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NASA NTRS2021

Hawai‘i Island Disasters: Using NASA Earth Observations to Assess Coastal Flood Risk with Measures of Land Cover Change, Flood Extent, and Vulnerability for Adaptation and Mitigation Planning on Hawai‘i Island

As the County of Hawai‘i faces an increased risk of extreme flooding events, sea-level rise, and other hazards associated with climate change, the need for building geospatial capacity to make better-informed decisions is critical. The County of Hawai‘i and Arizona State University partnered with NASA DEVELOP to complete a macro-scale risk analysis for the island of Hawai‘i analyzing flooding, land cover, vulnerability, and exposure factors using Earth observations and socio-economic data. The team assessed the variation in urban coastal vulnerability around the entire island of Hawai‘i, using satellite imagery of coastal land cover typology from satellite products such as Landsat 8 Operational Land Imager (OLI), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Sentinel-1 Synthetic Aperture Radar (SAR). The team made a sharable geodatabase containing datasets modeling vulnerability to coastal flooding as well as the Hawai‘i Flood Risk Toolbox (HiFloRT) which contains multiple tools for the County to map land cover, extreme rainfall and flood extent across the Island. The end products will allow the County of Hawai‘i to establish a protocol and standard framework for the utilization of Earth observations in future planning.

Garren Kalter↗
NASA NTRS2020

An Efficient Area-Based Algorithm for SAR Radiometric Terrain Correction and Map Projection

This article presents a projection algorithm based on the representation of radar samples as area elements, rather than point elements as traditionally done in previous works. Each area element in the geographic grid (geogrid) is associated with a set of samples in the radar grid that intersect completely or partially the area element according to the topography and the radar geometry. Accurate geocoding with adaptive multi-looking is achieved by successively assigning the weighted average of the radar samples to the corresponding geogrid elements. Analogously, the slant-range projection of geocoded data is improved by projecting the geogrid pixels onto the radar grid according to their projected area. When our slant-range projection approach is used within previously-published radiometric terrain correction (RTC) algorithms, the processing time is significantly reduced, performing 3.6 to 5.2 times faster over multi-looked data and up to 8.9 over single-look data. We demonstrate the strength of the area projection algorithm for RTC and geocoding using UAVSAR and Sentinel-1 data, and evaluate the results in the context of the upcoming NISAR mission.

Shiroma, Gustavo H↗
NASA NTRS2022

Generating Landslide Density Heatmaps for Rapid Detection Using Open-access Satellite Radar Data in Google Earth Engine

Rapid detection of landslides is critical for emergency response, disaster mitigation, and improving our understanding of landslide dynamics. Satellite-based synthetic aperture radar (SAR) can be used to detect landslides, often within days of a triggering event, because it penetrates clouds, operates day and night, and is regularly acquired worldwide. Here we present a SAR backscatter change approach in the cloud-based Google Earth Engine (GEE) that uses multi-temporal stacks of freely available data from the Copernicus Sentinel-1 satellites to generate landslide density heatmaps for rapid detection. We test our GEE-based approach on multiple recent rainfall- and earthquake-triggered landslide events. Our ability to detect surface change from landslides generally improves with the total number of SAR images acquired before and after a landslide event, by combining data from both ascending and descending satellite acquisition geometries and applying topographic masks to remove flat areas unlikely to experience landslides. Importantly, our GEE approach does not require downloading a large volume of data to a local system or specialized processing software, which allows the broader hazard and landslide community to utilize and advance these state-of-the-art remote sensing data for improved situational awareness of landslide hazards.

