Improving a regional peat thickness map using soil apparent electrical conductivity measurements at the field-scale
IntroductionThe increased adoption of proximal sensors has helped to generate peat mapping products: they gather data quickly and can detect the peat-mineral later boundary. A third layer, made of sedimentary peat (limnic layers, gyttja), can sometimes be found in between them. This material is high...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2023-12-01
|
| Series: | Frontiers in Soil Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fsoil.2023.1305105/full |
| _version_ | 1797427083029250048 |
|---|---|
| author | Raphaël Deragon Brandon Heung Nicholas Lefebvre Kingsley John Athyna N. Cambouris Jean Caron |
| author_facet | Raphaël Deragon Brandon Heung Nicholas Lefebvre Kingsley John Athyna N. Cambouris Jean Caron |
| author_sort | Raphaël Deragon |
| collection | DOAJ |
| description | IntroductionThe increased adoption of proximal sensors has helped to generate peat mapping products: they gather data quickly and can detect the peat-mineral later boundary. A third layer, made of sedimentary peat (limnic layers, gyttja), can sometimes be found in between them. This material is highly variable spatially and is associated with degraded soil properties when located near the surface.MethodsThis study aimed to assess the potential of direct current resistivity measurements to predict the maximum peat thickness (MPT), defined as the non-limnic peat thickness, to facilitate soil conservation and management practices at the field-scale. The results were also compared to a regional map of the MPT from a previous study used and also tested as a covariate. This study was conducted in a shallow (MPT = 8-138 cm) cultivated organic soil from Québec, Canada. The MPT was mapped using the apparent electrical conductivity (ECa) from a Veris Q2800, and a digital elevation model, with and without a regional MPT map (RM) as a covariate to downscale it. Three machine-learning algorithms (Cubist, Random Forest, and Support Vector Regression) were compared to ordinary kriging (OK), multiple linear regression, and multiple linear regression kriging (MLRK) models.Results and discussionThe best predictive performance was achieved with OK (Lin’s CCC = 0.89, RMSE = 13.75 cm), followed by MLRK-RM (CCC = 0.85, RMSE = 15.7 cm). All models were more accurate than the RM (CCC = 0.65, RMSE = 29.85 cm), although they underpredicted MPT > 100 cm. Moreover, the addition of the RM as a covariate led to a lower prediction error and higher accuracy for all models. Overall, a field-scale approach could better support precision soil conservation interventions by generating more accurate management zones. Future studies should test multi-sensor fusion and other geophysical sensors to further improve the model performance and detect deeper boundaries. |
| first_indexed | 2024-03-09T08:39:13Z |
| format | Article |
| id | doaj.art-b69be5c2d3204a0c838c8d17753ac495 |
| institution | Directory Open Access Journal |
| issn | 2673-8619 |
| language | English |
| last_indexed | 2024-03-09T08:39:13Z |
| publishDate | 2023-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Soil Science |
| spelling | doaj.art-b69be5c2d3204a0c838c8d17753ac4952023-12-02T17:22:04ZengFrontiers Media S.A.Frontiers in Soil Science2673-86192023-12-01310.3389/fsoil.2023.13051051305105Improving a regional peat thickness map using soil apparent electrical conductivity measurements at the field-scaleRaphaël Deragon0Brandon Heung1Nicholas Lefebvre2Kingsley John3Athyna N. Cambouris4Jean Caron5Département des sols et de génie agroalimentaire, Faculté des sciences de l’agriculture et de l’alimentation, Université Laval, Québec, QC, CanadaDepartment of Plant, Food, and Environmental Sciences, Faculty of Agriculture, Dalhousie University, Truro, NS, CanadaDépartement des sols et de génie agroalimentaire, Faculté des sciences de l’agriculture et de l’alimentation, Université Laval, Québec, QC, CanadaDepartment of Plant, Food, and Environmental Sciences, Faculty of Agriculture, Dalhousie University, Truro, NS, CanadaAgriculture and Agri-Food Canada—Québec Research and Development Centre, Québec, QC, CanadaDépartement des sols et de génie agroalimentaire, Faculté des sciences de