On exploring bivariate and trivariate maps as visualization tools for spatial associations in digital soil mapping: A focus on soil properties

Springer Science and Business Media LLC - Tập 24 Số 2 - Trang 511-532 - 2023
Kebonye, Ndiye M.1,2, Agyeman, Prince C.3, Seletlo, Zibanani4, Eze, Peter N.5
1Department of Geosciences, Chair of Soil Science and Geomorphology, University of Tübingen, Tübingen, Germany
2DFG Cluster of Excellence “Machine Learning: New Perspectives for Science”, University of Tübingen, Tübingen, Germany
3Department of Soil Science and Soil Protection, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Prague-Suchdol, Czech Republic
4Department of Animal Science and Production, Faculty of Animal and Veterinary Sciences, Botswana University of Agriculture and Natural Resources, Gaborone, Botswana
5Department of Earth and Environmental Science, Botswana International University of Science and Technology, Palapye, Botswana

Tóm tắt

The benefits of digital soil maps cannot be overemphasised. For many years, researchers have mapped different soil classes, properties and processes while identifying the spatial associations between soil properties using side-by-side visualization maps. Although this is acceptable, it may be difficult to identify complex spatial associations between the mapped soil properties. For some, the task may be challenging owing to multiple times of side-by-side placing of the maps and the possible application of none user-friendly colour palettes and or schemes. Innovative tools are proposed for visualizing and identifying spatial associations between digital soil maps (raster layers) using bivariate and trivariate maps. These tools are applied in a case study to identify the spatial interactions between pH and selected macro-nutrients [nitrogen (N) and potassium (K)] of similar locality (Czech Republic), resolution and scale. This study further gives a brief overview of the applicability of bivariate and trivariate maps following the digital soil mapping process. Results show that bivariate and trivariate maps are effective for visualizing complex associations between pH and macro-nutrients. However, precautionary measures should be taken while applying bivariate and trivariate maps to ensure they are self-explanatory and that the legend colour schemes applied are user-friendly. Also, the variables mapped should be related. In this case, pH is a key soil quality indicator that affects macro-nutrient availability in soils.

Tài liệu tham khảo

citation_title=Advancements in urban geochemical mapping of the Naples metropolitan area: Colour composite maps and results from an urban Brownfield site; citation_inbook_title=Mapping the chemical environment of urban areas; citation_publication_date=2011; citation_pages=410-423; citation_id=CR1; citation_author=S Albanese; citation_author=D Cicchella; citation_author=B Vivo; citation_author=A Lima; citation_author=D Civitillo; citation_author=A Cosenza; citation_publisher=Wiley Arnold, J. B., Daroczi, G., Werth, B., Weitzner, B., Kunst, J., & Auguie, B. (2015). Package ‘ggthemes’. Retrieved August, 2022, from https://cran.microsoft.com/snapshot/2015-07-10/web/packages/ggthemes/ggthemes.pdf Auguie, B., & Antonov, A. (2017). Package ‘gridExtra’. Retrieved August, 2022, from https://cran.r-project.org/web/packages/gridExtra/gridExtra.pdf citation_journal_title=International Journal of Environmental Research and Public Health; citation_title=China’s land uses in the multi-region input–output framework; citation_author=C Bao, M Xu, S Sun; citation_volume=16; citation_issue=16; citation_publication_date=2019; citation_pages=2940; citation_doi=10.3390/ijerph16162940; citation_id=CR4 citation_journal_title=Restoration Ecology; citation_title=Soil pH influences patterns of plant community composition after restoration with