Alexander L Handwerger↗
NASA NTRS2022

Thermal Hydraulic Disaggregation of SMAP Soil Moisture Over the Continental United States

Thermal Hydraulic disaggregation of Soil Moisture (THySM) algorithm was implemented to downscale NASA’s Soil Moisture Active Passive (SMAP) Enhanced soil moisture (SM) product to 1 km over the continental United States (CONUS). This algorithm was developed by combining thermal inertia theory with a soil hydraulic-based approach that considers fine-scale SM spatial distribution driven by both heat fluxes and hydraulic conductivity in soils. Relative soil wetness values were estimated using land surface temperature and normalized difference vegetation index for the thermal inertia model and using soil properties for the hydraulic model. The relative soil wetness values at 1 km from both models were then combined by using weighting functions whereby the spatial distribution of SM was governed more by thermal fluxes during times of strong heat transport and infiltration during moisture abundant soil conditions. THySM values were evaluated using in situ SM measurements from SMAP Core Validation Sites (CVS), the USDA Soil Climate Analysis Network, and the NOAA Climate Reference Network over CONUS. THySM shows higher accuracy than the SMAP / Sentinel-1 (SPL2SMAP_S) 1 km SM product when compared to in situ measurements. The accuracy of THySM is 0.048 m3/m3 based on unbiased root mean square error (ubRMSE), outperforming SPL2SMAP_S by 0.01-0.02 m3/m3. The ubRMSE of THySM 1km SM over the SMAP grassland/rangeland-dominated CVS sites is better than 0.04 m3/m3, which meets the SMAP mission SM accuracy requirement applied at 9 and 36 km.

SMAP↗
NASA NTRS2022

Okefenokee Water Resources: Using Earth Observations to Assess Hydrologic Changes and Wildfire Risk in the Okefenokee Swamp

The Okefenokee Swamp is a vital ecosystem known for its protection of biodiversity, peatland carbon sinks, and recreational and economic opportunities for local residents. The swamp has experienced several wildfires since the 1990s, and new development along the borders of Okefenokee National Wildlife Refuge (ONWR) threatens to alter hydrologic activity and increase fire frequency. NASA DEVELOP partnered with staff at the ONWR to determine the feasibility of using satellite imagery to assess wildfire risk and map changes in vegetation cover. Using data from NASA satellites Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI), Soil Moisture Active Passive (SMAP) data from the USDA’s Crop Condition and Soil Moisture Analytics Tool (Crop-CASMA), European Space Agency (ESA) Sentinel-1 C-Band Synthetic Aperture Radar (C-SAR), and Sentinel-2 Multispectral Instrument (MSI), the DEVELOP team assessed the relationship between hydrologic change, vegetation cover, and wildfire risk in the swamp. Results showed that the southern portion of ONWR has been burned the most since 1990 and has greater water stability than other areas of the refuge. The team also found that the largest pockets of mature forests remain in the northernmost regions. Soil moisture anomaly readings may serve as an indicator of fire conditions. The team used these results to create a vegetation map, a swamp water visibility time series map, a historical wildfire correlation analysis, and a methodology tutorial. These products will assist the ONWR in making informed management decisions about the future of the Okefenokee Swamp.

Brianne Kendall↗
NASA NTRSDate not supplied

Bhutan Agriculture II: Creating a Graphical User Interface, Crop Mask, and Data Collection Protocol for Analysis of Rice Crop in Bhutan Using Remotely Sensed Data

Agriculture is an important sector in Bhutan, accounting for 19.63% of Bhutan’s GDP in 2020 (World Bank) while also providing livelihoods for approximately 57% of the population (World Bank, 2017). The Department of Agriculture (DoA) in Bhutan still relies on in-field reporting for crop monitoring, which is time-consuming and labour intensive. To promote efficiency in these efforts, the team partnered with the DoA, the Bhutan Foundation, and the Ugyen Wangchuck Institute of Conservation and Environmental Research (UWICER). The team, with the help of the science advisors from NASA SERVIR, expanded the crop mask created in the previous term to the whole country of Bhutan and streamlined the sampling protocols for applicability to any available crop data. The team also created a graphical user interface (GUI) which provided a visual representation of current trends and rice distribution across Bhutan. The team utilized NASA Earth observations, including Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Landsat 8 Operational Land Imager (OLI), and Shuttle Radar Topography Mission (SRTM), as well as other Earth observations including Sentinel-1 C-band Synthetic Aperture Radar (C-SAR). This project refined the previous term’s methodology to help supplement crop monitoring and increase the frequency of data collected to aid decision-making processes with the use of remote sensing data.

