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Service Description: Date of Images:
Post-Event: June 26, 2025 at 00:59 UTC (about 6:59PM MT)
Pre-Event: June 20, 2025
Summary:
We developed a rapid-response workflow to map flooded areas during this emergency event using multi-temporal Sentinel-1 SAR data. Sentinel-1’s SAR backscatter characteristics are particularly effective for detecting surface water: water surfaces typically exhibit low backscatter, whereas non-water areas show higher backscatter. Leveraging this physical property, we distinguished between water and non-water surfaces from both pre- and post-flood observations.
To highlight flood-induced surface changes, we computed the differenced co-polarized VV backscatter (dVV = VV_post – VV_pre). This change detection technique enhances the visibility of newly inundated areas. We then applied a simple threshold to the dVV image to separate flooded from non-flooded areas.
Suggested Use:
The final output is a binary raster product, where pixels with a value of 1 represent flooded areas (dark blue color).
Satellite/Sensor:
Sentinel-1 /Synthetic Aperture Radar (SAR)
Resolution:
10 meters
Credits:
Dr. Khuong H. Tran (Khuong.tran@nasa.gov)
Service URL:
WMS Endpoint:
DISASTERS_202506_FIRE_NM/2506_sentinel1_flood
Description: Date of Images:
Post-Event: June 26, 2025 at 00:59 UTC (about 6:59PM MT)
Pre-Event: June 20, 2025
Summary:
We developed a rapid-response workflow to map flooded areas during this emergency event using multi-temporal Sentinel-1 SAR data. Sentinel-1’s SAR backscatter characteristics are particularly effective for detecting surface water: water surfaces typically exhibit low backscatter, whereas non-water areas show higher backscatter. Leveraging this physical property, we distinguished between water and non-water surfaces from both pre- and post-flood observations.
To highlight flood-induced surface changes, we computed the differenced co-polarized VV backscatter (dVV = VV_post – VV_pre). This change detection technique enhances the visibility of newly inundated areas. We then applied a simple threshold to the dVV image to separate flooded from non-flooded areas.
Suggested Use:
The final output is a binary raster product, where pixels with a value of 1 represent flooded areas (dark blue color).
Satellite/Sensor:
Sentinel-1 /Synthetic Aperture Radar (SAR)
Resolution:
10 meters
Credits:
Dr. Khuong H. Tran (Khuong.tran@nasa.gov)
Service URL:
WMS Endpoint:
false
Extent:
XMin: -105.35405719542383
YMin: 32.51874379054144
XMax: -103.50972608559805
YMax: 34.08881924412554
Spatial Reference: 4326
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LatestVCSWkid(0)
Initial Extent:
XMin: -105.35405719542383
YMin: 32.51874379054144
XMax: -103.50972608559805
YMax: 34.08881924412554
Spatial Reference: 4326
(4326)
LatestVCSWkid(0)
Full Extent:
XMin: -105.35405719542383
YMin: 32.51874379054144
XMax: -103.50972608559805
YMax: 34.08881924412554
Spatial Reference: 4326
(4326)
LatestVCSWkid(0)
Pixel Size X: 8.983152841195207E-5
Pixel Size Y: 8.983152841195192E-5
Band Count: 1
Pixel Type: U8
RasterFunction Infos: {"rasterFunctionInfos": [{
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Mensuration Capabilities: Basic
Inspection Capabilities:
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Has Multi Dimensions : false
Rendering Rule:
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Copyright Text: Dr. Khuong H. Tran (Khuong.tran@nasa.gov)
Service Data Type: esriImageServiceDataTypeGeneric
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Default Mosaic Method: Northwest
Allowed Mosaic Methods: NorthWest,Center,LockRaster,ByAttribute,Nadir,Viewpoint,Seamline,None
SortField:
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Mosaic Operator: First
Default Compression Quality: 75
Default Resampling Method: Bilinear
Max Record Count: 1000
Max Image Height: 4100
Max Image Width: 15000
Max Download Image Count: 20
Max Mosaic Image Count: 20
Allow Raster Function: true
Allow Copy: true
Allow Analysis: true
Allow Compute TiePoints: false
Supports Statistics: true
Supports Advanced Queries: true
Use StandardizedQueries: true
Raster Type Infos:
Name: Raster Dataset
Description: Supports all ArcGIS Raster Datasets
Help:
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Edit Fields Info: null
Ownership Based AccessControl For Rasters: null
Child Resources:
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Statistics
Key Properties
Legend
Raster Function Infos
Supported Operations:
Export Image
Query
Identify
Measure
Compute Histograms
Compute Statistics Histograms
Get Samples
Compute Class Statistics
Query GPS Info
Find Images
Image to Map
Map to Image
Measure from Image
Image to Map Multiray
Query Boundary
Compute Pixel Location
Compute Angles
Validate
Project