Insurers and reinsurers use Earth observation imagery to quantify damage after natural catastrophes, validate claims, detect fraud, and feed parametric triggers. The hard part is not buying a single image — it is planning which satellites can reach the affected area fast enough, at a usable look angle, through cloud, and with a clean pre-event baseline.
This guide explains how to plan that acquisition: how to choose sensors for the peril, how revisit and off-nadir angle (ONA) drive how quickly you get a usable scene, and how to build the feasibility check before you place an order with any imagery provider.
Start from the footprint of the event, not a single point — a flood, wildfire, or windstorm affects a region, and you want every pass whose swath touches any part of it. Set a tight date window around the event so you only evaluate passes that bracket it: ideally one clean pre-event scene as a baseline plus the first feasible post-event passes.
For rapid claims, the metric that matters is time-to-first-usable-pass: the earliest overpass that is in geometry, not blocked by cloud, and within your acceptable look angle. Widening the satellite set and the off-nadir tolerance shortens that time dramatically.
Flood and storm response is where SAR earns its place: radar sees through cloud and at night, so it delivers a post-event scene when optical sensors are grounded by weather — exactly when catastrophes happen. Use SAR for flood extent and structural collapse, then optical very-high-resolution (VHR) for damage grading once skies clear.
Wildfire and hail damage are primarily optical problems — burn scar extent, roof damage, and defoliation read best in VHR optical and shortwave/near-infrared bands. Plan optical passes with daylight required and a low-to-moderate ONA so building edges and roof condition stay interpretable.
Run a pass search over the affected AOI for the event window, filter by your acceptable ONA and (for optical) daylight, and sort by coverage so you see which passes actually cover the claim area versus clipping its edge. The result is a ranked, neutral list of feasible acquisitions across every operator — not a single vendor's catalogue.
Take that shortlist to the imagery provider or operator portal of your choice to place the tasking order. Keeping planning separate from purchasing means you compare the whole market on capability, then buy where it makes commercial sense.
It depends on how many satellites can reach the AOI and your look-angle tolerance. A wide SAR + optical satellite set with a generous off-nadir angle often yields a usable pass within hours; restricting to one sensor or a narrow ONA can push that to days.
Floods arrive with weather that blocks optical sensors. SAR penetrates cloud and works at night, so it captures flood extent during the event rather than after the water recedes.
For change-based damage and fraud detection, yes — plan one clean pre-event scene so post-event imagery can be differenced against a known good state.
No. PassPrediction is a neutral planning tool: it tells you which acquisitions are feasible across all operators, then you order from the provider of your choice.
Draw your Area of Interest, set the window and look-angle limits, and PassPrediction ranks every feasible pass across all operators — neutrally, in your browser, free to start.
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