Verdian Ag: Technical Brief & Methodology

1. Executive Summary

Verdian Ag provides agricultural intelligence infrastructure. Our primary objective is to bridge the data gap that prevents institutional capital from reaching smallholder farmers.

Traditional agricultural lending relies on collateral and credit history. Assets that rural farmers often lack. Verdian Ag replaces this requirement with verifiable, continuous agronomic data. By processing multi-spectral satellite imagery and localized weather data, we model field-level crop stress, vigor, and compliance with recommended agronomic practices.

This processing pipeline outputs the Verdian Score, a structured performance metric designed to support risk assessment. For financial institutions, this translates opaque farming operations into transparent, monitorable assets. For farmers, it provides a data-backed record of their reliability and yield potential.

2. The Verdian Score Architecture

The Verdian Score is a composite metric (0-1000) intended to help lenders and partners make informed decisions. It is an institutional-grade agronomic performance and behavioral compliance score.

The score is synthesized from four primary indices:

1. Vigor & Biomass Index: Derived from continuous multi-spectral satellite observation (Sentinel-2 NDVI), measuring the physiological health and growth stage of the crop.
2. Structural Stability Index: Derived from Synthetic Aperture Radar (SAR) Sentinel-1 VH/VV backscatter, providing cloud-independent monitoring of biomass accumulation and lodging risk.
3. Moisture & Stress Index: Evaluated by estimating soil moisture patterns, evapotranspiration rates, and historical drought stress penalties.
4. Behavioral Compliance Ratio: A measure of a farmer's adherence to localized agronomic advisories (e.g., timeliness of planting, responsiveness to irrigation alerts), acting as a proxy for operational reliability.

3. Carbon Sequestration Methodology

Verdian Ag formalizes satellite-based carbon monitoring to enable sustainable lending and carbon monetization. Our methodology utilizes Sentinel-1 (SAR) VH backscatter as the primary biomass proxy, with Sentinel-2 NDVI as an optical fallback.

3.1 Above-Ground Biomass (AGB) Estimation

We calculate Above-Ground Biomass (tonnes/ha) by normalizing SAR signal intensity against crop-specific expansion factors. For standard row crops, we utilize a base conversion factor where peak biomass is correlated with a normalized signal range of 0-15 tonnes/ha.

3.2 CO2e Conversion & Sequestration

The sequestration figure represents the net CO2 equivalent (CO2e) removed from the atmosphere and stored in the crop biomass:

• Carbon Content: 50% of the calculated dry Above-Ground Biomass is assumed to be carbon.
• CO2e Multiplier: We apply the international standard 3.67 multiplier (molecular weight of CO2 / Atomic weight of C) to convert stored carbon into CO2e tonnes.
• Monetization: These figures provide the basis for carbon-linked interest rate reductions (Sustainability Linked Loans) or the issuance of voluntary carbon credits, valued at a benchmark market rate (e.g., $12.50/tonne CO2e).

4. Data Ingestion & Processing

Verdian Ag operates entirely remotely, relying on multi-modal satellite telemetry rather than expensive on-site IoT sensors.

4.1 Satellite Analytics Engine

We ingest continuous optical and synthetic-aperture radar (SAR) data. This multi-modal approach ensures consistent data collection regardless of cloud cover.

Our preprocessing pipeline includes:

• Atmospheric Correction: Removing cloud shadows and atmospheric interference from optical imagery.
• Incidence Angle Normalization: Standardizing radar backscatter coefficients to ensure accurate structural modeling across different satellite passes.
• Spatial Aggregation: Processing pixel-level data precisely within the geofenced boundaries of individual smallholder farms.

5. Interactive Field Mapping

The Verdian dashboard incorporates a responsive, interactive satellite mapping interface built on Leaflet. This component provides real-time visualization of monitored field boundaries, health status, and geographic context.

5.1 Health Visualization Layer

Field boundaries render with dynamic styling based on continuous multi-spectral monitoring:

• Color Coding: Emerald for healthy (Score > 800), amber for warning (Score 600-800), orange for stress (Score 400-600), rose for critical (Score < 400).
• Selection State: Selected fields receive enhanced border weight (4px vs 2px) and slate-800 coloring for clear visual distinction.

6. Representative Output Schema

To support institutional integration, Verdian Ag structures its field intelligence into standardized data payloads.

(Note: This is a representative schema intended to illustrate the structure and types of data our models generate for pre-verification and risk assessment purposes.)

{
  "field_id": "uuid-v4-string",
  "assessment_date": "2026-02-22T00:00:00Z",
  "crop_type": "maize_white",
  "metrics": {
    "verdian_score": 820,
    "confidence_level": 0.88,
    "carbon_sequestration": {
      "co2e_tonnes_total": 4.25,
      "estimated_market_value_usd": 53.13,
      "methodology": "SAR-VH_BIOMASS_FUSION"
    },
    "vigor_index": {
      "current_value": 0.65,
      "7_day_trend": "stable"
    },
    "water_stress": {
      "status": "nominal",
      "days_since_rain": 4,
      "estimated_depletion_percent": 35
    },
    "compliance_ratio": 0.90
  },
  "flags": [
    {
      "type": "heat_stress_warning",
      "severity": "medium",
      "predicted_onset": "2026-02-25"
    }
  ]
}