Purpose and Scope of This Appendix
This appendix provides the technical background behind AgriProof’s satellite‑based assessments. It outlines the data sources, methods, limitations, and interpretation guidelines used when preparing independent evidence reports.
This document explains how satellite indicators are derived and interpreted, not a determination of liability or claim outcome.

A. Purpose of Satellite Evidence

Satellite observations provide an independent, objective record of surface conditions over time. When used correctly, they support agricultural assessments by documenting change, stability, or recovery within a defined spatial and temporal context.

B. When Satellite Evidence Is Most Useful

• Independent verification is required
• Ground access is limited or delayed
• Historical context is needed
• Patterns must be compared across multiple fields
• Weather conditions prevented timely field inspection

C. Data Sources & Resolution

• Data source: ESA Sentinel-1 and Sentinel-2 (Copernicus Programme)
• Spatial resolution: 10 m (field-scale zonal analysis)
• Temporal resolution:
  • Sentinel-2 (optical): ~5 days (weather dependent)
  • Sentinel-1 (radar): ~2–3 days
• Analysis performed using standard, peer-reviewed index formulations

D. Temporal Considerations & Observation Window

• Satellite data provides observations at discrete intervals rather than continuous monitoring.
• Crop response to events such as flooding, waterlogging, or stress may be delayed.
• For damage assessment, post-event observations should ideally extend 4–6 weeks after the reported incident, allowing multiple satellite passes and assessment of recovery or decline.
• Shorter post-event windows may confirm environmental exposure but may be insufficient to determine sustained crop damage.

E. Optical vs Radar Observations

Optical (Sentinel-2):

• Measures reflected light in visible and infrared bands
• Sensitive to vegetation condition and moisture
• Affected by cloud cover

Radar (Sentinel-1):

• Active sensor; unaffected by cloud or lighting
• Sensitive to surface structure and moisture
• Useful for confirming conditions during poor weather

F. Vegetation & Moisture Indices (Reference)

Vegetation indices are relative indicators of surface condition derived from spectral reflectance. Values should be interpreted comparatively across time and space rather than as absolute measures.

What is NDVI?

• NDVI (Normalized Difference Vegetation Index) is a widely used satellite-based measure of vegetation condition. It is calculated from how plants reflect red and near-infrared light.
• Red light is absorbed by chlorophyll
• Near-infrared light is reflected by healthy plant cells
• Values range from -1 to +1

Values	Meaning
< 0.2	Bare soil / very low vegetation
0.2 – 0.4	Sparse or stressed vegetation
0.4 – 0.6	Healthy vegetation
0.6 – 1.0	Dense, vigorous vegetation

G. Cloud Cover & Data Availability

• Optical imagery may be partially or fully obscured by cloud.
• Cloud-affected observations are excluded where appropriate.
• Radar imagery provides continuity where optical data is unavailable.

H. Limitations

• Satellite data cannot determine cause or liability.
• NDVI does not measure yield or financial loss.
• Cloud cover may reduce optical data availability.
• Radar indicates moisture and roughness, not crop type or health.
• Interpretation depends on crop stage and local conditions.

I. Why Satellite Evidence Is Used

• Objectivity
• Consistency across time
• Independent verification
• Historical record

J. Data Ethics & Independence

K. AgriProof – Service Facts

• Organisation: AgriProof Ltd. Registered in England & Wales. Registration No. 16912836
• Service: Independent satellite evidence for farm insurance and land-use queries
• Data sources: Copernicus Sentinel-1 & Sentinel-2 (ESA / EU)
• Outputs: NDVI, NDRE, NDWI, EVI, SAR metrics
• Use cases: Crop damage assessment, drought, flooding, compliance queries