United States of America imagery and land cover data

uav imagery by Skytracking Drone NC, acquired 2025-04-26 to 2025-04-26, 5.0 cm per pixel, published on OpenAerialMap.

uav imagery by Philip Gandy, acquired 2025-04-28 to 2025-04-28, 5.0 cm per pixel, published on OpenAerialMap.

uav imagery by Philip Gandy, acquired 2025-05-01 to 2025-05-01, 3.0 cm per pixel, published on OpenAerialMap.

Dates of Images: Post-Event: August 26, 2026 Pre-Event: August 4, 2026 Date of Next Image: Unknown Summary: Normalized Differential Vegetation Index (NDVI) maps are produced by converting pixel values to NDVI = (Red – NIR)/(Red + NIR), NIR indicates the near infrared band. The dynamically generated Normalized Difference Vegetation Index (NDVI) layer is an index for quantifying green vegetation. It reflects the state of vegetation health based on how vegetation reflects light at certain wavelengths. NDVI change was created by subtracting the cloud-masked post-event NDVI from the cloud-masked pre-event NDVI to show areas of potentially impacted vegetation as negative values (darker shades) and healthy/unimpacted vegetation in positive values (lighter shades). NDVI change can give insights to impacts from an event (like the scouring of vegetation from the riverbeds) but can also show changes in unrelated areas like the clearing of agriculture fields. A Normalized Difference Water Index (NDWI) threshold of less than 0 from August 4, 2026 was used to mask out permanent water bodies. These layers are binary NDVI change polygons (threshold of areas where NDVI decreased by -0.25 or more) for August 26, 2026 as compared to August 4, 2026. Suggested Use: NDVI change: Commonly used to distinguish healthy/active vegetation (positive values) from potentially unhealthy/impacted vegetation (negative values). This map shows a binary flag where NDVI decreased. Satellite/Sensor: MultiSpectral Instrument (MSI) on European Space Agency's (ESA) Copernicus Sentinel-2A/2B satellites Resolution: 10 meters Credits: NASA/MSFC, USGS, ESA Copernicus Esri REST Endpoint: See URL section on right side of page

uav imagery by Ben Scholer, acquired 2025-05-07 to 2025-05-07, 5.0 cm per pixel, published on OpenAerialMap.

uav imagery by DSHQ Drone Mapping, acquired 2025-05-11 to 2025-05-11, 5.0 cm per pixel, published on OpenAerialMap.

uav imagery by WebODM, acquired 2025-05-12 to 2025-05-12, 5.0 cm per pixel, published on OpenAerialMap.

uav imagery by Crayton Litton (Crayton Technologies), acquired 2025-05-24 to 2025-05-25, 5.0 cm per pixel, published on OpenAerialMap.

uav imagery by North Metro Fire Rescue District, acquired 2026-06-01 to 2026-06-01, 2.5 cm per pixel, published on OpenAerialMap.

uav imagery by North Metro Fire Rescue District, acquired 2026-02-27 to 2026-02-27, 3.2 cm per pixel, published on OpenAerialMap.

Dates of Images: Post-Event: February 17, 2025; February 19, 2025 Pre-Event: February 3, 2025 Date of Next Image: Unknown Summary: Shortwave Infrared: The Shortwave Infrared RGB provides a false composite look at the surface. This RGB uses a shortwave, the near-infrared, and red channels from the instrument. Color Infrared: The Color Infrared composite is created using the near-infrared, red, and green channels, allowing for the ability to see areas impacted from the fires. The near-infrared gives the ability to see through thin clouds. Healthy vegetation is shown as red, water is in blue. True Color: 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: Natural Color: areas of water will appear blue, healthy green vegetation will appear as a bright green, urban areas in various shades of magenta. Color Infrared: depicts healthy vegetation as red, water as blue. Some minor atmospheric corrections have occurred. True Color: provides a product of how the surface would look to the naked eye from space. 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: Landsat 9 Operational Land Imager (OLI) Resolution: 30 meters Credits: NASA/MSFC, USGS Esri REST Endpoint: See URL section on right side of page WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202502_FLOOD_OHVALLEY/2502_landsat/ImageServer/WMSServer

uav imagery by WebODM, acquired 2025-05-25 to 2025-05-25, 5.0 cm per pixel, published on OpenAerialMap.

uav imagery by North Metro Fire, acquired 2026-06-30 to 2026-06-30, 1.6 cm per pixel, published on OpenAerialMap.

