Articles | Volume 14, issue 10
https://doi.org/10.5194/essd-14-4719-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-14-4719-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
28 Oct 2022
Data description paper |
| 28 Oct 2022
| 28 Oct 2022
Colombian soil texture: building a spatial ensemble model
Viviana Marcela Varón-Ramírez
CORRESPONDING AUTHOR
Corporación Colombiana de Investigación Agropecuaria – AGROSAVIA, Mosquera-Cundinamarca, Colombia
Gustavo Alfonso Araujo-Carrillo
Corporación Colombiana de Investigación Agropecuaria – AGROSAVIA, Mosquera-Cundinamarca, Colombia
Mario Antonio Guevara Santamaría
Centro de Geociencias – Universidad Nacional Autónoma de México Campus Juriquilla, Querétaro, México
University of California, Riverside, Department of Environmental Sciences, Riverside, CA 92507, USA
United States Department of Agriculture, U.S. Soil Salinity National Laboratory, Riverside, CA 92507, USA
Related authors
Sergio Díaz-Guadarrama, Viviana M. Varón-Ramírez, Iván Lizarazo, Mario Guevara, Marcos Angelini, Gustavo A. Araujo-Carrillo, Jainer Argeñal, Daphne Armas, Rafael A. Balta, Adriana Bolivar, Nelson Bustamante, Ricardo O. Dart, Martin Dell Acqua, Arnulfo Encina, Hernán Figueredo, Fernando Fontes, Joan S. Gutiérrez-Díaz, Wilmer Jiménez, Raúl S. Lavado, Jesús F. Mansilla-Baca, Maria de Lourdes Mendonça-Santos, Lucas M. Moretti, Iván D. Muñoz, Carolina Olivera, Guillermo Olmedo, Christian Omuto, Sol Ortiz, Carla Pascale, Marco Pfeiffer, Iván A. Ramos, Danny Ríos, Rafael Rivera, Lady M. Rodriguez, Darío M. Rodríguez, Albán Rosales, Kenset Rosales, Guillermo Schulz, Víctor Sevilla, Leonardo M. Tenti, Ronald Vargas, Gustavo M. Vasques, Yusuf Yigini, and Yolanda Rubiano
Earth Syst. Sci. Data, 16, 1229–1246, https://doi.org/10.5194/essd-16-1229-2024, https://doi.org/10.5194/essd-16-1229-2024, 2024
Short summary
Short summary
In this work, the Latin America and Caribbean Soil Information System (SISLAC) database (https://54.229.242.119/sislac/es) was revised to generate an improved version of the data. Rules for data enhancement were defined. In addition, other datasets available in the region were included. Subsequently, through a principal component analysis (PCA), the main soil characteristics for the region were analyzed. We hope this dataset can help mitigate problems such as food security and global warming.
Sergio Díaz-Guadarrama, Viviana M. Varón-Ramírez, Iván Lizarazo, Mario Guevara, Marcos Angelini, Gustavo A. Araujo-Carrillo, Jainer Argeñal, Daphne Armas, Rafael A. Balta, Adriana Bolivar, Nelson Bustamante, Ricardo O. Dart, Martin Dell Acqua, Arnulfo Encina, Hernán Figueredo, Fernando Fontes, Joan S. Gutiérrez-Díaz, Wilmer Jiménez, Raúl S. Lavado, Jesús F. Mansilla-Baca, Maria de Lourdes Mendonça-Santos, Lucas M. Moretti, Iván D. Muñoz, Carolina Olivera, Guillermo Olmedo, Christian Omuto, Sol Ortiz, Carla Pascale, Marco Pfeiffer, Iván A. Ramos, Danny Ríos, Rafael Rivera, Lady M. Rodriguez, Darío M. Rodríguez, Albán Rosales, Kenset Rosales, Guillermo Schulz, Víctor Sevilla, Leonardo M. Tenti, Ronald Vargas, Gustavo M. Vasques, Yusuf Yigini, and Yolanda Rubiano
Earth Syst. Sci. Data, 16, 1229–1246, https://doi.org/10.5194/essd-16-1229-2024, https://doi.org/10.5194/essd-16-1229-2024, 2024
Short summary
Short summary
In this work, the Latin America and Caribbean Soil Information System (SISLAC) database (https://54.229.242.119/sislac/es) was revised to generate an improved version of the data. Rules for data enhancement were defined. In addition, other datasets available in the region were included. Subsequently, through a principal component analysis (PCA), the main soil characteristics for the region were analyzed. We hope this dataset can help mitigate problems such as food security and global warming.
Daphne Armas, Mario Guevara, Fernando Bezares, Rodrigo Vargas, Pilar Durante, Víctor Osorio, Wilmer Jiménez, and Cecilio Oyonarte
Earth Syst. Sci. Data, 15, 431–445, https://doi.org/10.5194/essd-15-431-2023, https://doi.org/10.5194/essd-15-431-2023, 2023
Short summary
Short summary
The global need for updated soil datasets has increased. Our main objective was to synthesize and harmonize soil profile information collected by two different projects in Ecuador between 2009 and 2015.The main result was the development of the Harmonized Soil Database of Ecuador (HESD) that includes information from 13 542 soil profiles with over 51 713 measured soil horizons, including 92 different edaphic variables, and follows international standards for archiving and sharing soil data.