l’agriculture et de l’alimentation, Université Laval, Québec, QC, CanadaIntroductionThe increased adoption of proximal sensors has helped to generate peat mapping products: they gather data quickly and can detect the peat-mineral later boundary. A third layer, made of sedimentary peat (limnic layers, gyttja), can sometimes be found in between them. This material is highly variable spatially and is associated with degraded soil properties when located near the surface.MethodsThis study aimed to assess the potential of direct current resistivity measurements to predict the maximum peat thickness (MPT), defined as the non-limnic peat thickness, to facilitate soil conservation and management practices at the field-scale. The results were also compared to a regional map of the MPT from a previous study used and also tested as a covariate. This study was conducted in a shallow (MPT = 8-138 cm) cultivated organic soil from Québec, Canada. The MPT was mapped using the apparent electrical conductivity (ECa) from a Veris Q2800, and a digital elevation model, with and without a regional MPT map (RM) as a covariate to downscale it. Three machine-learning algorithms (Cubist, Random Forest, and Support Vector Regression) were compared to ordinary kriging (OK), multiple linear regression, and multiple linear regression kriging (MLRK) models.Results and discussionThe best predictive performance was achieved with OK (Lin’s CCC = 0.89, RMSE = 13.75 cm), followed by MLRK-RM (CCC = 0.85, RMSE = 15.7 cm). All models were more accurate than the RM (CCC = 0.65, RMSE = 29.85 cm), although they underpredicted MPT > 100 cm. Moreover, the addition of the RM as a covariate led to a lower prediction error and higher accuracy for all models. Overall, a field-scale approach could better support precision soil conservation interventions by generating more accurate management zones. Future studies should test multi-sensor fusion and other geophysical sensors to further improve the model performance and detect deeper boundaries.https://www.frontiersin.org/articles/10.3389/fsoil.2023.1305105/fullsoil conservationproximal soil sensingdigital soil mappingcultivated organic soilshistosoldownscaling |
| spellingShingle | Raphaël Deragon Brandon Heung Nicholas Lefebvre Kingsley John Athyna N. Cambouris Jean Caron Improving a regional peat thickness map using soil apparent electrical conductivity measurements at the field-scale Frontiers in Soil Science soil conservation proximal soil sensing digital soil mapping cultivated organic soils histosol downscaling |
| title | Improving a regional peat thickness map using soil apparent electrical conductivity measurements at the field-scale |
| title_full | Improving a regional peat thickness map using soil apparent electrical conductivity measurements at the field-scale |
| title_fullStr | Improving a regional peat thickness map using soil apparent electrical conductivity measurements at the field-scale |
| title_full_unstemmed | Improving a regional peat thickness map using soil apparent electrical conductivity measurements at the field-scale |
| title_short | Improving a regional peat thickness map using soil apparent electrical conductivity measurements at the field-scale |
| title_sort | improving a regional peat thickness map using soil apparent electrical conductivity measurements at the field scale |
| topic | soil conservation proximal soil sensing digital soil mapping cultivated organic soils histosol downscaling |
| url | https://www.frontiersin.org/articles/10.3389/fsoil.2023.1305105/full |
| work_keys_str_mv | AT raphaelderagon improvingaregionalpeatthicknessmapusingsoilapparentelectricalconductivitymeasurementsatthefieldscale AT brandonheung improvingaregionalpeatthicknessmapusingsoilapparentelectricalconductivitymeasurementsatthefieldscale AT nicholaslefebvre improvingaregionalpeatthicknessmapusingsoilapparentelectricalconductivitymeasurementsatthefieldscale AT kingsleyjohn improvingaregionalpeatthicknessmapusingsoilapparentelectricalconductivitymeasurementsatthefieldscale AT athynancambouris improvingaregionalpeatthicknessmapusingsoilapparentelectricalconductivitymeasurementsatthefieldscale AT jeancaron improvingaregionalpeatthicknessmapusingsoilapparentelectricalconductivitymeasurementsatthefieldscale |