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Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon
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Generalizing soil properties in geographic space: Approaches used and ways forward
Carmen Cianfrani1, Aline Buri1, Eric Verrecchia1
1Institute of Earth Surface Dynamics (IDYST), University of Lausanne (UNIL), Lausanne, Switzerland.
Plos One
|December 22, 2018
Summary
Choosing the best digital soil mapping approach is crucial for understanding plant distributions. Hybrid approaches often outperform predictive-statistical approaches but are less utilized, necessitating further comparative studies.
Area of Science:
- Soil Science
- Ecology
- Geospatial Analysis
Background:
- Soils are complex Earth systems vital for plant water and nutrient capture.
- Soil properties are often overlooked in spatial plant distribution studies.
- Digital soil mapping is essential for regional soil property analysis.
Purpose of the Study:
- To review geostatistical (GSA), predictive-statistical (PSA), and hybrid (HA) approaches for digital soil mapping.
- To evaluate approach performance based on soil properties, study area, and data availability.
- To identify optimal methods for spatializing soil properties.
Main Methods:
- Literature review of digital soil mapping studies.
- Analysis of three statistical approach categories: GSA, PSA, and HA.
- Comparison of approach performance metrics.
Main Results:
- Hybrid approaches (HA) often show better performance than predictive-statistical approaches (PSA).
- PSA is widely used, but HA is less frequently applied despite superior results.
- The most effective approach varied depending on specific study contexts.
Conclusions:
- Current digital soil mapping practices may not align with optimal performance.
- More comparative studies are needed across diverse regions and scales.
- Further research is required to establish robust conclusions on best spatialization methods.
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