Spatial modelling of topsoil properties in Romania using geostatistical methods and machine learning
Various research topics from the field of soil science or agriculture require digital maps of soil properties as input data. Such maps can be achieved by digital soil mapping (DSM) techniques which have developed consistently during the last decades. Our research focuses on the application of geosta...
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| Published in | PloS one Vol. 18; no. 8; p. e0289286 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
San Francisco
Public Library of Science
23.08.2023
Public Library of Science (PLoS) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1932-6203 1932-6203 |
| DOI | 10.1371/journal.pone.0289286 |
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| Abstract | Various research topics from the field of soil science or agriculture require digital maps of soil properties as input data. Such maps can be achieved by digital soil mapping (DSM) techniques which have developed consistently during the last decades. Our research focuses on the application of geostatistical methods (including ordinary kriging, regression-kriging and geographically weighted regression) and machine learning algorithms to produce high resolution digital maps of topsoil properties in Romania. Six continuous predictors were considered in our study (digital elevation model, topographic wetness index, normalized difference vegetation index, slope, latitude and longitude). A tolerance test was performed to ensure that all predictors can be used for the purpose of digital soil mapping. The input soil data was extracted from the LUCAS database and includes 7 chemical properties (pH, electrical conductivity, calcium carbonate, organic carbon, N, P, K) and the particle-size fractions (sand, silt, clay). The spatial autocorrelation is higher for pH, organic carbon and calcium carbonate, as indicated by the partial sill / nugget ratio of semivariograms, meaning that these properties are more predictable than the others by kriging interpolation. The optimal DSM method was selected by independent sample validation, using resampled statistics from 100 samples randomly extracted from the validation dataset. Also, an additional independent sample of soil profiles, comprising legacy soil data, and the 200k Romania soil map were used for a supplementary validation. The results show that machine learning and regression-kriging are the optimal methods in most cases. Among the machine learning tested algorithms, the best performance is associated with Support Vector Machines and Random Forests methods. The geographically weighted regression is also among the optimum methods for pH and calcium carbonates spatial prediction. Good predictions were achieved for pH (R
2
of 0.417–0.469, depending on the method), organic carbon (R
2
of 0.302–0.443), calcium carbonates (R
2
of 0.300–0.330) and moderate predictions for electric conductivity, total nitrogen, silt and sand (R
2
of 0.155–0.331), while the lowest prediction characterizes the phosphorous content (R
2
of 0.015–0.044). LUCAS proved to be a reliable and useful soil database and the achieved spatial distributions of soil properties can be further used for national and regional soil studies. |
|---|---|
| AbstractList | Various research topics from the field of soil science or agriculture require digital maps of soil properties as input data. Such maps can be achieved by digital soil mapping (DSM) techniques which have developed consistently during the last decades. Our research focuses on the application of geostatistical methods (including ordinary kriging, regression-kriging and geographically weighted regression) and machine learning algorithms to produce high resolution digital maps of topsoil properties in Romania. Six continuous predictors were considered in our study (digital elevation model, topographic wetness index, normalized difference vegetation index, slope, latitude and longitude). A tolerance test was performed to ensure that all predictors can be used for the purpose of digital soil mapping. The input soil data was extracted from the LUCAS database and includes 7 chemical properties (pH, electrical conductivity, calcium carbonate, organic carbon, N, P, K) and the particle-size fractions (sand, silt, clay). The spatial autocorrelation is higher for pH, organic carbon and calcium carbonate, as indicated by the partial sill / nugget ratio of semivariograms, meaning that these properties are more predictable than the others by kriging interpolation. The optimal DSM method was selected by independent sample validation, using resampled statistics from 100 samples randomly extracted from the validation dataset. Also, an additional independent sample of soil profiles, comprising legacy soil data, and the 200k Romania soil map were used for a supplementary validation. The results show that machine learning and regression-kriging are the optimal methods in most cases. Among the machine learning tested algorithms, the best performance is associated with Support Vector Machines and Random Forests methods. The geographically weighted regression is also among the optimum methods for pH and calcium carbonates spatial prediction. Good predictions were achieved for pH (R.sup.2 of 