Mapping global floods with 10 years of satellite radar data

Domain: earth_science
Published: 2025-07-01
Editorial outcome: major_revision

Abstract
Floods cause extensive global damage annually, making effective monitoring essential. While satellite observations have proven invaluable for flood detection and tracking, comprehensive global flood datasets spanning extended time periods remain scarce. In this study, we introduce a deep learning flood detection model that leverages the cloud-penetrating capabilities of Sentinel-1 Synthetic Aperture Radar (SAR) satellite imagery, enabling consistent flood extent mapping through cloud cover and in both day and night conditions. By applying this model to 10 years of SAR data, we create a unique, longitudinal global flood extent dataset with predictions unaffected by cloud coverage, offering comprehensive and consistent insights into historically flood-prone areas over the past decade. We use our model predictions to identify historically flood-prone areas in Ethiopia and demonstrate real-time disaster response capabilities during the May 2024 floods in Kenya. Additionally, our longitudinal analysis reveals potential increasing trends in global flood extent over time, although further validation is required to explore links to climate change. To maximize impact, we provide public access to both our model predictions and a code repository, empowering researchers and practitioners worldwide to advance flood monitoring and enhance disaster response strategies.

Introduction
Floods are the deadliest natural hazards, striking numerous regions in the world each year1. Floods cause 40 billion dollars (2015 USD) in damages annually2 and affected 2.5 billion people between 1994 and 20143. Furthermore, the population of people living in flood-prone areas is increasing due to migration and population growth4,5. All of these impacts are expected to become even more severe with climate change6.

A key factor in understanding and mitigating impacts from flooding is knowing where flooding occurs on a regular basis. Accurate flood extent mapping provides essential data for various purposes. It helps urban planners design resilient infrastructure, aids in developing early warning systems, and supports insurance companies and policymakers in assessing risks and allocating resources. By understanding past flooding events, communities can better prepare for future occurrences, leading to safer and more resilient living environments. While mapping flood extent is important, doing this via on-the-ground efforts is often challenging, especially in developing countries, where resources for this time-intensive work are scarce. In addition, ground-based assessments are often for small areas.

Satellite data offers a powerful solution for mapping flood extent at scale. Two primary types of sensors are commonly used for this purpose: optical/infrared and Synthetic Aperture Radar (SAR). Optical/infrared sensors passively capture reflected light, yielding familiar photographic images with benefits like wide availability and frequent observations at various resolutions. However, their effectiveness is limited by cloud cover and a dependence on daylight. In contrast, SAR actively emits microwave signals and records their reflections, offering advantages such as being able to penetrate through cloud cover and operate in both day and night conditions. While SAR is often described as all-weather, its signal may be affected during extremely heavy rainfall7,8. However, SAR satellites like Sentinel-1 typically provide observations every 6–12 days for a given location, whereas optical satellite constellations have revisit times ranging from daily to several days, often enabling more frequent coverage than SAR. This lower temporal frequency can affect our ability to capture the full dynamics of flood events with SAR, particularly flash floods and peak flood extent that may occur between SAR observations. Additionally, both technologies face challenges in complex terrain such as narrow valleys, which are common worldwide.

To understand where flooding occurs regularly, researchers have attempted to track flood events systematically over time. Previous work on tracking global flood events over time has primarily relied on optical and infrared imagery, despite limitations from cloud coverage. One notable study4 used relatively coarse resolution (250 m) visible and infrared data from MODIS (Moderate Resolution Imaging Spectroradiometer) to map known flood events from the Dartmouth Flood Observatory9, a curated catalog of flood events. Similarly, the Global Surface Water (GSW) maps uses Landsat data (30 m resolution optical and infrared imagery) to track surface water and its changes over time10, though this study was not specifically focused on flooding. Neither the MODIS- or Landsat-based archives are being updated over time. In addition to these global, temporal datasets, there are existing tools for real-time flood mapping using optical and infrared imagery11–13. However, all of these approaches face a fundamental challenge in that cloud coverage often obscures flood events.

In light of the challenge of cloud coverage, SAR imagery offers a significant advantage for flood detection because SAR microwave signals penetrate cloud cover and can be used in both day and night conditions. SAR’s effectiveness in flood mapping stems from the distinct backscatter signature of water surfaces, which appear dark in SAR imagery due to specular reflection of the radar signal away from the sensor. This characteristic makes SAR particularly suitable for distinguishing water from other land cover types. Researchers have successfully applied various techniques to Sentinel-1 imagery for flood mapping, including threshold-based approaches, machine learning methods, and more recently, deep learning algorithms14. Combined with its high resolution capabilities (as fine as 10 m for Sentinel-1), SAR has become a valuable tool for flood mapping. SAR’s effectiveness in detecting flooding and its cloud-penetrating capabilities were highlighted in a comparison of flood detection between Sentinel-1 (SAR) and Sentinel-2 (optical) satellites over Europe, with SAR imagery detecting 58% of flood events while optical imagery captured only 28%, given the same number of satellites15.

Given these capabilities, SAR data has been used extensively for flood mapping16–18, especially over Europe and Canada, where satellite ownership enables more frequent observations than other regions in the world. However, unlike the comprehensive, multi-year datasets created using MODIS and Landsat imagery, there has not been an effort to create a similar dataset of flood extent using SAR imagery. While the Copernicus Global Flood Monitoring (GFM) system provides valuable SAR-based flood maps19, it is primarily designed for analyzing individual flood events rather than producing aggregate maps showing all flood detections across multiple years or tracking longitudinal flooding trends. Therefore, there remains a need for an analysis of flooding patterns over extended periods. A dataset that aggregates global SAR flood detections over multiple years, while carefully accounting for challenges like false positives, could provide additional insights into the spatial and temporal patterns of flooding.

Our aim is to address this gap by building a neural network model to detect flood extent from Sentinel-1 SAR imagery and apply this model to 10 years of available SAR data to provide a global flood extent database over time. Our model uses a change detection approach, comparing pairs of SAR images acquired before and during potential flood events to identify inundated areas. We focus solely on SAR to ensure consistent detection through cloud cover and in both day and night conditions, which is beneficial for creating reliable aggregate flood maps and enabling unbiased temporal analysis. We account for false positives with auxiliary datasets such as soil moisture, digital elevation models, temperature and land cover mappings.

Applying the model to a decade of Sentinel-1 SAR data enables the creation of a unique global dataset that supports several important use cases:

1. A comprehensive historical baseline: We generate a high-resolution map identifying historically flood-prone areas globally over the past decade. Leveraging SAR’s ability to observe through clouds, this dataset provides a consistent perspective that complements optical datasets often hindered by cloud cover during flood events. While historical flooding is not a perfect predictor of future risk, particularly as climate change and other dynamic factors (e.g., land use change) may alter future patterns, this baseline can inform risk assessments, mitigation planning, and resilient infrastructure development.
2. Enhanced rapid response: The underlying model and processing pipeline can provide near-real-time flood extent maps during crises, serving as a valuable tool for disaster response teams.
3. Observation-based trend analysis: The longitudinal nature of our datasets enables analysis of potential trends in flood extent over time. Although the 10-year span limits definitive climate attribution, this SAR-based approach offers a globally consistent, observation-driven view of flood dynamics over time. It also helps mitigate biases associated with report-based trend studies20,21 and provides a foundation for ongoing monitoring as the satellite record expands.

Results
We aggregated all flood detections over the 10 years of Sentinel 1 SAR data available at the time of our analysis (Oct 2014–Sep 2024) to create a global flood extent map, as shown in Fig. 1. While we are able to track flooding detected on specific dates and map flood rates over time, here we present flood extent as a binary output for ease of visualization, marking only whether or not flooding has been detected in that pixel after removing potential false positives. In SAR imagery, there are multiple potential causes of false positives that need to be accounted for when running a model over an extended time period and not just for specific flood events (see “Methods” Section for more details). Additionally, while we run our model at a 20 m spatial resolution, here we create the map at 250 m resolution to aid in visualization.

After creating the global flood map, we developed an exclusion mask to identify areas where flood detection may be unreliable or prone to false positives. This mask covers urban areas, where building interference makes flood detection challenging, arid regions where certain surface features can create false positives, and areas with rough terrain. In our visualizations, these excluded areas are shown in gray. This masking approach, similar to that used by the Copernicus GFM system, helps users understand where our flood detection capabilities may be limited. More details on the exclusion mask logic can be found in the “Methods” section.

To contextualize our flood detection approach, we compared our results with two existing GSW datasets: the Landsat-based GSW dataset10 and a MODIS-based dataset4 (see Table 1). For the GSW dataset, we considered anything with water occurrence less than 50% as a flood-prone area. Our analysis shows significant increases in detected flood extent compared to these existing datasets. Globally, we estimate that our results increase areas with detected historical flooding by 71%. Given that our dataset’s timespan (2014–2024) is largely covered in the GSW (1984–2021) and MODIS (2000–2018) datasets, this 71% increase suggests our approach is not merely capturing recent events, but rather detecting flood-prone areas that optical sensors missed during the same observation periods.

We also find strong overlap in locations where previous methods have detected flooding. When examining areas where the GSW dataset identifies flooding, our method detects flooding in 35% of these locations, increasing to 48% when restricting to areas outside our exclusion mask. The comparison with MODIS-based maps shows similar patterns, with our method detecting flooding in 28% of MODIS-identified flood areas (33% outside the exclusion mask). This is similar to the overlap between MODIS and GSW compared to each other (36% and 20%). Note that we do not expect perfect overlap between any of these datasets due to inherent differences in observation timespans, temporal resolution, and sensing modalities (optical/infrared vs SAR).