Katherine E. O. Todd-Brown, Rose Z. Abramoff, Jeffrey Beem-Miller, Hava K. Blair, Stevan Earl, Kristen J. Frederick, Daniel R. Fuka, Mario Guevara Santamaria, Jennifer W. Harden, Katherine Heckman, Lillian J. Heran, James R. Holmquist, Alison M. Hoyt, David H. Klinges, David S. LeBauer, Avni Malhotra, Shelby C. McClelland, Lucas E. Nave, Katherine S. Rocci, Sean M. Schaeffer, Shane Stoner, Natasja van Gestel, Sophie F. von Fromm, and Marisa L. Younger
Biogeosciences, 19, 3505–3522, https://doi.org/10.5194/bg-19-3505-2022, https://doi.org/10.5194/bg-19-3505-2022, 2022
Short summary
Short summary
Research data are becoming increasingly available online with tantalizing possibilities for reanalysis. However harmonizing data from different sources remains challenging. Using the soils community as an example, we walked through the various strategies that researchers currently use to integrate datasets for reanalysis. We find that manual data transcription is still extremely common and that there is a critical need for community-supported informatics tools like vocabularies and ontologies.
Related subject area
Domain: ESSD – Land | Subject: Pedology
Improving the Latin America and Caribbean Soil Information System (SISLAC) database enhances its usability and scalability
The patterns of soil nitrogen stocks and C : N stoichiometry under impervious surfaces in China
Mapping of peatlands in the forested landscape of Sweden using lidar-based terrain indices
Harmonized Soil Database of Ecuador (HESD): data from 2009 to 2015
ChinaCropSM1 km: a fine 1 km daily soil moisture dataset for dryland wheat and maize across China during 1993–2018
SGD-SM 2.0: an improved seamless global daily soil moisture long-term dataset from 2002 to 2022
A high spatial resolution soil carbon and nitrogen dataset for the northern permafrost region based on circumpolar land cover upscaling
A repository of measured soil freezing characteristic curves: 1921 to 2021
A compiled soil respiration dataset at different time scales for forest ecosystems across China from 2000 to 2018
Sergio Díaz-Guadarrama, Viviana M. Varón-Ramírez, Iván Lizarazo, Mario Guevara, Marcos Angelini, Gustavo A. Araujo-Carrillo, Jainer Argeñal, Daphne Armas, Rafael A. Balta, Adriana Bolivar, Nelson Bustamante, Ricardo O. Dart, Martin Dell Acqua, Arnulfo Encina, Hernán Figueredo, Fernando Fontes, Joan S. Gutiérrez-Díaz, Wilmer Jiménez, Raúl S. Lavado, Jesús F. Mansilla-Baca, Maria de Lourdes Mendonça-Santos, Lucas M. Moretti, Iván D. Muñoz, Carolina Olivera, Guillermo Olmedo, Christian Omuto, Sol Ortiz, Carla Pascale, Marco Pfeiffer, Iván A. Ramos, Danny Ríos, Rafael Rivera, Lady M. Rodriguez, Darío M. Rodríguez, Albán Rosales, Kenset Rosales, Guillermo Schulz, Víctor Sevilla, Leonardo M. Tenti, Ronald Vargas, Gustavo M. Vasques, Yusuf Yigini, and Yolanda Rubiano
Earth Syst. Sci. Data, 16, 1229–1246, https://doi.org/10.5194/essd-16-1229-2024, https://doi.org/10.5194/essd-16-1229-2024, 2024
Short summary
Short summary
In this work, the Latin America and Caribbean Soil Information System (SISLAC) database (https://54.229.242.119/sislac/es) was revised to generate an improved version of the data. Rules for data enhancement were defined. In addition, other datasets available in the region were included. Subsequently, through a principal component analysis (PCA), the main soil characteristics for the region were analyzed. We hope this dataset can help mitigate problems such as food security and global warming.
Qian Ding, Hua Shao, Chi Zhang, and Xia Fang
Earth Syst. Sci. Data, 15, 4599–4612, https://doi.org/10.5194/essd-15-4599-2023, https://doi.org/10.5194/essd-15-4599-2023, 2023
Short summary
Short summary
A soil survey in 41 Chinese cities showed the soil nitrogen (N) in impervious surface areas (ISA; NISA) was 0.59±0.35 kg m−2, lower than in pervious soils. Eastern China had the highest NISA but the lowest natural soil N in China. Soil N decreased linearly with depth in ISA but nonlinearly in natural ecosystems. Temperature was negatively correlated with C : NISA but positively correlated with natural soil C : N. The unique NISA patterns imply intensive disturbance in N cycle by soil sealing.
