR, Gonçalves DAM, Silva SHG, de Menezes MD, Curi N, Couto EG, dos Anjos LHC, Ceddia MB, Pinheiro ÉFM, Grunwald S, Vasques GM, Marques Júnior J, da Silva AJ, Barreto MCdV, Nóbrega GN, da Silva MZ, de Souza SF, Valladares GS, Viana JHM, da Silva Terra F, Horák-Terra I, Fiorio PR, da Silva RC, Frade Júnior EF, Lima RHC, Alba JMF, de Souza Junior VS, Brefin MDLMS, Ruivo MDLP, Ferreira TO, Brait MA, Caetano NR, Bringhenti I, de Sousa Mendes W, Safanelli JL, Guimarães CCB, Poppiel RR, e Souza AB, Quesada CA, do Couto HTZ (2019) The Brazilian Soil Spectral Library (BSSL): a general view, application and challenges. Geoderma 354, 113793
| The Brazilian Soil Spectral Library (BSSL): a general view, application and challenges.Crossref | GoogleScholarGoogle Scholar |
Demattê JAM, Ferreira FP, Alves MR, Campos RC (2011) Fotopedologia e pedologia espectral orbital associadas no estudo de solos desenvolvidos de basalto. Bragantia 70, 122–131.
| Fotopedologia e pedologia espectral orbital associadas no estudo de solos desenvolvidos de basalto.Crossref | GoogleScholarGoogle Scholar |
Demattê JAM, Fongaro CT, Rizzo R, Safanelli JL (2018) Geospatial Soil Sensing System (GEOS3): a powerful data mining procedure to retrieve soil spectral reflectance from satellite images. Remote Sensing of Environment 212, 161–175.
| Geospatial Soil Sensing System (GEOS3): a powerful data mining procedure to retrieve soil spectral reflectance from satellite images.Crossref | GoogleScholarGoogle Scholar |
Demattê JAM, Safanelli JL, Poppiel RR, Rizzo R, Silvero NEQ, Mendes WdS, Bonfatti BR, Dotto AC, Salazar DFU, Mello FAdO, Paiva AFdS, Souza AB, Santos NVd, Maria Nascimento C, Mello DCd, Bellinaso H, Gonzaga Neto L, Amorim MTA, Resende MEBd, Vieira JdS, Queiroz LGd, Gallo BC, Sayão VM, Lisboa CJdS (2020) Bare earth’s surface spectra as a proxy for soil resource monitoring. Scientific Reports 10, 4461
| Bare earth’s surface spectra as a proxy for soil resource monitoring.Crossref | GoogleScholarGoogle Scholar | 32157136PubMed |
dos Santos HG, Jacomine PKT, Dos Anjos LHC, De Oliveira VA, Lumbreras JF, Coelho MR, de Almeida JA, de Araujo Filho JC, de Oliveira JB, Cunha TJF (2018) ‘Sistema brasileiro de classificação de solos.’ (Embrapa: Brasília, DF)
Dotto AC, Demattê JAM, Viscarra Rossel RA, Rizzo R (2020) Soil environment grouping system based on spectral, climate, and terrain data: a quantitative branch of soil series. SOIL 6, 163–177.
| Soil environment grouping system based on spectral, climate, and terrain data: a quantitative branch of soil series.Crossref | GoogleScholarGoogle Scholar |
Ericksen PJ, Ingram JSI, Liverman DM (2009) Food security and global environmental change: emerging challenges. Environmental Science & Policy 12, 373–377.
| Food security and global environmental change: emerging challenges.Crossref | GoogleScholarGoogle Scholar |
Figueiredo SR, Giasson E, Tornquist CG, Nascimento PCd (2008) Uso de regressões logísticas múltiplas para mapeamento digital de solos no Planalto Médio do RS. Revista Brasileira de Ciência do Solo 32, 2779–2785.
| Uso de regressões logísticas múltiplas para mapeamento digital de solos no Planalto Médio do RS.Crossref | GoogleScholarGoogle Scholar |
Florinsky IV (2012) ‘Digital Terrain Analysis in Soil Science and Geology.’ (Academic Press)
| Crossref |
Flynn T, Rozanov A, de Clercq W, Warr B, Clarke C (2019) Semi-automatic disaggregation of a national resource inventory into a farm-scale soil depth class map. Geoderma 337, 1136–1145.