While we can create this global map, the primary significance is being able to go deeper and analyze any location on the globe using the same, scalable methodology. To illustrate this capability, we examined flood patterns in Ethiopia, a country that reflects broader trends observed across Africa. Across the continent, our model detects a 90% increase in flood extent compared to existing datasets. We focused on Ethiopia because we were able to work with organizations within the country with deep domain knowledge about expected flood patterns and get qualitative validation of the insights shown here22. At a country scale, our flood map identifies both well-known flood areas - such as regions near the Awash and Shabelle rivers and around Lake Tana in the northwest - and reveals additional flood-prone regions not captured in existing datasets, as shown in Fig. 2. We estimate that our results increase the flood detections in Ethiopia by 194%—nearly a 3× increase over existing methods.

In Fig. 3, we highlight two areas where our model reveals additional flood-prone regions not captured in existing MODIS and Landsat-based datasets: Semera in the Awash River Basin and Dolo Ado along the Ganale River. Near Semera, we see a 96% increase in flood-prone areas. Several factors contribute to our confidence that this represents improved flood detection rather than noise. First, SAR’s ability to penetrate cloud cover enables observation during flood events often missed by optical sensors, providing a more temporally complete view. Second, the model’s robust performance—particularly its high recall validated on the geographically diverse Kuro Siwo benchmark (see Methods subsection on “Model Validation”) —supports its ability to detect true flooding when present. Third, qualitative validation from local experts in Ethiopia with deep knowledge of regional flood behavior provides corroborating support that many of these detections are consistent with known flood-prone areas22. While our model identifies substantially more extent, we also note regions—especially wetlands near lakes—where optical datasets detect water not captured by our model, underscoring the complementarity of different sensing modalities.

In Dolo Ado, the increase in detected flood extent is even more pronounced, exceeding 1000% relative to the combined extent from MODIS and GSW. While the large flood event in November 2023, which falls outside the coverage of the comparison datasets, contributes significantly to this increase, our model also captured extensive flooding prior to 2021 that is not observed in the optical datasets. These additional detections, supported by the same validation factors described above, suggest our model identifies genuine flood events historically underrepresented in global optical datasets.

This enhanced flood detection capability significantly influences our understanding of flood risks to critical areas, such as cropland. By overlaying our flood map with land use/land cover data from ESRI (Environmental Systems Research Institute)23, we assessed the extent of cropland at risk near Semera, as shown in Fig. 4a. We estimate that 19% of cropland in this region falls within historically flooded areas according to our map, compared to 7% in the GSW dataset and 2% in the MODIS-based dataset. The contrast is even more pronounced in Dolo Ado, where our model identifies 52% of cropland in flood-prone areas versus just 1–3% in existing datasets. These regions primarily rely on rainfed agriculture for staple crops like sorghum and maize, making unplanned flooding a significant risk for the local population who depend on subsistence farming.

With the high resolution flood map, we can zoom in further to identify areas with historical flooding at even finer granularity, as in Fig. 4b, which provides a more detailed view of one particular area of Semera. Building on the previously stated assumption that historical flood patterns can inform future risk assessments, we can use these detailed maps to identify specific areas that may be vulnerable to future flooding. Given the importance of local agriculture for the populations in this area, it is important to understand which areas could be at risk of future flooding so that government agencies and other stakeholders, such as non-governmental aid organizations, would be able to target mitigation efforts as well as targeted infrastructure improvements or policies on future settlement and agriculture.

While the example above was for a specific region in Ethiopia, the methodology employed could be used anywhere in the world. This capability to produce high-resolution, detailed flood risk assessments anywhere in the world underscores the potential of our approach to significantly enhance flood preparedness and mitigation efforts across diverse geographic and socio-economic contexts. By enabling precise identification of flood-prone areas, our maps can support targeted interventions, ultimately contributing to more resilient communities worldwide.

Another application of the flood model is to be able to analyze SAR imagery for disasters. The spring rains of 2024 resulted in some of the worst flooding in Kenya’s history. During the flooding, we were able to collaborate with agencies within Kenya to provide near-real-time updates of flood extent with minimal human intervention.

Figure 5 shows the flood extent map (blue) and cropland map from ESRI (yellow) over the course of the flooding between March and May 2024 for the entire country. While we updated the map daily during the flood event, the versions shown is a composite of all areas where flooding was detected during this time period. As with the Ethiopia example above, we were able to overlay this with cropland maps to estimate the impact of the flooding. We estimated that roughly 75,000 hectares in the country was in or very near flooded areas (2% of all cropland in the ESRI land cover mapping for Kenya). This is roughly in line with the public government numbers of 168,000 acres/68,000 hectares affected24.

Being able to track flooding like this in real-time is a valuable asset during a disaster. While there are existing models and tools that can do this with SAR and other satellite data, having an additional model that can be run with minimal human intervention is another source of information that can be leveraged.

One major contribution from our work is the ability to track flood extent longitudinally. Climate change is expected to exacerbate flooding over time, but direct measurement of this trend is challenging. Because Sentinel-1 satellites have consistent return periods (with minor exceptions) and are minimally affected by cloud cover, we get as unbiased a view as possible of flood extent over time.

We are able to see statistical support for an increase in flooding over time, though we acknowledge the limitation of having only 10 years of data due to the operational period of the Sentinel-1 constellation. With limited data, it is difficult to attribute this to climate change, and we encourage others to extend this work and to refresh this over time as new data becomes available.

We aggregated global flood extent detections by month over the entire 10 year period. While the data is initially noisy, after removing the seasonal trend we see an increase over time. Figure 6 shows a seasonal decomposition of the overall trend. This decomposition visualizes how the flood extent signal can be separated into trend, seasonal, and residual components, helping illustrate the underlying patterns in our data. For our statistical analysis, we used a linear regression model with monthly dummy variables to control for seasonality while estimating the temporal trend. From the trend, we identified two potential data challenges: the potential outlier year of 2022, and the time before June 2017. Prior to June 2017, many of the observations were made with only one polarization channel instead of two, resulting in different rates of flood detections that we correct for before measuring the trend. The large observed flood extent in 2022 is likely due to specific flood events such as the flooding in Pakistan from June to October of that year. Given that these two factors could skew any potential estimate of the trend over time, we estimated the trend under different scenarios (see “Methods” Section for more details).

Table 2 shows the results for the different scenarios with one standard deviation. In general, we see a positive trend of a few percent per year, though in the most pessimistic case the result is not statistically significant (the result is within <2 standard deviations of 0), because of both the lower effect size and the result of removing nearly 40% of the data, which increases the uncertainty estimate. We view the middle row, where we exclude 2022 but include the earlier data and see a 5% yearly increase, as our current best estimate of the change in flooding over time. This 5% increase would result in a roughly 60% increase every decade if the growth compounded, resulting in great potential loss in human life and property if not mitigated.

In addition to understanding global trends in flooding, it can be beneficial to understand where flooding is increasing or decreasing across the globe. Figure 7 shows the estimated trend for 3° by 3° tiles. Tiles that have a p value for the trend greater than 0.2 or have low absolute observed trends are removed to reduce noise in the plot. While there is considerable uncertainty because of the limited time range of our analysis, there are some interesting regional insights. For example, there is an area between Nigeria and Ethiopia with an estimated increase in flooding. This is correlated with predictions of increased precipitation in this region under the CMIP6 (Coupled Model Intercomparison Project Phase 6) climate scenarios, a comprehensive climate modeling framework25. However, other regions do not necessarily have similar increases in predicted precipitation, so any such correlations should be interpreted with caution. These insights could be further corroborated by repeating the analysis as more data is made available and trying to combine with other data sources, such as the NOAA (National Oceanic and Atmospheric Administration) LEO Flood archive, which runs from 2012 to 2024.

Methods
Our approach for flood detection consists of two broad steps: first, running a neural network model trained on SAR data to detect flood candidates, and second, removing potential false positives using auxiliary datasets. After filtering out false positives, we used the aggregate results to create flood extent maps and to look at flooding trends over time.

We used a MobileNet early fusion change detection model, which combines spatial and temporal information early in the processing pipeline to analyze SAR images. We chose MobileNet as our model architecture because it can be effectively adapted for pixel-level classification tasks in satellite imagery while remaining computationally lightweight, making the model accessible to users with limited computing resources.

The training data consisted of manually labeled SAR images from significant flood events from the past few years. These events were chosen because we could clearly identify the flooding in SAR imagery and confirm the flooding using other sources, such as news reports, drone footage, and cloud-free Sentinel-2 imagery when available. Additionally, these flood events span multiple continents with diverse geographies, providing a robust dataset for training our model.

• Pakistan flooding in August 2022
• Greece flooding in September 2023
• Mozambique flooding in March 2023 (validation)
• Southeast Ethiopia flooding in November 2023 (test scene)

We opted for a change detection model because it leverages the temporal differences in SAR amplitudes to accurately represent flooding. In essence, by comparing SAR images captured before and after a flood event, the model can detect changes in the backscatter signal that indicate the presence of water in previously dry areas. Through experimentation on our validation dataset, we found that model performance improved when we explicitly applied filtering to the SAR amplitudes to identify ranges of pixel values consistent with the presence of water, rather than using raw amplitudes directly.

Our model uses four input features that capture both the presence of water-like signatures and significant changes in surface characteristics: binary change indicators for VV and VH polarizations (indicating transitions into typical water backscatter ranges), and the magnitude of backscatter changes (delta amplitudes) in both bands. Water typically appears dark in SAR imagery due to specular reflection, with characteristic backscatter values below −17.5 dB in the VV band and below −22.5 dB in the VH band. The delta amplitude features allow the model to distinguish between small changes that might be due to noise versus larger changes more likely to indicate actual flooding.

