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A Near-Real-Time Flood Mapping
Chain using Synthetic Aperture
Radar ImageriesQing Yang, Guangxi University, University of Connecticut,
Xinyi Shen and Emmanouil Anagnostou, University of Connecticut
Xi Chen, Peking University
Albert Kettner, and Robert Brakenridge, University of Colorado, Boulder
Jack Eggleston, Hydrological Remote Sensing Branch, USGS
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SAR Data Frequency During Recent Events
• Sentinel-1 (2014)
– 8 days interval
– 2-3 days potential
– 10 m spatial resolution• NISAR (2020)
– 4 times per month
– 5-10 m
Sentinel Revisits Aug. 27-Sept. 10,2017
Sentinel Revisits Sept. 10-Nov. 2, 2017
Shen, et al., (2019). Remote Sensing
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The RAdar Produced Inundation Diary (RAPID)-System Overview
Flood-Retrieval Trigger
SAR data Query
Retrieval AlgorithmExecution
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The Flood Trigger• Daily Flood status of
~4000 stations
– USGS Water Watch
• Drainage regions
– Watershed algorithm-
• Potential Flooded Area
– Flooded-unflooded
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A
Binary classification
B
Morphologic
processing
C
Multi-threshold
compensation
D
Machine
learning-based
refinement
Overview of the RAdar Produced Inundation Diary(RAPID)Shen, et al., (2019). Remote Sensing of Environment
Theoretical PDF
20 40 60 80 100
10
20
30
40
50
60
70
80
90
100
Real PDF
20 40 60 80 100
10
20
30
40
50
60
70
80
90
100 0
1
2
3
4
5
6
x 1012
𝑝 𝐼1, 𝐼2 →ℎ 𝐼1, 𝐼2∆𝐼1∆𝐼2
• Water source tracing (WST)
• Improved change detection (ICD)
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Input DatasetsDataset Name Source/Type Producer Time span Coverage Spatial Res. Revisiting
intervals
Sentinel-1 SAR ESA Since 2014 Global 3.5/10 m 12 days (after 2013 Oct)
6 days (after 2015 Oct)
NLCD Landsat/LCC USGS 1992-2011 US 30 m 5 years
GLR-FROM Landsat/LCC Tsinghua Unvi. 2010, 2015 Global 30 m 5 years
Water Occurance Landsat/water
probability
Pekel et al. 1984-2015 Global 30 m static
Hydrography NHD-HR USGS N/A US 30 m static
DEM STRM USGS N/A Global 10 m US
30 m global
static
NARWidth TM/River Width George Allen
et al.
N/A North
America
30 m static
GWD-LR STRM/River Width &
Hydrograph
Dai Yamazaki
et al.
N/A Global 90 m static
HydroLakes STRM WWF N/A Global 90 m static
US-Detailed
Stream Body
Survey USGS/USEPA/E
SRI
N/A US 10 m static
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Case study-Typhoon Nepartak,
July 17, 2016 Yangtze River, China
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Case study-Oct. 10, 2017, Vietnam
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Case Study- Hurricane Harvey, Aug. 27-Sept. 10, 2017
Confusion Matrix
Reference User accuracyWet Dry
Ret
riev
al Wet 11.09% 3.29% 77%
Dry 3.73% 81.90%
Producer accuracy
75% 93%
RAPID Map DFO Comprehensive Map
https://floodobservatory.colorado.edu/Events/2017USA4510/2017USA4510.html
Aug. 30, 2017
Aug. 27-Sept. 10, 2017
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Case Study- Hurricane Florence Sept. 18, 2018
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Case Study- Hurricane Florence
Sept. 19, 2018
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2019 Midwestern U.S. floods
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Summary
• An automated open-flood
mapping chain has been
mapped
• Quantitative validation shows
satisfactory
• It has been tested over many
events
Next
• Synergizing active/passive
microwave data
• Wetlands/Vegetation flood
mapping by SAR
• Base flow estimation
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Combining the Active and Passive
Inundation MappingFlood Scan M/C
April 16, 2008
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Inundation mapping beneath vegetation
• Backscatter enhancement-4a & 4b
𝑆𝐻𝐻2 0 𝑆𝐻𝐻𝑆𝑉𝑉
∗
0 2 𝑆𝐻𝑉2 0
𝑆𝑉𝑉𝑆𝐻𝐻∗ 0 𝑆𝑉𝑉
2
=
𝑓𝑆 𝛽2 + 𝑓𝐷 𝛼 2 +
3
8𝑓𝑉 0 𝑓𝑆𝛽 + 𝑓𝐷𝛼 +
𝑓𝑉8
02
8𝑓𝑉 0
𝑓𝑆𝛽∗ + 𝑓𝐷𝛼
∗ +𝑓𝑉8
0 𝑓𝑆 + 𝑓𝐷 +3
8𝑓𝑉
• Freeman-Durden 3-component Model
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Open Water
Dry Lands Wetlands
σSDc
σHVc
σSDc
σHVc
σSDc
σHVc
𝜎𝑆𝐷𝑐 = σ𝐻𝐻
𝑐 − 3σ𝐻𝑉𝑐
𝜎𝑞𝑝𝑐 𝑡2 = 𝑓𝑞𝑝
21𝜎𝑞𝑝0 𝑡2
• Temporal Calibration
• Incomplete polarimetric decomposition
• Wetland Signature
Inundation beneath
Vegetation Detection
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Acknowledgement• John W. Jones, Hydrologic Remote Sensing Branch, USGS
• David M. Bjerklie, New England Water Science Center, USGS
• John F. Galantowicz, Atmospheric and Environmental Research, Boston