Flood-risk mapping for agricultural insurance in vegetated terrain depends on computed-aperture radar interferometric coherence, yet canopy decoherence attenuates the signal that classifiers must detect. the present analysis formalises the detection problem as a before-after-control-impact design and derives the operating characteristics of a coherence-based flood detector under a declared variance structure with intraclass correlation 0.64, between-cluster standard deviation 6.0 and residual standard deviation 4.5. Power was estimated across 2000 replications per design cell for cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points. The design required 56 clusters to detect a 5 percentage-point effect at 80% power. The analysis characterises the detection-power bound that canopy decoherence imposes on payout-equity claims, showing that the cluster counts typical of agricultural insurance portfolios are insufficient to separate genuine flood signal from decoherence noise. The contribution is a benchmark-derived bound that links remote-sensing detection power to the actuarial fairness of index-based insurance payouts.
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Mavinga et al. (2026) studied this question.
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