Evaluating Digital Soil Mapping approaches for mapping GlobalSoilMap soil properties from legacy data in Languedoc-Roussillon (France)
Digital Soil Mapping is becoming increasingly operational because of shared approaches, clear specifications (e.g., GlobalSoilMap) and more “practical” applications across the planet. In a 27,236km2 French region located in the Northern Mediterranean area, this study evaluated four well-known Digita...
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| Published in | Geoderma Regional Vol. 4; pp. 20 - 30 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.04.2015
Elsevier |
| Subjects | |
| Online Access | Get full text |
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| Abstract | Digital Soil Mapping is becoming increasingly operational because of shared approaches, clear specifications (e.g., GlobalSoilMap) and more “practical” applications across the planet. In a 27,236km2 French region located in the Northern Mediterranean area, this study evaluated four well-known Digital Soil Mapping approaches that were possibly applicable in the French context for inferring the GlobalSoilMap (GSM) grid using freely available data from the French spatial data infrastructure. These approaches used for soil input either a legacy 1:250,000 scale soil map—an area-weighted mean of soil mapping units and spatial disaggregation of soil mapping units (simulated)—or a set of 2024 legacy-measured soil profiles (1 profile/13.5km2) associated with a set of soil covariates using random forest and random forest plus kriging. These methods provided estimates of 29 soil properties over a 100m by 100m grid, i.e., clay, sand, silt, coarse fragment, organic carbon contents, pH and CEC, at the four upper GSM-specified depth intervals, plus soil depth.
This experiment showed that the performances in mapping soil properties were highly variable (from R2=0 to R2=0.79). They were strongly correlated with the amount of spatially-structured variance of the soil properties captured by the available soil data. For example better performances were observed for organic carbon that is driven by large-scale climate variations than for texture and soil depth that are driven by short scale variations of parent materials and erosion–deposition ratio along a slope. Furthermore, the profile-based DSM models generally outperformed the soil map-based DSM models, and kriging did not improve the results of random forest. Finally improving the quality of soil data input was considered as the best way to improve the current mapping performances.
In addition to providing a GlobalSoilMap proof-of-concept for Northern Mediterranean areas, this paper illustrates how a prior analysis of the available soil data may help in the anticipated estimation of the mapping performances and in the prior selection of the most promising DSM models to reach these performances.
•Four DSM models were tested to map 29 GSM-specified soil properties in Southern France.•Mapping performances were highly variable (from R2=0 to R2=0.79).•Soil profile-based DSM models generally outperformed soil map-based DSM models.•The soil database used as an input of DSM models was the most limiting factor. |
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| AbstractList | Digital Soil Mapping is becoming increasingly operational because of shared approaches, clear specifications (e.g., GlobalSoilMap) and more “practical” applications across the planet. In a 27,236 km2 French region located in the Northern Mediterranean area, this study evaluated four well-known Digital Soil Mapping approaches that were possibly applicable in the French context for inferring the GlobalSoilMap (GSM) grid using freely available data from the French spatial data infrastructure. These approaches used for soil input either a legacy 1:250,000 scale soil map—an area-weighted mean of soil mapping units and spatial disaggregation of soil mapping units (simulated)—or a set of 2024 legacy-measured soil profiles (1 profile/13.5 km2) associated with a set of soil covariates using random forest and random forest plus kriging. These methods provided estimates of 29 soil properties over a 100 m by 100 m grid, i.e., clay, sand, silt, coarse fragment, organic carbon contents, pH and CEC, at the four upper GSM-specified depth intervals, plus soil depth. This experiment showed that the performances in mapping soil properties were highly variable (from R2 = 0 to R2 = 0.79). They were strongly correlated with the amount of spatially-structured variance of the soil properties captured by the available soil data. For example better performances were observed for organic carbon that is driven by large-scale climate variations than for texture and soil depth that are driven by short scale variations of parent materials and erosion–deposition ratio along a slope. Furthermore, the profile-based DSM models generally outperformed the soil map-based DSM models, and kriging did not improve the results of random forest. Finally improving the quality of soil data input was considered as the best way to improve the current mapping performances. In addition to providing a GlobalSoilMap proof-of-concept for Northern Mediterranean areas, this paper illustrates how a prior analysis of the available soil data may help in the anticipated estimation of the mapping performances and in the prior selection of the most promising DSM models to reach these performances. Digital Soil Mapping is becoming increasingly operational because of shared approaches, clear specifications (e.g., GlobalSoilMap) and more “practical” applications across the planet. In a 27,236km² French