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

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

The RAdar Produced Inundation Diary (RAPID)-System Overview

Flood-Retrieval Trigger

SAR data Query

Retrieval AlgorithmExecution

The Flood Trigger• Daily Flood status of

~4000 stations

– USGS Water Watch

• Drainage regions

– Watershed algorithm-

• Potential Flooded Area

– Flooded-unflooded

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)

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

Case study-Typhoon Nepartak,

July 17, 2016 Yangtze River, China

Case study-Oct. 10, 2017, Vietnam

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

Case Study- Hurricane Florence Sept. 18, 2018

Case Study- Hurricane Florence

Sept. 19, 2018

2019 Midwestern U.S. floods

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

Combining the Active and Passive

Inundation MappingFlood Scan M/C

April 16, 2008

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

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

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