Cuba Hurricane Melissa: Damage and Flood Proxy Maps, Oct 2025

Dates of Images: Post-Event: October 29, 2025; October 30, 2025; October 31, 2025; November 1, 2025; November 2, 2025; November 4, 2025; November 7, 2025; November 8, 2025; November 9, 2025. Pre-Event: September 2025 Please Note: Images with majority cloud cover are not shown in the Map Viewer, but are available in the service. Summary: These Black Marble Day-Night Band (BRDF-Corrected) cloud masks were created by the NASA Black Marble Science team. This is the layer to displaying where clouds are present. This comparison between the images is meant as a visual assessment of outage impacts to aid various partners who are working to deliver emergency aids to local communities. Power outage maps like these help disaster response efforts in the short-term as well as long-term monitoring during the crucial stages of disaster recovery. Suggested Use: NASA's Black Marble Nighttime Light product suite is a state-of-the-art daily global collection of standard products for monitoring nighttime lights (NTL). Utilizing the Visible Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) aboard the Suomi-NPP, NOAA-20, and NOAA-21 satellites. Grey represents cloud cover. Satellite/Sensor: Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) onboard the Suomi National Polar-orbiting Platform (SNPP), NOAA-20, and NOAA-21 Resolution: 500 meters Credits: NASA Black Marble Science team Please cite the following two references when using this data: Román MO, Stokes EC, Shrestha R, Wang Z, Schultz L, Carlo EA, Sun Q, Bell J, Molthan A, Kalb V, Ji C. Satellite-based assessment of electricity restoration efforts in Puerto Rico after Hurricane Maria. PloS one. 2019 Jun 28;14(6):e0218883. Román MO, Wang Z, Sun Q, Kalb V, Miller SD, Molthan A, Schultz L, Bell J, Stokes EC, Pandey B, Seto KC. NASA's Black Marble nighttime lights product suite. Remote Sensing of Environment. 2018 Jun 1;210:113-43. Point of Contact: Ranjay Shrestha NASA Goddard Space Flight Center E-mail: ranjay.m.shrestha@nasa.gov Additional Links: NASA’s Black Marble Product Suite Román, M.O. et al. (2019) Satellite-based assessment of electricity restoration efforts in Puerto Rico after Hurricane Maria. PLoS One, 14 (6). Román, M.O. et al. (2018) NASA’s Black Marble nighttime lights product suite. Remote Sensing of Environment. 210, 113–143. REST Endpoint: See URL on the right side of the page. WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202510_HURRICANE_MELISSA/202511_blackmarble_brdf_cloud/ImageServer/WMSServer

GLIDEnumbers are GLobal unique IDEntifiers for Disasters. Each disaster is registered in a record that contains a GLIDEnumber that uniquely identifies it and allows information to be interoperable across multiple disaster information sources, both structured and textual. Accessing disaster information can be a time consuming and laborious task. Not only is data scattered but frequently identification of the disaster can be confusing in countries with many disaster events and across borders. To address both of these issues, Asian Disaster Reduction Center (ADRC) proposed a globally common Unique ID code for disasters. Among the institutions using actively GLIDEnumbers are UN-OCHA (ReliefWeb, FSCC, HDX), UNDRR, UNDP, WMO, IFRC, FAO, the European Commission and the Centre for Research on the Epidemiology of Disasters (CRED), among many others. Country datasets are only produced for countries with at least one GLIDE event since 1 January 2022. The global dataset contains all events available from the GLIDE API, including those not assigned to a country.

The Soil and Terrain database for Cuba primary data (version 1.0), at scale 1:1 million (SOTER_Cuba), was compiled of enhanced soil informtion within the framework of the FAO's program Land Degradation Assessment in Drylands (LADA). Primary soil and terrain data for Cuba were obtained from the SOTERLAC database (ver. 2) at scale 1:5 million. This update includes changes in the GIS file, based on the SRTM-DEM derived surface information and supplementary attributes data changes of the pedon database. SOTER forms a part of the ongoing activities of ISRIC, FAO and UNEP to update the world's baseline information on natural resources.The project involved collaboration with national soil institutes from the countries in the region as well as individual experts.