This dataset contains georeferenced aerial imagery from 1927 and 1930 covering the District of Columbia. The original photographs were created as part of early aerial survey efforts and are held within a combined National Archives and Records Administration (NARA) catalog entry (NARA ID 139056920), Record Group 328. The images were scanned by the National Capital Park and Planning Commission, Federal Projects Planning Division (ca. 1926–ca. 1952). A multi-agency collaboration—including the Historic Preservation Office, OCTO, DHS, OP, DOEE, DOH, and FEMS—completed the georeferencing and mosaic development. As part of this effort, the team georeferenced and cataloged the full 1927 aerial photo series of the District, derived from more than 700 high‑resolution image scans. The resulting mosaic and index provide a historic baseline for land use, urban development, environmental analysis, and cultural resource research.

uav imagery by North Metro Fire Rescue District, acquired 2026-08-20 to 2026-08-20, 3.1 cm per pixel, published on OpenAerialMap.

uav imagery by Ben Scholer, acquired 2025-05-31 to 2025-06-01, 4.6 cm per pixel, published on OpenAerialMap.

Dates of Images: 4/30/2024, 5/7/2024 Date of Next Image: Varies by region, typically 12 days since previous pass. Set time slider to most recent interval and click on area of interest to identify date of last pass. Summary: The Alaska Satellite Facility has developed false color Red, Green, Blue (RGB) and Radiometrically Terrain-Correct (RTC) composites of the Sentinel-1A/B Synthetic Aperture Radar (SAR) instrument which assigns the co- and cross-polarization information to a channel in the composite. When used to support a flooding event, areas in blue denotes water present at the time of the satellite overpass before or after the start of the flooding event. Sentinel-1 RGB Decomposition of RTC VV and VH imagery over United States coastlines. Blue areas have low returns in VV and VH (smooth surfaces such as calm water, but also frozen/crusted soil or dry sand), Green areas have high returns in VH (volume scatterers such as vegetation or some types of snow/ice), and Red areas have relatively high VV returns and relatively low VH returns (such as urban or sparsely vegetated areas). Suggested Use: In this image, water appears in blue, vegetated areas in shades of green and urban areas in bright orange. It is recommended to use this product with ancillary information to derive flooded areas. Satellite/Sensor: Synthetic Aperture Radar on European Space Agency's (ESA) Copernicus Sentinel-1A satellite Resolution: 30 meters Service URL: https://gis.earthdata.nasa.gov/portal/home/item.html?id=e893329c61b6428bbe903d3452a8ba0a WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202405_FLOOD_TX/2408_sentinel1/ImageServer/WMSServer

Dates of Images: Post-Event: 3/14/2026 Pre-Event: 1/25/2026 Date of Next Image: Unknown Summary: True Color: 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: True Color: provides a product of how the surface would look to the naked eye from space. 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: Landsat 9 Operational Land Imager (OLI) Resolution: 30 meters Credits: NASA/MSFC, USGS Esri REST Endpoint: See URL section on right side of page WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202603_FLOOD_HI/2605_landsat/ImageServer/WMSServer

uav imagery by NetApex, acquired 2026-02-24 to 2026-02-24, 5.0 cm per pixel, published on OpenAerialMap.

Dates of Images: Post-Event: January 12, 2025 Pre-Event: December 18, 2024 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. The Short Wave Infrared (SWIR) RGB is a product that is created using the SWIR, NIR, and Red channels of the respective instrument. The Color Infrared composite is created using the near-infrared, red, and green channels, allowing for the ability to see areas impacted by the fires. The near-infrared gives the ability to see through thin clouds. Healthy vegetation is shown as red, water is in blue. The Normalized Burn Ratio identifies burned areas using this calculation. Darker areas are potentially burned areas. Suggested Use: The True Color RGB provides a product of how the surface would look to the naked eye from space. 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. The Short Wave Infrared (SWIR) RGB is a product that can provides value in flood detection. Areas of water will appear blue, healthy green vegetation will appear as a bright green, urban areas in various shades of magenta, snow will appear as a bright blue/cyan, and bare soils being multicolor dependent on their makeup. Compare pre-event imagery to post-event imagery to identify potential affected areas. A Color Infrared composite depicts healthy vegetation as red, water as blue. Some minor atmospheric corrections have occurred. Satellite/Sensor: MultiSpectral Instrument (MSI) on European Space Agency's (ESA) Copernicus Sentinel-2A/2B satellites Resolution: True Color and Color Infrared: 10 meters Shortwave Infrared: 20 meters Credits: NASA/MSFC, USGS, ESA Copernicus Service URL: https://gis.earthdata.nasa.gov/portal/home/item.html?id=cc620f9a325a4c8a9e10fa90f1ce7e11 WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202501_FIRE_CA/2501_sentinel2/ImageServer/WMSServer

