Validating Remote-Sensing Measures of Natural Hazards: Granular-Level Links to Insured Loss during a Cyclone

Wang, Ke; Noy, Ilan

doi:10.5194/egusphere-2026-3539

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https://doi.org/10.5194/egusphere-2026-3539

Preprints

07 Aug 2026

| 07 Aug 2026

Status: this preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).

Validating Remote-Sensing Measures of Natural Hazards: Granular-Level Links to Insured Loss during a Cyclone

Ke Wang and Ilan Noy

Abstract. Remote-sensing products are widely used after disasters as indicators of incurred damage, yet it remains uncertain which mapped surface-disturbance remote-sensing signals are most informative about residential damages. This study examines the damage from the 2023 Cyclone Gabrielle in New Zealand by linking four publicly available remote-sensing layers—SAR-detected standing water, post-event wetness, soil- or silt-related disturbance, and inferred slope-related disturbance—to residential insurance claims from the public insurer at a fine spatial scale. We construct claim-rate and insurance payout outcomes and estimate cross-sectional models, investigating their association with the data from remote-sensing products. The two insurance outcomes capture recorded claim intensity relative to local building stock and the monetary intensity of insured loss. Slope-related disturbance is most strongly associated with claim occurrence and loss variation in hazard-positive rural areas. Wetness-related disturbance becomes the strongest predictor of loss severity once claims are observed. Standing-water and soil-related indicators provide smaller but more stable signals, and their composite index is positively associated with both claim rates and payouts. Urban areas show higher baseline loss levels, whereas rural areas show stronger marginal responses to additional physical disturbance. Our findings show that remote-sensing indicators are not interchangeable hazard proxies. Their value as proxies for disaster damage depends on the physical signal captured, the damage outcome measured, and the settlement context in which damage occurs.

Received: 17 Jun 2026 – Discussion started: 07 Aug 2026

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Ke Wang and Ilan Noy

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RC1:
'Review for egusphere-2026-3539', Anonymous Referee #1, 03 Sep 2026 reply
The study ‘Validating Remote-Sensing Measures of Natural Hazards: Granular-Level Links to Insured Loss during a Cyclone’ looks at the correlation between four different remote-sensing indicators and the occurrence and magnitude of residential losses represented through natural-hazard insurance claims following Cyclone Gabrielle, which hit the North Island of New Zealand in February 2023.
The study is an interesting and timely contribution to improvements in the validation of risk and hazard indices. While the results are interesting and worth publishing in NHESS, several key points made in the manuscript require further clarification. I would therefore recommend the manuscript for publication after major revisions.
General comments
I think the manuscript will do well in future reviews if the authors are able to address the following issues:
The study has a slightly inconsistent conceptualisation of how hazards and losses are linked. It often ignores the fact that losses are not a direct function of the hazard intensity but are also driven by the exposure and vulnerability of dwellings. I would recommend using the standard IPCC disaster risk definition of Risk = Hazard x Exposure x Vulnerability for consistency across the manuscript.

The approach using mesh blocks, which seem to represent administrative units, to normalise both the remote sensing and claims data is counterintuitive as it seems to throw away the most valuable properties of the two types of data which is spatial granularity. Lumping together the data in large unevenly sized geographical units removes the potential to study the heterogeneous effects that drive the variation in damages in different areas, often at much smaller scales than represented in the mesh blocks used in this study. Based on the description of the data and the robustness checks, my understanding is that you would be able to pair hazard indicators directly with the specific geolocation of a claim avoiding issues of spatial mismatches between hazard indicators and claims described in the discussion. I assume there is a reason why the authors have not done that, but the study currently lacks a justification for why they decided to aggregate data on administrative units that seem to have nothing to do with either how claims are reported or how the hazard indicators are constructed.

If I interpret footnote 1 on page 5 correctly, then the claims data used in the study exclude any flood damages to residential dwellings. I find this confusing as the study uses at least two flood-related hazard indicators and previous studies on cyclones have shown that flood damages to dwellings are one of the main drivers of total damages during cyclones or hurricanes. If the claims data actually do not contain information about flood losses to private dwellings, the authors would need to explain how to interpret the results if presumably a large part of the overall damage is not included in the data. If the claims data do contain flood damages to dwellings, it would be good to rephrase footnote 1 to make this point clear.

I think LogPayout should be normalised by the replacement value of buildings in an area to avoid losses being primarily a function of how expensive it is to fix a building. Averaging payouts over a larger geographical area can potentially be seen as an alternative to normalising payouts per claim, but comes with the limitations mentioned under point 2).

Overall, the regression results are well described and seem robust, but I would suggest to also discuss the fact that the overall explained variance in all presented model specifications is quite small (Rsq < 0.1). This is very common when studying losses and damages from natural hazards, as the processes are complex and cannot be easily captured by individual hazard metrics, but if the declared goal of the paper is to validate remote sensing measures of natural hazards, this seems an important point to discuss. The results suggest that while the chosen hazard indicators do explain some of the variability in claim occurrence and payout, the unexplained variability is very high.