native-based seed mixes; citation_author=KM Barlow, DA Mortensen, PJ Drohan; citation_volume=28; citation_issue=4; citation_publication_date=2020; citation_pages=869-879; citation_doi=10.1111/rec.13141; citation_id=CR5 citation_journal_title=Ecological Applications; citation_title=Wild, connected, and diverse: Building a more resilient system of protected areas; citation_author=RT Belote, MS Dietz, CN Jenkins, PS McKinley, GH Irwin, TJ Fullman; citation_volume=27; citation_issue=4; citation_publication_date=2017; citation_pages=1050-1056; citation_doi=10.1002/eap.1527; citation_id=CR6 citation_journal_title=Frontiers in Environmental Science; citation_title=Interpretation of convolutional neural networks for acid sulfate soil classification; citation_author=A Beucher, CB Rasmussen, TB Moeslund, MH Greve; citation_volume=9; citation_publication_date=2022; citation_pages=809995; citation_doi=10.3389/fenvs.2021.809995; citation_id=CR7 Bivand, R., Denney, B., Dunlap, R., Hernangómez, D., Ono, H., & Parry, J. (2022). Package ‘classInt’. Retrieved August, 2022, from https://cran.r-project.org/web/packages/classInt/classInt.pdf citation_journal_title=Geoderma Regional; citation_title=Application of regression-kriging and sequential Gaussian simulation for the delineation of forest areas potentially suitable for liming in the Jizera Mountains region, Czech Republic; citation_author=L Borůvka, R Vašát, K Němeček, R Novotný, V Šrámek, O Vacek; citation_volume=21; citation_publication_date=2020; citation_pages=e00286; citation_doi=10.1016/j.geodrs.2020.e00286; citation_id=CR9 citation_title=The nature and properties of soils; citation_publication_date=1990; citation_id=CR10; citation_author=NC Brady; citation_publisher=Macmillan Publishing Company citation_journal_title=Machine Learning; citation_title=Random forests; citation_author=L Breiman; citation_volume=45; citation_publication_date=2001; citation_pages=5-32; citation_doi=10.1023/A:1010933404324; citation_id=CR11 Breiman, L., & Cutler, A. (2018). randomForest: Breiman and Cutler’s random forests for classification and regression. Version 4.6–14. Retrieved September, 2021, from https://CRAN.R-project.org/package=randomForest citation_title=Nitrogen-total; citation_inbook_title=Methods of soil analysis: Part 3 chemical methods; citation_publication_date=1996; citation_pages=1085-1121; citation_id=CR13; citation_author=JM Bremner; citation_publisher=American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America citation_journal_title=Frontiers in Plant Science; citation_title=Nitrogen metabolism and biomass production in forest trees; citation_author=FM Cánovas, RA Cañas, FN Torre, MB Pascual, V Castro-Rodríguez, C Avila; citation_volume=9; citation_publication_date=2018; citation_pages=1449; citation_doi=10.3389/fpls.2018.01449; citation_id=CR14 citation_journal_title=The Cartographic Journal; citation_title=Perceptions of variable similarity on bivariate choroplethic maps; citation_author=LW Carstensen; citation_volume=21; citation_issue=1; citation_publication_date=1984; citation_pages=23-29; citation_doi=10.1179/caj.1984.21.1.23; citation_id=CR15 citation_journal_title=Agronomy; citation_title=Mapping the spatial variability of soil acidity in Zambia; citation_author=LM Chabala, A Mulolwa, O Lungu; citation_volume=4; citation_issue=4; citation_publication_date=2014; citation_pages=452-461; citation_doi=10.3390/agronomy4040452; citation_id=CR16 citation_journal_title=Science of the Total Environment; citation_title=A high-resolution map of soil pH in China made by hybrid modelling of sparse soil data and environmental covariates and its implications for pollution; citation_author=S Chen, Z Liang, R Webster, G Zhang, Y Zhou, H Teng; citation_volume=655; citation_publication_date=2019; citation_pages=273-283; citation_doi=10.1016/j.scitotenv.2018.11.230; citation_id=CR17 