The Sentinel-1 SAR data used in this study is from Catalyst (via the Microsoft Planetary Computer) and has undergone standard SAR preprocessing, including orbit application, gamma-nought correction to normalize backscatter across different incidence angles, radiometric terrain correction using PlanetDEM, and speckle filtering. The gamma-nought correction adds an additional cosine correction to better normalize backscatter across different incidence angles. We utilize both ascending and descending passes to maximize temporal coverage. The model was trained on consecutive pairs of SAR images with the same viewing geometry and time of day taken within 30 days of each other. While Sentinel-1 typically has a 12-day repeat cycle, we allow up to 30 days between observations to account for potential missing acquisitions while maintaining consistent imaging conditions. Prior work17 has suggested that including an additional pre-event image can provide some improvement in flood detection, but the gains in F1 score were small (typically less than 1 percentage point) and were often confounded with model architecture changes. Given these modest gains and the increased computational cost of processing additional images, we opted for a paired-image approach, using one pre-event and one post-event image.

While we tested incorporating additional inputs such as soil moisture and elevation data directly into the model, our labeled flood examples did not contain enough diversity across different soil moisture conditions and terrain types to effectively train on these variables. Namely, we did not see sufficient flooded and non-flooded pixels across a range of different soil moisture and slope values. Additionally, the coarse resolution of soil moisture data (10 km) meant we essentially had one measurement per scene, providing insufficient data for the model to learn meaningful relationships. We therefore elected to use these auxiliary datasets in post-processing instead, where they help reduce false positives through heuristic filtering.

We validated the model using two approaches: evaluation on our internal test set and comparison to the Kuro Siwo dataset, a newly-released comprehensive global dataset of expert-annotated Sentinel-1 flood images17.

The model’s performance on the test set showed promising results. To understand these results, we use several standard metrics: the Intersection over Union (IOU) measure shows well our predicted flood areas overlap with actual flood areas, with our score of 0.67 meaning that 67% of the combined predicted and actual flood area was correctly identified. The Precision score of 0.68 indicates that when our model predicts a flood, it’s correct 68% of the time. Our Recall score of 0.99 means we successfully detected 99% of all actual flood events. Finally, the F1 Score of 0.80 represents the overall balance between precision and recall, where 1.0 would be perfect performance. These metrics indicate that the model is highly effective at detecting flood events, with a high recall ensuring most flood events are captured, albeit with some false positives, or pixels predicted as flooded but that are non-flooded.

To ensure robustness, we also test the model’s performance against the Kuro Siwo dataset. This dataset was selected due to multiple favorable factors. First, it contains both pre- and post-flood SAR imagery, making it suitable for a change detection model such as ours. Second, it contains 43 distinct flood events distributed across six continents and varied climate zones. This is essential for validating a model that is being applied globally. Lastly, the dataset contains manually annotated images to account for shortcomings in existing annotation approaches such as the Copernicus Emergency Management System outputs17. Given the known challenges of SAR in complex terrains like steep mountains and dense forests—areas where our model’s predictions are masked using the exclusion mask (see “Methods” subsection on Post-Processing)—this validation focuses on assessing model performance in the diverse global regions where detections are expected to be most reliable.

Our model performs well against the Kuro Siwo dataset, achieving an F1 score on the test set of 0.77. This is comparable to the models published in the Kuro Siwo paper, which have F1 scores ranging from 0.75 to 0.80, and greater than the performance of the Copernicus GFM system’s F1 score of 0.72, as shown in Table 3. We note that in the Kuro Siwo paper they report results for multi-class classification models, so that the F1 scores are not necessarily directly comparable to the task we have for binary classification. Additionally, the paper only provides F1 scores for the flooding task. Nevertheless, their numbers provide a useful benchmark for comparison. We also note that while our model provides binary flood/no-flood classifications, GFM produces probabilistic flood likelihood values that can be thresholded for different applications. For this comparison, we used an optimal threshold of 0.3 for GFM, determined through validation on a subset of flood events (see SI Section S1.5.2).

There are two types of post-processing we apply to the model results. First, we filter out potential false positives using both time-varying auxiliary data sources and static features. Second, we provide a static exclusion mask that identifies areas where flood detection may be unreliable due to false negatives or false positives.

There are several factors that can result in false positives in SAR data. In SAR imagery, water appears very dark. Any surface that appears similarly dark can therefore mimic water and therefore flooding. For example, arid regions often look dark in SAR because the surface absorbs much of the SAR signal or reflects it away from the satellite, resulting in low backscatter measured by the satellite. Another cause of false positives is mountainous areas, where terrain shadows can result in areas where the signal sent out by the SAR satellite does not reach the ground, again resulting in a low SAR amplitude. Similar effects occur in heavily vegetated areas and urban areas. Lastly, freeze/thaw cycles can result in areas that technically flood, in the sense that water is present where it was not present before, but typically this flooding is of a different nature than most of the flood events we are concerned with, and so we aim to filter those out as false positives.

To address potential false positives, we incorporated both static landscape features and time-varying characteristics. The static filtering was applied based on the ESA land cover mapping33 and a digital elevation model34. Specifically, we filter out areas marked as “Bare Ground” in the land cover mapping along with areas with terrain slopes greater than 10°. We include two time-varying features: soil moisture estimates35–37 and land surface temperature38, to identify areas with low soil moisture or low temperatures to exclude. As noted above, while we tested incorporating soil moisture and elevation data directly into the model training, we found it more effective to use it as a post-processing filter to remove false positives. We also used the ESA land cover mapping to remove permanent water bodies like lakes and rivers. While our change detection approach should inherently ignore permanent water bodies since they appear as water in both pre- and post-event imagery, we found that explicitly masking them helped reduce noise in our predictions.

We first run the neural network model to generate potential candidates for detected floods, then apply heuristic thresholds with the auxiliary datasets to remove false positives. The heuristic thresholds were determined by analyzing model predictions and auxiliary dataset values in regions with a very low likelihood of flooding, such as deserts and mountainous areas. For instance, regions with soil moisture levels below a certain threshold were excluded as false positives. We note that in the dataset we have provided, we include all potential flood candidates with the auxiliary data included, enabling other researchers to apply their own thresholds and make informed decisions on which events to filter out. Detailed methods and the specific thresholds for removing false positives are provided in Supplementary Information Section S2.

For our static map products (GeoTIFF format), we include an exclusion mask that serves two purposes. First, it identifies areas where we expect unreliable flood detection due to static landscape features based on the filtering used to minimize false positives: areas marked as “bare ground” in the ESA land cover mapping and regions with steep terrain (slopes exceeding 10°) in their immediate surroundings. We include these surrounding areas because steep terrain can influence radar signals in nearby pixels. Second, the mask identifies areas where we expect a high rate of false negatives, specifically areas marked as “Built-Up” in the ESA land cover map, which typically indicates urban areas where building interference makes flood detection challenging.

We analyzed flooding trends over a decade by aggregating flood extent data by month and normalizing by the number of available SAR observations. This normalization is necessary as observation frequency can vary- for example, one Sentinel-1 satellite went offline in late 2021, reducing observations by nearly 50%. To estimate changes in flooding over time, we fit a linear model to the time series under various scenarios. We chose a linear model for its simplicity and interpretability, given the noisy nature of the flood extent data and the relatively short time period.

We explored different scenarios to address potential data challenges. For example, 2022 showed much higher than average flood extent per observation, likely due to large flood events like in Pakistan. Given the short time period, these outliers could skew our results towards artificially high estimates of increased flooding over time. Another challenge was in the earlier Sentinel-1 observations. Many of these used only one polarization channel, and therefore had different rates of flood detection than the typical two-channel observations that are ubiquitous after June 2017. We can correct for these differences, or remove those earlier years. Both sets of results are presented as different scenarios in Table 2. Detailed methodology for estimating flooding trends over time is provided in Supplementary Information Section S3.

One challenge inherent in high-resolution flood modeling using SAR imagery is the precise delineation of flood extents. While SAR imagery is effective in penetrating cloud cover and providing continuous monitoring, it may not always capture every pixel of a flooded area accurately. Factors such as SAR signal scattering, speckle noise, and surface roughness variations can result in imperfect flood detection, leading to potential underestimation of the flood extent39–42.