Lukas Rimondini, Thomas Gumbricht, Anders Ahlström, and Gustaf Hugelius
Earth Syst. Sci. Data, 15, 3473–3482, https://doi.org/10.5194/essd-15-3473-2023, https://doi.org/10.5194/essd-15-3473-2023, 2023
Short summary
Short summary
Peatlands have historically sequestrated large amounts of carbon and contributed to atmospheric cooling. However, human activities and climate change may instead turn them into considerable carbon emitters. In this study, we produced high-quality maps showing the extent of peatlands in the forests of Sweden, one of the most peatland-dense countries in the world. The maps are publicly available and may be used to support work promoting sustainable peatland management and combat their degradation.
Daphne Armas, Mario Guevara, Fernando Bezares, Rodrigo Vargas, Pilar Durante, Víctor Osorio, Wilmer Jiménez, and Cecilio Oyonarte
Earth Syst. Sci. Data, 15, 431–445, https://doi.org/10.5194/essd-15-431-2023, https://doi.org/10.5194/essd-15-431-2023, 2023
Short summary
Short summary
The global need for updated soil datasets has increased. Our main objective was to synthesize and harmonize soil profile information collected by two different projects in Ecuador between 2009 and 2015.The main result was the development of the Harmonized Soil Database of Ecuador (HESD) that includes information from 13 542 soil profiles with over 51 713 measured soil horizons, including 92 different edaphic variables, and follows international standards for archiving and sharing soil data.
Fei Cheng, Zhao Zhang, Huimin Zhuang, Jichong Han, Yuchuan Luo, Juan Cao, Liangliang Zhang, Jing Zhang, Jialu Xu, and Fulu Tao
Earth Syst. Sci. Data, 15, 395–409, https://doi.org/10.5194/essd-15-395-2023, https://doi.org/10.5194/essd-15-395-2023, 2023
Short summary
Short summary
We generated a 1 km daily soil moisture dataset for dryland wheat and maize across China (ChinaCropSM1 km) over 1993–2018 through random forest regression, based on in situ observations. Our improved products have a remarkably better quality compared with the public global products in terms of both spatial and time dimensions by integrating an irrigation module (crop type, phenology, soil depth). The dataset may be useful for agriculture drought monitoring and crop yield forecasting studies.
Qiang Zhang, Qiangqiang Yuan, Taoyong Jin, Meiping Song, and Fujun Sun
Earth Syst. Sci. Data, 14, 4473–4488, https://doi.org/10.5194/essd-14-4473-2022, https://doi.org/10.5194/essd-14-4473-2022, 2022
Short summary
Short summary
Compared to previous seamless global daily soil moisture (SGD-SM 1.0) products, SGD-SM 2.0 enlarges the temporal scope from 2002 to 2022. By fusing auxiliary precipitation information with the long short-term memory convolutional neural network (LSTM-CNN) model, SGD-SM 2.0 can consider sudden extreme weather conditions for 1 d in global daily soil moisture products and is significant for full-coverage global daily hydrologic monitoring, rather than averaging monthly–quarterly–yearly results.
Juri Palmtag, Jaroslav Obu, Peter Kuhry, Andreas Richter, Matthias B. Siewert, Niels Weiss, Sebastian Westermann, and Gustaf Hugelius
Earth Syst. Sci. Data, 14, 4095–4110, https://doi.org/10.5194/essd-14-4095-2022, https://doi.org/10.5194/essd-14-4095-2022, 2022
Short summary
Short summary
The northern permafrost region covers 22 % of the Northern Hemisphere and holds almost twice as much carbon as the atmosphere. This paper presents data from 651 soil pedons encompassing more than 6500 samples from 16 different study areas across the northern permafrost region. We use this dataset together with ESA's global land cover dataset to estimate soil organic carbon and total nitrogen storage up to 300 cm soil depth, with estimated values of 813 Pg for carbon and 55 Pg for nitrogen.
Élise G. Devoie, Stephan Gruber, and Jeffrey M. McKenzie
Earth Syst. Sci. Data, 14, 3365–3377, https://doi.org/10.5194/essd-14-3365-2022, https://doi.org/10.5194/essd-14-3365-2022, 2022
Short summary
Short summary
Soil freezing characteristic curves (SFCCs) relate the temperature of a soil to its ice content. SFCCs are needed in all physically based numerical models representing freezing and thawing soils, and they affect the movement of water in the subsurface, biogeochemical processes, soil mechanics, and ecology. Over a century of SFCC data exist, showing high variability in SFCCs based on soil texture, water content, and other factors. This repository summarizes all available SFCC data and metadata.
Hongru Sun, Zhenzhu Xu, and Bingrui Jia
Earth Syst. Sci. Data, 14, 2951–2961, https://doi.org/10.5194/essd-14-2951-2022, https://doi.org/10.5194/essd-14-2951-2022, 2022
Short summary
Short summary
We compiled a new soil respiration (Rs) database of China's forests from 568 studies published up to 2018. The hourly, monthly, and annual samples were 8317, 5003, and 634, respectively. Most of the Rs data are shown in figures but were seldom exploited. For the first time, these data were digitized, accounting for 82 % of samples. Rs measured with common methods was selected (Li-6400, Li-8100, Li-8150, gas chromatography) and showed small differences of ~10 %. Bamboo had the highest Rs.
