(EXERCISE - DaNCE March 2026) Flood Extents from UAVSAR for Hurricane Florence September 2018

Date of Images: September 19, 2018; August 9, 2018 Summary: Scientists at NASA's Marshall Space Flight Center created these water extents using Landsat imagery. These images can be used to see where open water is visible at the time of the satellite overpass. This product shows all water detected and differentiates between normal water areas and some flooded areas. This product was classified using the ESA Worldview. It's important to note that all flooded areas may not be captured do to the sensors limitations of not being able to "see" through vegetation and buildings. To determine where additional flooding may have occurred, combine this layer with other data sets. Suggested Use: This product shows water that is detected by the sensor with different colors indicating different land cover/land use classifications from CDL that appear to have water and are potentially flooded. Blue (1): Known Water Red (2): Flooded Developed Green (3): Flooded Vegetation Orange (4): Flooded Cropland/Grassland Gray (5): Clouds/Cloud Shadow (0): No Data Satellite/Sensor: Copernicus Sentinel-2 Resolution: 10 meters Credits: NASA/MSFC, USGS, ESA Copernicus Esri REST Endpoint: See URL section on the right side of page. WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_EX2601_201809_HURRICANE_FLORENCE/ex2601_landsat_WaterExtent/MapServer/WMSServer

Dates of Images: 20180914T1114, 20180914T2305, 20180915T1106, 20180915T2257, 20180917T2329, 20180918T2320, 20180919T1122, 20180919T2313 (GMT) (YYYYMMDD: HHMM) Summary: The Advanced Rapid Imaging and Analysis (ARIA) and Observational Products for End-Users from Remote Sensing Analysis (OPERA) teams at the Jet Propulsion Laboratory, California Institute of Technology produced flood mapping products using the OPERA Dynamic Surface Water Extent from Sentinel-1 (DSWx-S1) dataset. These products provide a rapid assessment of surface water extent to support emergency response and situational awareness during flood events. The dataset provided includes two products: Surface Water Extent maps on individual dates Maximum Surface Water Extent maps, showing the full extent of water observed over a defined time period Dates and Input Data: Post-event images were collected on September 14, 2018; September 15, 2018; September 17, 2018; September 18, 2018; and September 19, 2018. Two acquisitions were available on September 14, 15, and 19, with one acquired in the morning and the other in the late evening. The products were generated using OPERA DSWx-S1 satellite imagery and mosaicked across tiles to provide continuous spatial coverage. Product Description: Surface water is identified from Synthetic Aperture Radar (SAR) satellite imagery and mapped at 30 m spatial resolution. Maximum Surface Water Extent maps show all locations where water was detected at any time during the analysis period. Caveats and Limitations: Detecting inundation under vegetation, in the urban perimeter, in arid environments, and along water body edges is known to be challenging. The local accuracy of the flood maps in such environments may be impacted. These results are intended to support rapid emergency response. All information is provisional for use under emergency response guidelines. These data are provided with absolutely no warranty of any kind. Use at your own risk. Suggested Use: The OPERA DSWx-S1 Water product classifies the Sentinel-1 (S1) input imagery into "not water", "open surface water", and "inundated vegetation”, masks include Height Above Nearest Drainage (HAND) mask and layover/shadow mask), or no data classes. File names:  OPERA_L3_DSWX-S1_V1_WTR_20180914T1114_mosaic.tif OPERA_L3_DSWX-S1_V1_WTR_20180914T2305_mosaic.tif  OPERA_L3_DSWX-S1_V1_WTR_20180915T1106_mosaic.tif OPERA_L3_DSWX-S1_V1_WTR_20180915T2257_mosaic.tif  OPERA_L3_DSWX-S1_V1_WTR_20180917T2329_mosaic.tif OPERA_L3_DSWX-S1_V1_WTR_20180918T2320_mosaic.tif  OPERA_L3_DSWX-S1_V1_WTR_20180919T1122_mosaic.tif OPERA_L3_DSWX-S1_V1_WTR_20180919T2313_mosaic.tif  Water (WTR) Layer Values:  0: Not Water - an area with valid data that is not open water (class 1),inundated vegetation (class 3)height above nearest drainage masked (class 250)or layover/shadow masked (class 251) (suggested color: #ffffff)  1: Open Water - an area that is entirely water and unobstructed to the sensor, including obstructions by vegetation, terrain, and buildings. (suggested color: #0000ff)  3: Inundated vegetation - an area that is considered inundated, extracted from the high value in dual polarization ratio and the wetland class in land cover map. (suggested color: #00ff00)  250: Height Above Nearest Drainage (HAND) masked - an area where topographic height is higher than the HAND threshold (suggested color: #c8c8c8)  251: Layover/shadow masked - an area identified as layover or shadow computed from the geometry of the digital elevation model and sensor. The area is directly copied from input burst RTC-S1 products (suggested color: #808080)  255: No data (suggested color: transparent) Maximum Water Extent Layer Values: The maximum water extent observed during the event was created by aggregating OPERA DSWx-S1 water products.  File names: ARIA-OPERA_L3_DSWX-S1_V1_WTR_20180831T2321_20180919T2313_max_extent.tif  0: Not Water. No water was detected within the time period of the images. Masked pixels are set to No data. (suggested color: #ffffff)  1: Water - an area where open water or partial surface water was detected during the time period of the images. (suggested color: #ff15e4) Satellite/Sensor/Resolution: Satellite/Sensor: Synthetic Aperture Radar (SAR) instrument on European Space Agency's (ESA) Sentinel-1A/C satellites. Spatial Resolution: 30 meters Temporal Resolution: sub-daily to bi-weekly Esri REST Endpoint: See URL to the right. WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_EX2601_201809_HURRICANE_FLORENCE/ex2601_opera_dswx_s1/MapServer/WMSServer