To mitigate these limitations, we apply a buffering or dilation to the flood detections39,41,42. This process involves expanding the detected flood pixels by a specified distance to account for potential inaccuracies in the SAR data. The buffer helps include areas likely at risk of flooding that may not have been explicitly detected by the model. Buffering is particularly important in urban areas or vegetated areas (such as cropland), where buildings and vegetation can prevent the SAR signal from reaching the ground, making it difficult to detect flooding in some cases. For the geotiffs in our public dataset, we apply two versions, with buffers of 240 and 80 m. The 240 m (12 pixels) buffer ensures a conservative estimate, in the sense that we err on the side of marking something as flooded. For getting accurate estimates of cropland affects by flooding, we found that 80 m (4 pixels) gave us the closest alignment with other sources of flood extent (see Supplementary Information for details). We note that since we provide the raw model outputs in our dataset, other researchers have the flexibility to apply their own choice of buffer distances based on their specific needs.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Mapping Global Floods with 10 Years of Satellite Radar Data\nI enjoyed reading this manuscript and congratulations on doing this huge amount of important work.\nHowever, with important work comes great responsibility. If this gets published in Nature Communications, and with the\nMicrosoft association, it will be widely picked up. It is vital to properly assess the capabilities of the data and make crystal\nclear the points the data can be used for and what it perhaps should not be used for or where users should be mindful. I have\nbeen involved in disaster response around flooding for a few years now and I see data, especially satellite derived data\nmisused frequently. Users tend to view the data as the true flood which of course is nonsense as the image is only a\nrepresentation of an image. Making sure this is well understood is crucial and indeed ethical.\nWith this in mind, I think if this data is going to be in Nature Communications and likely widely publicised, more careful\nevaluation and communication is needed. I have outlined my considerations below. Please do not take these observations\nto heart too much as I really believe you have done some great work. A few years ago I was in a similar position being solely\na data creator and not really considering the implications of incorrect use in the real world, but after a few years of seeing\nand working in more applications, I have switched my view a little. You could easily publish this in a more specialised\njournal, but for a journal with so much clout I cannot emphasise enough how important it is to be robust.\nGood points:\n- The multi-temporality is really interesting\n- A truly amazing amount of data processing\n- Buffering is a good addition\n- The consideration of false positives is quite robust\n- The data record is nice\nMajor points to consider\n- The figures can be very difficult to read. For instance Figure 5 is unreadable.\n- Is the calculation in Figure 7 correct? Or more accurately, should a different threshold be used to determine what is\nsignificant or not? Assuming a 1 degree tile is 111km, the area of a 3x3 degree tile would be 110,889km2. One hectare is\n0.01km2, so a large increase would be 0.2km2. This is a really small fraction of the total area. I wonder would it be better to\nreport the % total land area flooded and any trends in that. That would also get over the issue of unequal areas in each 3x3\ndegree tiles. You have a really nice story here, but I feel it could be told a bit clearer.\n- Paragraph starting at Line 232. You make some important claims in this paragraph, but the analysis for it is not in the main\ntext. Many people use Landsat/MODIS data so this analysis should be supported adequately.\n- I would highly recommend including an exclusion layer like Copernicus Global Flood Monitoring does. You correctly\nmention the difficulties in recording flooding in difficult areas like urban areas, but it is important to exclude these (or at least\nflag the inability I these areas) in your data products. Many potential users may not quite grasp this in my experience so\nhaving an exclusion layer is very useful.\n- The model validation section could be improved. I would love to see some maps for each location to better understand\nwhat is really going on. What type of flood is being detected? If it is a fast onset (say pluvial), then the differences in timing of\nthe high resolution optical imagery with the SAR imagery could be quite challenging. Could you also confirm the overpass\ntiming of each imagery so the reader can get a better idea of the timing so we know if like for like is being compared?\n- I believe you are comparing against the Global Flood Monitoring product (GFS, ref 14). Did you use the likelihood values? I\nhave found in model evaluation and forecasting work the likelihood maps to be very useful in visualising how sensitive an\narea can be to the different SAR processing algorithms. If you evaluate against different thresholds of likelihood that might\nproduce some interesting results. It would be fantastic if your product could have some sort of uncertainty/ensemble attached\nto it. I have no affiliation to this group whatsoever by the way, but in my experience I have found it really useful. I note the\ngreat work in the post-processing to remove false positives as I have read on. Well done!\n- As you noted, there is a lack of flood events used in the training of your model. Even if it might not improve your metrics too\nmuch, it would be very beneficial to make your work more convincing if you had more training image. I note you discuss this\nmore in supplementary materials. I can see that 4 locations may suffice, but there others may need more convincing. How\nabout labelled images from (from a quick google):\no Montello, F., Arnaudo, E., & Rossi, C. (2022). MMFlood: A Multimodal Dataset for Flood Delineation from Satellite Imagery\n(1.0.0) [Data set]. Zenodo.\n- If you cannot use more images to train, it would be great to describe why the areas you used are enough for the model to\npaint an accurate and robust global picture.\n- Again you have rightfully noted there is a lack of validation/evaluation. It is vital to note that your confidence in your results\nare less in places that you have not trained or are not similar. This product could well be used in a disaster situation and it is\nvital (and ethical) to make people aware of what the product can achieve and roughly how confident you are in it.\n- Could you give some further explanation on you chose the ESRI landcover product. Is it the multi-temporality (I’m assuming\nit’s the Dynamic world one). Or are you using a static landcover mask? Does cropland contain irrigated areas as that could\nsignificantly impact the flood results (irrigated areas, particularly in your Pakistan example are detected as flooded. They are\nflooded, BUT are deliberately flooded.\n- I think it would be beneficial to describe why a MobileNet architecture is appropriate for this analysis. By this I mean how it\nprocesses and classifies images. I note you discuss the performance compare to ResNet etc., but for someone not familiar\nwith ML/Deep learning it would be good to briefly describe why it is appropriate.\n- Did you test the local thresholding for the SAR imagery? In my experience the thresholding is so important and what works\nin the floodplain of the Ganges can be wildly different to that in the Zambezi. You could effectively create ensemble maps by\nchoosing different thresholds. I do later note the super large 2 month compute time so that may not be possible. That seems\nsuper long to me.\n- Delving a bit more into the model validation in supplementary materials. The time difference between the Planet and SAR\nimages is quite significant, especially for a relatively short flood. For a long lasting flood where the flood waters are fairly\nstatic that might suffice, but for this relatively short flood you need to highlight more the potential issues. Did you get any on\nthe ground photos, or try to use gauge data or something like that to unpick the flood event? Or any ground reports?\n- I understand why you used the CNN derived Planet delineation, but it is difficult to be convinced whether this can be truly\ntrusted. It is tempting to believe a very fine resolution image because it looks crisper, but it can be very wrong.\n- It would be good to compare your extents visually to the Pekel Global surface water, both quantitatively and visually\nMinor\n- There should be a mention that SAR imagery most likely misses the flood peak\n- Figure 4 – Needs a legend on the actual figure itself\n- Figure 6 uses square meters as the unit of analysis but others use hectares. Is this deliberate? If so why was this choice\nmade?\n- Line 230 – I would be tempted to just say other optical flood products and not constrain it to NOAA. There’s a lot out there\nand you don’t want to risk annoying anyone.\n- Line 382. It would be good to describe what each metric means so a non expert could understand what a good score is.\nPerhaps compare these scores to something similar. Basically just make it clear what they really mean.\n- Line 426 – why did you chose a linear model?\n- Perhaps clarify the resolution of the product. I see on the data record it is 30m for the rest of the world, but you mention 20m\nin the text. When describing the buffer you are using multiple of 30m.\n- Line 40 supplementary materials – what are the other sources you are describing?\n- Can you run the model at different locations in different areas? In large floodplains, 20m or coarser would more than suffice.\nBut in smaller areas/peri-urban you could run at 10m?\n- I think model evaluation is better than model validation in this context as we cannot be that confident in the ‘ground\ntruth’/benchmark data.\n- Figure S2 – the maps could be zoomed in more to see the areas of agreement etc. It is impossible to tell at this scale what\nis going on.\n- Instead of slope, you could use a slope roughness metric. Check out the vast range in WhiteBox Tools", "reviewer_2": "General Comments:\nThe authors utilized deep learning (DL) for global flood extent mapping using 10-meter resolution SAR imagery. While the\nmethod shows potential, the study's test cases are insufficient for claiming a global-scale applicability. Additionally, there are\nseveral areas where the methodology, discussion, and framing of results can be improved for scientific rigor and clarity.\n1. Clarity and Wording Issues:\n• Line 9: The phrase \"any weather condition\" is misleading. SAR can penetrate clouds and operate both day and night, but it\nhas limitations in rainy conditions due to rain droplets affecting backscatter. Please rephrase this line for accuracy.\n• Line 35: The term \"regardless of weather condition\" is not accurate. SAR operates day and night and can function under\ncloudy conditions, but rainy weather can impact backscatter values.\n• Lines 29–38: The wording in this section needs revision for better clarity and flow.\n• Lines 35–36: Please clarify what is meant by this line. Are you referring to SAR’s temporal resolution? If so, specify how it\nrelates to flood mapping.\n• Line 66: Rephrase this sentence for improved readability and accuracy.\n2. Missing Points in the Introduction:\nThe introduction lacks critical context regarding SAR capabilities and related studies. I recommend addressing the following:\n• The capability of SAR for water mapping and flood detection, emphasizing its ability to detect dynamic water changes.\n• A brief literature review on prior studies that used SAR (especially Sentinel-1) for water and flood mapping.\n• The rationale for using only SAR imagery. Why was SAR-optical fusion not considered? Optical imagery, while affected by\ncloud cover, can complement SAR and improve flood mapping accuracy.\n• The number of Sentinel-1 images required before and after a flood event to ensure reliable analysis.\n3. SAR Data and Processing:\n• Lines 404–421: The discussion on SAR imagery is not scientifically sound. Key points to address:\no Include the range of SAR backscatter values typically associated with water.\no Discuss the need for incidence angle correction, as this is critical for comparing backscatter across different tiles. The\nincidence angle could also be included as an input feature in the model.