Date of Image(s): 1/11/2025 Date of Next Image: 1/16/2025 Summary: This product visualizes three dominant principal components derived from the surface reflectance estimates from airborne imaging spectroscopy measurements taken by NASA JPL’s AVIRIS-3. The image visualizes the relative strength of three dominant eigenvectors, estimated from data across the full scene. Eigenvectors from this high (284) dimensional dataset do not specifically identify physical features but are correlated to physical phenomena; similar to how ‘red’, ‘green’, or ‘blue’ in a traditional image are correlated to, but not directly indicative of, specific physical processes. Instead of just “red,” “green,” and “blue,” this analysis considers 284 unique “colors” in the visible to shortwave infrared spectrum, providing a much more robust depiction of spectral differences. Thus, contrast between different colored regions in this image can be used to infer strong differences in surface type. For example, the fire boundary at the time of image collection is readily apparent. Explained another way, within a given scene, burned structures will share a unique spectral reflectance signature in “284-color-space” compared to other surfaces and display as the same color in the PCA image. The product ground spatial resolution is approximately 2.8 m, and flights occurred between 19:40 and 21:00 UTC on Jan 11th 2025. Raw data are available for download here: https://popo.jpl.nasa.gov/pub/LA_Fires/dist/eaton_pca_20250111.tif, https://popo.jpl.nasa.gov/pub/LA_Fires/dist/palisades_pca_20250111.tif. In the absence of a supervised image classification, the PCA image can demonstrate areas with similar spectral reflectance properties. On visual inspection of the PCA image, we can intuitively differentiate burned areas from non-burned areas, even for different types of burned surfaces (e.g. burned vegetation vs. burned structures). Note: PCA images are produced on a scene-specific basis. This means unique PCA color representation in one scene does not translate to the same colors in another scene. For example, colors in the Eaton Fire scene do not relate to the same colors in the Palisades fire scene. Suggested Use: As an unsupervised method to reduce dimensionality and improve feature extraction in high-spectral resolution datasets, the PCA image can serve as an intermediate visual guide to reveal the information content and diversity of surfaces in the AVIRIS-3 datasets. Analysts may use this image to visually understand the spectral diversity of fire impacted areas, especially in conjunction with ancillary data that characterizes the unique underlying physical phenomena represented by unique colors in the PCA image. POC: Philip G. Brodrick (JPL) and David R. Thompson (JPL) Citation: AVIRIS-3 Radiance Data: Eckert, R., D.R. Thompson, A.M. Chlus, J.W. Chapman, M. Eastwood, M. Bernas, S. Geier, M. Helmlinger, D. Keymeulen, E. Liggett, S. Nadgauda, L.M. Rios, L.A. Shaw, W. Olson-Duvall, P.G. Brodrick, and R.O. Green. 2024. AVIRIS-3 L1B Calibrated Radiance, Facility Instrument Collection. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/2356 AVIRIS-3 Reflectance Data: Brodrick, P.G., A.M. Chlus, U.N. Bohn, E. Greenberg, J. Montgomery, J.W. Chapman, M. Eastwood, S.R. Lundeen, R. Eckert, W. Olson-Duvall, D.R. Thompson, and R.O. Green. 2025. AVIRIS-3 L2A Orthocorrected Surface Reflectance, Facility Instrument Collection. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/2357 Service URL: https://gis.earthdata.nasa.gov/portal/home/item.html?id=6898f96fcb1d4b12ba0ba02fb7df28e3 WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202501_FIRE_CA/2501_aviris_pca/ImageServer/WMSServer

Dates of Images Post-Event: 5/21/2026 Summary Skysat is a high-resolution satellite constellation operated by Planet and allows for multiple viewing opportunities of any location on Earth daily. Skysat collects 4-band imagery which allows for multiple derived products to be produced. 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. The Color Infrared composite is created using the near-infrared, red, and green channels. The near-infrared gives the ability to see through thin clouds. Healthy vegetation is shown as red, water is in blue. Resolution ~1 m Credits NASA Disasters Program, Processing from MSFC, Planet. For derived products: “Includes copyrighted material of Skysat. All rights reserved.” This work utilized data made available through the NASA Commercial Satellite Data Acquisition (CSDA) program.