Cited articles
Aitchison, J.: The statistical analysis of compositional data, Chapman and
Hall, Blackburn Press, 460 pp., ISBN-10 1930665784, 1986. a
Amirian-Chakan, A., Minasny, B., Taghizadeh-Mehrjardi, R., Akbarifazli, R.,
Darvishpasand, Z., and Khordehbin, S.: Some practical aspects of predicting
texture data in digital soil mapping, Soil Till. Res., 194,
104289, https://doi.org/10.1016/j.still.2019.06.006, 2019. a, b, c
Angelini, M., Kempen, B., Heuvelink, G., Temme, A., and Ransom, M.:
Extrapolation of a structural equation model for digital soil mapping,
Geoderma, 367, 114226, https://doi.org/10.1016/j.geoderma.2020.114226, 2020. a
Angelini, M. E., Heuvelink, G. B., Kempen, B., and Morrás, H. J.: Mapping the
soils of an Argentine Pampas region using structural equation modelling,
Geoderma, 281, 102–118, https://doi.org/10.1016/j.geoderma.2016.06.031, 2016. a
Araujo, M. A., Zinn, Y. L., and Lal, R.: Soil parent material, texture and
oxide contents have little effect on soil organic carbon retention in
tropical highlands, Geoderma, 300, 1–10,
https://doi.org/10.1016/j.geoderma.2017.04.006, 2017. a
Araujo-Carrillo, G. A., Varón-Ramírez, V. M., Jaramillo-Barrios,
C. I., Estupiñan-Casallas, J. M., Silva-Arero, E. A., Gómez-Latorre,
D. A., and Martínez-Maldonado, F. E.: IRAKA: The first Colombian soil
information system with digital soil mapping products, Catena, 196, 104940,
https://doi.org/10.1016/j.catena.2020.104940, 2021. a
Arrouays, D., Grundy, M. G., Hartemink, A. E., Hempel, J. W., Heuvelink, G. B.,
Hong, S. Y., Lagacherie, P., Lelyk, G., McBratney, A. B., McKenzie, N. J.,
d.L. Mendonca-Santos, M., Minasny, B., Montanarella, L., Odeh, I. O.,
Sanchez, P. A., Thompson, J. A., and Zhang, G.-L.: Chapter Three –
GlobalSoilMap: Toward a Fine-Resolution Global Grid of Soil Properties, vol.
125 of Advances in Agronomy, Academic Press, 93–134,
https://doi.org/10.1016/B978-0-12-800137-0.00003-0, 2014. a
Beaudette, D. E., Roudier, P., and O'Geen, A.: Algorithms for quantitative
pedology: A toolkit for soil scientists, Comput. Geosci., 52,
258–268, https://doi.org/10.1016/j.cageo.2012.10.020, 2013. a
Bischl, B., Lang, M., Kotthoff, L., Schiffner, J., Richter, J., Studerus, E.,
Casalicchio, G., and Jones, Z. M.: mlr: Machine Learning in R, J.
Mach. Learn. Res., 17, 1–5,
2016. a
Bishop, T., McBratney, A., and Laslett, G.: Modelling soil attribute depth
functions with equal-area quadratic smoothing splines, Geoderma, 91, 27–45,
https://doi.org/10.1016/S0016-7061(99)00003-8, 1999. a
Bönecke, E., Meyer, S., Vogel, S.,
Schröter, I., Gebbers, R., Kling, C., Kramer,
E., Lück, K., Nagel, A., Philipp, G., Gerlach,
F., Palme, S., Scheibe, D., Zieger, K., and
Rühlmann, J.: Guidelines for precise lime
management based on high-resolution soil pH, texture and SOM maps generated
from proximal soil sensing data, Precis. Agric., 22, 493–523,
https://doi.org/10.1007/s11119-020-09766-8, 2021. a
Breiman, L.: Statistical Modeling: The Two Cultures (with comments and a
rejoinder by the author), Statist. Sci., 16, 199–231,
https://doi.org/10.1214/ss/1009213726, 2001. a
Brenning, A.: Spatial cross-validation and bootstrap for the assessment of
prediction rules in remote sensing: The R package sperrorest, in: 2012 IEEE
International Geoscience and Remote Sensing Symposium, 5372–5375,
https://doi.org/10.1109/IGARSS.2012.6352393, 2012. a
Brus, D., Kempen, B., and Heuvelink, G.: Sampling for validation of digital
soil maps, Eur. J. Soil Sci., 62, 394–407,
https://doi.org/10.1111/j.1365-2389.2011.01364.x, 2011. a
Campbell, P. M. d. M., Fernandes, E. I., Francelino, M. R., Demattê, J.