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 Red    = 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://maps.disasters.nasa.gov/ags03/services/texas_flood_202507/uavsar/MapServer/WMSServer Data Download: https://uavsar.jpl.nasa.gov/cgi-bin/download-files.pl

Dates of Images: 20180914T1609, 20180917T1553, 20180918T1549, 20180919T1540, 20180920T1539 (GMT) (YYYYMMDD: HHMM) Summary: The Advanced Rapid Imaging and Analysis (ARIA) and Observational Products for End-Users from Remote Sensing Analysis (OPERA) teams at the Jet Propulsion Laboratory, California Institute of Technology produced flood mapping products using the OPERA Dynamic Surface Water Extent from Harmonized Landsat Sentinel-2 (DSWx-HLS) dataset. These products provide a rapid assessment of surface water extent to support emergency response and situational awareness during flood events. The dataset provided includes two products: Surface Water Extent maps on individual dates Maximum Surface Water Extent maps, showing the full extent of water observed over a defined time period Dates and Input Data: Flood images: September 14, 2018, September 17, 2018, September 18, 2018, September 19, 2018, September 20, 2018 Products are generated from OPERA DSWx-HLS satellite imagery and mosaicked across tiles to provide continuous coverage. Product Description: Surface water is identified from optical satellite imagery and mapped at 30 m spatial resolution. Maximum Surface Water Extent maps show all locations where water was detected at any time during the analysis period. Caveats and Limitations: We note the HLS snow/ice mask often misclassifies sediment rich water as snow/ice. Detecting inundation under vegetation, in the urban perimeter, in arid environments, and along water body edges is known to be challenging. The local accuracy of the flood maps in such environments may be impacted. These results are intended to support rapid emergency response. All information is provisional for use under emergency response guidelines. These data are provided with absolutely no warranty of any kind. Use at your own risk. Suggested Use: The OPERA DSWx-HLS Water product classifies the Harmonized Landsat Sentinel-2 (HLS) input imagery into "not water", "open surface water", and “partial surface water”. The "HLS cloud/cloud shadow" and "HLS snow/ice" layers are direct inputs from the HLS Fmask. Water (WTR) Layer Values: File names: OPERA_L3_DSWX-HLS_V1_WTR_20180914T1609_mosaic.tif, OPERA_L3_DSWX-HLS_V1_WTR_20180917T1553_mosaic.tif, OPERA_L3_DSWX-HLS_V1_WTR_20180918T1549_mosaic.tif, OPERA_L3_DSWX-HLS_V1_WTR_20180919T1540_mosaic.tif, OPERA_L3_DSWX-HLS_V1_WTR_20180920T1539_mosaic.tif Values:0: Not Water - an area with valid reflectance data that is not open water (class 1), partial surface water (class 2), snow/ice (class 252), cloud/cloud shadow (class 253), or ocean masked (class 254). Masking can result in a not water (class 0) where land cover masking is applied. (suggested color: #ffffff) 1: Open Water - an area that is entirely water and unobstructed to the sensor, including obstructions by vegetation, terrain, and