\no Explain why soil moisture data was not considered as an input feature to improve model performance. Soil moisture can\nsignificantly impact backscatter and flood detection.\n• Preprocessing:\no Specify the preprocessing techniques applied to the 10-meter SAR data. Given the resolution, speckle noise is a\nsignificant issue, and speckle filtering is essential. Clarify if and how this was handled.\no Did the study use ascending and descending passes, or only one of them? This should be stated explicitly.\n• Feature Importance:\no Consider performing a feature importance analysis to identify which auxiliary data contributes most to model performance.\nThis would help reduce redundancy and improve the model's efficiency. Refer to studies that demonstrate the importance of\nfeature selection in deep learning.\no Specifically, it is necessary to determine the most important SAR features for flood mapping. For example, features like\nbackscatter intensity, polarization (VV/VH), or temporal change metrics may vary in their significance depending on the\nregion and type of flooding.\no This approach could also guide the integration of auxiliary data, ensuring only the most impactful variables are included in\nthe model. Refer to existing studies that highlight the benefits of feature selection in deep learning models.\n• Reliability of Backscatter:\no Acknowledge the limitations of SAR data on rainy days. Rain droplets and dew can alter backscatter values and impact\nclassification accuracy. This should be explicitly discussed.\n4. Differentiating Between Water Types:\n• Provide an explanation of how the model differentiates:\no Permanent surface water versus flooded areas.\n• Flooded cropland (e.g., rice paddies in Southeast Asia) versus actual flood events.\n• Special test cases should target regions prone to misclassification, such as paddy fields or other seasonally flooded\ncroplands. For example, line 105 mentions that 11% of cropland lies in historically flooded areas—these regions need\nfocused testing.\n5. Comparison and Validation:\n• It would strengthen the study to compare the performance of:\no SAR-only models.\no Optical-only models.\n• Fusion of SAR and optical data. This would provide insights into the complementary nature of these datasets for flood\nmapping.\n• Discuss SAR’s unique ability to detect dynamic water changes and compare it with prior studies.\n6. Testing and Evaluation:\n• The study needs to expand its testing regions, particularly targeting areas with complex hydrological dynamics (e.g.,\nSoutheast Asia, which has flooded cropland like rice fields). The current test cases are insufficient to generalize the findings\nto a global scale.", "reviewer_3": "This paper describes the use of a ten-year record of Sentinel-1 SAR images to map floods globally, using a neural network along auxiliary dataset, including soil moisture, DEM, temperature, and land cover to minimize false positive errors.\nIt demonstrates trends at the global scale and assess some use cases around the world where the authors also validate the\nmaps with high-resolution optical images from Planet Labs.\nThe paper is generally very well written and the methodology is sound, both technically and statistically.\nIn the main text of the paper, an in-depth comparison with and discussion of the Copernicus GFM S1 based flood maps,\nespecially on the use cases presented would be much appreciated by a larger audience, I think. In my opinion, the authors\nshould add this and also describe better the benefits and advantages/limitations of their global method compared to the\nCopernicus GFM.\nIn the Supplementary material. IOU (F1) is indeed low, maybe the authors should have some cases with S2 as a validation\nset and discuss the differences and thus maybe prove their point about the difficultly with high res optical images as a\nvalidation set."}, "author_response": "Response: We thank the reviewer for their kind words and their feedback. Point well taken on\nthe need to more careful and deliberate in evaluation and communication of the results. We\nbelieve we have addressed this by making the suggestions made below, specifically by doing\nmuch more extensive validation of the model against other datasets and including an exclusion\nmask in our maps to show areas where the model results should be interpreted with caution.\nResponse: We’ve redone many of the figures to make them more legible.\nResponse: This is a good suggestion – we’ve converted his to changes in percentage of land area\nflooded and added a filter on the magnitude of the change so that we don’t plot very small\nabsolute changes that are large in percentages. To your point about the magnitude of the trend,\nthere was initially an error in the calculation because we did not properly account for the\ndilation/buffering, but your overall point still stands – these are not huge increases relative to\nthe total land area in each tile. In fact, most tiles are <<1% flooded on average. Overall the plot\nlooks very similar, but we believe that following your suggestion will help the story come across\nmore clearly.\nOne note on the new plot – you’ll see that the estimated trends are relatively large – often in\nthe 1-2% range per month, which translates to quite large increases over 10 years. Because of\nthe limited time frame of our analysis, we typically don’t observe a steady increase over time in\nflooding. Instead, the trends are often dominated by a couple large flood events in the dataset,\nresulting in large estimated slopes. We’ve tried to capture this concept in the text and figure\ncaption.\nResponse: We've added a detailed comparison to both the Pekel Global Surface Water (GSW)\nand MODIS-based datasets in the Results section. We now present global estimates of overlap\nand potential new flood-prone regions our method identifies, along with estimates for each of\nthe regions we call out in the text and figures. This is all in the “Global Flood Map and\nApplication to Ethiopia” subsection under the Results section. Overall, we find that there is\nreasonable overlap with these datasets, but also a large amount of net new flooding detected\nby our model.\nResponse: This is a great suggestion, and in the aggregate maps we provide we've added\nexclusion layers, similar to the approach used by Copernicus GFM. To be more specific, the\nexclusion mask includes areas we either expect false negatives (urban areas) or have excluded\nareas due to potential false positives (arid regions and rough terrain). This is shown in the new\nplots, along with additional text explaining this idea in the Results and Methods section\nResponse: We've added maps for the new model validation approach (see later comment +\nresponse for more details). More importantly, we've shifted our primary validation to use the\nKuro Siwo dataset, a comprehensive SAR-specific dataset, which eliminates the challenges of\ntiming mismatches between SAR and optical imagery. We’ve included some maps from the Kuro\nSiwo validation in the SI to highlight areas where the model does well and not so well.\nResponse: Regarding the likelihood values, in the new model validation approach we did\nexperiment with these and seeing how the performance changes with different thresholds. We\ndo find that model performance can be improved by reducing the threshold, so we report\nresults in the Supplementary Information under different scenarios. Detailed results across\ndifferent thresholds are reported in the Supplementary Information Section S1.5.2\nFor our current model, we do not have well-calibrated uncertainties. We agree that would be a\nvaluable thing to include, however, this is a known challenge with deep learning models. Our\ncurrent model’s predictions tend to be highly overconfident in either direction, which doesn’t\nlend itself well to proper calibration of uncertainties or probabilities. It is possible to build a\nbetter calibrated model to produce uncertainties like the GFM does, but for now we leave that\nfor future work.\nResponse: Based on comments from multiple reviewers, we have done additional model\nvalidation. While we considered adding more training data, our validation results suggest that\nour current training set, though small, is sufficiently diverse and representative. We found the\nrecently published Kuro Siwo dataset to be the best match for validating our approach, since the\ndataset includes pre and post SAR imagery and labels for 43 locations across the globe. We've\nre-written the model validation section of the paper to reflect this new work. See “Model\nValidation” under the Methods section and S1.5.2 in the SI. In summary, we find that our model\nperforms well on this dataset, showing strong average performance across continents and\nsimilar aggregate performance to what the authors report in the Kuro Siwo paper, along with\nslightly better performance than the GFM ensemble product.\nResponseOne challenge is that we don't have well calibrated uncertainties from the model.\nWhile we cannot fully address this issue in the current work, we've taken several steps to help\nusers understand the model's limitations and reliability. Following your suggestion, we've added\nexclusion layers to the maps to clearly indicate areas where the model is known to be less\nreliable - such as arid regions and mountainous regions (prone to false positives) or urban areas\n(prone to false negatives). We've also added explicit discussion of these limitations in the\npaper's Discussion section to ensure users, particularly those in disaster response situations, are\naware of where and when the model's predictions should be used with additional caution.\nResponse: We are using a static land cover map from ESRI. It does have a temporal component,\nbut given that it doesn't cover the entire time period of the SAR data, we decided to just use the\nmost recent mapping. We're using the ESRI one for this particular use case because it's been\nfound to be more accurate for cropland than ESA world cover (see Zentner et al 2022). The\ncropland does contain irrigated areas, and we've added a caveat in the Discussion section about\nhow to interpret results in irrigated cropland, particularly noting the case of rice paddies and\nother intentionally flooded agricultural areas.\nResponse: We’ve added this to the Methodology Section: ‘We chose MobileNet as our model\narchitecture because it can be effectively adapted for pixel-level classification tasks in satellite\nimagery while remaining computationally lightweight, making the model accessible to users\nwith limited computing resources.’\nResponse: We did not implement local thresholding for this work. While we agree this would be\nvaluable, our validation against the global Kuro Siwo dataset shows that global thresholding\nperforms well across diverse geographic regions (with F1 scores ranging from 0.68-0.80 across\ncontinents, as detailed in the SI). We've documented specific cases where global thresholding\nmay be suboptimal in the SI, but leave the implementation of local thresholding for future work.\nResponse: For this particular flood, we did not have on the ground photos. We agree that the\ntime difference is a major issue. To help address concerns over model accuracy, we have shifted\nour primary validation to the new SAR-specific Kuro Siwo dataset, which shows strong\nperformance across diverse global locations. While we've maintained the Kenya flood as a\nvaluable real-world case study, it is no longer the primary validation of the model.\nResponse: This is a fair point about the limitations of fine-resolution imagery. We've addressed\nthis concern by shifting to the Kuro Siwo SAR dataset as our primary validation source. While we\nretain the Planet imagery comparison as part of our Kenya case study, it is no longer used for\nmodel validation.\nResponse: We’ve added plots and a table comparing our flood map to the Pekel map and the\nMODIS-based flood dataset globally and for the specific regions we mention in the Discussion.\nOverall, we find that the overlap between our model and the other datasets is consistent with\nhow closely the two other dataset match each other – some amount of overlap but many\nunique flood detections in each one. This is not surprising – all three datasets cover different\ntime periods with different observing instruments with different temporal resolution. Overall,\nthe point we’re making is that there are areas (like the ones in Ethiopia we highlight in the\npaper) where our model is capturing significantly more flood-prone area than the other two\ndatasets. This is shown in Results section, specifically the subsection “Global Flood Map and\nApplication to Ethiopia”\nResponse: We’ve added a line on the temporal limitations of the SAR imagery in the\nIntroduction.