citation_journal_title=Catena; citation_title=Digital mapping of the soil thickness of loess deposits over a calcareous bedrock in central France; citation_author=S Chen, AC Richer-de-Forges, VL Mulder, G Martelet, T Loiseau, S Lehmann; citation_volume=198; citation_publication_date=2021; citation_pages=105062; citation_doi=10.1016/j.catena.2020.105062; citation_id=CR18 citation_title=Current vegetation of the Czech Republic; citation_inbook_title=Flora and vegetation of the Czech Republic; citation_publication_date=2017; citation_pages=229-337; citation_id=CR19; citation_author=M Chytrý; citation_publisher=Springer Dowle, M., Srinivasan, A., Gorecki, J., Chirico, M., Stetsenko, P., & Short, T. (2021). Package ‘data.table’. Retrieved August, 2022, from https://cran.r-project.org/web/packages/data.table/data.table.pdf European Soil Data Centre (ESDAC). (2021). European Commission, Joint Research Centre. Retrieved August, 2022, from https://esdac.jrc.ec.europa.eu citation_journal_title=Journal of the American Statistical Association; citation_title=On grouping for maximum homogeneity; citation_author=WD Fisher; citation_volume=53; citation_publication_date=1958; citation_pages=789-798; citation_doi=10.1080/01621459.1958.10501479; citation_id=CR22 citation_title=Statistical mapping (enumeration, normalization, classification); citation_inbook_title=The geographic information science and technology body of knowledge; citation_publication_date=2019; citation_id=CR23; citation_author=M Foster; citation_publisher=USC citation_journal_title=PLoS ONE; citation_title=Comparing spatial regression to random forests for large environmental data sets; citation_author=EW Fox, JM Ver Hoef, AR Olsen; citation_volume=15; citation_issue=3; citation_publication_date=2020; citation_pages=e0229509; citation_doi=10.1371/journal.pone.0229509; citation_id=CR24 citation_journal_title=Journal of Geochemical Exploration; citation_title=Uranium, thorium and potassium insights on Campania region (Italy) soils: Sources patterns based on compositional data analysis and fractal model; citation_author=I Guagliardi, D Zuzolo, S Albanese, A Lima, P Cerino, A Pizzolante; citation_volume=212; citation_publication_date=2020; citation_pages=106508; citation_doi=10.1016/j.gexplo.2020.106508; citation_id=CR25 citation_title=The elements of statistical learning; citation_publication_date=2009; citation_id=CR26; citation_author=T Hastie; citation_author=R Tibshirani; citation_author=J Friedman; citation_publisher=Springer citation_journal_title=Geoderma; citation_title=Tier 4 maps of soil pH at 25 m resolution for the Netherlands; citation_author=A Helfenstein, VL Mulder, GB Heuvelink, JP Okx; citation_volume=410; citation_publication_date=2022; citation_pages=115659; citation_doi=10.1016/j.geoderma.2021.115659; citation_id=CR27 Hengl, T., Rossiter, D. G., & Husnjak, S. (2002). Mapping soil properties from an existing national soil data set using freely available ancillary data. In Proceedings of the 17th World Congress of Soil Science, Paper no. 1140 (p. 1481). IUSS. Hijmans, R. J., van Etten, J., Sumner, M., Cheng, J., Baston, D., & Bevan, A. (2022). Package ‘raster’. Retrieved August, 2022, from https://cran.r-project.org/web/packages/raster/raster.pdf citation_journal_title=Soil and Tillage Research; citation_title=Comparing laboratory and airborne hyperspectral data for the estimation and mapping of topsoil organic carbon: Feature selection coupled with random forest; citation_author=Y Hong, S Chen, Y Chen, M Linderman, AM Mouazen, Y Liu; citation_volume=199; citation_publication_date=2020; citation_pages=104589; citation_doi=10.1016/j.still.2020.104589; citation_id=CR30 Horneck, D. A., Sullivan, D. M., Owen, J. S., & Hart, J. M. (2011). Soil test interpretation guide. Retrieved October, 2021, from