Temporal coverage: Current calendar year - Today Data Sources: VIIRS Sensor on NOAA-20 Time Interval: Every 12 Hours Data Latency: ~ 9 - 12 hours from satellite overpass Projection: EPSG:4326 Spatial Extent: Contiguous United States, Alaska, and Canada. Portions of Alaska west of 169 Degrees west and portions of Canada north of 75 degrees north are excluded. Resolution: Vector data derived from the 375 m nadir resolution of the VIIRS sensors Purpose / use case: The FEDS algorithm is an “event-based” approach that tracks the growth and intensity of individual fires every 12 hours and treats them as discrete objects that persist, expand, and evolve together. This tracking strategy allows for detailed monitoring of large fires in space and time as they spread across a landscape. The algorithm clusters VIIRS active fire observations into specific fire events, and then estimates a fire perimeter polygon, the active segment of the perimeter, and metrics for each fire, such as the total estimated area (km^2) and length of the active fire front (km). Each 12-hour increment also includes information on a fire’s mean Fire Radiative Power (FRP), which is a metric that describes fire intensity as estimated by the satellite sensor. This dataset is best used for near real-time situational awareness and retrospective scientific analysis of fire behavior. Notes on Interpretation of the Data: As a satellite-based data product, FEDS has lower spatial resolution and accuracy than fire perimeters derived in operational contexts, such as those from airborne imagers. However, FEDS can provide information when other sources are unavailable, and the algorithm runs automatically in fixed 12-hr intervals for all clusters of fire activity greater than 4 km^2. The FEDS algorithm inherits the same uncertainties associated with the VIIRS active fire product. These include potential geolocation errors that can be relevant for interpreting fire locations and fire spread. For example, hot plumes of smoke can generate false positive detections that overestimate the size of the fire at that time step. In addition, active fire detections may be clustered into events that do not align directly with operational definitions of fire events (i.e., FEDS may group multiple agency-designated fires into a single fire event, or consider a single agency-designated fire to be multiple fire events). Finally, clouds or thick smoke may lead to the omission of VIIRS active fire detections, leading to an underestimate of fire activity and fire spread. More information on the interpretation of the VIIRS Active Fire Dataset for fires can be found in the NASA Fire Information for Resource Management System (FIRMS) FAQ. Source information/ Program Information: This dataset was developed as part of the NASA Earth Information Systems (EIS) project. This dataset was generated through the Wildfire Tracking Lab –a collaboration between scientists at NASA Goddard, NASA Langley, NASA Marshall, University of California Irvine, and University of Maryland. Citation: Chen et al. “California wildfire spread derived using VIIRS satellite observations and an object-based tracking system.” Scientific data 9.1 (2022): 249 https://doi.org/10.1038/s41597-022-01343-0. Repository: The underlying algorithm that generates these data layers is hosted and developed at the Fire Events Data Suite repository. Parameters: Column Name: t Description: Time of VIIRS detection. Data collected during approximately 1:30 AM and 1:30 PM local solar time overpasses. The time component of the datetime is assigned a local time of 00:00:00 for 1:30 AM overpasses, and 12:00:00 for 1:30 PM overpasses. Unit is Datetime. yyyy-mm-ddThh:mm:ss. Time component is in local solar time. Date component is the UTC date. UTC date will match local date for all of CONUS and Canada, but will not match for Alaska. Column Name: region Description: Descriptive name of larger region where tracking is occurring. Column Name: fireid Description: Fire ID. Unique for each fire within a given region. Unit is Numeric ID Column: geometry Description: The location of the pixels. Unit is Geometry. CRS is EPSG:4326. Column: Primary Key Description: Globally unique identifier for a given layer entry. This key is formatted by [region]|[fireid]|[timestamp]. Unitless. Alternate Forms Of Data Access: This data can also be accessed through an API. Documentation for the API can be found here. The API distributes data in open source data formats. Contact: For inquiries about service performance please contact support@earthdata.nasa.gov or post/view questions on the Earthdata Forum. For questions about the data, please contact the science team with this form.

Dates of Images: July 9, 2025 Date of Next Image: None Expected Summary: These maps show the results of a machine learning classification applied to UAVSAR data to identify open water flooding and inundation under tree canopy.  Colored areas in the image show detected flooding, where the color shows the type of landcover which was flooded (see legend below).  Non-flooded areas are transparent.  The classification uses a U-Net machine learning algorithm which was trained using UAVSAR data from previous flood events.  UAVSAR provides polarimetric synthetic aperture radar data from which different types of scattering mechanisms can be observed, which provides information on the type of flooding.  For example, flooding underneath vegetation produces strong double-bounce scattering from the water surface and tree trunks.  However, strong double-bounce scattering from urban areas aligned with the radar viewing direction, such as in Austin, TX, can produce erroneous classifications.  Non-flooded vegetation is generally dominated by volume scattering from the forest canopy.  In comparison, open water flooding exhibits weak radar returns in all polarizations. Suggested Use: Classified Images: Blue   = Open Water Yellow = Flooded Developed Green  = Flooded Vegetation Orange = Flooded Crop Satellite/Sensor: UAVSAR airborne L-band synthetic aperture radar (SAR) aboard a NASA Gulfstream C-20A jet. Resolution: 10 meters Credits: UAVSAR data courtesy of NASA/JPL-Caltech Esri REST Endpoint: See URL section on right side of the page WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202507_FLOOD_TX/202507_UAVSAR/MapServer/WMSServer Data Download: https://uavsar.jpl.nasa.gov/cgi-bin/download-files.pl

NOTE: Due to the higher resolution of this data, it may be slow to load or require the user to zoom to a smaller area of interest. Date of Images: Post-Event: 10/24/2025, 10/23/2025, 10/21/2025, 10/20/2025, 10/18,2025 Pre-Event: 9/10/2025, 9/13/2025, 9/14/2025, 9/15/2025, 9/20/2025 Date of Next Image: Unknown Summary: The True Color RGB composite provides a product of how the surface would look to the naked eye from space. The RGB is created using the red, green, and blue channels of the respective instrument. Suggested Use: The True Color RGB provides a product of how the surface would look to the naked eye from space and may show damage caused by severe weather. The True Color RGB is produced using the 3 visible wavelength bands (red, green, and blue) from the respective sensor. Some minor atmospheric corrections have occurred. Satellite/Sensor: PlanetScope Resolution: 3 Meters Esri REST Endpoint: See URL section on right side of page