Date(s) of Images: Post-Event: 5/21/2026 Summary: Satellogic’s operates a commercial constellation of satellites and sensors that offers 4 band (R,G, B and NIR) acquisitions. True Color: 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. Color Infrared: The Color Infrared composite is created using the near-infrared, red, and green channels, allowing for the ability to see areas impacted from the fires. The near-infrared gives the ability to see through thin clouds. Healthy vegetation is shown as red, water is in blue. Resolution: 0.5 m to 1 m depending on generation of sensor used and viewing angle. Credits: NASA Disasters Program, Satellogic. For derived products: “Includes copyrighted material of Satellogic. All rights reserved.” This work utilized data made available through the NASA Commercial Satellite Data Acquisition (CSDA) program."

Dates of Images: Post-Event: May 20, 2026; May 19, 2026 Pre-Event: May 7, 2026; April 20, 2026 Date of Next Image: None 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. The Color Infrared composite is created using the near-infrared, red, and green channels. The near-infrared gives the ability to see through thin clouds. Healthy vegetation is shown as red, water is in blue. Suggested Use: The True Color RGB provides a product of how the surface would look to the naked eye from space. 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. A Color Infrared composite depicts healthy vegetation as red, water as blue. Some minor atmospheric corrections have occurred. Satellite/Sensor: MultiSpectral Instrument (MSI) on European Space Agency's (ESA) Copernicus Sentinel-2A/2B satellites Resolution: 10 meters Credits: NASA Disasters/MSFC, USGS, ESA Copernicus Esri REST Endpoint: See URL section on right side of page WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202605_FIRE_CA/202609_sentinel2/ImageServer/WMSServer

Dates of Images: Post-Event: 3/12, 3/14, 3/15, 3/16, 3/17, 3/18, 3/19, 3/20, 3/21, 3/22 Pre-Event: 3/10, 3/11,  Date of Next Image: Varies by region, typically 6 days since previous pass. Summary: The Alaska Satellite Facility has developed false color Red, Green, Blue (RGB) and Radiometrically Terrain-Correct (RTC) composite products of the Sentinel-1A/B Synthetic Aperture Radar (SAR) instrument which assigns the co- and cross-polarization information to a channel in the composite. When used to support a flooding event, areas in blue denotes water present at the time of the satellite overpass before or after the start of the flooding event. Sentinel-1 RGB Decomposition of RTC VV and VH imagery over United States coastlines. Blue areas have low returns in VV and VH (smooth surfaces such as calm water, but also frozen/crusted soil or dry sand), Green areas have high returns in VH (volume scatterers such as vegetation or some types of snow/ice), and Red areas have relatively high VV returns and relatively low VH returns (such as urban or sparsely vegetated areas). Suggested Use: In the RGB images, water appears in blue, vegetated areas in shades of green and urban areas in bright orange. It is recommended to use this product with ancillary information to derive flooded areas. Satellite/Sensor: Synthetic Aperture Radar on European Space Agency's (ESA) Copernicus Sentinel-1A satellite Resolution: 20 meters REST Endpoint: See URL below to the right. WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202603_FLOOD_HI/2605_sentinel1/ImageServer/WMSServer

uav imagery by Garrett Jobes, acquired 2026-05-29 to 2026-05-29, 13.0 cm per pixel, published on OpenAerialMap.

Date of Image(s): 12/18/2024; UTC: 183709 1/12/25; UTC: 183731 Summary: These pre-fire and post-fire images pair were analyzed for normalized burn ratio (NBR). The input imagers were generated from the ESA’s Sentinel-2’s Multi-Spectral imager. They were created from the Level 2A products which have been atmospherically corrected and orthorectified. NBR is defined mathematically as (NIR – SWIR)/(NIR + SWIR) where NIR is near-infrared and SWIR is short-wave infrared. For Sentinel 2, NIR = Band 8 and SWIR = Band 12. Using these definitions, the 20-meter resolution Band 8 and Band 12 images were used to generate these products. Sentinel-2 NBR methods: https://custom-scripts.sentinel-hub.com/custom-scripts/sentinel-2/nbr/ Finally dNBR is simply the difference between the pre-fire NBR and the post-fire NBR. Thus, we calculated dNBR as dNBR = NBR(12/18/2024) - NBR(01/12/2025). Suggested Use: NBR is commonly used as a proxy to indicate areas which have charred vegetation. Darker areas (more negative values) in the NBR image more strongly represent the presence of burned vegetation. Since the dNBR considers the condition of the scene before the fire occurred, the resulting value has been used as a proxy for burn severity. More positive values in the dNBR analysis represent a proxy for greater burn severity. Negative values in dNBR represent a re-greening of or growth of vegetation in between pre and post imagery. The use of this dNBR product as a quantitative metric of burn severity of the Los Angeles Fires at the time of posting this dataset should be strongly caveated. This is due to several dNBR Limitations: The spectral band selections used in the NBR and dNBR calculations, and the implication of changes observed following fire in those wavelengths, primarily pertain to how vegetation spectral signatures change in NIR and SWIR wavelengths following charring. Because of this, dNBR may not accurately describe burned surfaces that are not vegetation (e.g. human built infrastructure). This dataset has not been validated by independent burn severity assessments. The degree to which dNBR is accurately determined depends on careful selection of pre and post event imagery. An effort was made to use the highest quality imagery (i.e. cloud free) with the shortest time difference possible in the image pair; however, it is unknown at the time of this posting how selection of different pre/post image pairs could affect the derived dNBR values. Satellite/Sensor: ESA Copernicus Sentinel-2 Resolution: 20 m in infrared bands Credits: Copernicus Sentinel-2 data was downloaded from the Sentinel-2 data explorer (https://dataspace.copernicus.eu/explore-data/data-collections/sentinel-data/sentinel-2) Data processed by Kyle Kabasares (ARC; kyle.k.kabasares@nasa.gov) References: Normalize Burn Ratio theory: https://un-spider.org/advisory-support/recommended-practices/recommended-practice-burn-severity/in-detail/normalized-burn-ratio Service URL: https://gis.earthdata.nasa.gov/portal/home/item.html?id=3bc872886af24b799acd2ef881691a6a WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202501_FIRE_CA/2501_sentinel2_dnbr/ImageServer/WMSServer