\nResponse: Added\nResponse: We’ve changed this to hectares. Also, the units in the plot were initially normalized to\nthe number of observations in each month, since we do this for the trend analysis (to account\nfor variations in observations per month, for example when S1B went down). The new plot is in\nour best estimate of hectares/month flooded\nResponse: Done\nResponse: This has been added to the Model validation section when the metrics are\nintroduced.\nResponse: We added this line to the text: “We chose a linear model for its simplicity and\ninterpretability, given the noisy nature of the flood extent data and the relatively short time\nperiod.”\nResponse: Yes you’re correct – we initially ran this at 30m resolution but have been updating\neverything to 20m resolution. We’ve updated the results to be 20m resolution with buffers that\nare multiples of 20 and have addressed this in the text.\nResponse: It’s the datasets that were mentioned two paragraphs earlier: Sentinel-2 imagery,\nnews articles, drone footage and UN reports. We’ve clarified this in the text.\nResponse: We did not do this, but it’s one of the options for running the model. Since the model\nand code are available on github, this is something other researchers could do based on their\nneeds. We’ve added this point to the text.\nResponse: Agreed – we now refer to validation only against the Kuro Siwo dataset, and discuss\nthe Kenya analysis as an ‘Evaluation’ instead\nResponse: For the new validation, we have some more zoomed in maps. Since the Kenya\nvalidation/evaluation is no longer central to the paper, we’ve left those images as is.\nResponse: Interesting suggestion, we hadn’t seen this before! We’ll consider this in any future\nwork.\nResponse: We thank the reviewer for their helpful suggestions. Per their feedback, we have\nmade a significant change to the Model Validation. As detailed below, we have evaluated our\nmodel against a newly released global dataset of SAR images and flood labels from 43 locations\ncalled Kuro Siwo. Our model shows good performance compared to both the Kuro Siwo baseline\nin their paper and to Copernicus GFM model ensemble predictions.\nWe have also revised and added much of the text to address comments on scientific rigor and\nclarity. Details of our responses to the individual comments are below\nResponse: Thank you for this important technical clarification. We have modified the text to\nremove phrases like 'any weather condition' and instead focus on SAR's cloud-penetrating\ncapabilities and day/night operation. We have added appropriate citations acknowledging the\npotential impact of extremely heavy rainfall on SAR backscatter in the introduction.\nResponse: We have substantially revised this paragraph to improve clarity and flow. The new\ntext provides a more structured comparison of optical/infrared and SAR technologies, with\nprecise details about their respective capabilities and limitations. We have also added specific\ninformation about temporal coverage (e.g., 6-12 days for Sentinel-1 versus daily for some\noptical satellites) and clearly articulated the trade-offs between the different satellite systems\nResponse: Yes we are referring to the temporal resolution. We have clarified how the 6-12 day\nrevisit period for a satellite constellation like Sentinel-1 can affect our ability to full capture the\ndynamics of flooding, particularly for short-lived events such as flash floods.\nResponse: We’ve changed this to: “Developing a comprehensive, high-resolution map that\nidentifies all locations where floods were detected globally over the past decade” to clarify what\nwe mean and provide a more accurate statement\nResponse: These points are now addressed in the introduction, where we discuss SAR's\ncapabilities for water mapping, particularly its distinct backscatter signature due to specular\nreflection. Given space constraints, we have focused on citing a comprehensive review of SAR\nflood mapping techniques (from threshold-based approaches to deep learning) to cover the\nbasics of how SAR is used for water and flood mapping.\nResponse: We have clarified in the text our rationale for focusing solely on SAR imagery in the\nintroduction stating “We focus solely on SAR to ensure consistent detection through cloud cover\nand in both day and night conditions, which is crucial for creating reliable aggregate flood maps\nand enabling unbiased temporal analysis.”\nAdditionally, since previous work has created aggregate flood maps using optical data (from\nMODIS and Landsat), we view our work with SAR as being a novel contribution to the field.\nLastly, we have added fusion techniques as a potential future research direction and now\ninclude a paragraph on this in the Discussion.\nIn our methodology, we use pairs of consecutive SAR images taken within 30 days of each other.\nRecent results from the Kuro Siwo paper (Bountos et al., 2024) indicate that while incorporating\nan additional pre-event image can provide some improvement, the gains in F1 score are small\n(typically less than 1 percentage point) and often confounded with changes in model\narchitecture. Given these modest benefits and the increased computational cost, a paired-\nimage approach is appropriate. We have added this explanation to the Methods section.\nWhile time-series approaches, such as those used in the Sen12 dataset, may offer further\nimprovements in flood classification, their significantly higher computational requirements\nmade them out of scope for our work.\nResponse: We have added this to the Methods section. Specifically, we identified characteristic\nbackscatter values below -17.5 dB in the VV band and below -22.5 dB in the VH band as typical\nfor water surfaces due to specular reflection\nResponse: Agreed, and we’ve added details on this to the text. We’re using data that is gamma-\nnought corrected, which includes an extra cosine correction to better normalize backscatter\nacross different incidence angles. Including incidence angle as a feature is a good point that we\nhad not considered – this is a great idea for future work.\nResponse: Yes, we agree on the importance of soil moisture. We actually did test this approach,\nas briefly noted in the text. However, given your interest in this point, we have expanded this\ntext in the main paper under the ‘Neural Network Model’ subsection. In short, because of the\ncoarse resolution for soil moisture (~10km), we did not have sufficient diversity in flood and\nnon-flood events across different soil moistures to adequately learn the relationships between\nsoil moisture and flooding in the model, and therefore opted to include soil moisture and slope\ndata in the post-processing instead.\nResponse: We use the data directly from the planetary computer (who gets data from Catalyst\nas a data processor), which uses standard SAR processing techniques such as orbit application,\ngamma correction, radiometric terrain correction using PlanetDEM, and speckle filtering. We’ve\nadded a paragraph on this to the text in the ‘Neural Network Model’ subsection.\nResponse: We utilize both ascending and descending passes to maximize temporal coverage,\nwhile ensuring we only compare image pairs with the same viewing geometry. We've added this\nclarification to the text.\nResponse: We want to clarify that our model uses only 4 input features from the SAR data:\n Binary indicator for whether the VV band is within the range of estimated water\nbackscatter\n Binary indicator for whether the VH band is within the range of estimated water\nbackscatter\n Delta in VV band intensity between the pre- and post-event imagery\n Delta in VH band intensity between the pre- and post-event imagery\nWe did experiment with including auxiliary data directly into the model, such as elevation,\nslope, and soil moisture, but did not see an improvement in the validation metrics. This is likely\ndue to the lack of diverse flooding outcomes by slope (i.e., it is difficult to identify flooded areas\nin areas with high slope) and for soil moisture, the spatial resolution is so low that we essentially\nget one measurement per scene – it’s not enough data for the model to learn from (this is\nalready discussed in the Discussion). Given the lack of improvement by adding additional\nfeatures, we stuck with the 4 dimensional input features.\nGiven the relatively low-dimensional input space, traditional feature importance analysis\ntypically used for high-dimensional problems would add unnecessary complexity without\nproviding meaningful insights. We acknowledge that some of the ideas presented could be\nincluded as additional features, but given the model’s relatively strong performance (see\nupdates on model validation), we leave these for future work.\nIn the new text, we have revised language on the features used and briefly described the tests\non adding additional features and our justification for not including them in the final model.\nResponse: As noted in our earlier response, we have modified the text in the introduction to\nacknowledge that SAR signals can be affected during extremely heavy rainfall. Specifically,\nwhere we discuss SAR capabilities, we now state: 'SAR can penetrate through clouds and\noperate in both day and night conditions, though its signal may be affected during extremely\nheavy rainfall.' We believe this placement in the introduction, where we discuss the general\ncapabilities and limitations of SAR technology, is appropriate for contextualizing these effects.\nResponse: The change detection approach naturally highlights only flooded areas vs permanent\nwater. Qualitatively, permanent water will appear dark in both the pre- and post-flood SAR\nimagery, while flooded areas will only appear dark in the post-flood imagery. Additionally, we\nremove permanent water bodies during post-processing for any spurious flood detections that\nmay occur. To better clarify this in the text, we’ve added a this to the Methods section on post-\nprocessing: “We also used the ESA land cover mapping to remove permanent water bodies like\nlakes and rivers. While our change detection approach should inherently ignore permanent\nwater bodies since they appear as water in both pre- and post-event imagery, we found that\nexplicitly masking them helped reduce noise in our predictions.”\nAs for rice paddies, this is a great point. Our method does not differentiate between flooding\ncaused by natural processes and manmade flooding. This is a limitation of our model and\nfrankly most satellite detection models. We have added this important caveat to the Discussion\nsection, noting that applications in regions with significant managed flooding (such as rice\ncultivation) would require additional contextual data or temporal analysis to distinguish\nbetween natural floods and agricultural practices. Here’s the text we’ve added to the\nDiscussion: “In particular, certain land use patterns require careful consideration when\ninterpreting model results. A prime example is rice cultivation, where paddies are deliberately\nflooded as part of the agricultural cycle. While our model correctly identifies these areas as\nflooded, these detections represent intentional agricultural practices rather than natural flood\nevents. Users should exercise particular caution when applying this model in regions with\nextensive rice cultivation or similar agricultural practices, where distinguishing between\nintentional and unintended flooding is crucial for proper interpretation.”\nResponse: As noted below, we have completely redone the model validation to include areas\nwith paddy fields. The updated validation results can be found in the Model Validation section\nof the paper (with additional information in the Supplementary Information). Our response to\nthis point can be found below under your comment 6. Testing and Evaluation.\nThe 11% figure specifically refers to cropland in flood-prone areas within our Ethiopian study\nregion, based on discussions with local partners who have on-the-ground knowledge of\nagricultural practices in these areas. We have clarified this contextual detail in the text to avoid\nany confusion about the scope of this statistic. We have also noted the crop types in this area\nand the fact that they are rainfed. As noted above, we have added a note to the Discussion\nabout paddy fields and other deliberately flooded croplands.