Wangdrak Dorji​↗
NASA NTRSDate not supplied

Lower Illinois River Valley Ecological Forecasting: Inundation Mapping of the Lower Illinois River Valley Using Synthetic Aperture Radar and Optical Satellite Imagery for Wetland Conservation and Restoration Prioritization Efforts

The Lower Illinois River Valley (LIRV) is home to some of the richest agricultural lands in the United States and its wetlands provide key ecosystem services like clean water and flood reduction. It has also experienced extensive degradation due to development and urban pollution. The Great Rivers Land Trust (GRLT), the National Great Rivers Research & Education Center, Principia College, and the American Geophysical Union’s (AGU) Thriving Earth Exchange sought to incorporate inundation and surface water extent layers into their geodatabases to more accurately identify priority areas for wetland restoration. This project aimed to determine the feasibility of detecting inundation extent and duration along the valley using remotely sensed data. The team used Sentinel-1 C-band Synthetic Aperture Radar (SAR) data to classify open water and inundated vegetation within the study site. The open water classification was compared to Dynamic Surface Water Extent (DSWE) derived from Landsat 8 Operational Land Imager. The team successfully created layers of inundation minimum and maximum extent, as well as inundation duration across the study area for 2019 and 2020. The open water classification resulted in an overall accuracy of 86% when validated against DSWE classifications. These analyses will help end users to identify high priority areas along the LIRV best suited for land conversion projects in the future.

Vanessa Machuca↗
NASA NTRSDate not supplied

NASA GSFC Flood Mapping System (In beta testing)

The authors are developing the NASA GSFC Flood Mapping System, designed to provide near real time, on-demand satellite-based products to disaster management practitioners for requests provided through a website interface. The initial version of the system, which only provides products based on Planetscope data provided by Planet Labs to NASA through the NASA CSDA Program, is currently being beta tested in partnership with the disaster management community and interested research partners. Products include images, NDWI maps, NDVI maps, and water extent maps for flood scenes of interest, the same four products for a specified reference scene, and water extent change maps based on the flood scenes and reference scenes. The team’s vision for the system includes adding products based on all appropriate sources of data that are available at no cost to the project (Landsat, Sentinel-1, Sentinel-2), and obtaining funding for commercial SAR data to provide products in the case where none of the “no-cost” data meets end-user needs. We provide an overview of the system, and discuss the products offered, lessons learned from initial testing, and plans to evolve the system in accordance with our overall vision and the lessons learned.

F. Policelli↗
NASA NTRS2022

Lower Illinois River Valley Ecological Forecasting: Inundation Mapping of the Lower Illinois River Valley Using Synthetic Aperture Radar and Optical Satellite Imagery for Wetland Conservation and Restoration Prioritization Efforts

The Lower Illinois River Valley (LIRV) is home to some of the richest agricultural lands in the United States and its wetlands provide key ecosystem services like clean water and flood reduction. It has also experienced extensive degradation due to development and urban pollution. The Great Rivers Land Trust (GRLT), the National Great Rivers Research & Education Center, Principia College, and the American Geophysical Union’s (AGU) Thriving Earth Exchange sought to incorporate inundation and surface water extent layers into their geodatabases to more accurately identify priority areas for wetland restoration. This project aimed to determine the feasibility of detecting inundation extent and duration along the valley using remotely sensed data. We used Sentinel-1 C-band Synthetic Aperture Radar (SAR) data to classify open water and inundated vegetation within the study site. The open water classification was compared to Dynamic Surface Water Extent(DSWE) derived from Landsat 8 Operational Land Imager. We successfully created layers of inundation minimum and maximum extent, as well as inundation duration across the study area for 2019 and 2020. The open water classification resulted in an overall accuracy of 86% when validated against DSWE classifications. These analyses will help end users to identify high priority areas along the LIRV best suited for land conversion projects in the future.