0.417-0.469, depending on the method), organic carbon (R.sup.2 of 0.302-0.443), calcium carbonates (R.sup.2 of 0.300-0.330) and moderate predictions for electric conductivity, total nitrogen, silt and sand (R.sup.2 of 0.155-0.331), while the lowest prediction characterizes the phosphorous content (R.sup.2 of 0.015-0.044). LUCAS proved to be a reliable and useful soil database and the achieved spatial distributions of soil properties can be further used for national and regional soil studies. Various research topics from the field of soil science or agriculture require digital maps of soil properties as input data. Such maps can be achieved by digital soil mapping (DSM) techniques which have developed consistently during the last decades. Our research focuses on the application of geostatistical methods (including ordinary kriging, regression-kriging and geographically weighted regression) and machine learning algorithms to produce high resolution digital maps of topsoil properties in Romania. Six continuous predictors were considered in our study (digital elevation model, topographic wetness index, normalized difference vegetation index, slope, latitude and longitude). A tolerance test was performed to ensure that all predictors can be used for the purpose of digital soil mapping. The input soil data was extracted from the LUCAS database and includes 7 chemical properties (pH, electrical conductivity, calcium carbonate, organic carbon, N, P, K) and the particle-size fractions (sand, silt, clay). The spatial autocorrelation is higher for pH, organic carbon and calcium carbonate, as indicated by the partial sill / nugget ratio of semivariograms, meaning that these properties are more predictable than the others by kriging interpolation. The optimal DSM method was selected by independent sample validation, using resampled statistics from 100 samples randomly extracted from the validation dataset. Also, an additional independent sample of soil profiles, comprising legacy soil data, and the 200k Romania soil map were used for a supplementary validation. The results show that machine learning and regression-kriging are the optimal methods in most cases. Among the machine learning tested algorithms, the best performance is associated with Support Vector Machines and Random Forests methods. The geographically weighted regression is also among the optimum methods for pH and calcium carbonates spatial prediction. Good predictions were achieved for pH (R2 of 0.417-0.469, depending on the method), organic carbon (R2 of 0.302-0.443), calcium carbonates (R2 of 0.300-0.330) and moderate predictions for electric conductivity, total nitrogen, silt and sand (R2 of 0.155-0.331), while the lowest prediction characterizes the phosphorous content (R2 of 0.015-0.044). LUCAS proved to be a reliable and useful soil database and the achieved spatial distributions of soil properties can be further used for national and regional soil studies. Various research topics from the field of soil science or agriculture require digital maps of soil properties as input data. Such maps can be achieved by digital soil mapping (DSM) techniques which have developed consistently during the last decades. Our research focuses on the application of geostatistical methods (including ordinary kriging, regression-kriging and geographically weighted regression) and machine learning algorithms to produce high resolution digital maps of topsoil properties in Romania. Six continuous predictors were considered in our study (digital elevation model, topographic wetness index, normalized difference vegetation index, slope, latitude and longitude). A tolerance test was performed to ensure that all predictors can be used for the purpose of digital soil mapping. The input soil data was extracted from the LUCAS database and includes 7 chemical properties (pH, electrical conductivity, calcium carbonate, organic carbon, N, P, K) and the particle-size fractions (sand, silt, clay). The spatial autocorrelation is higher for pH, organic carbon and calcium carbonate, as indicated by the partial sill / nugget ratio of semivariograms, meaning that these properties are more predictable than the others by kriging interpolation. The optimal DSM method was selected by independent sample validation, using resampled statistics from 100 samples randomly extracted from the validation dataset. Also, an additional independent sample of soil profiles, comprising legacy soil data, and the 200k Romania soil map were used for a supplementary validation. The results show that machine learning and regression-kriging are the optimal methods in most cases. Among the machine learning tested algorithms, the best performance is associated with Support Vector Machines and Random Forests methods. The geographically weighted regression is also among the optimum methods for pH and calcium carbonates spatial prediction. Good predictions were achieved for pH (R2 of 0.417-0.469, depending on the method), organic carbon (R2 of 0.302-0.443), calcium carbonates (R2 of 0.300-0.330) and moderate predictions for electric conductivity, total nitrogen, silt and sand (R2 of 0.155-0.331), while the lowest prediction characterizes the phosphorous content (R2 of 0.015-0.044). LUCAS proved to be a reliable and useful soil database and the achieved