Soil Landscape Classification

What This Tool Does

Soil Landscape Classification classifies a DEM into Pennock-inspired landform units using multiscale terrain analysis. It returns a landform raster, an optional polygon layer, a summary JSON, and an HTML report.

Typical Questions This Tool Helps Answer

• Which landform positions dominate this field (summit, backslope, footslope, depression), and how do their typical erosion, leaching, and moisture characteristics inform site-specific management decisions?
• Which terrain positions show convergent or depressional characteristics that signal moisture accumulation or elevated compaction vulnerability?
• Are the predicted soil landscape classes consistent with existing field observations and available soil survey records?

When To Use

• Soil survey preparation
• Terrain-based land management planning
• Identifying convergent and depositional terrain positions
• Building a simple landform map from a DEM

What You Need

Key Settings

What You Get

The summary also includes dominant_class_code, dominant_class_name, and class_distribution entries with confidence ranges for each class.

It also includes:

• output_semantics
• confidence_contract
• interpretation_warnings

Runtime Output Keys

result.outputs["landform_units"]
result.outputs["multiscale_signature"]
result.outputs["landform_polygons"]
result.outputs["summary"]
result.outputs["html_report"]
result.outputs["path"]

Common Questions

Q: Which output should agronomy teams review first? A: Start with summary.dominant_class_name plus class confidence ranges in summary.class_distribution, then verify map patterns in landform_units.

Q: What is a common interpretation mistake? A: Treating class boundaries as hard soil boundaries; transition zones with low confidence need field checks.

Q: Which settings most change class distribution? A: profile_curvature_threshold, plan_curvature_threshold, fine_scale, and coarse_scale usually produce the largest redistribution.

Q: How should teams use these outputs operationally? A: Use landform_polygons to plan sampling zones and target lower-confidence areas for pedology verification.

Results Delivery Checklist

• summary["valid_cells"] is non-zero
• summary["dominant_class_name"] matches the expected terrain setting
• class_distribution looks plausible for the study area
• If requested, landform_polygons was created and opens correctly in GIS software
• The report was reviewed before delivery

Operational Notes

• Curvature thresholds and smoothing scales are the most sensitive controls; keep them fixed when comparing fields or seasons.
• Use confidence ranges from class_distribution to prioritize field checks in transitional terrain.
• Treat mapped class boundaries as guidance for sampling and management zoning, not hard pedologic boundaries.

Related Tools

• yield_data_conditioning_and_qa
• precision_ag_yield_zone_intelligence
• precision_irrigation_optimization

References

• Runtime implementation: wbtools_pro/src/tools/geomorphometry/soil_landscape_classification.rs
• Precision Agriculture Intelligence bundle overview: manual/pro-tools-customer/src/precision_agriculture/overview.md

When To Use This Workflow

Use Soil Landscape Classification when you need terrain-informed soil-position units for sampling design and variable-rate agronomic planning.