https://catalog.extension.oregonstate.edu/sites/catalog/files/project/pdf/ec1478.pdf citation_title=Soil fertility and forage production; citation_inbook_title=Harvested forages; citation_publication_date=1999; citation_pages=187-224; citation_id=CR32; citation_author=RD Horrocks; citation_author=JF Vallentine; citation_publisher=Academic Press citation_journal_title=PLoS ONE; citation_title=Defining ecological regions in Italy based on a multivariate clustering approach: A first step towards a targeted vector borne disease surveillance; citation_author=C Ippoliti, L Candeloro, M Gilbert, M Goffredo, G Mancini, G Curci; citation_volume=14; citation_issue=7; citation_publication_date=2019; citation_pages=e0219072; citation_doi=10.1371/journal.pone.0219072; citation_id=CR33 citation_title=LUCAS 2015 topsoil survey. Presentation of dataset and results, EUR 30332 EN; citation_publication_date=2020; citation_id=CR34; citation_author=A Jones; citation_author=O Fernandez-Ugalde; citation_author=S Scarpa; citation_publisher=Publications Office of the European Union citation_journal_title=Smart Agricultural Technology; citation_title=Optimized modelling of countrywide soil organic carbon levels via an interpretable decision tree; citation_author=NM Kebonye, PC Agyeman, JKM Biney; citation_volume=3; citation_publication_date=2022; citation_pages=100106; citation_doi=10.1016/j.atech.2022.100106; citation_id=CR77 Kebonye, N. M., Agyeman, P. C., & Biney, J. K. (2022b). Using an innovative bivariate colour scheme to infer spatial links and patterns between prediction and uncertainty: an example based on an explainable soil CN ratio model. Modeling Earth Systems and Environment, 1–8. citation_journal_title=Geoderma Regional; citation_title=Long term treated wastewater impacts and source identification of heavy metals in semi-arid soils of Central Botswana; citation_author=NM Kebonye, PN Eze, FO Akinyemi; citation_volume=10; citation_publication_date=2017; citation_pages=200-214; citation_doi=10.1016/j.geodrs.2017.08.001; citation_id=CR35 citation_journal_title=Journal of Geochemical Exploration; citation_title=Self-organizing map artificial neural networks and sequential Gaussian simulation technique for mapping potentially toxic element hotspots in polluted mining soils; citation_author=NM Kebonye, PN Eze, K John, A Gholizadeh, J Dajčl, O Drábek; citation_volume=222; citation_publication_date=2021; citation_pages=106680; citation_doi=10.1016/j.gexplo.2020.106680; citation_id=CR36 citation_journal_title=Geoderma; citation_title=Comparison of multivariate methods for arsenic estimation and mapping in floodplain soil via portable X-ray fluorescence spectroscopy; citation_author=NM Kebonye, K John, S Chakraborty, PC Agyeman, SK Ahado, PN Eze, K Němeček, O Drábek, L Borůvka; citation_volume=384; citation_publication_date=2021; citation_pages=114792; citation_doi=10.1016/j.geoderma.2020.114792; citation_id=CR37 Kuhn, M., Wing, J., Weston, S., Williams, A., Keefer, C., & Engelhardt, A. (2022). Package ‘caret’. Retrieved August, 2022, from https://cran.r-project.org/web/packages/caret/caret.pdf citation_journal_title=Remote Sensing; citation_title=Complementarity between textural and radiometric indices from airborne and spaceborne multi VHSR Data: Disentangling the complexity of heterogeneous landscape matrix; citation_author=M Lang, S Alleaume, S Luque, N Baghdadi, JB Féret; citation_volume=11; citation_issue=6; citation_publication_date=2019; citation_pages=693; citation_doi=10.3390/rs11060693; citation_id=CR39 citation_journal_title=Remote Sensing; citation_title=Mapping and characterization of phenological changes over various farming systems in an arid and semi-arid region using multitemporal moderate spatial resolution data; citation_author=Y Lebrini, A Boudhar, A Laamrani, A Htitiou, H Lionboui, A