Vanessa Machuca↗
NASA NTRSDate not supplied

Evaluating SAR Radiometric Terrain Correction Solutions: Optimal products for applied users

Operational applications of Synthetic Aperture Radar (SAR) are under development around the world, driven by the regularly-acquired, free-and-open source C-band SAR observations provided by ESA’s Sentinel-1 sensor constellation since 2014. Groups like SERVIR, a joint NASA and USAID initiative, are at the forefront of remote sensing applications for societal benefit. A takeaway from SERVIR’s experience is the need for appropriately geocoded and fully calibrated SAR data that is ready to use for a range of ecosystems-related applications. Radiometric Terrain Corrected (RTC) data are key entry-level products for multiple applications that range from ecosystems to hazards. This work fills a gap in current research by evaluating several RTCs produced by open-source software solutions (SNAP-7 and ISCE-2), the gold standard commercial software (GAMMA), a Google Earth Engine (GEE) based workflow, and the uncorrected GRD products currently available in GEE. RTCs were analyzed for geolocation quality, absolute radiometric calibration, and fidelity of the radiometric terrain flattening over ten sites representing varied terrains. In addition, a time series analysis was conducted over two locations. Overall, no significant differences for radiometric calibration were found across RTC products. However, all RTCs performed better than uncorrected GRD products. The main differences between products were found in geolocation quality. These results not only demonstrate the need for the uptake and distribution of RTC products for ecosystems applications, but demonstrate the ability to do so with open source methods, adding value to developing affordable operational applications.

Helen Blue Parache↗
NASA NTRS2022

Measurement of Surface Deformation Related to the December 2018 Mt. Etna Eruption Using Time-Series Interferometry and Magma Modeling for Hazard Zone Mapping

Mount Etna has erupted several times since it was first formed. Recently, Mount Etna began erupting again over 24–27 December 2018. Because it erupts frequently, Mount Etna should be observed on a frequent basis. From June 2018 to October 2019, 34 and 56 interferometric synthetic aperture radar (InSAR) images were acquired from the ascending and descending tracks of the Sentinel-1 satellite, respectively. We employed the Stanford Method for Persistent Scatterers (StaMPS) and a refined small baseline subset (SBAS) InSAR method to produce a surface deformation time-series map. In the time-series analysis, the phase signal remained unaltered with time. The Okada model was then applied to the result to generate a modeled interferogram, and the Q-LavHA program was run to generate a lava flow prediction model. A direct comparison of the results showed that Persistent Scatterers Interferometry (PSI)-StaMPS and the refined SBAS technique were comparable in terms of the displacement pattern, with slightly different velocity values obtained for individual points. In particular, a velocity range of −25 to 21 cm/yr was obtained from PSI-StaMPS, whereas a range of −30 to 25 cm/yr was obtained from the refined SBAS method. Upon computation of the vertical and east-west displacement components based on ascending and descending track data using both methods, deformation velocities of 51.5 and 52.5 cm/yr in the westerly direction on the western flank of Mount Etna were obtained from PSI-StaMPS and the refined SBAS method, respectively, whereas on the eastern flank, deformation toward the east was estimated to occur at a velocity of 50.1 or 54.2 cm/yr, respectively. PSIStaMPS estimated a vertical deformation velocity of −5.3 to 18.3 cm/yr, whereas the refined SBAS method produced a velocity range of approximately −7 to 19 cm/yr. The interferogram obtained via Okada modeling showed two fault sources in the 2018 Mount Etna eruption and a total volume change of approximately 12.39 × 10 6 m 3 . From the modeling results, a lava flow prediction model was generated using the Q-LavHA program. The approaches described in this study can be used by government officials, authorities, and other decision-makers to monitor and assess the risk of volcanic activity in the region.

