Large-scale flood monitoring based on time series Sentinel-1 images and Z-index

Xing, Runmei; Zhao, Jun

doi:10.5194/egusphere-2025-5770

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https://doi.org/10.5194/egusphere-2025-5770

Preprints

03 Feb 2026

| 03 Feb 2026

Large-scale flood monitoring based on time series Sentinel-1 images and Z-index

Runmei Xing and Jun Zhao

Abstract. Synthetic Aperture Radar (SAR) satellites have emerged as a crucial technology for real-time flood monitoring in the face of escalating flood disaster hazards. However, current flood extraction techniques still have a lot of drawbacks when it comes to solving the twin problems of accurate urban flood detection and extensive monitoring. To address this challenge, this study proposes a novel approach based on an analysis of backscatter characteristics across different land cover types, combined with multiple auxiliary datasets, enabling effective monitoring of both extensive flooding and inundation in building areas. First, an analysis of scattering behavior in time-series SAR images revealed that in natural areas, the consistency of backscatter intensity is strongly influenced by vegetation growth status. In urban areas, rainfall can intensify double-bounce scattering, also disrupting intensity consistency. Based on these findings, a Z-score-based flood classification tree was developed. This method uses reference images and flood-period images to compute Z-score maps, enabling pixel-level flood probability estimation and establishing flood detection thresholds with clear statistical significance. The integration of VV and VH polarizations within the classification tree further improves the reliability of flood identification. Applied to the 2021 Weihui flood event, the method demonstrated strong performance, achieving a critical success index (CSI) of 60 % and overall accuracy (OA) of 90 % in natural areas, and a CSI of 62 % and OA of 73 % in building areas. The proposed approach shows significant advantages in accurately classifying flood-affected areas and offers the capability to monitor both large-scale floods and urban inundation.

Received: 20 Nov 2025 – Discussion started: 03 Feb 2026

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Runmei Xing and Jun Zhao

Status: final response (author comments only)

CC1:
'Comment on egusphere-2025-5770', Nima Zafarmomen, 22 Mar 2026
As a reviewer, I find this paper timely and relevant, since it addresses the important problem of combining large-scale flood mapping with urban inundation detection using Sentinel-1 time series. The main contribution is the proposed Z-score-based classification tree that uses VV/VH polarizations together with auxiliary datasets to distinguish flood responses in natural and built-up areas. The Weihui 2021 case study shows that the method is reasonably effective, with strong overall accuracy in natural areas and moderate but useful performance in urban areas. The paper is also valuable because it discusses the physical scattering mechanisms behind the different responses of cropland, water, and buildings, rather than presenting the method as a purely empirical workflow. Overall, the manuscript is promising for publishing in HESS. I will put some minor comments:
The manuscript would benefit from careful English editing, as there are many grammatical issues and repeated singular/plural mismatches such as “images is” and awkward phrasing throughout the text.

The novelty relative to prior Sentinel-1 time-series flood mapping studies, especially Z-score or anomaly-based approaches, should be stated more explicitly in the Introduction and Discussion. At present, the distinction from earlier methods is somewhat diffuse.

The choice of thresholds for natural areas (-2, -2.5, -3) and urban areas (2, 2.5, 3) is statistically motivated, but the final recommended threshold should be justified more systematically, perhaps with a table summarizing OA, UA, PA, and CSI for all cases.

The validation strategy deserves clearer discussion, especially because the Sentinel-2 and Sentinel-1 acquisitions are not fully synchronous, and the paper itself notes that flood extent changed rapidly between dates. This temporal mismatch should be treated more explicitly as a source of uncertainty.

Please define all abbreviations at first use and keep terminology consistent, for example “building areas,” “urban areas,” and “urban (building) areas,” which are currently used interchangeably.

In the methods section, it would help to clarify how the “reference images” were selected, how many were used for each flood-date analysis, and whether seasonality was controlled equally for natural and urban surfaces.

Figures are generally helpful, but some captions are too brief. The reader would benefit from more explicit legends, clearer color explanations, and a more direct connection between figure content and the interpretation in the main text, especially for Figs. 5–8.

I strongly recommend that the authors cite the recent paper Spatiotemporal SWMM-LSTM surrogate modeling for efficient node-level water depth and inflow prediction in urban drainage networks as it is highly relevant to the topic of urban flood prediction and would strengthen the manuscript’s engagement with recent advances in the field.
Citation: https://doi.org/10.5194/egusphere-2025-5770-CC1
RC1: 'Comment on egusphere-2025-5770', Anonymous Referee #1, 13 Apr 2026

Review of egusphere-2025-5770

“A Time-Series SAR-Based Anomaly Detection Method for Simultaneous Monitoring of Large-Scale Floods and Urban Inundation”
1. General Assessment
This study introduces a Z-score-based anomaly detection framework using Sentinel‑1 time series (VV+VH) integrated with auxiliary datasets (GSW, DEM) for flood mapping in both natural and built‑up areas. The method is validated on the 2021 Weihui flood event. The topic is relevant and the statistical approach is theoretically grounded. However, several methodological and validation issues require clarification and further analysis prior to acceptance.
2. Major Technical Comments
（1）Normality assumption of temporal backscatter

The Z‑score transformation assumes normality of the underlying pixel‑wise backscatter distributions. Although skewness and kurtosis are mentioned (Section 3.1), no quantitative results for the study area are presented (e.g., actual coefficients, histograms, or Q‑Q plots). Please provide these diagnostics for representative natural and urban land cover classes. Additionally, discuss how deviations from normality affect the interpretability of confidence levels associated with the chosen Z‑score thresholds.

