Module 3
Coarse Digital Soil Mapping (Linear Model)
Objectives: Generate a baseline DSM for soil texture and SOC at multiple depths.
Requirements
Participants should have:
-
Surface Layer Mapping (0–0.3 m)
- Use a multiple linear regression model with covariates (NDWI, BSI, elevation, etc.) to predict:
- Sand (%)
- Clay (%)
- SOC (%)
-
Subsurface Layers
Analysis of profile data shows:
- Texture variation within profile layers < texture model error → assume same texture for deeper layers.
- SOC variation within profiles > model error → predict SOC by depth.
Thus, SOC for deeper layers is estimated using:
\[
SOC\left( z \right) = SOC_{surface} \cdot e^{-kz}\]
where \(k\) is the fitted exponential decay coefficient.
- Outcome
- Coarse DSMs for:
- Sand (%), Clay (%), SOC (%) at 0–0.3 m
- SOC at 0.3–0.6 m and 0.6–0.9 m
- Field-wide initial soil parameter maps for MONICA simulations.