A. M., Pereira, M. G., Guimarães, C. C. B., and Pinto, L. A. D. S. R.:
Digital soil mapping of soil properties in the “Mar de Morros”
environment using spectral data, Revista Brasileira de Ciência do Solo,
42, e0170413, https://doi.org/10.1590/18069657rbcs20170413, 2019. a
Catoni, M., D'Amico, M. E., Zanini, E., and Bonifacio, E.: Effect of pedogenic
processes and formation factors on organic matter stabilization in alpine
forest soils, Geoderma, 263, 151–160, https://doi.org/10.1016/j.geoderma.2015.09.005,
2016. a
Caubet, M., Román Dobarco, M., Arrouays, D.,
Minasny, B., and Saby, N. P. A.: Merging country, continental and global
predictions of soil texture: Lessons from ensemble modelling in France,
Geoderma, 337, 99–110, https://doi.org/10.1016/j.geoderma.2018.09.007, 2019. a
Cortés, A., Cortés, M., Guevara, J., and Palacino, A.: Mapas de suelos
de Colombia, Memoria explicativa, Instituto Geográfico Agustín
Codazzi (IGAC), Subdirección Agrológica, Bogotá, 1982. a
Dharumarajan, S. and Hegde, R.: Digital mapping of soil texture classes using
Random Forest classification algorithm, Soil Use Manage., 38, 135–149,
https://doi.org/10.1111/sum.12668, 2020. a
FAO: Sistema de Información de Suelos de Latinoamérica y el Caribe –
SISLAC, http://54.229.242.119/sislac/es (last access: 12 April 2021), 2020. a
Flórez, A.: Colombia: evolución de sus relieves y modelados, Unilibros,
Bogotá, https://repositorio.unal.edu.co/handle/unal/53415 (last access: 24 August 2022), 2003. a
Grunwald, S., Thompson, J. A., and Boettinger, J. L.: Digital Soil Mapping and
Modeling at Continental Scales: Finding Solutions for Global Issues, Soil
Sci. Soc. Am. J., 75, 1201–1213, https://doi.org/10.2136/sssaj2011.0025, 2011. a
Guevara, M., Olmedo, G. F., Stell, E., Yigini, Y., Aguilar Duarte, Y., Arellano Hernández, C., Arévalo, G. E., Arroyo-Cruz, C. E., Bolivar, A., Bunning, S., Bustamante Cañas, N., Cruz-Gaistardo, C. O., Davila, F., Dell Acqua, M., Encina, A., Figueredo Tacona, H., Fontes, F., Hernández Herrera, J. A., Ibelles Navarro, A. R., Loayza, V., Manueles, A. M., Mendoza Jara, F., Olivera, C., Osorio Hermosilla, R., Pereira, G., Prieto, P., Ramos, I. A., Rey Brina, J. C., Rivera, R., Rodríguez-Rodríguez, J., Roopnarine, R., Rosales Ibarra, A., Rosales Riveiro, K. A., Schulz, G. A., Spence, A., Vasques, G. M., Vargas, R. R., and Vargas, R.: No silver bullet for digital soil mapping: country-specific soil organic carbon estimates across Latin America, SOIL, 4, 173–193, https://doi.org/10.5194/soil-4-173-2018, 2018. a
Hengl, T. and MacMillan, R. A.: Predictive soil mapping with R, OpenGeoHub foundation, Wageningen,
Netherlands, 370 pp., ISBN 978-0-359-30635-0,
2019. a
Hengl, T., de Jesus, J. M., MacMillan, R. A., Batjes, N. H., Heuvelink, G. B.,
Ribeiro, E., Samuel-Rosa, A., Kempen, B., Leenaars, J. G., Walsh, M. G.,
and Ruiperez Gonzalez, M.: SoilGrids1km – global soil information based on automated mapping,
PloS one, 9, e105992, https://doi.org/10.1371/journal.pone.0105992, 2014. a, b
Hengl, T., Miller, M. A. E., Križan, J.,
Shepherd, K. D., Sila, A., Kilibarda, M.,
Antonijević, O.,
Glušica, L., Dobermann, A., Haefele, S. M.,
McGrath, S. P., Acquah, G. E., Collinson, J., Parente, L., Sheykhmousa, M.,
Saito, K., Johnson, J.-M., Chamberlin, J., Silatsa, F. B. T., Yemefack, M.,
Wendt, J., MacMillan, R. A., Wheeler, I., and Crouch, J.: African soil
properties and nutrients mapped at 30 m spatial resolution using two-scale
ensemble machine learning – Scientific Reports, Sci. Rep., 11, 1–18,
https://doi.org/10.1038/s41598-021-85639-y, 2021. a, b, c
IDEAM: Mapa de Coberturas de la Tierra Metodología Corine Land Cover adaptada
para Colombia Escala 1:100.000 (Período 2010–2012), 2014. a
IDEAM: Climatological atlas of Colombia – Interactive year 2015,
