Bell, Jordan R.

Molthan, Andrew L.

Schultz, Lori A.

NASA Technical Reports Server

Advantages and Applications of Synthetic Aperture Radar as a Decision Support Tool
Andrew L. Molthan1, Jordan R. Bell2, Lori A. Schultz2
1Earth Science Branch, Marshall Space Flight Center, Huntsville, Alabama
2Earth System Science Center, University of Alabama in Huntsville, Huntsville, Alabama
Introduction
Synthetic Aperture Radar (SAR)
Hurricane Harvey
EF-2 Tornado near Clear Lake, WI 
18 June 2016 Hail Damage in NW Iowa
Future Work
• Satellite remote sensing can provide large-scale mapping of 
areas impacted by a disaster, contributing value to situational 
awareness, monitoring the event, and decision support 
services. 
• The constellation of NASA, NOAA, commercial, and 
international partner satellites provides a wide range of 
information at varying wavelengths, spatial resolutions, and 
repeat cycles. 
• Many efforts focus on optical remote sensing (VIS, NIR, IR) but 
in weather-related disasters are often blocked by cloud cover.
• For example, a major hurricane or long-term rainfall 
event produce widespread cloud cover that blocks the 
view of resulting floods
• Synthetic aperture radar is an active remote sensing approach 
that, sending out a signal with known wavelength and 
measures backscatter.
• Compared to a passive system that receives thermal emissions 
or reflected components, SAR allows for penetration through 
most clouds and precipitation, varying with wavelength.
Acknowledgements
Colleagues at the Alaska Satellite Facility (ASF) at the University of Alaska, 
Fairbanks in Fairbanks, Alaska were instrumental in helping develop products 
through those collaborations.  Support in oSTEM provided by NASA 
Headquarters and Marshall’s Office of Diversity and Equal Opportunity.
• Continued development of SAR RGBs for qualitative analysis for decision 
makers as well as supplemental training for interpretation.
• Work with end users on how quantitative products can be best integrated 
into their analysis and response activities, including training with end users 
so that they can draw the correct inference from the imagery products.
• Expanding the use of SAR products presented in this research to also cover 
DSS support of wildfires, as both optical remote sensing and SAR analysis 
provide numerous opportunities for mapping wildfires.
• Focus on objective algorithms for automatic extraction of affected regions.
a) b)
c)
Known Water
Possible Flood Water
• SAR imagery from the ESA Sentinel 1A and 
1B were used, along with International 
Charter assets (not shown) to map potential 
flooding from Harvey, focusing on Houston.
• Areas of substantially lower backscatter were 
mapped to identify “potential flood” as 
water located where water was not expected 
to be under normal conditions.
• Resulting products were used by FEMA and 
partners alongside other GIS data sets to 
predict area and population impacted by 
flooding.
• FEMA, National Guard, and other partners 
combine information about the remote 
sensing of a flood with other population and 
structure information to develop preliminary 
assessments.
• Assessment information feeds into the 
broader response process and planning.
Figure 4. a) Aqua MODIS image of T.S. Harvey producing 
cloud cover over the Houston area, b) Sentinel 1B 
mapping (false color image) of the Houston area despite 
cloud cover with water depicted in blue shades and 
other darkening, and c) final threshold-based map of 
water and flooding in the region.
• SAR backscatter is sensitive to the reflecting surface, and because vegetation provides unique 
differences in co-polarized and cross-polarized returns, damage to vegetation is apparent in 
SAR imagery, particularly where the vegetation has been changed by the tornado.
• Changes in backscatter among different images and false color compositing of parameters 
identified a tornado damage track near Clear Lake, WI.
Figure 5. False color compositing of backscatter and change over time identifies an east-west oriented pattern 
of change associated with a tornado near Clear Lake, WI, data provided to the NWS for their assessment.
S1A - 30 June 2016MODIS -- 19 June 2016
• Severe thunderstorm winds, combined with hail, led to corn and other crop 
damage in northwestern Iowa on 18 June 2016 and was evident as a scar 
(browns) in MODIS imagery the following day.
• In MODIS vegetation index imagery, the scar remains apparent through 
much of the growing season when compared to surrounding vegetation.
• Because SAR backscatter is sensitive to the amount of vegetation and 
orientation of leaves, height of crop, etc., damage to the corn is evident in 
false color compositing of co- and cross-polarized backscatter.
• Development work continues to extract these signals as damage maps.
Figure 6. a) MODIS image of hail damage scar in northwestern Iowa from 19 June 2016. b-d) 
Temporal evolution of the hail damage scar as depicted in false color compositing of co-
polarized and cross-polarized backscatter of the Sentinel 1A instrument through mid-July.  
7 July
Figure 1. Comparison of passive 
and active remote sensing, this 
and other figures courtesy of 
NASA ARSET training module on 
Synthetic Aperture Radar.
• Some SAR systems also penetrate through dense vegetation 
canopy, depending on wavelength.
• Allows for through-cloud observations of floods and other 
severe weather damage.
• Backscattering mechanisms depend upon the surface that is 
being sampled:
• Smooth surfaces like undisturbed water can appear dark 
(low dB)
• Vegetation orientation impacts dB return from various 
polarizations.
Figure 2. Example of a SAR system 
transmitting a signal (left) and receiving 
a backscattered component (right) based 
upon the surface that has been 
intercepted by the signal, similar to 
weather radar.
Figure 3. Various types and 
styles of backscattering 
mechanisms that contribute 
to the strength of signal 
received, as a function of 
what has been sampled.
19 July
SAR backscatter 
reveals initial 
damage and recovery 
of vegetation 
regardless of cloud 
cover conditions.
undamaged vegetation
(primarily corn, soybeans)
loss of vegetation from hail
https://ntrs.nasa.gov/search.jsp?R=20170011226 2019-08-30T18:48:08+00:00Z


Advantages and Applications of Synthetic Aperture Radar as a Decision Support Tool

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