This dataset was generated to publish a geospatial inventory of Shopping Centers in Mecklenburg County, NC. This data serves to support and assist Housing & Neighborhood Services.

uav imagery by NMFRD, acquired 2026-03-25 to 2026-03-25, 1.6 cm per pixel, published on OpenAerialMap.

uav imagery by NYenvironcom, acquired 2026-02-22 to 2026-02-22, 3.7 cm per pixel, published on OpenAerialMap.

Dates of Images: Post-Event: July 4, 2026 Pre-Event: June 19, 2026 Date of Next Image: Unknown Summary: Normalized Differential Vegetation Index (NDVI) maps are produced by converting pixel values to NDVI = (Red – NIR)/(Red + NIR), NIR indicates the near infrared band. The dynamically generated Normalized Difference Vegetation Index (NDVI) layer is an index for quantifying green vegetation. It reflects the state of vegetation health based on how vegetation reflects light at certain wavelengths. NDVI change was created by subtracting the cloud-masked post-event NDVI from the cloud-masked pre-event NDVI to show areas of potentially impacted vegetation as negative values (darker shades) and healthy/unimpacted vegetation in positive values (lighter shades). NDVI change can give insights to impacts from an event (like the scouring of vegetation from the riverbeds) but can also show changes in unrelated areas like the clearing of agriculture fields. This layer is a binary NDVI change polygon (threshold of areas where NDVI decreased by -0.25 or more) for June 19, 2026 as compared to July 4, 2026. Suggested Use: NDVI change: Commonly used to distinguish healthy/active vegetation (positive values) from potentially unhealthy/impacted vegetation (negative values). This map shows a binary flag where NDVI decreased. Satellite/Sensor: MultiSpectral Instrument (MSI) on European Space Agency's (ESA) Copernicus Sentinel-2A/2B satellites Resolution: 10 meters Credits: NASA/MSFC, USGS, ESA Copernicus Esri REST Endpoint: See URL section on right side of page

uav imagery by SAS, acquired 2026-02-22 to 2026-02-22, 3.5 cm per pixel, published on OpenAerialMap.

satellite imagery by North Metro Fire Rescue District, acquired 2026-06-15 to 2026-06-15, 0.9 cm per pixel, published on OpenAerialMap.

uav imagery by Judco, acquired 2026-09-19 to 2026-09-19, 2.5 cm per pixel, published on OpenAerialMap.

Dates of Images: Pre-Event: October 13, 2025 Post-Event: December 17, 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. The Color Infrared composite is created using the near-infrared, red, and green channels. The near-infrared gives the ability to see through thin clouds. Healthy vegetation is shown as red, water is in blue. Suggested Use: The True Color RGB provides a product of how the surface would look to the naked eye from space. 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. A Color Infrared composite depicts healthy vegetation as red, water as blue. Some minor atmospheric corrections have occurred. Satellite/Sensor: MultiSpectral Instrument (MSI) on European Space Agency's (ESA) Copernicus Sentinel-2A/2B satellites Resolution: 10 meters Credits: NASA/MSFC, USGS, ESA Copernicus Esri REST Endpoint: See URL section on right side of page WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202512_FLOOD_WA/2512_sentinel2/ImageServer/WMSServer

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