Salhi; citation_volume=13; citation_issue=4; citation_publication_date=2021; citation_pages=578; citation_doi=10.3390/rs13040578; citation_id=CR40 Leutner, B., Horning, N., Schwalb-Willmann, J., & Hijmans, R. J. (2022). Package ‘RStoolbox’. Retrieved August, 2022, from https://cran.r-project.org/web/packages/RStoolbox/RStoolbox.pdf citation_journal_title=International Journal of Environmental Research and Public Health; citation_title=Bivariate spatial pattern between smoking prevalence and lung cancer screening in US counties; citation_author=B Liu, J Sze, L Li, KA Ornstein, E Taioli; citation_volume=17; citation_issue=10; citation_publication_date=2020; citation_pages=3383; citation_doi=10.3390/ijerph17103383; citation_id=CR42 citation_journal_title=Etude Et Gestion Des Sols; citation_title=Essais de représentation cartographique de l’incertitude pour les utilisateurs de cartographies des sols par modélisation statistique (Attempts to map uncertainty for users of soil maps by statistical modeling); citation_author=T Loiseau, AR Forges, P Roudier, C Ducommun, S Chen, P Lagacherie; citation_volume=27; citation_issue=1; citation_publication_date=2020; citation_pages=257-275; citation_id=CR43 citation_journal_title=Geoderma Regional; citation_title=Could airborne gamma-spectrometric data replace lithological maps as co-variates for digital soil mapping of topsoil particle-size distribution? A case study in Western France; citation_author=T Loiseau, AC Richer-de-Forges, G Martelet, A Bialkowski, P Nehlig, D Arrouays; citation_volume=22; citation_publication_date=2020; citation_pages=e00295; citation_doi=10.1016/j.geodrs.2020.e00295; citation_id=CR44 citation_journal_title=Sustainable Cities and Society; citation_title=Exploring spatiotemporal effects of the driving factors on COVID-19 incidences in the contiguous United States; citation_author=A Maiti, Q Zhang, S Sannigrahi, S Pramanik, S Chakraborti, A Cerda; citation_volume=68; citation_publication_date=2021; citation_pages=102784; citation_doi=10.1016/j.scs.2021.102784; citation_id=CR45 citation_journal_title=Studies in Environmental Science; citation_title=Acidification of forests and forest soils: Current status; citation_author=E Matzner; citation_volume=50; citation_publication_date=1992; citation_pages=77-86; citation_doi=10.1016/S0166-1116(08)70103-1; citation_id=CR46 citation_journal_title=Geoderma; citation_title=On digital soil mapping; citation_author=AB McBratney, MM Santos, B Minasny; citation_volume=117; citation_issue=1–2; citation_publication_date=2003; citation_pages=3-52; citation_doi=10.1016/S0016-7061(03)00223-4; citation_id=CR47 citation_journal_title=Agriculture, Ecosystems and Environment; citation_title=Soil pH increase under paddy in South Korea between 2000 and 2012; citation_author=B Minasny, SY Hong, AE Hartemink, YH Kim, SS Kang; citation_volume=221; citation_publication_date=2016; citation_pages=205-213; citation_doi=10.1016/j.agee.2016.01.042; citation_id=CR48 citation_journal_title=ISPRS International Journal of Geo-Information; citation_title=Geo-spatial analysis of population density and annual income to identify large-scale socio-demographic disparities; citation_author=N Moos, C Juergens, AP Redecker; citation_volume=10; citation_issue=7; citation_publication_date=2021; citation_pages=432; citation_doi=10.3390/ijgi10070432; citation_id=CR49 citation_journal_title=Science of the Total Environment; citation_title=GlobalSoilMap France: High-resolution spatial modelling the soils of France up to two meter depth; citation_author=VL Mulder, M Lacoste, AC Richer-de-Forges, D Arrouays; citation_volume=573; citation_publication_date=2016; citation_pages=1352-1369; citation_doi=10.1016/j.scitotenv.2016.07.066; citation_id=CR50 citation_journal_title=Geoderma; citation_title=Further results on prediction of soil properties from terrain