http://atlas.ideam.gov.co/visorAtlasClimatologico.html (last access: 4 May 2021), 2015. a
IGAC: Mapa Suelos de Colombia, IGAC, Bogotá, Instituto Geográfico Agustín
Codazzi (IGAC), ISBN 9589067670,
2003. a
James, G., Witten, D., Hastie, T., and Tibshirani, R.: An Introduction to
Statistical Learning: with Applications in R, Springer,
https://doi.org/10.1007/978-1-0716-1418-1, 2013. a
Kaya, F. and Başayiğit, L.: Spatial Prediction and Digital Mapping
of Soil Texture Classes in a Floodplain Using Multinomial Logistic
Regression, in: Intelligent and Fuzzy Techniques for Emerging Conditions and
Digital Transformation, edited by: Kahraman, C., Cebi, S., Cevik Onar, S.,
Oztaysi, B., Tolga, A. C., and Sari, I. U., Springer
International Publishing, Cham, 463–473, https://doi.org/10.1007/978-3-030-85577-2_55, 2022. a
Kempen, B., Brus, D. J., Heuvelink, G. B., and Stoorvogel, J. J.: Updating the
1:50,000 Dutch soil map using legacy soil data: A multinomial logistic
regression approach, Geoderma, 151, 311–326,
https://doi.org/10.1016/j.geoderma.2009.04.023, 2009. a
Kempen, B., Brus, D. J., Stoorvogel, J. J., Heuvelink, G. B., and de Vries, F.:
Efficiency comparison of conventional and digital soil mapping for updating
soil maps, Soil Sci. Soc. Am. J., 76, 2097–2115,
https://doi.org/10.2136/sssaj2011.0424, 2012. a
Khaledian, Y. and Miller, B. A.: Selecting appropriate machine learning methods
for digital soil mapping, Appl. Math. Modell., 81, 401–418, 2020. a
Laborczi, A., Szatmári, G., Kaposi, A. D., and Pásztor, L.: Comparison
of soil texture maps synthetized from standard depth layers with directly
compiled products, Geoderma, 352, 360–372,
https://doi.org/10.1016/j.geoderma.2018.01.020, 2019. a, b
Lagacherie, P., Arrouays, D., Bourennane, H., Gomez, C., and Nkuba-Kasanda, L.:
Analysing the impact of soil spatial sampling on the performances of Digital
Soil Mapping models and their evaluation: A numerical experiment on Quantile
Random Forest using clay contents obtained from Vis-NIR-SWIR hyperspectral
imagery, Geoderma, 375, 114503, https://doi.org/10.1016/j.geoderma.2020.114503, 2020. a
Lang, M., Binder, M., Richter, J., Schratz, P., Pfisterer, F., Coors, S., Au,
Q., Casalicchio, G., Kotthoff, L., and Bischl, B.: mlr3: A modern
object-oriented machine learning framework in R, J. Open Source
Softw., 4, 44, https://doi.org/10.21105/joss.01903, 2019. a
Lark, R. and Bishop, T.: Cokriging particle size fractions of the soil,
Eur. J. Soil Sci., 58, 763–774,
https://doi.org/10.1111/j.1365-2389.2006.00866.x, 2007. a
Lawrence, I. and Lin, K.: A concordance correlation coefficient to evaluate
reproducibility, Biometrics, 45, 255–268, https://doi.org/10.2307/2532051, 1989. a
Li, J., Wan, H., and Shang, S.: Comparison of interpolation methods for mapping
layered soil particle-size fractions and texture in an arid oasis, CATENA,
190, 104514, https://doi.org/10.1016/j.catena.2020.104514, 2020. a, b, c, d
Liu, F., Zhang, G.-L., Song, X., Li, D., Zhao, Y., Yang, J., Wu, H., and Yang,
F.: High-resolution and three-dimensional mapping of soil texture of China,
Geoderma, 361, 114061, https://doi.org/10.1016/j.geoderma.2019.114061, 2020. a, b, c, d
Llamas, R. M., Guevara, M., Rorabaugh, D., Taufer, M., and Vargas, R.: Spatial
Gap-Filling of ESA CCI Satellite-Derived Soil Moisture Based on
Geostatistical Techniques and Multiple Regression, Remote Sens., 12, 665,
https://doi.org/10.3390/rs12040665, 2020. a
Lovelace, R., Nowosad, J., and Muenchow, J.: Geocomputation with R, CRC Press, ISBN-10 1138304514,
2019. a
Mallavan, B., Minasny, B., and McBratney, A.: Homosoil, a Methodology for
Quantitative Extrapolation of Soil Information Across the Globe,
Springer Netherlands, Dordrecht, 137–150,
https://doi.org/10.1007/978-90-481-8863-5_12, 2010. a