spatial distributions of soil properties can be further used for national and regional soil studies.Various research topics from the field of soil science or agriculture require digital maps of soil properties as input data. Such maps can be achieved by digital soil mapping (DSM) techniques which have developed consistently during the last decades. Our research focuses on the application of geostatistical methods (including ordinary kriging, regression-kriging and geographically weighted regression) and machine learning algorithms to produce high resolution digital maps of topsoil properties in Romania. Six continuous predictors were considered in our study (digital elevation model, topographic wetness index, normalized difference vegetation index, slope, latitude and longitude). A tolerance test was performed to ensure that all predictors can be used for the purpose of digital soil mapping. The input soil data was extracted from the LUCAS database and includes 7 chemical properties (pH, electrical conductivity, calcium carbonate, organic carbon, N, P, K) and the particle-size fractions (sand, silt, clay). The spatial autocorrelation is higher for pH, organic carbon and calcium carbonate, as indicated by the partial sill / nugget ratio of semivariograms, meaning that these properties are more predictable than the others by kriging interpolation. The optimal DSM method was selected by independent sample validation, using resampled statistics from 100 samples randomly extracted from the validation dataset. Also, an additional independent sample of soil profiles, comprising legacy soil data, and the 200k Romania soil map were used for a supplementary validation. The results show that machine learning and regression-kriging are the optimal methods in most cases. Among the machine learning tested algorithms, the best performance is associated with Support Vector Machines and Random Forests methods. The geographically weighted regression is also among the optimum methods for pH and calcium carbonates spatial prediction. Good predictions were achieved for pH (R2 of 0.417-0.469, depending on the method), organic carbon (R2 of 0.302-0.443), calcium carbonates (R2 of 0.300-0.330) and moderate predictions for electric conductivity, total nitrogen, silt and sand (R2 of 0.155-0.331), while the lowest prediction characterizes the phosphorous content (R2 of 0.015-0.044). LUCAS proved to be a reliable and useful soil database and the achieved spatial distributions of soil properties can be further used for national and regional soil studies. Various research topics from the field of soil science or agriculture require digital maps of soil properties as input data. Such maps can be achieved by digital soil mapping (DSM) techniques which have developed consistently during the last decades. Our research focuses on the application of geostatistical methods (including ordinary kriging, regression-kriging and geographically weighted regression) and machine learning algorithms to produce high resolution digital maps of topsoil properties in Romania. Six continuous predictors were considered in our study (digital elevation model, topographic wetness index, normalized difference vegetation index, slope, latitude and longitude). A tolerance test was performed to ensure that all predictors can be used for the purpose of digital soil mapping. The input soil data was extracted from the LUCAS database and includes 7 chemical properties (pH, electrical conductivity, calcium carbonate, organic carbon, N, P, K) and the particle-size fractions (sand, silt, clay). The spatial autocorrelation is higher for pH, organic carbon and calcium carbonate, as indicated by the partial sill / nugget ratio of semivariograms, meaning that these properties are more predictable than the others by kriging interpolation. The optimal DSM method was selected by independent sample validation, using resampled statistics from 100 samples randomly extracted from the validation dataset. Also, an additional independent sample of soil profiles, comprising legacy soil data, and the 200k Romania soil map were used for a supplementary validation. The results show that machine learning and regression-kriging are the optimal methods in most cases. Among the machine learning tested algorithms, the best performance is associated with Support Vector Machines and Random Forests methods. The geographically weighted regression is also among the optimum methods for pH and calcium carbonates spatial prediction. Good predictions were achieved for pH (R 2 of 0.417–0.469, depending on the method), organic carbon (R 2 of 0.302–0.443), calcium carbonates (R 2 of 0.300–0.330) and moderate predictions for electric conductivity, total nitrogen, silt and sand (R 2 of 0.155–0.331), while the lowest prediction characterizes the phosphorous content (R 2 of 0.015–0.044). LUCAS proved to be a reliable and useful soil database and the achieved spatial distributions of soil properties can be further used for national and regional soil studies. Various research topics from the field of soil science or agriculture require digital maps of soil properties as input data. Such maps can be achieved by digital soil mapping (DSM) techniques which have developed consistently during the last decades. Our research focuses on the application of