| Semi-automatic disaggregation of a national resource inventory into a farm-scale soil depth class map.Crossref | GoogleScholarGoogle Scholar |
Fongaro CT, Demattê JAM, Rizzo R, Safanelli JL, Mendes WDS, Dotto AC, Vicente LE, Franceschini MHD, Ustin SL (2018) Improvement of clay and sand quantification based on a novel approach with a focus on multispectral satellite images. Remote Sensing 10, 1555
| Improvement of clay and sand quantification based on a novel approach with a focus on multispectral satellite images.Crossref | GoogleScholarGoogle Scholar |
Formaggio A, Epiphanio J, Valeriano M, Oliveira J (1996) Comportamento espectral (450-2.450 nm) de solos Tropicals de Sao Paulo. Revista brasileira de ciência do solo 20, 467–474.
Gallo BC, Demattê JAM, Rizzo R, Safanelli JL, Mendes WDS, Lepsch IF, Sato MV, Romero DJ, Lacerda MPC (2018) Multi-temporal satellite images on topsoil attribute quantification and the relationship with soil classes and geology. Remote Sensing 10, 1571
| Multi-temporal satellite images on topsoil attribute quantification and the relationship with soil classes and geology.Crossref | GoogleScholarGoogle Scholar |
Goktepe AB, Altun S, Sezer A (2005) Soil clustering by fuzzy c-means algorithm. Advances in Engineering Software 36, 691–698.
| Soil clustering by fuzzy c-means algorithm.Crossref | GoogleScholarGoogle Scholar |
Gonçalves TG, Pons NAD, Melloni EGP, Mancini M, Curi N (2021) Digital soil mapping: predicting soil classes distribution in large areas based on existing soil maps from similar small areas. Ciência e Agrotecnologia 45, e007921
| Digital soil mapping: predicting soil classes distribution in large areas based on existing soil maps from similar small areas.Crossref | GoogleScholarGoogle Scholar |
Gorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D, Moore R (2017) Google Earth Engine: planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202, 18–27.
| Google Earth Engine: planetary-scale geospatial analysis for everyone.Crossref | GoogleScholarGoogle Scholar |
Grinand C, Arrouays D, Laroche B, Martin MP (2008) Extrapolating regional soil landscapes from an existing soil map: sampling intensity, validation procedures, and integration of spatial context. Geoderma 143, 180–190.
| Extrapolating regional soil landscapes from an existing soil map: sampling intensity, validation procedures, and integration of spatial context.Crossref | GoogleScholarGoogle Scholar |
Hou D, O’Connor D, Nathanail P, Tian L, Ma Y (2017) Integrated GIS and multivariate statistical analysis for regional scale assessment of heavy metal soil contamination: a critical review. Environmental Pollution 231, 1188–1200.
| Integrated GIS and multivariate statistical analysis for regional scale assessment of heavy metal soil contamination: a critical review.Crossref | GoogleScholarGoogle Scholar | 28939126PubMed |
IBGE (2019) Províncias estruturais, compartimentos de relevo, tipos de solos, regiões fitoecológicas e outras áreas. (IBGE, Coordenação de Recursos Naturais e Estudos Ambientais: Rio de Janeiro) Available at https://biblioteca.ibge.gov.br/index.php/biblioteca-catalogo?view=detalhes&id=2101648
IUSS 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. World Soil Resources Report 106, FAO, Rome. Available at http://www.fao.org/3/i3794en/I3794EN.pdf
Jenny H (1994) ‘Factors of soil formation: a system of quantitative pedology.’ (Courier Corporation)
Ker JC, Curi N, Schaefer CEGR, Vidal-Torrado P (2015) ‘Pedologia: fundamentos.’ (Sociedade Brasileira de Ciência do Solo: Viçosa)
Koch A, McBratney A, Adams M, Field D, Hill R, Crawford J, Minasny B, Lal R, Abbott L, O’Donnell A, Angers D, Baldock J, Barbier E, Binkley D, Parton W, Wall DH, Bird M, Bouma J, Chenu C, Flora CB, Goulding K, Grunwald S, Hempel J, Jastrow J, Lehmann J, Lorenz K, Morgan CL, Rice CW, Whitehead D, Young I, Zimmermann M (2013) Soil security: solving the global soil crisis. Global Policy 4, 434–441.
| Soil security: solving the global soil crisis.Crossref | GoogleScholarGoogle Scholar |
Lacoste M, Lemercier B, Walter C (2011) Regional mapping of soil parent material by machine learning based on point data. Geomorphology 133, 90–99.