Malone, B., Searle, R., Malone, B., and Searle, R.: Updating the Australian
digital soil texture mapping (Part 2∗): spatial modelling of merged
field and lab measurements, Soil Res., 59, 435–451, https://doi.org/10.1071/SR20284,
2021. a
Malone, B. P., Jha, S. K., Minasny, B., and McBratney, A. B.: Comparing
regression-based digital soil mapping and multiple-point geostatistics for
the spatial extrapolation of soil data, Geoderma, 262, 243–253,
https://doi.org/10.1016/j.geoderma.2015.08.037, 2016. a
McBratney, A. B., Mendonça Santos, M. L., and
Minasny, B.: On digital soil mapping, Geoderma, 117, 3–52,
https://doi.org/10.1016/S0016-7061(03)00223-4, 2003. a, b
Minasny, B. and McBratney, A.: Methodologies for global soil mapping, in:
Digital soil mapping, Springer, 429–436,
https://doi.org/10.1007/978-90-481-8863-5_34, 2010. a
Møller, A. B., Beucher, A. M., Pouladi, N., and Greve, M. H.: Oblique geographic coordinates as covariates for digital soil mapping, SOIL, 6, 269–289, https://doi.org/10.5194/soil-6-269-2020, 2020. a
Mulder, V. L., Lacoste, M., Richer-de Forges, A., and Arrouays, D.:
GlobalSoilMap France: High-resolution spatial modelling the soils of France
up to two meter depth, Sci. Total Environ., 573, 1352–1369,
https://doi.org/10.1016/j.scitotenv.2016.07.066, 2016. a, b
Niang, M. A., Nolin, M. C., Jégo, G., and Perron, I.: Digital Mapping of
Soil Texture Using RADARSAT-2 Polarimetric Synthetic Aperture Radar Data,
Soil Sci. Soc. Am. J., 78, 673–684,
https://doi.org/10.2136/sssaj2013.07.0307, 2014. a
Odeh, I. O., Todd, A. J., and Triantafilis, J.: Spatial prediction of soil
particle-size fractions as compositional data, Soil Sci., 168, 501–515,
https://doi.org/10.1097/01.ss.0000080335.10341.23, 2003. a
Orton, T., Pringle, M., and Bishop, T.: A one-step approach for modelling and
mapping soil properties based on profile data sampled over varying depth
intervals, Geoderma, 262, 174–186, https://doi.org/10.1016/j.geoderma.2015.08.013,
2016. a
Osman, K. T.: Soils: principles, properties and management, Dordrecht, New
York, Springer, ISBN 978-94-007-5662-5, https://doi.org/10.1007/978-94-007-5663-2, 2013. a
Patel, K. F., Fansler, S. J., Campbell, T. P., Bond-Lamberty, B., Smith, A. P.,
RoyChowdhury, T., McCue, L. A., Varga, T., and Bailey, V. L.: Soil texture
and environmental conditions influence the biogeochemical responses of soils
to drought and flooding, Commun. Earth Environ., 2, 127,
https://doi.org/10.1038/s43247-021-00198-4, 2021. a
Pawlowsky-Glahn, V. and Olea, R. A.: Geostatistical analysis of compositional
data, Oxford University Press, Online ISBN
9780197565513, https://doi.org/10.1093/oso/9780195171662.001.0001, 2004. a
Poggio, L., de Sousa, L. M., Batjes, N. H., Heuvelink, G. B. M., Kempen, B., Ribeiro, E., and Rossiter, D.: SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty, SOIL, 7, 217–240, https://doi.org/10.5194/soil-7-217-2021, 2021. a, b
Polley, E. C. and Van der Laan, M. J.: Super learner in prediction, U.C.
Berkeley Division of Biostatistics Working Paper Series, 266, http://biostats.bepress.com/ucbbiostat/paper266 (last access: 25 October 2021), 2010. a
Poveda, G.: La hidroclimatología de Colombia: una síntesis desde la
escala inter-decadal hasta la escala diurna, Rev. Acad. Colomb. Cienc, 28,
201–222, 2004. a
Radočaj, D.,
Jurišić, M.,
Zebec, V., and
Plaščak, I.:
Delineation of Soil Texture Suitability Zones for Soybean Cultivation: A
Case Study in Continental Croatia, Agronomy, 10, 823,
https://doi.org/10.3390/agronomy10060823, 2020. a
Ramcharan, A., Hengl, T., Nauman, T. W., Brungard, C. W., Waltman, S. W.,
Wills, S., and Thompson, J.: Soil Property and Class Maps of the Conterminous
United States at 100-Meter Spatial Resolution, Soil Sci. Soc.