Suci Ramayanti↗
NASA NTRSDate not supplied

WET Water Resources: A Google Earth Engine Python API Tool to Automate Wetland Extent Mapping Using Radar Satellite Sensors for Wetland Management and Monitoring

Wetland ecosystems are annually or seasonally wet transition zones between land and water. They provide a range of ecosystem services such as water filtration, flood mitigation, and carbon sequestration, as well as hosting biodiversity hotspots. Although they fulfill fundamental physical and natural processes, wetland extent and health are threatened by anthropogenic influences related to urbanization, population increase, pollution, and climate change. Recognizing the need to quantitatively monitor changes in these recently threatened ecosystems in a timely and cost-effective way, we developed a Google Earth Engine (GEE) Python API tool for automated wetland extent mapping using optical and radar satellite sensors that can be applied globally. The tool will significantly improve wetland change analysis and monitoring as the optical and SAR data proves high resolution (5-10 m) imagery, and SAR data is unaffected by cloud cover and light availability (day vs. night), which are common limitations for other remotely sensed sensors. The tool utilizes Copernicus Sentinel-1 C-band and NISAR L-band synthetic aperture radar (SAR) imagery. During image preprocessing, we applied a MODIS snow mask product to mask global snow coverage, which would affect land classification sensitivity. Calibration and validation were conducted through a historical change and sensitivity analysis of the Sudd watershed located in central Sudan. The tool was the first of its kind, as it enables NISAR data processing through an open-source GEE repository, further expanding and improving the utility of NASA Earth observations and contributing to NASA Open Science initiatives. We anticipate the tool will be used by researchers and practitioners interested in wetland monitoring and management..

Lori Berberian↗
NASA NTRS2023

Georgia Disasters II: Evaluating the Impacts of Hurricane Irma on Georgia Heirs Property Owners Using NASA Earth Observations

Heirs property owners are especially vulnerable to natural and manmade disasters. This group of people have inherited property left with no clear title and thus have unclear group ownership with the other legal owners, which are all spouses, children, etc. of past owners. After Hurricane Irma made landfall in Georgia in September of 2017, heirs property owners became more likely to be denied access to federal relief due to the legal status of their property title. To observe how this group was impacted by Hurricane Irma, the NASA DEVELOP team partnered with The Georgia Heirs Property Law Center (The Center), a non-profit law firm that works with heirs properties owners. The team used computer assisted mass appraisal (CAMA) data to identify likely heirs property owners. They cross referenced this map with a flood map produced with surface reflectance and backscatter imagery from Landsat 8 OLI, Sentinel-2 MSI, and Sentinel-1 C-SAR, sensors to identify communities in need of relief or assistance. The flood extent maps were validated against United States Geological Survey (USGS) Hurricane Irma High Water Mark in situ data taken the same day Irma crossed into Georgia. To further evaluate the impacted group, the team correlated the flood and heirs property likelihood maps to FEMA denials based on titles issues. The team’s end products were handed off to the Georgia Heirs Property Law Center for use in community outreach, educational materials, and to help direct where The Center can work to prioritize its limited legal resources.