Specific comments:
L37ff: ‘This distinction matters for loss analysis because insurance claims better reflect realised damage rather than meteorological intensity alone. Remote-sensing indicators can therefore provide a more direct spatial link between physical disturbance and observed residential losses and damages.’ These two sentences confuse hazard, risk and vulnerability. Both the meteorological intensity and the used remote sensing indicators are just different ways to measure hazard intensity. Losses are a function of properties exposed to the measured hazard and how vulnerable these properties are to the hazard.
L164:’This “standing water” image has not been validated against ground observations of flood extent.’ That seems like a useful thing to do as I would assume that standing water can be the result of various processes including in-situ rainfall which is different from standing flood water from rivers. Is there a reason why the authors have not done that?
L188f: What is the rationale for normalising LogPayouts by the total number of buildings and not by the number of affected buildings in a mesh block? Normalising by the total number of buildings makes it difficult to distinguish between areas that have few buildings with high damages and areas with many buildings with low damages.
L288ff: It would be good to get at least a bit more detail about the remote sensing indicators used and how they were constructed. For example, for D-Wet, how was the threshold defined to distinguish between higher soil moisture levels following rainfall and higher soil moisture levels caused by flooding?
L423: ‘This sample’ repetition

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Citation: https://doi.org/10.5194/egusphere-2026-3539-RC1
RC2: 'Comment on egusphere-2026-3539', Anonymous Referee #2, 16 Sep 2026 reply

Validating remote-sensing measures of natural hazards: Granular-level links to insured loss during a cyclone
This paper examines the relationship between residential property insurance claims—specifically the proportion of claim counts and payout ratios—and remote sensing indicators in regions affected by the 2023 cyclone in New Zealand. As remote sensing indicators, the study employs flood extent estimated from SAR imagery (S-flood), alongside three indicators derived from optical sensors (D-wet, D-soil, and D-slope). Through statistical analysis, the paper reveals that both the claim count ratio and the payout ratio show relatively higher correlations with S-flood and D-soil, and that the performance of these indicators varies between urban and suburban/rural areas. Based on these findings, the paper concludes that estimating damage-related metrics from remote sensing indicators is still challenging.
The use of insurance loss data is invaluable for evaluating and estimating damage scale, and the study deserves credit for utilizing such a precious dataset. However, significant flaws remain regarding the remote sensing indicators selected. While S-flood measures inundated areas using SAR imagery—a metric clearly relevant to cyclone damage—the other three indicators are calculated from pre- and post-disaster changes in NDVI. Because these NDVI-based indicators inherently include seasonal vegetation fluctuations, they lack a direct connection to actual disaster damage. Performing statistical analyses with metrics that have such weak physical ties to actual losses makes it virtually impossible to infer meaningful relationships with monetary damage, leaving the validity of the selected remote sensing indicators questionable.
Furthermore, NDVI is primarily an index used to assess the condition of vegetation and green spaces. In contrast, residential properties (buildings and immediate lots) subject to insurance claims are predominantly non-vegetated, built environments. Asserting that a drop in NDVI in surrounding farmland or woodland directly correlates with physical destruction or inundation of residential structures represents a major leap in physical logic.
If the authors intend to conclude that estimating damage from remote sensing indicators is difficult, it is essential to explore indicators that directly represent cyclone-induced physical damage. A study that skips the critical step of evaluating indicator validity and simply fits basic statistical models remains insufficient. Linking data that fails to separate baseline background noise from true disaster damage directly to insurance claims yields extremely low academic validity and utility.
For these reasons, the reviewer concludes that the paper suffers from flawed methodological premises regarding the physical and disaster-related validity of the input data. As the reliability of its conclusions cannot be guaranteed, this manuscript is recommended for Reject.

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Citation: https://doi.org/10.5194/egusphere-2026-3539-RC2
RC3: 'Comment on egusphere-2026-3539', Anonymous Referee #3, 16 Sep 2026 reply

Although the title presents the study as a validation exercise, the methodology describes an exploratory cross-sectional analysis. The study assesses correspondence with insurance outcomes but does not validate the accuracy of the remote-sensing measures against independent ground-truth hazard or damage observations.
The selection and construction of the variables requires clear justification. ClaimRate is calculated by dividing residential claims by the number of building footprints in each meshblock, but the building footprints may include garages, sheds and other roofed structures that are not equivalent to insured dwellings. The authors should explain why this is an appropriate denominator and assess the sensitivity of the results to this measurement mismatch. Similarly, hazard intensity is defined as the affected pixel area divided by the total land area of the meshblock. It is unclear why this represents residential hazard exposure, since affected pixels may cover areas without connection to a residential asset.
Re: the PCA discussion - Line 534, 541-543 highlights identical loading as something that supports the study but this is just a natural result of reducing the dimensions to two, you get sqrt(2)/2. A 2-variable PCA reveals 36.3% unexplained variance.

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Citation: https://doi.org/10.5194/egusphere-2026-3539-RC3

Ke Wang and Ilan Noy

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Short summary

Cyclone Gabrielle caused severe damage across New Zealand’s North Island. We compared four satellite-based measures of cyclone-related surface disturbance with residential insurance losses. Different indicators captured different aspects of loss, and no single measure explained all impacts. Combining multiple indicators provides a more complete picture of disaster losses and can improve future risk assessment and recovery planning.


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