Why shouldn't we just buy satellite data from Planet or Maxar instead of building our own constellation?

Commercial data subscriptions are fast to deploy but create strategic dependency: licence terms can restrict redistribution, government-sensitive site data passes through foreign jurisdictions, and prices are set by suppliers who may prioritise other customers in a crisis. A sovereign constellation gives the nation perpetual, unrestricted access to its own territory at a predictable cost amortised over the satellite lifetime, typically 5–7 years per LEO platform.

What satellite data inputs feed a site-suitability model?

The core inputs are multispectral imagery for land cover and vegetation indices, SAR for terrain roughness and soil moisture, satellite-derived solar irradiance (e.g. from EUMETSAT's Meteosat or NOAA GOES products), wind reanalysis from scatterometry and numerical weather models, and digital elevation models from missions such as TanDEM-X or SRTM. These are fused with grid-infrastructure layers and environmental sensitivity maps.

How accurate are satellite-derived solar resource estimates compared with ground measurements?

Best-in-class products achieve ±4% annual GHI uncertainty at a single point, according to NREL benchmarking. This is sufficient for preliminary site screening and portfolio prioritisation but falls short of the ±2% threshold most project lenders require. At least 12 months of co-located ground-station data is still expected for bankable energy assessments.

Can satellite analytics replace environmental impact assessment (EIA) fieldwork?

No, but they dramatically reduce the footprint and duration of fieldwork. Satellite-derived biodiversity proxies (NDVI, habitat fragmentation indices) and hydrological models narrow the area requiring in-person survey from hundreds of square kilometres to a handful of candidate polygons, cutting EIA costs by an estimated 20–35% according to World Bank ESMAP guidance.

What orbit is best for renewable energy site suitability analytics?

Low Earth orbit (LEO) at 500–600 km is the standard choice: it delivers sub-10 m resolution at manageable data volumes and enables the multi-day revisit needed to track seasonal land-cover changes. Geostationary orbit adds value specifically for continuous solar irradiance mapping (as EUMETSAT Meteosat and NOAA GOES demonstrate) but is not required for the core site-screening workflow.

How many satellites does a nation actually need for meaningful coverage of its territory?

For a mid-sized nation (500,000–2,000,000 km²), a constellation of 3–6 microsatellites in a sun-synchronous LEO orbit provides near-daily revisit at 5–10 m resolution when combined with open-data inputs from Sentinel-2 and Landsat. Smaller nations or those focused on specific resource corridors can achieve adequate screening with as few as two satellites supplemented by commercial tasking agreements.

What data-sharing obligations apply once satellite data is collected?

There is no binding international obligation to share EO data, but WMO Resolution 40 and the Committee on Earth Observation Satellites (CEOS) Open Data Principles encourage non-commercial sharing of meteorological and land-observation data. Nations should design their ground-segment licensing framework to distinguish classified-tasking imagery from the broader non-sensitive archive they may wish to share with regional partners or the UN system.

How do we ensure the analytics platform doesn't become a new vendor dependency?

Insist on open standards at every interface: OGC API Features (OGC 17-069r3) for spatial data services, ISO 19115 metadata, and open-source processing frameworks such as ESA's SNAP or GDAL. Governments that anchor their sovereign constellation ground segment on proprietary analytics platforms simply trade one dependency for another; the software stack must be as sovereign as the hardware.

11.2.3Live

Site Suitability Analytics

Two engineers in high-visibility vests reviewing equipment on a flat, sun-baked landscape with sparse vegetation — Wind turbines rise across open grassland, their placement guided by satellite data identifying the most productive renewable energy sites.

Satellite Data Pinpoints Optimal Renewable Energy Sites

1
Satellite Sensors
Captures solar irradiance, wind, terrain data
2
Downlink & Ingestion
Raw imagery streamed to ground processing hubs
3
Suitability Modelling
GIS layers rank land by energy yield potential
4
Developer Dashboard
Interactive maps highlight top-scoring candidate sites

Multispectral satellite data is processed into ranked suitability maps, guiding renewable energy developers toward highest-yield locations.

Page in the build queue

This application has its place in the atlas. The full page — short and long descriptions, what-matters bullets, sovereignty rationale, and the eight-field architecture block — is being written next.

Maturity reading: Operational systems exist with paying customers. Phase 1 built §4.1 (Maritime Intelligence) end-to-end as the depth-and-format reference. Section 4 is the next section to graduate from scaffold to full content; the rest follow on the roadmap.

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