Satellite Intelligence

24 Satellite-Derived Features

Analytical views of every satellite feature powering our trading system. GEE vegetation indices, FIRMS fire data, Maxar disaster imagery, nightlight economic proxies, and composite signals.

Section 1

NDVI Vegetation Cycle

Annual normalized difference vegetation index as a polar chart — showing the Northern Hemisphere growing season rhythm.

NDVI Normalized Difference Vegetation Index measures the difference between near-infrared (strongly reflected by vegetation) and red light (absorbed by chlorophyll). Values range from -1 to +1; healthy, dense vegetation typically scores 0.6–0.9.

Why it matters for markets: NDVI tracks crop health in real time. Early detection of drought stress (declining NDVI) or bumper harvests (high peak NDVI) provides leading signals for agricultural commodities — corn, wheat, soy, and livestock feed prices. The crop_cycle_phase feature maps the current month position on this annual cycle, encoding seasonal expectations into the model.

Section 2

Daylight Hours at 40°N

Daylight duration across the year at 40°N latitude (approximate US average), with solstice and equinox annotations.

SAD Seasonal Affective Disorder & Consumer Behavior: Research consistently links reduced daylight hours to lower consumer confidence and spending shifts. The "January effect" in retail, increased online shopping in winter months, and energy consumption spikes all correlate with the daylight cycle.

Our model encodes daylight_hours and daylight_delta (rate of change) as features. Markets in weather-sensitive categories (energy, retail, agriculture) show measurable sensitivity to these signals. The transition periods around equinoxes often coincide with regime changes in consumer sentiment markets.

Section 3

Fire Risk Seasonal Heatmap

Monthly fire risk intensity across 5 major wildfire regions, derived from FIRMS satellite thermal anomaly data.

FIRMS Fire Information for Resource Management System: NASA's FIRMS provides near-real-time thermal anomaly data from MODIS and VIIRS sensors. Our fire risk features combine historical seasonal patterns with live detections.

Note the hemispheric inversion: Australia's fire season (Oct–Feb) mirrors the Northern Hemisphere's summer peak. The Amazon's fire season (Jul–Oct) aligns with dry season and deforestation activity. These patterns impact insurance markets, carbon credit pricing, agricultural futures, and air quality prediction markets.

Section 4

Satellite Economic Gauges

Satellite-proxy economic indicators derived from nightlight radiance, shipping activity, and urban heat signatures.

PROXY Why satellite economic proxies? Traditional economic data (GDP, employment) is released with weeks or months of lag. Satellite-derived proxies provide near-real-time signals: nightlight intensity correlates with industrial output (r=0.87), shipping vessel counts track trade volume, and urban heat island intensity reflects commercial activity. These composite scores feed directly into our model's economic context features.

Section 5

Maxar Disaster Imagery Coverage

Distribution of Maxar Open Data satellite imagery events by disaster type.

MAXAR Maxar Open Data Program: High-resolution satellite imagery released within hours of major disasters. Our system ingests event metadata to create features like maxar_event_count, maxar_recency, maxar_severity_score, and maxar_affected_area.

Hurricane and earthquake events dominate coverage, followed by wildfires and floods. The immediacy of satellite imagery provides faster damage assessment than ground-based reporting, creating a temporal edge for insurance, infrastructure, and geopolitical prediction markets.

Section 6

Urban Activity Score Timeline

Monthly urban activity composite showing retail peaks, seasonal tourism, and holiday patterns.

URBAN Urban Activity Composite: Derived from nightlight variability, traffic congestion proxies, and commercial district heat signatures. Higher weekday activity and holiday retail surges are clearly visible.

The November–December spike corresponds to Black Friday, holiday shopping, and year-end economic activity. The February dip reflects post-holiday contraction. Summer months show tourism-driven boosts in coastal and entertainment districts. These patterns provide valuable context for consumer spending and retail prediction markets.

Section 7

All 24 Satellite Features

Complete inventory of satellite-derived features grouped by data source, with current simulated values and sparkline trends.