region located in the Northern Mediterranean area, this study evaluated four well-known Digital Soil Mapping approaches that were possibly applicable in the French context for inferring the GlobalSoilMap (GSM) grid using freely available data from the French spatial data infrastructure. These approaches used for soil input either a legacy 1:250,000 scale soil map—an area-weighted mean of soil mapping units and spatial disaggregation of soil mapping units (simulated)—or a set of 2024 legacy-measured soil profiles (1 profile/13.5km²) associated with a set of soil covariates using random forest and random forest plus kriging. These methods provided estimates of 29 soil properties over a 100m by 100m grid, i.e., clay, sand, silt, coarse fragment, organic carbon contents, pH and CEC, at the four upper GSM-specified depth intervals, plus soil depth.This experiment showed that the performances in mapping soil properties were highly variable (from R²=0 to R²=0.79). They were strongly correlated with the amount of spatially-structured variance of the soil properties captured by the available soil data. For example better performances were observed for organic carbon that is driven by large-scale climate variations than for texture and soil depth that are driven by short scale variations of parent materials and erosion–deposition ratio along a slope. Furthermore, the profile-based DSM models generally outperformed the soil map-based DSM models, and kriging did not improve the results of random forest. Finally improving the quality of soil data input was considered as the best way to improve the current mapping performances.In addition to providing a GlobalSoilMap proof-of-concept for Northern Mediterranean areas, this paper illustrates how a prior analysis of the available soil data may help in the anticipated estimation of the mapping performances and in the prior selection of the most promising DSM models to reach these performances. Digital Soil Mapping is becoming increasingly operational because of shared approaches, clear specifications (e.g., GlobalSoilMap) and more “practical” applications across the planet. In a 27,236km2 French region located in the Northern Mediterranean area, this study evaluated four well-known Digital Soil Mapping approaches that were possibly applicable in the French context for inferring the GlobalSoilMap (GSM) grid using freely available data from the French spatial data infrastructure. These approaches used for soil input either a legacy 1:250,000 scale soil map—an area-weighted mean of soil mapping units and spatial disaggregation of soil mapping units (simulated)—or a set of 2024 legacy-measured soil profiles (1 profile/13.5km2) associated with a set of soil covariates using random forest and random forest plus kriging. These methods provided estimates of 29 soil properties over a 100m by 100m grid, i.e., clay, sand, silt, coarse fragment, organic carbon contents, pH and CEC, at the four upper GSM-specified depth intervals, plus soil depth. This experiment showed that the performances in mapping soil properties were highly variable (from R2=0 to R2=0.79). They were strongly correlated with the amount of spatially-structured variance of the soil properties captured by the available soil data. For example better performances were observed for organic carbon that is driven by large-scale climate variations than for texture and soil depth that are driven by short scale variations of parent materials and erosion–deposition ratio along a slope. Furthermore, the profile-based DSM models generally outperformed the soil map-based DSM models, and kriging did not improve the results of random forest. Finally improving the quality of soil data input was considered as the best way to improve the current mapping performances. In addition to providing a GlobalSoilMap proof-of-concept for Northern Mediterranean areas, this paper illustrates how a prior analysis of the available soil data may help in the anticipated estimation of the mapping performances and in the prior selection of the most promising DSM models to reach these performances. •Four DSM models were tested to map 29 GSM-specified soil properties in Southern France.•Mapping performances were highly variable (from R2=0 to R2=0.79).•Soil profile-based DSM models generally outperformed soil map-based DSM models.•The soil database used as an input of DSM models was the most limiting factor. |
| Author | Vaysse, K. Lagacherie, P. |
| Author_xml | – sequence: 1 givenname: K. surname: Vaysse fullname: Vaysse, K. email: kevin.vaysse@supagro.inra.fr organization: INRA, UMR LISAH, Montpellier, France – sequence: 2 givenname: P. surname: Lagacherie fullname: Lagacherie, P. organization: INRA, UMR LISAH, Montpellier, France |
| BackLink | https://hal.inrae.fr/hal-02629696$$DView record in HAL |
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| Keywords | Digital Soil Mapping GlobalSoilMap.net Random forest Legacy soil survey Mediterranean region France Kriging mediterranean region globalSoilMap.net digital Soil Mapping kriging france random forest legacy soil survey |
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| SubjectTerms | carbon clay climate Digital Soil Mapping Environmental Sciences France GlobalSoilMap.net infrastructure Kriging Legacy soil survey Life Sciences Mediterranean region Random forest sand silt soil depth soil profiles soil properties soil surveys spatial data texture variance |
| Title | Evaluating Digital Soil Mapping approaches for mapping GlobalSoilMap soil properties from legacy data in Languedoc-Roussillon (France) |
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