\nResponse: We agree that integrating SAR and optical data can potentially enhance flood\nmapping accuracy, and we acknowledge the benefits of such fusion approaches. While\nperforming that comparison on our own work is beyond the scope of the current paper (see\ncomment above), we have made additions to the text in the Discussion reflecting the relative\nstrengths and weaknesses of the approaches, highlighting the complimentary nature of SAR and\noptical and the benefits of fusion models. And as noted above, because of the strengths of\noptical imagery (higher spatial resolution, true color representation plus infrared bands that can\nresult in better detection of flooding), we are considering this for future work, repeating this\ntype of analysis with both SAR and optical data.\nResponse: Based on comments from multiple reviewers, we have done additional model\nvalidation. Based on the currently available datasets, we found that the recently published Kuro\nSiwo dataset to be the best match for our setup, since the dataset includes pre and post SAR\nimagery and expert-annotated labels. We’ve re-written much of the model validation section of\nthe paper to reflect this new work. In summary, we find that in general our model performs well\non this dataset, showing strong average performance across continents and similar aggregate\nperformance to what the authors report in the Kuro Siwo paper, along with better performance\nthan the Copernicus GFM ensemble.\nWe note that the Kuro Siwo dataset includes multiple scenes in south and southeast Asia that\ninclude rice fields. Performance in these scenes is quite good, with F1 scores >=0.77. While\nthere is always more validation that can be done, we believe the performance of the model is\nthe best that can be done with the currently available flood datasets.\nResponse: We thank the reviewer for their kind feedback!\nResponse: Based on feedback from all 3 reviewers, we have done extensive revisions to our\nModel Validation. Based on the currently available datasets, we found that the recently\npublished Kuro Siwo dataset to be the best match for our setup, since the dataset includes pre\nand post SAR imagery and labels. We’ve re-written much of the model validation section of\nthe paper to reflect this new work. In summary, we find that in general our model performs well on\nthis dataset, showing strong average performance across continents and similar aggregate\nperformance to what the authors report in the Kuro Siwo paper.\nWe also compare against GFM predictions and find that our model on average performs better\nthan GFM. We do a detailed comparison for specific scenes in the Supplemental information,\nhighlighting where our model outperforms GFM and where it doesn’t, with implications for\npotential future work.\nThe previous model validation is still included in the Supplemental Information, but are not a\nkey result in our work anymore.\nResponse: We have a public github repository for the model here:\nhttps://github.com/microsoft/ai4g-flood\nThe model predictions are also freely available here: https://huggingface.co/datasets/ai-for-\ngood-lab/ai4g-flood-dataset"}, {"round": 2, "reviewer_comments": {"reviewer_1": "Well done to the authors for the pain-staking response to all the reviewers comments. I particularly appreciate the additional\nvalidation and inclusion of a exclusion mask.\nThe improvement to the Figures are appreciated. Personally I would like to see a scale bar on some of the more detailed\nmap, but that's just being cartographically picky!\nI find the large increase in flood detection from your method very intriguing!\nIt would be very interesting to evaluate your approach with this new Bayesian based work from Roth et al (2025) but that is\nprobably out of the scope of this paper\nRoth, F., Tupas, M.E., Navacchi, C., Zhao, J., Wagner, W. and Bauer-Marschallinger, B., 2025. Evaluating the robustness of\nBayesian flood mapping with Sentinel-1 data: A multi-event validation study. Science of Remote Sensing, 11, p.100210.", "reviewer_2": "Dear Authors,\nI appreciate your effort in compiling a global flood extent dataset and acknowledge the importance of large-scale flood\nmapping initiatives. However, I would like to offer several comments and suggestions that I believe can help improve the\nclarity, rigor, and overall impact of your manuscript.\n1. Limited Case-Based Evaluation One key concern is the generalization of algorithm performance based on a limited\nnumber of flood cases. It is difficult to conclude whether one algorithm is superior to another without extensive and\nsystematic validation across diverse flood events and environmental conditions. Many similar studies fall short in offering\ninsights into the strengths and limitations of individual algorithms, making it challenging for readers to understand their\napplicability across different geographies and weather conditions.\nI would strongly encourage the authors—and our research community at large—to place more emphasis on rigorous\nvalidation and physical understanding, rather than solely on developing increasingly complex algorithms. In this regard, I\nwould like to highlight the exemplary work by Florian Roth et al. (2025), who conducted a robust multi-event evaluation of a\nBayesian flood mapping algorithm using 18 flood events across five continents with Sentinel-1 data. His study offers critical\ninsights into the sensitivity of VV and VH polarizations over vegetated flood areas and identifies important limitations in\nexisting approaches. This paper is currently missing from your references and I highly recommend reviewing it:\nRoth, F., Tupas, M.E., Navacchi, C., Zhao, J., Wagner, W., Bauer-Marschallinger, B., 2025. Evaluating the robustness of\nBayesian flood mapping with Sentinel-1 data: A multi-event validation study. Science of Remote Sensing, 100210. Link\nNotably, this work has already informed improvements in the operational Copernicus Global Flood Monitoring (GFM)\nservice.\n2. Motivation for Temporal Aggregation While aggregating 10 years of flood extent data may be useful from a historical\nperspective, the added value is not fully clear in your current discussion. What does this aggregated product offer, and how\ncan it be linked to broader trends such as climate change, land use changes, or other hydrometeorological drivers? Without\nsuch context, it is difficult to interpret the observed increase or decrease in flood extents across regions.\n3. Specific Comments on the Manuscript\n• Lines 33–36: This sentence needs rephrasing for clarity and to avoid redundancy.\n• Lines 39–40: The claim that \"daily observations from constellations\" are available is incorrect. For HLS\n(Harmonized Landsat-Sentinel), the typical revisit time is 2–3 days, not daily.\n• Lines 66–67: Please clarify the specific benefit of the proposed approach in this context.\n• Line 72: What is the practical advantage of creating a 10-year SAR-based flood extent database, especially considering\nSentinel-1's limitations (e.g., revisit time, difficulty with forested flood areas)?\n• Line 83–84: While historical flooding can indicate areas of potential future risk, it is important to acknowledge the role of\nclimate change and other dynamic factors that may alter future flood patterns. This aspect is missing from your discussion.\n4. Issues with the Introduction and Figures\n• The introduction could be improved for clarity and conciseness. The last paragraph, in particular, repeats earlier points and\nwould benefit from restructuring.\n• Figure 1: The color scheme and visual quality are suboptimal. The figure is difficult to interpret, and the visualization should\nbe improved.\n• The masking of regions (due to urbanization, desert areas, etc.) appears to have excluded countries like Iran, which have\nexperienced significant flood events. Please explain the rationale and implications of this exclusion.\n• The validation approach remains unclear, particularly for flood-prone areas such as mountainous regions and flooded\nforests, which are often excluded or poorly captured.\n• Lines 153–163: The justification provided for why your approach captures \"additional\" flood areas more accurately than\nprevious methods is not convincing. More evidence or validation is needed.\n• Figure 8: To enhance its value, consider linking it to climate-related drivers of flood change. Without this context, the\nfigure’s contribution to advancing flood mapping knowledge remains limited.", "reviewer_3": "I have read the responses to comments and the revised version, and I thank the authors for their extensive revisions.\nIn my opinion, the responses and revisions with regard to my points of concern raised are adequate and sufficient."}, "author_response": "We thank the reviewer for their kind words and thoughtful feedback. We agree that the Roth et al.\n(2025) paper presents a valuable example of detailed algorithm validation. While its specific focus lies\nbeyond the scope of our global-scale approach, we have now cited the study and added a brief\ndiscussion of how similar evaluations could inform future work.\nWe also updated several figures to include scale bars and enhance visual clarity, a point raised by\nanother reviewer.\nWe again thank the reviewer for their constructive comments and believe the paper is significantly\nstronger as a result.\nResponse: We thank the reviewers for their helpful comments. We have carefully reviewed the\ncomments below and adjusted the manuscript accordingly.\nResponse: We thank the reviewer for their constructive comments and for highlighting the recently\npublished Roth et al. (2025) paper. We agree this is an important study demonstrating a valuable\napproach for in-depth algorithm evaluation, providing critical insights into specific sensitivities like VV vs\nVH polarization. We have now cited Roth et al. in the manuscript and incorporated it into our discussion,\nincluding acknowledging that such detailed investigation represents a potential avenue for future work\nbuilding upon our findings.\nRegarding the validation of our approach, we wish to respectfully clarify that our validation, significantly\nexpanded in the previous revision based on valuable reviewer feedback (detailed in Methods section\n(subsection ‘Model Validation’) and SI Section S1.5.2), is already quite extensive. It utilizes the\ncomprehensive Kuro Siwo benchmark dataset, which encompasses 43 diverse flood events spanning 6\ncontinents. We selected this broad benchmark specifically because our primary contributions are the\nintroduction of a novel model intended for global-scale application and the creation of a decade-long\nglobal dataset. While the Roth et al. (2025) study provides crucial insights via a deep dive into a specific\nalgorithm's performance sensitivities across 18 events – a valuable approach we now cite and discuss as\na potential direction for future, focused investigations (see Discussion, page 13, lines 353-362) – we\nbelieve our validation across 43 diverse global events robustly assesses our model's performance and\ngeneralizability for the broad geographic scope and stated objectives of this manuscript.\nResponse: We thank the reviewer for highlighting the need to more clearly articulate the motivation and\nadded value of our 10-year temporal aggregation. We agree this is crucial context and understand the\nconcern relates to clarifying the specific contributions of both the aggregated historical map and the\nlongitudinal trend analysis derived from it.\nTo address this, we have made specific revisions to the manuscript to better articulate the unique value\nproposition of this decade-long, SAR-based dataset:\n1. Revised Introduction: We have significantly revised the paragraph outlining the motivations and\nuse cases enabled by our approach (see revised text on page 2, lines 78-92). This revision now\nmore explicitly states the value of generating:\no A comprehensive, high-resolution historical baseline map using cloud-penetrating SAR,\nemphasizing its importance for robust risk assessment and its complementarity to\noptical datasets often affected by cloud cover during floods.\no An observation-based trend analysis, highlighting its contribution as the first globally\nconsistent SAR perspective on potential decadal dynamics, establishing a crucial\nbaseline for future monitoring, and offering a less biased view compared to report-\nbased methods, while carefully maintaining appropriate caution regarding climate\nattribution given the 10-year record length.