Am. J., 82, 186–201, https://doi.org/10.2136/sssaj2017.04.0122, 2018. a
Rangel-Ch, J. O. and Aguilar, M.: Una aproximación sobre la diversidad
climática en las regiones naturales de Colombia, Diversidad Biótica
I. Instituto de Ciencias Naturales-Universidad Nacional de Colombia-Inderena,
Bogotá, 25–77, 1995. a
Richer-de Forges, A. C., Arrouays, D., Chen, S., Dobarco, M. R., Libohova, Z.,
Roudier, P., Minasny, B., and Bourennane, H.: Hand-feel soil texture and
particle-size distribution in central France, Relationships and implications,
Catena, 213, 106155, https://doi.org/10.1016/j.catena.2022.106155, 2022. a
Samuel-Rosa, A., Heuvelink, G., Vasques, G., and Anjos, L.: Do more detailed
environmental covariates deliver more accurate soil maps?, Geoderma, 243,
214–227, https://doi.org/10.1016/j.geoderma.2014.12.017, 2015. a
Soil Survey Staff: Keys to Soil Taxonomy, 12th Edn., USDA-Natural Resources
Conservation Service, 360 pp., https://www.nrcs.usda.gov/sites/default/files/2022-09/Keys-to-Soil-Taxonomy.pdf (last access: 23 November 2021), 2014. a
Soropa, G., Mbisva, O. M., Nyamangara, J., Nyakatawa, E. Z., Nyapwere, N., and
Lark, R. M.: Spatial variability and mapping of soil fertility status in a
high-potential smallholder farming area under sub-humid conditions in
Zimbabwe, SN Appl. Sci., 3, 1–19, https://doi.org/10.1007/s42452-021-04367-0, 2021. a
Tsagris, M., Giorgos, A., Alenazi, A., and Adam, C.: Compositional:
Compositional Data Analysis,
https://cran.r-project.org/web/packages/Compositional/index.html (lst access: 19 April 2022), 2022. a
Varón-Ramírez, V. and Araujo-Carrillo, G.: Textural soil data,
Colombia, 0–100 cm, Environmental Data Initiative [data set], https://doi.org/10.6073/pasta/3f91778c2f6ad46c3cc70b61f02532db,
2022. a, b
Varón-Ramírez, V., Araujo-Carrillo, G., and Guevara, M.: Textural soil
maps, Colombia, 0–100 cm, Environmental Data Initiative [data set],
https://doi.org/10.6073/pasta/d6c0bf5847aa40836b42dcc3e0ea874e, 2022. a, b
VimiVaron: VimiVaron/Textural-maps-Colombia: Colombian Soil Texture Code,
Version 1, Zenodo [code], https://doi.org/10.5281/zenodo.7185675, 2022. a
Wadoux, A. M.-C., Román-Dobarco, M., and McBratney, A. B.: Perspectives on
data-driven soil research, Eur. J. Soil Sci., 72, 1675–1689,
https://doi.org/10.1016/j.apm.2019.12.016, 2021a. a
Wadoux, A. M. J.-C., Minasny, B., and McBratney, A. B.: Machine learning for
digital soil mapping: Applications, challenges and suggested solutions,
Earth-Sci. Rev., 210, 103359, https://doi.org/10.1016/j.earscirev.2020.103359, 2020. a
Wadoux, A. M. J.-C., Heuvelink, G. B. M., de Bruin, S., and Brus, D. J.:
Spatial cross-validation is not the right way to evaluate map accuracy,
Ecol. Model., 457, 109692, https://doi.org/10.1016/j.ecolmodel.2021.109692,
2021b. a
Wang, Z. and Shi, W.: Mapping soil particle-size fractions: A comparison of
compositional kriging and log-ratio kriging, J. Hydrol., 546,
526–541, https://doi.org/10.1016/j.jhydrol.2017.01.029, 2017. a
Wang, Z., Shi, W., Zhou, W., Li, X., and Yue, T.: Comparison of additive and
isometric log-ratio transformations combined with machine learning and
regression kriging models for mapping soil particle size fractions, Geoderma,
365, 114214, https://doi.org/10.1016/j.geoderma.2020.114214, 2020.
a
Webster, R. and Oliver, M. A.: Geostatistics for environmental scientists, John
Wiley & Sons, https://doi.org/10.1002/9780470517277, 2007. a, b
Witten, I., Frank, E., Hall, M., and Pal, C.: What's It All About?, Data
Mining: Practical machine learning tools and techniques, 3–38,
https://doi.org/10.1016/C2009-0-19715-5, 2011. a
Yang, Y.: Chapter 4 – Ensemble Learning, in: Temporal Data Mining Via
Unsupervised Ensemble Learning, edited by: Yang, Y., Elsevier, 35–56,
https://doi.org/10.1016/B978-0-12-811654-8.00004-X, 2017. a
Yigini, Y., Olmedo, G., Reiter, S., Baritz, R., Viatkin, K., and Vargas, R.:
Soil organic carbon mapping: Cookbook, 223 pp., ISBN 978-92-5-130440-2, 2018. a
Zhang, C. and Ma, Y.: Ensemble machine learning: methods and applications,
Springer, 332 pp., ISBN 978-1-4419-9326-7, https://doi.org/10.1007/978-1-4419-9326-7, 2012. a
Zhang, Y. and Hartemink, A. E.: Quantifying short-range variation of soil
texture and total carbon of a 330-ha farm, Catena, 201, 105200,
https://doi.org/10.1016/j.catena.2021.105200, 2021. a
Zounemat-Kermani, M., Batelaan, O., Fadaee, M., and Hinkelmann, R.: Ensemble
machine learning paradigms in hydrology: A review, J. Hydrol., 598,
126266, https://doi.org/10.1016/j.jhydrol.2021.126266, 2021. a
Short summary
These are the first national soil texture maps obtained via digital soil mapping. We built clay, sand, and silt maps using spatial assembling with the best possible predictions at different depths. Also, we identified the better model for each pixel. This work was done to address the lack of soil texture maps in Colombia, and it can provide soil information for water-related applications, ecosystem services, and agricultural and crop modeling.
These are the first national soil texture maps obtained via digital soil mapping. We built clay,...
Altmetrics
Final-revised paper
Preprint