| Regional mapping of soil parent material by machine learning based on point data.Crossref | GoogleScholarGoogle Scholar |
Lamichhane S, Kumar L, Adhikari K (2021) Updating the national soil map of Nepal through digital soil mapping. Geoderma 394, 115041
| Updating the national soil map of Nepal through digital soil mapping.Crossref | GoogleScholarGoogle Scholar |
Leizica E, Frank Buss ME, Noellemeyer E (2022) Geomorphology as a tool to digitize homogeneous management zones based on soil properties in the semiarid central Argentinean Pampas. Geoderma Regional 28, e00458
| Geomorphology as a tool to digitize homogeneous management zones based on soil properties in the semiarid central Argentinean Pampas.Crossref | GoogleScholarGoogle Scholar |
Ma Y, Minasny B, Malone BP, Mcbratney AB (2019) Pedology and digital soil mapping (DSM). European Journal of Soil Science 70, 216–235.
| Pedology and digital soil mapping (DSM).Crossref | GoogleScholarGoogle Scholar |
Mc Carthy U, Uysal I, Badia-Melis R, Mercier S, O’Donnell C, Ktenioudaki A (2018) Global food security – issues, challenges and technological solutions. Trends in Food Science & Technology 77, 11–20.
| Global food security – issues, challenges and technological solutions.Crossref | GoogleScholarGoogle Scholar |
McBratney AB, Mendonça Santos ML, Minasny B (2003) On digital soil mapping. Geoderma 117, 3–52.
| On digital soil mapping.Crossref | GoogleScholarGoogle Scholar |
McBratney A, Field DJ, Koch A (2014) The dimensions of soil security. Geoderma 213, 203–213.
| The dimensions of soil security.Crossref | GoogleScholarGoogle Scholar |
Mello FAO, Bellinaso H, Mello DC, Safanelli JL, Mendes WDS, Amorim MTA, Gomez AMR, Poppiel RR, Silvero NEQ, Gholizadeh A, Silva SHG, Curi N, Demattê JAM (2021a) Soil parent material prediction through satellite multispectral analysis on a regional scale at the Western Paulista Plateau, Brazil. Geoderma Regional 26, e00412
| Soil parent material prediction through satellite multispectral analysis on a regional scale at the Western Paulista Plateau, Brazil.Crossref | GoogleScholarGoogle Scholar |
Mello FAO, Demattê JAM, Rizzo R, Dotto AC, Poppiel RR, Mendes WdS, Guimarães CCB (2021b) Expert-based maps and highly detailed surface drainage models to support digital soil mapping. Geoderma 384, 114779
| Expert-based maps and highly detailed surface drainage models to support digital soil mapping.Crossref | GoogleScholarGoogle Scholar |
Mello FAO, Demattê JAM, Rizzo R, Mello DCd, Poppiel RR, Silvero NEQ, Safanelli JL, Bellinaso H, Bonfatti BR, Gomez AMR, Sousa GPB (2022) Complex hydrological knowledge to support digital soil mapping. Geoderma 409, 115638
| Complex hydrological knowledge to support digital soil mapping.Crossref | GoogleScholarGoogle Scholar |
Memarsadeghi N, Mount DM, Netanyahu NS, Le Moigne J (2007) A fast implementation of the isodata clustering algorithm. International Journal of Computational Geometry & Applications 17, 71–103.
| A fast implementation of the isodata clustering algorithm.Crossref | GoogleScholarGoogle Scholar |
Mendes WdS, Medeiros Neto LG, Demattê JAM, Gallo BC, Rizzo R, Safanelli JL, Fongaro CT (2019) Is it possible to map subsurface soil attributes by satellite spectral transfer models? Geoderma 343, 269–279.
| Is it possible to map subsurface soil attributes by satellite spectral transfer models?Crossref | GoogleScholarGoogle Scholar |
Mendes WdS, Demattê JAM, de Resende MEB, Chimelo Ruiz LF, César de Mello D, Fim Rosas JT, Quiñonez Silvero NE, Ferracciú Alleoni LR, Colzato M, Rosin NA, Campos LR (2022) A remote sensing framework to map potential toxic elements in agricultural soils in the humid tropics. Environmental Pollution 292, 118397
| A remote sensing framework to map potential toxic elements in agricultural soils in the humid tropics.Crossref | GoogleScholarGoogle Scholar |
Mezzalira S (1966) (Boletim no. 45) Os fosseis do Estado de São Paulo. Available at https://www.infraestruturameioambiente.sp.gov.br/wp-content/uploads/sites/233/2019/02/Boletim_IG_15_Os_Fosseis_do_Estado_de_Sao_Paulo_Parte_II-1996.pdf
Miller JP (1958) ‘High mountain streams: effects of geology on channel characteristics and bed material.’ (State Bureau of Mines and Mineral Resources, New Mexico Institute of Mining and Technology)
Miller BA (2012) The need to continue improving soil survey maps. Soil Horizons 53, 11
| The need to continue improving soil survey maps.Crossref | GoogleScholarGoogle Scholar |
Miller BA, Schaetzl RJ (2016) History of soil geography in the context of scale. Geoderma 264, 284–300.