attributes: Heterotopic cokriging and regression-kriging; citation_author=IO Odeh, AB McBratney, DJ Chittleborough; citation_volume=67; citation_issue=3–4; citation_publication_date=1995; citation_pages=215-226; citation_doi=10.1016/0016-7061(95)00007-B; citation_id=CR51 Pedersen, T. L. (2020). Package ‘patchwork’. Retrieved August, 2022, from https://cran.r-project.org/web/packages/patchwork/patchwork.pdf Probst, P., & Janitza, S. (2020). Package ‘varImp’. Version 0.4. Retrieved February, 2022, from https://cran.r-project.org/web/packages/varImp/varImp.pdf citation_journal_title=Remote Sensing; citation_title=National scale 3D mapping of soil pH using a data augmentation approach; citation_author=P Roudier, OR Burge, SJ Richardson, JK McCarthy, GJ Grealish, AG Ausseil; citation_volume=12; citation_issue=18; citation_publication_date=2020; citation_pages=2872; citation_doi=10.3390/rs12182872; citation_id=CR54 citation_journal_title=Ecography; citation_title=Explainable artificial intelligence enhances the ecological interpretability of black-box species distribution models; citation_author=M Ryo, B Angelov, S Mammola, JM Kass, BM Benito, F Hartig; citation_volume=44; citation_issue=2; citation_publication_date=2021; citation_pages=199-205; citation_doi=10.1111/ecog.05360; citation_id=CR55 citation_journal_title=ESAIM: Proceedings and Surveys; citation_title=Tuning parameters in random forests; citation_author=E Scornet; citation_volume=60; citation_publication_date=2017; citation_pages=144-162; citation_doi=10.1051/proc/201760144; citation_id=CR56 citation_title=Multi-hazard groundwater risks to the drinking water supply in Bangladesh: Challenges to achieving the sustainable development goals; citation_publication_date=2019; citation_id=CR57; citation_author=M Shamsudduha; citation_author=G Joseph; citation_author=MR Khan; citation_author=A Zahid; citation_author=KMU Ahmed; citation_publisher=World Bank citation_journal_title=Geoderma; citation_title=Assessing countrywide soil organic carbon stock using hybrid machine learning modelling and legacy soil data in Cameroon; citation_author=FB Silatsa, M Yemefack, FO Tabi, GB Heuvelink, JG Leenaars; citation_volume=367; citation_publication_date=2020; citation_pages=114260; citation_doi=10.1016/j.geoderma.2020.114260; citation_id=CR58 citation_journal_title=Geoderma; citation_title=Multi-task convolutional neural networks outperformed random forest for mapping soil particle size fractions in central Iran; citation_author=R Taghizadeh-Mehrjardi, M Mahdianpari, F Mohammadimanesh, T Behrens, N Toomanian, T Scholten; citation_volume=376; citation_publication_date=2020; citation_pages=114552; citation_doi=10.1016/j.geoderma.2020.114552; citation_id=CR59 citation_journal_title=International Journal of Climatology; citation_title=Bivariate colour maps for visualizing climate data; citation_author=AJ Teuling, R Stӧckli, SI Seneviratne; citation_volume=31; citation_publication_date=2011; citation_pages=1408-1412; citation_doi=10.1002/joc.2153; citation_id=CR60 citation_journal_title=The American Statistician; citation_title=A theory for coloring bivariate statistical maps; citation_author=BE Trumbo; citation_volume=35; citation_issue=4; citation_publication_date=1981; citation_pages=220-226; citation_id=CR61 citation_journal_title=Soil Science and Plant Nutrition; citation_title=Copper micronutrient fixation kinetics and interactions with soil constituents in semi-arid alkaline soils; citation_author=TK Udeigwe, M Eichmann, PN Eze, GM Ogendi, MN Morris, MR Riley; citation_volume=62; citation_issue=3; citation_publication_date=2016; citation_pages=289-296; citation_doi=10.1080/00380768.2016.1197046; citation_id=CR62 Urbanek, S. (2015). Package ‘png’. Retrieved August, 2022, from https://cran.r-project.org/web/packages/png/png.pdf USDA–NRCS. (2004). Soil survey