geostatistical methods (including ordinary kriging, regression-kriging and geographically weighted regression) and machine learning algorithms to produce high resolution digital maps of topsoil properties in Romania. Six continuous predictors were considered in our study (digital elevation model, topographic wetness index, normalized difference vegetation index, slope, latitude and longitude). A tolerance test was performed to ensure that all predictors can be used for the purpose of digital soil mapping. The input soil data was extracted from the LUCAS database and includes 7 chemical properties (pH, electrical conductivity, calcium carbonate, organic carbon, N, P, K) and the particle-size fractions (sand, silt, clay). The spatial autocorrelation is higher for pH, organic carbon and calcium carbonate, as indicated by the partial sill / nugget ratio of semivariograms, meaning that these properties are more predictable than the others by kriging interpolation. The optimal DSM method was selected by independent sample validation, using resampled statistics from 100 samples randomly extracted from the validation dataset. Also, an additional independent sample of soil profiles, comprising legacy soil data, and the 200k Romania soil map were used for a supplementary validation. The results show that machine learning and regression-kriging are the optimal methods in most cases. Among the machine learning tested algorithms, the best performance is associated with Support Vector Machines and Random Forests methods. The geographically weighted regression is also among the optimum methods for pH and calcium carbonates spatial prediction. Good predictions were achieved for pH (R 2 of 0.417–0.469, depending on the method), organic carbon (R 2 of 0.302–0.443), calcium carbonates (R 2 of 0.300–0.330) and moderate predictions for electric conductivity, total nitrogen, silt and sand (R 2 of 0.155–0.331), while the lowest prediction characterizes the phosphorous content (R 2 of 0.015–0.044). LUCAS proved to be a reliable and useful soil database and the achieved spatial distributions of soil properties can be further used for national and regional soil studies. |
| Audience | Academic |
| Author | Pîrnău, Radu Gabriel Patriche, Cristian Valeriu Vasiliniuc, Ionuţ Roşca, Bogdan |
| AuthorAffiliation | 1 Geographic Research Center, Romanian Academy, Iaşi Branch, Iaşi, Romania 2 Department of Geography, Faculty of Geography and Geology, “Alexandru Ioan Cuza” University of Iaşi, Iaşi, Romania Universiti Teknologi Malaysia, MALAYSIA |
| AuthorAffiliation_xml | – name: 2 Department of Geography, Faculty of Geography and Geology, “Alexandru Ioan Cuza” University of Iaşi, Iaşi, Romania – name: 1 Geographic Research Center, Romanian Academy, Iaşi Branch, Iaşi, Romania – name: Universiti Teknologi Malaysia, MALAYSIA |
| Author_xml | – sequence: 1 givenname: Cristian Valeriu orcidid: 0000-0003-4970-0860 surname: Patriche fullname: Patriche, Cristian Valeriu – sequence: 2 givenname: Bogdan surname: Roşca fullname: Roşca, Bogdan – sequence: 3 givenname: Radu Gabriel orcidid: 0000-0003-3368-6662 surname: Pîrnău fullname: Pîrnău, Radu Gabriel – sequence: 4 givenname: Ionuţ orcidid: 0000-0003-0923-0358 surname: Vasiliniuc fullname: Vasiliniuc, Ionuţ |
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| CitedBy_id | crossref_primary_10_1371_journal_pone_0316940 crossref_primary_10_1016_j_geodrs_2024_e00874 crossref_primary_10_3390_agronomy14122828 crossref_primary_10_1080_10095020_2025_2454523 crossref_primary_10_1016_j_ecoinf_2024_102634 crossref_primary_10_1007_s12665_024_11468_7 crossref_primary_10_1590_1809_4430_eng_agric_v44e20240027_2024 |
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| Copyright | COPYRIGHT 2023 Public Library of Science 2023 Patriche et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. Copyright: © 2023 Patriche et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 2023 Patriche et al 2023 Patriche et al 2023 Patriche et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| DOI | 10.1371/journal.pone.0289286 |
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| SubjectTerms | Agriculture Algorithms Analysis Biology and Life Sciences Calcium carbonate Carbon Carbonates Chemical properties Climate change Computer and Information Sciences Data mining Digital Elevation Models Digital mapping Digital maps Earth Sciences Ecology and Environmental Sciences Electric properties Electrical conductivity Electrical resistivity Geographic information systems Geology Geostatistics Interpolation Kriging interpolation Land use Learning algorithms Machine learning Mapping Methods Mountains Neural networks Normalized difference vegetative index Optimization Organic carbon People and Places pH effects Physical Sciences Precipitation Predictions Properties Regression Regression analysis Research and Analysis Methods Sand Silt Software Soil mapping Soil maps Soil profiles Soil properties Soil sciences Spatial distribution Statistical analysis Statistical methods Support vector machines Taxonomy Topsoil Vegetation index Wetness index |
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| Title | Spatial modelling of topsoil properties in Romania using geostatistical methods and machine learning |
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