| History of soil geography in the context of scale.Crossref | GoogleScholarGoogle Scholar |
Milne G (1935) Composite units for the mapping of complex soil associations. In ‘The Transactions of the third international congress of soil science, Oxford, England, Vol. 1’, pp. 345–347. (Thomas Murby & Co.: London)
Minasny B, McBratney AB (2008) Regression rules as a tool for predicting soil properties from infrared reflectance spectroscopy. Chemometrics and Intelligent Laboratory Systems 94, 72–79.
| Regression rules as a tool for predicting soil properties from infrared reflectance spectroscopy.Crossref | GoogleScholarGoogle Scholar |
Møller AB, Malone B, Odgers NP, Beucher A, Iversen BV, Greve MH, Minasny B (2019) Improved disaggregation of conventional soil maps. Geoderma 341, 148–160.
| Improved disaggregation of conventional soil maps.Crossref | GoogleScholarGoogle Scholar |
Naimi S, Ayoubi S, Demattê JAM, Zeraatpisheh M, Amorim MTA, Mello FAdO (2021) Spatial prediction of soil surface properties in an arid region using synthetic soil image and machine learning. Geocarto International
| Spatial prediction of soil surface properties in an arid region using synthetic soil image and machine learning.Crossref | GoogleScholarGoogle Scholar |
Odgers NP, Sun W, McBratney AB, Minasny B, Clifford D (2014) Disaggregating and harmonising soil map units through resampled classification trees. Geoderma 214–215, 91–100.
| Disaggregating and harmonising soil map units through resampled classification trees.Crossref | GoogleScholarGoogle Scholar |
Oldeman L (1997) Soil degradation: a threat to food security? Paper presented at the international conference on time ecology: time for soil culture – temporal perspectives on sustainable use of soil. (ISRIC) Available at https://edepot.wur.nl/296559
Oliveira JB, Prado H (1989) ‘Carta pedológica de Piracicaba.’ (Instituto Agronômico: Campinas)
Parsons AJ, Wainwright J, Powell DM, Kaduk J, Brazier RE (2004) A conceptual model for determining soil erosion by water. Earth Surface Processes and Landforms 29, 1293–1302.
| A conceptual model for determining soil erosion by water.Crossref | GoogleScholarGoogle Scholar |
Pei T, Qin CZ, Zhu AX, Yang L, Luo M, Li B, Zhou C (2010) Mapping soil organic matter using the topographic wetness index: A comparative study based on different flow-direction algorithms and kriging methods. Ecological Indicators 10, 610–619.
| Mapping soil organic matter using the topographic wetness index: A comparative study based on different flow-direction algorithms and kriging methods.Crossref | GoogleScholarGoogle Scholar |
Pelegrino MHP, Silva SHG, Menezes MDd, Silva Ed, Owens PR, Curi N (2016) Mapping soils in two watersheds using legacy data and extrapolation for similar surrounding areas. Ciência e Agrotecnologia 40, 534–546.
| Mapping soils in two watersheds using legacy data and extrapolation for similar surrounding areas.Crossref | GoogleScholarGoogle Scholar |
Philipson WR (1960) ‘Manual of photographic interpretation.’ (American Society of Photogrammetry)
Poppiel RR, Lacerda MPC, de Oliveira Junior MP, Demattê JAM, Romero DJ, Sato MV, de Almeida Júnior LR, Cassol LFM (2018) Surface spectroscopy of oxisols, entisols and inceptisol and relationships with selected soil properties. Revista Brasileira de Ciência do Solo 42, e0160519
| Surface spectroscopy of oxisols, entisols and inceptisol and relationships with selected soil properties.Crossref | GoogleScholarGoogle Scholar |
Poppiel RR, Lacerda MPC, Demattê JAM, Oliveira MP, Gallo BC, Safanelli JL (2019) Pedology and soil class mapping from proximal and remote sensed data. Geoderma 348, 189–206.