\n2. Enhanced Discussion: We have also reinforced these points within the Discussion section:\no In the paragraph discussing the benefits of our approach over existing datasets (page 12,\nlines 312-322), we have added a sentence explicitly connecting SAR's cloud-penetrating\ncapability to the value of creating the robust historical baseline map mentioned in the\nIntroduction.\no We have rebalanced the paragraph focused on the trend analysis limitations (page 14,\nlines 388-398). This section now leads by reiterating the positive contribution and value\nof the trend analysis (e.g., first SAR-based observation look at flooding over time,\nbaseline for future monitoring) before detailing the necessary and important caveats\nregarding the limitations for climate attribution over the current time frame. The\nconcluding sentences have also been strengthened to emphasize the foundation this\nwork lays for future long-term monitoring.\nResponse: Thank you for pointing this out. We have revised the sentences describing optical/infrared\nand SAR sensors (original Lines 33-36) for better clarity and flow, removing the potentially redundant\nsentence. The revised text can be found on page 1, lines 32-42.\nResponse: We thank the reviewer for pointing out the potential ambiguity in our original statement\nabout 'daily coverage' and for providing the example of the HLS constellation's 2-3 day revisit time. We\nagree that clarification was needed for greater accuracy. Our initial statement was based on the revisit\ntime of VIIRS and commercial satellite offerings such as Planet labs, which do have daily optical data.\nTo address this, we have revised the sentence in the Introduction (page 1, line 39) to more accurately\nreflect the range of optical revisit frequencies. The revised text now clarifies that revisit times typically\nrange from near-daily to several days, providing better context for the comparison with Sentinel-1 SAR.\nResponse: We thank the reviewer for these two related points requesting clarification on the specific\nbenefit and practical advantage of our 10-year aggregated SAR dataset approach (around Lines 66–72 in\nthe original), particularly in light of Sentinel-1’s known limitations. These comments are closely tied to\nthe overall motivation for our work, which we also address in Point 2.\nAs detailed in our response to Point 2, we have substantially revised the Introduction paragraph (page 2,\nlines 78-92) that outlines the motivations and use cases for our approach. This revised section,\nwhich follows the context around Lines 66–72 in the original (now lines 62-70), clearly articulates the\nspecific benefits and practical contributions of our work—including the creation of a unique, cloud-\nresilient historical baseline and the ability to perform observation-based trend analysis. We believe this\nadded explanation directly addresses the core questions raised in both reviewer comments 3.3 and 3.4.\nRegarding the specific Sentinel-1 limitations mentioned (e.g., revisit frequency, challenges in\nforested/complex terrain), we agree these are important considerations and are acknowledged in the\nmanuscript. The implications of revisit time are discussed in the Introduction (page 1, lines 38-42) when\ncomparing sensor types. Challenges related to complex terrain (including forests), along with the\nrationale and implications of our exclusion mask and its impact on validation scope, are addressed in the\nMethods (page 17, lines 550-553) and Discussion (page 13, lines 341-345).\nResponse: We thank the reviewer for highlighting the importance of explicitly acknowledging that future\nflood patterns may be altered by dynamic factors such as climate change. We agree that this is an\nimportant caveat when discussing the utility of historical flood data.\nTo address this, we have revised the relevant sentence in the Introduction's motivation paragraph (page\n2, lines 82-85) to explicitly incorporate this point. The updated sentence now reads:\n“While historical flooding is not a perfect predictor of future risk, particularly as climate change and\nother dynamic factors (e.g., land use change) may alter future patterns, this baseline can inform risk\nassessments, mitigation planning, and resilient infrastructure development.”\nResponse: We agree with the reviewer on the need for improvement in the last paragraph. As detailed in\nour responses to Point 2 and Point 3 above, the final paragraph of the Introduction outlining the\nmotivations and practical advantages of our approach has been substantially revised and restructured\n(page 2, lines 78-92).\nResponse: We appreciate the reviewer’s feedback regarding the clarity of Figure 1. We agree that the\nexclusion mask’s light gray tone in the original figure may have been difficult to distinguish from other\nbackground elements at a global scale. We experimented with alternative color schemes, including tan\nand blue hues, but found they competed visually with the flood detections or distracted from the main\nfocus of the map. As a result, we revised Figure 1 to use a darker gray for the exclusion mask, improving\ncontrast while preserving visual balance and interpretability. We have also updated the other figures (2,\n3, and 5) that have an exclusion mask layer.\nResponse: We thank the reviewer for raising this concern. We agree that at global scale, the exclusion\nmask can give the impression that entire countries—such as Iran—are fully excluded from the analysis.\nHowever, our exclusion mask operates at the pixel level, based on land surface properties like terrain\nsteepness, aridity, and urbanization. While parts of Iran, particularly its central desert and some\nmountainous regions, are masked due to SAR limitations in those environments, flood detection is still\nperformed in all unmasked areas within the country.\nTo clarify this point, we include below a zoomed-in map of Iran showing flood detections (blue) and\nexcluded areas (gray). As this plot illustrates, substantial regions—especially in the west and along\nrivers—are not excluded and show active flood detections in our dataset.\nWe hope this visualization clarifies that countries like Iran are not entirely excluded, and that our\nmasking approach preserves analysis in regions where SAR is effective. Given the pixel-level basis of the\nexclusion mask, this pattern is common in geographically diverse countries.\nResponse: We thank the reviewer for raising this important point. As noted in the Methods section and\nfurther elaborated in the Discussion, we explicitly mask out areas where SAR flood detection is known to\nbe unreliable due to terrain or land cover complexity — including steep mountains, dense forests, and\nurban areas — using an exclusion mask (see Post-Processing subsection). These areas are not only\nexcluded from our flood maps but are also excluded from our validation exercises.\nTo address the reviewer’s concern more directly in the text, we have revised the paragraph in the Model\nValidation section (page 13, lines 550-553) to clarify this point. Specifically, we now note that while the\nKuro Siwo dataset includes diverse global flood events, the validation focuses on regions where our\nmodel is expected to perform reliably. The new sentence reads:\n“Given the known challenges of SAR in complex terrains like steep mountains and dense forests — areas\nwhere our model’s predictions are masked using the exclusion mask (see Methods subsection on Post-\nProcessing) — this validation focuses on assessing model performance in the diverse global regions\nwhere detections are expected to be most reliable.”\nResponse: We thank the reviewer for pushing for a more convincing justification regarding the accuracy\nof the additional flood areas captured by our method (around Lines 153-163 in the original). We agree\nthat citing SAR's cloud-penetration capability alone is insufficient and that providing stronger, multi-\nfaceted evidence is necessary.\nOur confidence in the accuracy of these additional detections stems from several converging lines of\nevidence, which we have now made more explicit in the revised Results section (page 4, lines 149-164 in\nthe revised version):\n1. Mechanism: We reiterate that SAR's ability to penetrate cloud cover provides the mechanism by\nwhich it can detect events missed by optical sensors.\n2. Quantitative Validation: We now explicitly reference the model's strong performance in\nquantitative validation, particularly its high recall demonstrated on the diverse Kuro Siwo\nbenchmark dataset (72%), indicating its general effectiveness in identifying true flood pixels\nwhen present.\n3. Qualitative Local Validation: Crucially for the Ethiopia examples discussed in this section, our\nassessment was corroborated through qualitative validation conducted with local partner\norganizations (IOM Ethiopia), whose deep domain knowledge confirmed the plausibility and\naccuracy of the additional flood patterns identified by our model in these specific regions. This\ncollaborative validation process and associated findings are documented in a related technical\nreport (https://environmentalmigration.iom.int/sites/g/files/tmzbdl1411/files/documents/2024-\n11/vulnerable-communities-ethiopia-communities-at-risk-of-flooding-final.pdf), which we now\ncite in the manuscript (page 4, lines 144 and 157).\nTogether, these combined lines of evidence—the physical mechanism (cloud penetration),\nquantitative performance metrics, and qualitative validation by local experts—provides the robust and convincing\njustification requested by the reviewer.\nResponse: We thank the reviewer for the suggestion to enhance the value of Figure 8 by considering\nlinks to potential drivers of flood change. We agree that providing context is valuable for interpretation.\nAs noted in the manuscript text, we maintain scientific caution regarding definitive attribution of the\nobserved 10-year trends, given the challenges of distinguishing long-term signals from climate variability\nand episodic events over this relatively short timeframe.\nHowever, to explicitly address the reviewer’s suggestion and better frame the figure’s contribution\ntoward future understanding, we have revised the caption for Figure 8 (page 11, lines 292-299). The new\nconcluding sentence reads: \"These regional patterns merit follow-up in future work, especially to assess\nwhether any align with climate-related drivers as longer observational records become available.\"\nWe thank the reviewer for their kind words and for their previous comments which helped strengthen\nthe paper!"}, {"round": 3, "reviewer_comments": {"reviewer_2": "I would like to thank the authors for their thoughtful responses to my previous comments. I\nappreciate the effort they have put into revising the manuscript and addressing the\nconcerns raised.\nThat said, for several of the comments, I had expected more substantial revisions—\nparticularly beyond citing prior work or adding a few lines in the introduction or discussion.\nWith all due respect to the authors' efforts and the use of an extensive 10-year Sentinel-1\ndataset, I am unable to identify a clear added value or novel contribution that significantly\nadvances the field or benefits future research. As such, I remain unconvinced of the\nbroader impact of this study on the SAR flood mapping community.\nAlthough I have no further technical comments at this stage, I do not believe the\nmanuscript meets the threshold for publication in its current form. Given the authors’\nconsiderable efforts, I defer the final decision to the editor and would also welcome input\nfrom an additional reviewer for a broader perspective."}, "author_response": "Response: Regarding the reviewer's comments on the broader impact and novelty: while\nwe understand their perspective, we have aimed in the revised manuscript to clearly\narticulate the primary contributions. These include the generation of a globally consistent\n10-year SAR-based flood dataset which identifies flood-prone areas often missed in\nexisting datasets (as quantified in Table 1 and shown for specific regions in Figure 3), and\nthe longitudinal analysis of global flood trends derived from this SAR data (Figures 6 and 7,\nTable 2). We also highlight the public release of our model predictions and codebase.\nWe note the reviewer's confirmation that all technical points are resolved, and we\nappreciate them deferring the final decision to the editor."}]

Decision letter