Shakirah Rogers↗
NASA NTRS2023

Bhutan Agriculture III: Monitoring Cropland Changes in Bhutan using Remote Sensing to Bolster Food Security and Support Crop Monitoring

The Bhutan Agriculture III team aimed to improve agricultural efficiency in Bhutan. Bhutan is a nation heavily reliant on agriculture, but it faces challenges such as geophysical limitations and lack of scientific agricultural practice. The team partnered with a primary end user, Bhutan’s Department of Agriculture (DoA), and with collaborators; the Bhutan Foundation, National Plant Protection Centre (NPPC), Agricultural Research Department Centre (ARDC), National Statistics Bureau (NSB), and the Ugyen Wangchuck Institute for Conservation and Environment Research (UWICER). Advised by NASA SERVIR, the team developed crop masks and monitored rice distribution from 2015 to 2022 utilizing Earth observations such as Landsat 8 Operational Land Imager (OLI), Landsat 9 OLI-2, Sentinel-1 C-Band Synthetic Aperture Radar (C-SAR), Sentinel-2 MultiSpectral Instrument (MSI) and Shuttle Radar Topography Mission (SRTM). The team gathered 5,000 points from the five dzongkhags that yield the most rice in Bhutan (Paro, Punakha, Samtse, Sarpang and Wangue Phodrang) using Collect Earth Online (CEO). With the data collected, the team split the data into training and validation data on Google Earth Engine (GEE) for a random forest (RF) classifier for rice and non-rice classification. After running the data on the Random Forest (RF) model, the team got an accuracy score of 81.48%, a kappa score of 55.75% and an F1 score of 86.11%. This data supports better agricultural decision-making for the governing body of Bhutan, helps enhance farming efficiency and foster sustainable practices, assists in overcoming data inaccuracy and bolsters food security in the country.

Sonam Seldon Tshering↗
NASA NTRS2023

Unalakleet Climate: Analyzing Permafrost Degradation and Drainage Networks in Unalakleet, Alaska

The coastal community of Unalakleet is currently the 8th most at-risk community in Alaska due to the adverse effects of climate change that include permafrost degradation, severe coastal erosion, and sea-level rise-induced flood inundation caused by increasingly frequent storm surges. In response, the community has started a managed relocation with support from the Native Village of Unalakleet (NVU) and the National Renewable Energy Lab (NREL)’s Alaska campus in Fairbanks. The Unalakleet Climate NASA DEVELOP team partnered with NREL to provide remote sensing support and analysis for resilience planning in Unalakleet, supporting their ongoing relocation efforts and guiding future expansion. The team utilized Sentinel-1 C-Synthetic Aperture Radar (SAR), WorldView-2, and WorldView-3 datasets from 2017 – 2023 to analyze seasonal summer subsidence and utilized a 2014 Ancillary USGS 5 m Alaska DEM to conduct drainage network analyses that included watershed delineation and Height Above Nearest Drainage (HAND) analysis. The team also used high-resolution WorldView images to locate stable reference points that served as quality control for the team’s analyses. The team’s end products included maps containing subsidence and drainage zones information at and surrounding the relocation site. The team’s products provide NREL with valuable data that enables them to better assist Unalakleet’s managed relocation and assists Unalakleet with adapting to the catastrophic effects of climate change and build resilience in a community on the front lines of climate change.

Ian Lee↗
NASA NTRS2023

Understanding Volume Estimation Uncertainty of Lakes and Wetlands Using Satellites and Citizen Science

We studied variations in the volume of water stored in small lakes and wetlands using satellite remote sensing and lake water height data contributed by citizen scientists. A total of 94 water bodies across the globe were studied using satellite data in the optical and microwave wavelengths from Landsat 8, Sentinel-1, and Sentinel-2. The uncertainty in volume estimation as a function of geography and geophysical factors, such as cloud cover, precipitation, and water surface temperature, was studied. The key finding that emerged from this global study is that uncertainty is highest in regions with a distinct precipitation season, such as in the monsoon dominated South Asia or the Pacific Northwestern region of the USA. This uncertainty is further compounded when small lakes and wetlands are seasonal with alternating land use as a water body and agricultural land, such as the wetlands of Northeastern Bangladesh. On an average, 45% of studied lakes could be estimated of their volume change with a statistical significant uncertainty that is less than the expected volume in South Asia. In North America, this statistically significant uncertainty in volume estimation was found to be around 50% in lakes eastward of the 108th meridian with lowest uncertainty found in lakes along the East coast of the USA. The article provides a baseline for understanding the current state of the art in estimating volumetric change of lakes and wetlands using citizen science in anticipation of the recently launched Surface Water and Ocean Topography Mission.

