Mapping Tornado Tracks with Satellite Data

Mark Altaweel


Tornadoes have been notoriously difficult to predict, causing not only great damage and loss of life but they continue to hinder how well emergency services can prepare for them.

Tornadoes are probably among the most significant weather-related factors causing damage and destruction that occurs in the United States in any given year.

Now, using satellite imagery, we might be able to at least better detect where tornadoes moved so that future prediction and understanding of tornadoes can improve. 

To improve tornado forecasting, it is essential to pinpoint their exact paths, as this allows for the analysis of specific conditions that influenced their trajectory. Determining the precise locations where tornadoes strike and progress can be challenging, especially when their tracks are obscured due to a lack of vegetation or minimal damage, as is often the case in more rural areas.

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An annotated satellite image over Kentucky showing with arrows a tornado track.
The trajectory of the December 2012 tornado that carved out a track through Kentucky is visible in images captured by the MODIS instrument aboard NASA’s Aqua satellite.

On 10–11 December 2021, a tornado touched down in northeastern Arkansas.[1] Using Moderate Resolution Imaging Spectroradiometer (MODIS), short-wave multi-spectral observations reveal scaring on farmland that indicates the exact pathway the tornado took.

Wang et al. used satellite MODIS imagery to help detect changes on soil surfaces after a tornado had moved through the region studied.[2] 

Mapping tornado tracks

The principal in locating tornado tracks is straightforward and has been around for some time. The idea is to locate disturbed surfaces that are caused by tornadoes which should demonstrate abrupt changes in reflective properties where given light wavelengths help detect these changes  using remote sensors.

Side by side satellite images, one before a tornado, and one after a tornado.
Before and after satellite images of an area in Arkansas that had been struck by a tornado on August 27, 2014. Images: NASA Aqua satellite.

Using before and after imagery can make changes more apparent and easier to detect tornadoes pathways.[3] 

Other techniques scientists have used also focus specifically on changes to vegetation properties, including measures such as changes to the amount of leaf material (Leaf Area Index), which demonstrate changes to vegetation that also make tornado tracks apparent.[4]

Using measurements of higher soil moisture content to map tornado tracks

In the study by Wang et al., the authors use (MODIS) short-wave infrared SWIR channels, which help to demonstrate significant soil moisture scars on Arkansas’ aquert farmland. Aquert soil has a high clay content that effectively maintains the significant alterations in soil structure that occur as a tornado passes through.

The concept behind using this is tornadoes should leave behind a long swath of moister-than-usual soil because drier soils should be disturbed or sucked up by the tornado as it moves through an area.

Tornadoes remove the top layer of soil as they pass through, revealing underlying soil with a higher moisture content. The study revealed a 60-kilometer-long track of wet soil.

Over the study region, an absolute increase that was more than 0.15 in NSDSI1, a measures of channel frequency change in SWIR that demonstrates surface moisture, was detected, which indicates a surge in soil moisture that is statistically significant. This could only happen after the removal of top soil that would reveal the deeper and wetter soil.

Changes to normalized difference vegetation index (NDVI), a measure of live green vegetation, also supports this.

Statistical significance testing of the region and of imagery from different times also shows a significant change in soil surface moisture. Overall, the imagery makes it clear soil moisture surges at the surface after a tornado has moved through an area.

In other words, this moisture serves as a marker of where the tornado was.

While this is an exciting discovery that may help others detect tornado pathways, and thus enable us to better known where tornadoes were to better study them, the limitation of the technique is it best works in clay-rich soils that retain water better. Very likely, this also works in regions that tend to be wetter when given tornadoes are likely to strike. 

Remote sensing technique works best with clay-rich soils

The technique demonstrated by Wang is relatively simple to apply but it helps to demonstrate important properties that tornadoes affect on clay-rich soils. Scientists can now better study even older tornadoes using before and after imagery, while future tornadoes can also be studied.

By detecting pathways tornadoes took, and combining that data with weather information from given dates, it will potentially better inform us why given tornadoes developed and moved through given regions. This could potentially enable researchers to better determine key characteristics that demonstrate the likelihood of tornadoes forming and moving so that forecasting of tornadoes improves.

We are likely still a long way from developing accurate forecasts of tornadoes, particularly because tornadoes appear to occur in a variety of places, but this recent research demonstrating their tracks could make a significant contribution to understanding these weather phenomenon. 


[1]    A recent article looking at the study focused on locating tornado tracks from the Arkansas tornado can be found here:

[2]    For more on the study by Wang et al. on detecting surface moisture change using MODIS after a tornado struck, see:  Jingyu Wang, Yun Lin, Greg M. McFarquhar, Edward Park, Yu Gu, Qiong Su, Rong Fu, Kee Wei Lee, and Tianhao Zhang. 2023. Soil Moisture Observations From Shortwave Infrared Channels Reveal Tornado Tracks: A Case in 10–11 December 2021 Tornado Outbreak. Geophysical Research Letters 50, 6 (March 2023), e2023GL102984. DOI:

[3]    For more on techniques in locating tornado tracks, see:  Gary J. Jedlovec, Udaysankar Nair, and Stephanie L. Haines. 2006. Detection of Storm Damage Tracks with EOS Data. Weather and Forecasting 21, 3 (June 2006), 249–267. DOI:

[4]    For more on measures using Leaf Area Index, see:  Aosier, B., and Kaneko, M. 2007. Evaluation of the forest damage by typhoon using remote sensing technique. In IEEE international conference on geoscience and remote sensing symposium, IGARSS 2007, Barcelona, Spain, 23–27 July 2007. 


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About the author
Mark Altaweel
Mark Altaweel is a Reader in Near Eastern Archaeology at the Institute of Archaeology, University College London, having held previous appointments and joint appointments at the University of Chicago, University of Alaska, and Argonne National Laboratory. Mark has an undergraduate degree in Anthropology and Masters and PhD degrees from the University of Chicago’s Department of Near Eastern Languages and Civilizations.