Articles | Volume 6, issue 1
https://doi.org/10.5194/soil-6-163-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/soil-6-163-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Original research article
| 
12 May 2020
Original research article |
| 12 May 2020
| 12 May 2020
Soil environment grouping system based on spectral, climate, and terrain data: a quantitative branch of soil series
Andre Carnieletto Dotto
Department of Soil Science, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, SP, 13418-900, Brazil
Jose A. M. Demattê
CORRESPONDING AUTHOR
Department of Soil Science, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, SP, 13418-900, Brazil
Raphael A. Viscarra Rossel
School of Molecular and Life Sciences, Curtin University, Perth, WA 6102, Australia
Rodnei Rizzo
Department of Soil Science, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, SP, 13418-900, Brazil
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- Evaluating the use of explicitly spatial models and non-spatial models to estimate soil particle-size fractions and soil texture using Sentinel 1 and covariates S. Deodoro et al. https://doi.org/10.1016/j.soisec.2026.100235
- A sensors-based profile heterogeneity index for soil characterization A. Barros e Souza et al. https://doi.org/10.1016/j.catena.2021.105670
- Can environmental clustering reveal soil profile patterns? A depth-based approach at field scale Z. Rasaei et al. https://doi.org/10.1016/j.catena.2025.109695
- Improving spectral estimation of soil inorganic carbon in urban and suburban areas by coupling continuous wavelet transform with geographical stratification Y. Hong et al. https://doi.org/10.1016/j.geoderma.2022.116284
- A Moroccan soil spectral library use framework for improving soil property prediction: Evaluating a geostatistical approach T. Asrat et al. https://doi.org/10.1016/j.geoderma.2024.117116
- Quantitative classification of ‘excessively drained’ and ‘somewhat excessively drained’ of Korean soils using soil properties D. Lee et al. https://doi.org/10.7745/KJSSF.2025.58.1.144
- Complex hydrological knowledge to support digital soil mapping F. Mello et al. https://doi.org/10.1016/j.geoderma.2021.115638
- The Brazilian Soil Spectral Library data opening J. Novais et al. https://doi.org/10.19047/0136-1694-2024-119-261-305
- An interlaboratory comparison of mid-infrared spectra acquisition: Instruments and procedures matter J. Safanelli et al. https://doi.org/10.1016/j.geoderma.2023.116724
- Spectral Characteristics of Hydromorphic Soils: A Review R. Döbröntey et al. https://doi.org/10.1134/S1064229326601733
- A binary prediction model to assess the oxidation of Gley Lowland soils in Japan M. Morishita et al. https://doi.org/10.1080/00380768.2024.2448453
- Fine-scale soil mapping with Earth Observation data: a multiple geographic level comparison J. Safanelli et al. https://doi.org/10.36783/18069657rbcs20210080
- An assessment of Sentinel‐1 synthetic aperture radar, geophysical and topographical covariates for estimating topsoil particle‐size fractions S. Deodoro et al. https://doi.org/10.1111/ejss.13414
- Proximal sensing approach for soil characterization and discrimination: a case of study in Brazil A. Gómez et al. https://doi.org/10.1080/10106049.2022.2102228
17 citations as recorded by crossref.
- Combining multiple methods for automated soil delineation: from traditional to digital F. Mello et al. https://doi.org/10.1071/SR21067
- Using the surface scattering mechanism from dual-pol SAR data to estimate topsoil particle-sizefractions S. Deodoro et al. https://doi.org/10.1016/j.jag.2024.103742
- Data mining of urban soil spectral library for estimating organic carbon Y. Hong et al. https://doi.org/10.1016/j.geoderma.2022.116102
- Evaluating the use of explicitly spatial models and non-spatial models to estimate soil particle-size fractions and soil texture using Sentinel 1 and covariates S. Deodoro et al. https://doi.org/10.1016/j.soisec.2026.100235
- A sensors-based profile heterogeneity index for soil characterization A. Barros e Souza et al. https://doi.org/10.1016/j.catena.2021.105670
- Can environmental clustering reveal soil profile patterns? A depth-based approach at field scale Z. Rasaei et al. https://doi.org/10.1016/j.catena.2025.109695
- Improving spectral estimation of soil inorganic carbon in urban and suburban areas by coupling continuous wavelet transform with geographical stratification Y. Hong et al. https://doi.org/10.1016/j.geoderma.2022.116284
- A Moroccan soil spectral library use framework for improving soil property prediction: Evaluating a geostatistical approach T. Asrat et al. https://doi.org/10.1016/j.geoderma.2024.117116
- Quantitative classification of ‘excessively drained’ and ‘somewhat excessively drained’ of Korean soils using soil properties D. Lee et al. https://doi.org/10.7745/KJSSF.2025.58.1.144
- Complex hydrological knowledge to support digital soil mapping F. Mello et al. https://doi.org/10.1016/j.geoderma.2021.115638
- The Brazilian Soil Spectral Library data opening J. Novais et al. https://doi.org/10.19047/0136-1694-2024-119-261-305
- An interlaboratory comparison of mid-infrared spectra acquisition: Instruments and procedures matter J. Safanelli et al. https://doi.org/10.1016/j.geoderma.2023.116724
- Spectral Characteristics of Hydromorphic Soils: A Review R. Döbröntey et al. https://doi.org/10.1134/S1064229326601733
- A binary prediction model to assess the oxidation of Gley Lowland soils in Japan M. Morishita et al. https://doi.org/10.1080/00380768.2024.2448453
- Fine-scale soil mapping with Earth Observation data: a multiple geographic level comparison J. Safanelli et al. https://doi.org/10.36783/18069657rbcs20210080
- An assessment of Sentinel‐1 synthetic aperture radar, geophysical and topographical covariates for estimating topsoil particle‐size fractions S. Deodoro et al. https://doi.org/10.1111/ejss.13414
- Proximal sensing approach for soil characterization and discrimination: a case of study in Brazil A. Gómez et al. https://doi.org/10.1080/10106049.2022.2102228
Saved (final revised paper)
Latest update: 12 Sep 2026
Short summary
The objective of this study was to develop a soil grouping system based on spectral, climate, and terrain variables with the aim of developing a quantitative way to classify soils. To derive the new system, we applied the above-mentioned variables using cluster analysis and defined eight groups or "soil environment groupings" (SEGs). The SEG system facilitated the identification of groups with similar characteristics using not only soil but also environmental variables for their distinction.
The objective of this study was to develop a soil grouping system based on spectral, climate,...