buildings. (suggested color: #0000ff) 2: Partial Surface Water - an inundated area that is at least 20% and less than 100% open water. This may be referred to as “subpixel inundation” when referring to a pixel’s area. Examples include wetlands, water bodies with emergent vegetation, and pixels bisected by coastlines. (suggested color: #b4d5f4) 252: Snow/Ice/Sediment Rich Water - an area identified as snow/ice according to input HLS Fmask quality assurance (QA) data (suggested color: #00ffff) 253: Cloud/Cloud Shadow - an area identified as cloud or cloud shadow or adjacent to cloud/cloud shadow according to input HLS Fmask quality assurance (QA) data (suggested color: #afafaf) 254: Ocean Masked - an area identified as ocean using a shoreline database with an added margin (suggested color: #00007f) 255: No data (suggested color: transparent) Maximum Water Extent Layer Values: File names: ARIA-OPERA_L3_DSWX-HLS_V1_WTR_20180830T1604_20180920T1539_max_extent.tif 0: Not Water. No water was detected within the time period of the images. Masked pixels are set to No data. (suggested color: #ffffff) 1: Water - an area where open water or partial surface water was detected during the time period of the images. (suggested color: #ff15e4) Satellite/Sensor/Resolution: Satellite/Sensor: Harmonized Landsat Sentinel-2 (HLS) derived from MultiSpectral Instrument (MSI) on European Space Agency's (ESA) Copernicus Sentinel-2A/2B/2C satellites and Operational Land Imager 2 (OLI-2) on NASA/USGS Landsat 8/9 satellites. Spatial Resolution: 30 meters Temporal Resolution: Sub-weekly Esri REST Endpoint: See URL to the right. WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_EX2601_201809_HURRICANE_FLORENCE/ex2601_opera_dswx_hls/MapServer/WMSServer

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

Date of Images: 8/29/2018 Summary: Scientists at NASA's Marshall Space Flight Center created these water extents using Landsat imagery. These images can be used to see where open water is visible at the time of the satellite overpass. This product shows all water detected and differentiates between normal water areas and some flooded areas. This product was classified using the ESA Worldview. It's important to note that all flooded areas may not be captured do to the sensors limitations of not being able to "see" through vegetation and buildings. To determine where additional flooding may have occurred, combine this layer with other data sets. Suggested Use: This product shows water that is detected by the sensor with different colors indicating different land cover/land use classifications from CDL that appear to have water and are potentially flooded. Blue (1): Known Water Red (2): Flooded Developed Green (3): Flooded Vegetation Orange (4): Flooded Cropland/Grassland Gray (5): Clouds/Cloud Shadow (0): No Data Satellite/Sensor: Copernicus Sentinel-2 Resolution: 10 meters Credits: NASA/MSFC, USGS, ESA Copernicus Esri REST Endpoint: See URL section on the right side of page. WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_EX2601_201809_HURRICANE_FLORENCE/ex2601_sentinel2_WaterExtent/MapServer/WMSServer