From Satellite to Survival: Quantifying the Scale of Mexico's Catastrophic Floods
The torrential rains and subsequent flooding that struck Central and Eastern Mexico in October 2025 brought immense tragedy and immediate challenges to recovery efforts across Veracruz, Hidalgo, and Puebla. For someone who has lived many years in Mexico, I can imagine how this size of natural catastrophe affects the communities, rural and urban—the swift isolation of villages, the destruction of harvests, and the displacement of families.
The blue areas represent the regions/zones that were flooded. This map is from the Tabasco region.
While official response teams and aid agencies mobilize, remote sensing technology provides the critical initial data necessary for a strategic response. This analysis utilizes the Copernicus Sentinel-1 Synthetic Aperture Radar (SAR) satellite data, processed using Google Earth Engine (GEE), to move beyond simple headlines and provide a measurable assessment of the disaster’s exact footprint.
Phase I: The Hard Data—Mapping the Inundation
My initial analysis focused on establishing the maximum extent of the floodwaters by employing Multi-Temporal Change Detection. The ability of SAR to penetrate the heavy cloud cover—a limitation that renders traditional optical satellites useless during severe weather—was essential, delivering clear delineation of the submerged areas.
The quantification of this maximum flood extent is the foundation upon which all subsequent impact assessments must be built.
All regions have been affected by the flooding and situation got worse towards the weekend of 11th to 12th October.
This initial quantification directly supports the critical early operational decisions, including the mobilization of large government assets, such as the deployment of over 10,000 Marine Corps personnel and relief workers, which has been widely reported. Knowing the exact spatial extent of the water is the first step in directing this massive logistical effort.
According to this analysis the flood extent changed very fast towards 11th of October in the region of San Vicente Tancuayalab (San Luis Potosi).
Methodology Overview: Deconstructing the Flood Signal
The analysis can be divided into three phases, relying on the all-weather, cloud-penetrating capability of SAR:
- Data Preparation: I filtered the Sentinel-1 archive to create two median composites—one for the non-flooded baseline (Late September 2025) and one for the flood event (till 13th of October 2025).
- Change Detection: We calculated the pixel-by-pixel difference between the post-flood and pre-flood images. Pixels where the radar backscatter dropped significantly—indicating a transition from land to water—were identified using a specific flood threshold.
- Cleaning and Validation: The resulting flood mask was cleaned by filtering out noise, removing small pixel clusters, and, crucially, using the JRC Global Surface Water dataset to mask permanent water bodies, ensuring the output reflects only the newly inundated area.
Conclusion: A Geospatial Blueprint for Resilience
The true value of this geospatial intelligence is realized when the flood map is overlaid with exposure and vulnerability data. This translates the hazard into measurable, policy-relevant metrics:
- Exposed Population (using data from e.g. JRC, European Commission: GHSL)
- Agricultural and Structural loss (using e.g. ESA Worldcover)
- Isolation Analysis (using e.g. OSM road Network or Overture data)
- Estimated Flood Depth (using e.g. SRTM, NASA or local data on Mexico from INEGI)
By harnessing the power of GEE and Sentinel-1, we move beyond reporting damage to driving intelligent disaster response and building quantifiable resilience into national policy.
For any reader interested in replicating, validating, or extending this analytical framework—particularly colleagues in the NatCat, academic, or governmental sectors—the entire Python script used to filter, process, and quantify the flood extent, has been uploaded to my Github.
You can find the complete, documented Python code here (in a GIST, a way to share code snippets via Github):
Disclaimer: Scope and Limitation of Analysis
The data and maps presented in this blog series are derived from the Copernicus Sentinel-1 satellite imagery and processed using the Google Earth Engine (GEE) platform. This content is published strictly for non-commercial, educational, and exploratory purposes only.