5.9.5 — Climate Risk Intelligence — maturity: live

Asset-Level Climate Exposure

Quantifying physical climate hazard at the level of individual infrastructure assets—power plants, ports, roads, hospitals—using satellite-derived observational data rather than modelled proxies.

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National regulators, finance ministries and infrastructure owners are under mounting pressure to disclose how specific physical assets will perform under intensifying climate hazards. The problem is that most risk frameworks rely on coarse, globally averaged model grids that cannot resolve a single dam, transformer yard or coastal highway. When a foreign data vendor fills that gap, the underlying hazard layers, confidence intervals and update schedules are opaque—and can be withdrawn at contract renewal.

A sovereign constellation combining optical imagery, synthetic aperture radar and multispectral thermal sensors can observe every significant national asset on a sub-weekly cadence. Satellite-derived land surface temperature, inundation extent, ground subsidence (via InSAR), vegetation health and coastal erosion rates are ingested against a national asset register to produce per-asset exposure scores tied to observed reality rather than interpolated models. The result is a living ledger: when a cyclone crosses a power corridor or a heatwave depresses a river level below a cooling-water intake, the system flags affected assets within hours.

Operationally, this changes how governments allocate adaptation capital. Infrastructure owners can prioritise hardening budgets with actuarial precision. Sovereign regulators can mandate disclosure standards anchored to nationally verified data rather than accepting a commercial vendor's proprietary score. Central banks conducting climate stress tests stop depending on information they cannot audit. The feedback loop closes: satellite observation drives investment, investment is tracked by the same satellites, and outcomes are verifiable.

What matters

A 30m or finer spatial resolution is required to resolve individual structures; global model grids at 0.25° (~25km) are useless at asset level.
InSAR-derived ground subsidence measured to millimetre precision is a leading indicator of foundation risk that no ground sensor network can provide at national scale.
Foreign commercial providers operate under their own governments' export-control and data-localisation rules, meaning hazard data for critical national infrastructure can be withheld or degraded.
IFRS S2 and the TCFD framework require disclosure of physical climate risk at asset granularity; a sovereign data layer prevents regulatory dependence on a single commercial score.

Sovereignty score: 8/10 — A nation that cannot independently observe the physical climate exposure of its own critical infrastructure has ceded a core sovereign risk-management function to whichever foreign commercial provider currently holds the contract.

Critical infrastructure coordinates are themselves sensitive national security data; transmitting a full national asset register to a foreign analytics vendor creates an intelligence exposure that no procurement clause can fully mitigate.
Commercial climate-risk scores are proprietary algorithms with no public auditability; a sovereign regulator accepting them for financial stability stress tests or infrastructure investment decisions cannot challenge, reproduce or update the methodology.
Export controls and sanctions regimes can interrupt access to satellite imagery or derived data products at precisely the moment of a climate emergency, when real-time asset status is most operationally critical.
Domestic adaptation finance, insurance pricing and green bond frameworks all require a common, auditable national hazard baseline; a patchwork of foreign vendor scores produces incompatible numbers that undermine policy coherence.

Reference architecture

Payload: Primary: multispectral optical imager, 3–5m resolution, 15 spectral bands including SWIR and thermal infrared (8–12 µm), 20km swath. Secondary: L-band SAR at 5m resolution for InSAR subsidence mapping and all-weather flood inundation; 30km swath, HH+HV polarisation.
Bus class: ESPA-class microsat, 120–180kg, 600W solar array; dual payload accommodation on a single bus to reduce per-asset revisit cost; cold-gas propulsion for station-keeping.
Orbit: Sun-synchronous LEO at 520–550km; 18-satellite walker constellation providing sub-5-day global revisit with optical and SAR cross-cueing; paired SAR satellites in the same orbital plane offset by 180° for 12-day InSAR baseline.
Ground segment: National network of 4 X-band downlink stations distributed across climate zones for latency reduction; S-band TT&C on each site; on-premise data archive with no mandatory cloud egress; SatNOGS-compatible UHF/VHF housekeeping backup.
Software & data
Latency
Cost band
Lead time

References

TCFD Recommendations: Physical Risk Guidance — The Task Force on Climate-related Financial Disclosures recommends that organisations assess physical climate risks at the level of specific assets and locations. Aggregate or sector-level scoring is explicitly noted as insufficient for material disclosure.
IFRS S2 Climate-related Disclosures Standard — IFRS S2 requires entities to disclose climate-related physical risks that could affect specific assets, operations or supply chains. The standard places the evidentiary burden on the reporting entity, not the data vendor.
ESA Climate Change Initiative — Land Surface Temperature — ESA's CCI Land Surface Temperature product provides consistent, multi-decadal thermal records derived from satellite sensors, enabling baseline characterisation of heat exposure at sub-kilometre scales.
USGS Landsat — Monitoring Land Change — Landsat's continuous archive since 1972 underpins land-cover change and inundation mapping; USGS notes its direct application to infrastructure resilience assessment through multi-temporal analysis.
World Bank — Investing in Urban Resilience — The World Bank estimates that climate-exposed urban infrastructure losses could exceed $314 billion annually by 2030, and argues that granular, asset-level hazard data is the prerequisite for cost-effective adaptation investment.