Detecting Clay and Sand with SBI

Use satellite imagery to quickly map clay-rich vs. sand-dominant zones without full resurvey.

Business Case

Soil texture influences water holding capacity, nutrient availability, and yield stability.

Traditional maps are costly and becoming outdated quite quickly.

With GeoPard, you can:

• Flag clay (darker) vs. sand (brighter) areas fast

• Focus soil sampling where it matters

• Create variable-rate seeding, irrigation, and fertilizer zones

• Refresh insights each year with minimal effort

Dynamic Tutorial

Results match soil scans from German fields measuring sand and clay content.

A few targeted ground samples help fine-tune thresholds.

The tutorial below supports multiple languages. Choose the one that best fits you from the language icon .

Solution

How it works:

• SBI (Soil Brightness Index) rises with surface reflectance (red + NIR).

• Clay: finer particles, more moisture/oxides → lower SBI (darker).

• Sand: coarser, drier, higher albedo → higher SBI (brighter).

Workflow in GeoPard:

1. Collect SBI layers from bare-soil dates (several per year).

2. Normalize each scene (removes illumination/acquisition bias).

3. Average across time (cuts random noise).

4. Cluster spatially into 0.25-0.5 ha (0.5-1 ac) zones (reduces pixel speckle).

5. Classify the distribution:

   • Lower SBI → clay-leaning

   • Higher SBI → sand-leaning

6. Export zones for scouting and VR prescriptions.

Normalization + multi-date averaging + small clusters make SBI a stable proxy for texture and improve separability between darker (clay) and brighter (sand) areas.