laboratory methods manual. Soil survey investigations report no. 42. Version 4.0. Retrieved August, 2022, from https://www.nrcs.usda.gov/Internet/FSE_DOCUMENTS/nrcseprd1026807.pdf citation_journal_title=Vegetation History and Archaeobotany; citation_title=Vegetation history of chernozems in the Czech Republic; citation_author=B Vysloužilová, L Danková, D Ertlen, J Novák, D Schwartz, L Šefrna; citation_volume=23; citation_issue=1; citation_publication_date=2014; citation_pages=97-108; citation_doi=10.1007/s00334-014-0441-7; citation_id=CR65 citation_journal_title=Ecological Modelling; citation_title=Spatial cross-validation is not the right way to evaluate map accuracy; citation_author=AMC Wadoux, GB Heuvelink, S Bruin, DJ Brus; citation_volume=457; citation_publication_date=2021; citation_pages=109692; citation_doi=10.1016/j.ecolmodel.2021.109692; citation_id=CR66 citation_journal_title=Geoderma; citation_title=Ten challenges for the future of pedometrics; citation_author=AMC Wadoux, GB Heuvelink, RM Lark, P Lagacherie, J Bouma, VL Mulder; citation_volume=401; citation_publication_date=2021; citation_pages=115155; citation_doi=10.1016/j.geoderma.2021.115155; citation_id=CR67 citation_journal_title=Geoderma; citation_title=Beyond prediction: Methods for interpreting complex models of soil variation; citation_author=AMC Wadoux, C Molnar; citation_volume=422; citation_publication_date=2021; citation_pages=115953; citation_doi=10.1016/j.geoderma.2022.115953; citation_id=CR68 citation_journal_title=European Journal of Soil Science; citation_title=A note on knowledge discovery and machine learning in digital soil mapping; citation_author=AMC Wadoux, A Samuel-Rosa, L Poggio, VL Mulder; citation_volume=71; citation_issue=2; citation_publication_date=2020; citation_pages=133-136; citation_doi=10.1111/ejss.12909; citation_id=CR69 citation_journal_title=The American Statistician; citation_title=An empirical inquiry concerning human understanding of two-variable color maps; citation_author=H Wainer, CM Francolini; citation_volume=34; citation_issue=2; citation_publication_date=1980; citation_pages=81-93; citation_id=CR70 citation_title=Geostatistics for environmental scientists; citation_publication_date=2007; citation_id=CR71; citation_author=R Webster; citation_author=MA Oliver; citation_publisher=Wiley Wickham, H., Chang, W., Henry, L., Pedersen, T. L., Takahashi, K., & Wilke, C. (2022). Package ‘ggplot2’. Retrieved August, 2022, from https://cran.r-project.org/web/packages/ggplot2/ggplot2.pdf Wickham, H., & RStudio. (2022). Package ‘tidyverse’. Retrieved August, 2022, from https://cran.r-project.org/web/packages/tidyverse/tidyverse.pdf citation_journal_title=Science of the Total Environment; citation_title=Prediction of soil organic carbon and the C: N ratio on a national scale using machine learning and satellite data: A comparison between Sentinel-2, Sentinel-3 and Landsat-8 images; citation_author=T Zhou, Y Geng, C Ji, X Xu, H Wang, J Pan; citation_volume=755; citation_publication_date=2021; citation_pages=142661; citation_doi=10.1016/j.scitotenv.2020.142661; citation_id=CR74 citation_journal_title=Journal of Maps; citation_title=The potential risk of combined effects of water and tillage erosion on the agricultural landscape in Czechia; citation_author=D Žížala, A Juřicová, J Kapička, I Novotný; citation_volume=17; citation_issue=2; citation_publication_date=2021; citation_pages=428-438; citation_doi=10.1080/17445647.2021.1942251; citation_id=CR75 citation_journal_title=Catena; citation_title=High-resolution agriculture soil property maps from digital soil mapping methods, Czech Republic; citation_author=D Žížala, R Minařík, J Skála, H Beitlerová, A Juřicová, JR Rojas; citation_volume=212; citation_publication_date=2022; citation_pages=106024; citation_doi=10.1016/j.catena.2022.106024; citation_id=CR76