（2）Threshold selection across polarizations

The classification tree applies identical confidence thresholds (e.g., 95%, 98.8%, 99.7%) to both VV and VH Z‑score maps without justification. Given the differing sensitivity of co‑polarization and cross‑polarization to surface roughness and vegetation structure, polarization‑specific thresholds may be more appropriate. Please justify the current approach or provide a sensitivity analysis comparing uniform versus polarization‑adaptive thresholds.

（3）Exclusion of persistent water bodies using GSW

A fixed occurrence frequency threshold of 25% from the GSW dataset is used to mask seasonal/permanent water. The rationale for this specific value is not provided. Please justify this threshold or conduct a sensitivity analysis. Furthermore, discuss how spatial resolution mismatches (GSW at 30 m vs. Sentinel‑1 GRD at ~10 m) and geolocation errors are handled, particularly at land‑water boundaries.

（4）Temporal mismatch in validation

Validation for natural areas relies on Sentinel‑2 images acquired 8–9 hours apart from the corresponding Sentinel‑1 acquisitions. Given the demonstrated rapid flood dynamics (e.g., differences between July 26 and July 31), such a temporal gap may introduce non‑negligible uncertainty. Quantify the potential impact of this time lag on accuracy metrics. For urban areas, UAV validation with a 2‑hour gap is more robust, but its spatial coverage is limited; please provide a map indicating the extent of UAV validation relative to the full study domain.

（5）Failure mechanisms in urban flood detection

The authors correctly identify that near‑roof‑level inundation suppresses double‑bounce scattering, leading to detection failure. The suggested remedy (using smaller incidence angle SAR) is often infeasible in operational settings. Please discuss whether auxiliary data, such as high‑resolution DEM (already listed in Section 2.4), could be used to estimate building‑water height differences and thus improve detection. Also, estimate the proportion of urban area where this failure mechanism is expected to occur.

（6）Post‑processing: pixel aggregation

The removal of flood patches consisting of ≤4 connected pixels may eliminate small but genuine water bodies (e.g., isolated puddles or narrow drainage channels). Justify this threshold based on physical considerations (e.g., minimum detectable water area given speckle characteristics) or provide a sensitivity analysis with alternative thresholds.

（7）Absence of baseline comparison

No comparison is made with established flood detection methods (e.g., simple change detection, Otsu thresholding, or single‑polarization anomaly detection). Such a comparison is necessary to demonstrate the relative advantage of the proposed dual‑polarization Z‑score classification tree. At a minimum, compare against a single‑polarization baseline using the same Z‑score framework.
3. Minor Comments
Line 145: The acronym “RRB images” is undefined. Please clarify (presumably “reference images”).

Line 200: Subject‑verb agreement – “baseline images … was acquired” should be “were acquired”.

Figures 3 and 8: Copyright notices for base map imagery (NASA, Google) are included. Please confirm that reproduction rights are secured for open‑access publication.

Equation (1) and accompanying text: The description of excess kurtosis is redundant (“subtracting 3 … known as excess kurtosis” while the formula already subtracts 3). Please revise for clarity.

Section 4.1.2: Rainfall can alter surface roughness prior to inundation. Did the authors attempt to disentangle rainfall‑induced backscatter changes from flood‑induced changes in the time series?
4. Recommendation
Major revisions required. The study addresses an important topic and presents a conceptually interesting approach. However, the concerns regarding normality validation, threshold justification, temporal validation mismatches, and the lack of baseline comparisons must be adequately addressed before the manuscript can be accepted for publication.

Citation: https://doi.org/10.5194/egusphere-2025-5770-RC1
RC2: 'Comment on egusphere-2025-5770', Guy J.-P. Schumann, 19 Apr 2026

This paper presents a method to process SAR images of floods. It seems to be a good method and scientifically very sound and robust but I have three major comments that should be addressed before publication:
- Like with so many other SAR processing techniques that now exist, it is very unclear why this method should be preferred to other methods published that seem to perform equally well. The author need to explain the innovation presented relative to other published methods and better justify why their method is preferred. Is it better accuracy, more robustness/consistency, better scaling.
- the authors also need to benchmark their presented method. Why not compare it to the S1 GFM for some flood events?
- the authors should also compare it to the simplest SAR method, such as Otsu or a simple image differencing. How much better is it to justify the increased computational complexity?

Citation: https://doi.org/10.5194/egusphere-2025-5770-RC2

Runmei Xing and Jun Zhao

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Short summary

This study introduces a method for monitoring floods using Synthetic Aperture Radar. The approach analyzes how radar signals interact with different land surfaces. It finds vegetation growth affects signal consistency in natural landscapes, while rainfall changes signal reflection in cities. Using statistical analysis, the method creates flood probability maps. Combining different radar signal types improves identification of flooded areas in both natural and urban environments.


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