| Pedology and soil class mapping from proximal and remote sensed data.Crossref | GoogleScholarGoogle Scholar |
Ray RG (1960) ‘Aerial photographs in geologic interpretation and mapping.’ (U.S. Government Printing Office)
| Crossref |
Rizzo R, Demattê JAM, Terra FdS (2014) Using numerical classification of profiles based on Vis-NIR spectra to distinguish soils from the Piracicaba Region, Brazil. Revista Brasileira de Ciência do Solo 38, 372–385.
| Using numerical classification of profiles based on Vis-NIR spectra to distinguish soils from the Piracicaba Region, Brazil.Crossref | GoogleScholarGoogle Scholar |
Rizzo R, Demattê JAM, Lepsch IF, Gallo BC, Fongaro CT (2016) Digital soil mapping at local scale using a multi-depth Vis–NIR spectral library and terrain attributes. Geoderma 274, 18–27.
| Digital soil mapping at local scale using a multi-depth Vis–NIR spectral library and terrain attributes.Crossref | GoogleScholarGoogle Scholar |
Rizzo R, Medeiros LG, Mello DCd, Marques KPP, Mendes WdS, Quiñonez Silvero NE, Dotto AC, Bonfatti BR, Demattê JAM (2020) Multi-temporal bare surface image associated with transfer functions to support soil classification and mapping in southeastern Brazil. Geoderma 361, 114018
| Multi-temporal bare surface image associated with transfer functions to support soil classification and mapping in southeastern Brazil.Crossref | GoogleScholarGoogle Scholar |
Ruhe RV (1960) Elements of the soil landscape. In ‘Transactions of the 7th international congress of soil science, Vol. 4’, pp. 165–170. (International Society of Soil Science)
Safanelli JL, Chabrillat S, Ben-Dor E, Demattê JAM (2020) Multispectral models from bare soil composites for mapping topsoil properties over Europe. Remote Sensing 12, 1369
| Multispectral models from bare soil composites for mapping topsoil properties over Europe.Crossref | GoogleScholarGoogle Scholar |
Santos HG, Hochmüller DP, Cavalcanti AC, Rêgo RS, Ker JC, Panoso LA, Amaral JAMd (1995) ‘Procedimentos normativos de levantamentos pedológicos.’ (EMBRAPA-SPI: Brasília, DF; EMBRAPA-CNPS: Rio de Janeiro)
Sarmento EC, Giasson E, Weber EJ, Flores CA, Hasenack H (2017) Disaggregating conventional soil maps with limited descriptive data: a knowledge-based approach in Serra Gaúcha, Brazil. Geoderma Regional 8, 12–23.
| Disaggregating conventional soil maps with limited descriptive data: a knowledge-based approach in Serra Gaúcha, Brazil.Crossref | GoogleScholarGoogle Scholar |
Schaetzl R, Anderson S (2005) ‘Soils: genesis and geomorphology.’ (Cambridge University Press: New York)
Silva Júnior JFd, Siqueira DS, Teixeira DDB, Panosso AR, Marques Júnior J, Pereira GT (2021) Multivariate split moving windows and magnetic susceptibility for locating soil boundaries of São Paulo, Brazil. Geoderma Regional 26, e00418
| Multivariate split moving windows and magnetic susceptibility for locating soil boundaries of São Paulo, Brazil.Crossref | GoogleScholarGoogle Scholar |
Silvero NEQ, Demattê JAM, Vieira JdS, Mello FAdO, Amorim MTA, Poppiel RR, Mendes WdS, Bonfatti BR (2021) Soil property maps with satellite images at multiple scales and its impact on management and classification. Geoderma 397, 115089
| Soil property maps with satellite images at multiple scales and its impact on management and classification.Crossref | GoogleScholarGoogle Scholar |
Siqueira DS, Marques J, Pereira GT (2010) The use of landforms to predict the variability of soil and orange attributes. Geoderma 155, 55–66.
| The use of landforms to predict the variability of soil and orange attributes.Crossref | GoogleScholarGoogle Scholar |
Souza CG (1995) ‘Manual técnico de pedologia.’ (IBGE)
Tarboton DG, Bras RL, Rodriguez-Iturbe I (1988) The fractal nature of river networks. Water Resources Research 24, 1317–1322.
| The fractal nature of river networks.Crossref | GoogleScholarGoogle Scholar |
ten Caten A, Dalmolin RSD, Pedron FdA, Santos MdLM (2011) Extrapolação das relações solo-paisagem a partir de uma área de referência. Ciência Rural 41, 812–816.
| Extrapolação das relações solo-paisagem a partir de uma área de referência.Crossref | GoogleScholarGoogle Scholar |
Thomas AL, King D, Dambrine E, Couturier A, Roque J (1999) Predicting soil classes with parameters derived from relief and geologic materials in a sandstone region of the Vosges mountains (Northeastern France). Geoderma 90, 291–305.
| Predicting soil classes with parameters derived from relief and geologic materials in a sandstone region of the Vosges mountains (Northeastern France).Crossref | GoogleScholarGoogle Scholar |
Urbina-Salazar D, Vaudour E, Baghdadi N, Ceschia E, Richer-de-Forges AC, Lehmann S, Arrouays D (2021) Using Sentinel-2 images for soil organic carbon content mapping in croplands of Southwestern France. The usefulness of Sentinel-1/2 derived moisture maps and mismatches between Sentinel images and sampling dates. Remote Sensing 13, 5115
| Using Sentinel-2 images for soil organic carbon content mapping in croplands of Southwestern France. The usefulness of Sentinel-1/2 derived moisture maps and mismatches between Sentinel images and sampling dates.Crossref | GoogleScholarGoogle Scholar |
U.S.G.S. (2019a) ‘Landsat 4-7 Surface Reflectance (Ledaps) product guide.’ (U.S.G.S.) Available at https://www.usgs.gov/media/files/landsat-4-7-collection-1-surface-reflectance-code-ledaps-product-guide
U.S.G.S. (2019b) ‘Landsat 8 Surface Reflectance Code (LASRC) product guide. (No. LSDS-1368 Version 2.0).’ (U.S.G.S.) Available at https://www.usgs.gov/media/files/landsat-8-collection-1-land-surface-reflectance-code-product-guide
van Breemen N, Buurman P (2002) ‘Soil formation.’ 2nd edn. (Kluwer Academic Publishers)
Vasques GM, Demattê JAM, Viscarra Rossel RA, Ramírez López L, Terra FS, Rizzo R, De Souza Filho CR (2015) Integrating geospatial and multi-depth laboratory spectral data for mapping soil classes in a geologically complex area in southeastern Brazil. European Journal of Soil Science 66, 767–779.
| Integrating geospatial and multi-depth laboratory spectral data for mapping soil classes in a geologically complex area in southeastern Brazil.Crossref | GoogleScholarGoogle Scholar |
Vaysse K, Lagacherie P (2015) Evaluating Digital Soil Mapping approaches for mapping GlobalSoilMap soil properties from legacy data in Languedoc-Roussillon (France). Geoderma Regional 4, 20–30.
| Evaluating Digital Soil Mapping approaches for mapping GlobalSoilMap soil properties from legacy data in Languedoc-Roussillon (France).Crossref | GoogleScholarGoogle Scholar |
Vincent S, Lemercier B, Berthier L, Walter C (2018) Spatial disaggregation of complex Soil Map Units at the regional scale based on soil-landscape relationships. Geoderma 311, 130–142.
| Spatial disaggregation of complex Soil Map Units at the regional scale based on soil-landscape relationships.Crossref | GoogleScholarGoogle Scholar |
Wolski MS, Dalmolin RSD, Flores CA, Moura-Bueno JM, Caten At, Kaiser DR (2017) Digital soil mapping and its implications in the extrapolation of soil-landscape relationships in detailed scale. Pesquisa Agropecuária Brasileira 52, 633–642.
| Digital soil mapping and its implications in the extrapolation of soil-landscape relationships in detailed scale.Crossref | GoogleScholarGoogle Scholar |
Young FJ, Hammer RD (2000) Defining geographic soil bodies by landscape position, soil taxonomy, and cluster analysis. Soil Science Society of America Journal 64, 989–998.
| Defining geographic soil bodies by landscape position, soil taxonomy, and cluster analysis.Crossref | GoogleScholarGoogle Scholar |
Zeraatpisheh M, Ayoubi S, Brungard CW, Finke P (2019) Disaggregating and updating a legacy soil map using DSMART, fuzzy c-means and k-means clustering algorithms in Central Iran. Geoderma 340, 249–258.
| Disaggregating and updating a legacy soil map using DSMART, fuzzy c-means and k-means clustering algorithms in Central Iran.Crossref | GoogleScholarGoogle Scholar |