Shahzaib Khan↗
NASA NTRSDate not supplied

Utilizing Remote Sensing to Detect and Assess the Impacts of Estuarine Breach Events for Improved Coastal Wetland Monitoring and Management

Estuaries are extremely dynamic environments that provide a host of vital ecosystem services. California’s Marine Life Protection Act protects such ecosystems by creating Marine Protected Areas (MPAs). California has approximately 440,000 acres of estuarine habitat as well as 23 Estuarine Marine Protected Areas (EMPAs). Thus, in situ data collection is often difficult due to time and resource constraints. This project used remote sensing to gather data that examined the health and dynamics of California EMPAs to supplement ground-based field measurements. Through the use of Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS), Sentinel-2 MultiSpectral Instrument (MSI), and Sentinel-1 C-band Synthetic Aperture Radar (C-SAR), this project assessed mouth state, inundation extent, turbidity, temperature, and tidal measurements for observable estuaries. The Normalized Difference Water Index (NDWI) from Sentinel-2 MSI captured estuary mouth state and inundation extent. Landsat 8 OLI and Sentinel-2 MSI detected differences in water quality metrics that correlated to changes in estuary mouth state (i.e., open or closed) through algorithms in Google Earth Engine (GEE). The GEE California Estuary Assessment (CEA) tool was created with partner input throughout development and culminated in a graphical user interface tailored to management needs. The CEA tool will allow the partners, the Ocean Protection Council, Moss Landing Marine Laboratories’ Central Coast Wetlands Group, the Southern California Coastal Water Research Project, and University of California Los Angeles (UCLA) and Davis (UCD), to better understand estuary dynamics and facilitate informed management decisions through satellite-based Earth observations.

Alex Gunnerson↗
NASA NTRSDate not supplied

Post-Severe Thunderstorm Damage Assessment from March 2-3, 2020, Nashville, TN, Using Synthetic Aperture Radar Observations

Severe weather events (e.g., hurricanes, tornadoes) are responsible for most weather-related infrastructure and building damages. The destruction caused by these events can cross several states, making damage estimates difficult, especially in heavily vegetated or rural areas. Drone or optical imagery is often relied upon in these cases but is limited by solar and atmospheric conditions. Synthetic Aperture Radar (SAR) is an active sensor allowing for day and night collections in all weather conditions. This research highlights the benefits and limitations of using SAR to detect tornado tracks and associated damages while also assessing the strengths and weaknesses of two SAR sensors with varying wavelengths and spatial resolutions from the March 2020 Tornado Outbreak. The outbreak occurred overnight on March 2-3, when several supercell thunderstorms tracked across multiple states producing numerous tornadoes (EF-0 through EF-4) and large hail. Most of the damage occurred in central Tennessee, resulting in 25 fatalities, hundreds of injuries, and over a billion dollars worth of damage. Publicly available C-band (~6 cm) imagery from the European Space Agency’s Sentinel-1 satellite and commercial X-band (~3 cm) imagery from Airbus’s TerraSAR-X satellite were used to generate amplitude and coherence products. The closest post-event collections were used for the amplitude products, and the closest pre- and post-collections were used to create the coherence pairs. If damage tracks were identified in any of the SAR products, the length (miles) and max width (yards) were recorded and compared to the National Weather Service Storm Data official records. SAR successfully detected five rated EF-1 or higher out of ten recorded tornadoes. However, none of the four EF-0 tornadoes were identified. X- and C-band SAR sensors are heavily impacted by dense vegetation, underestimating the extent of damage. Despite these limitations, SAR products can be beneficial when looking at severe weather impacts, especially during the winter and early spring when the coherence products are less influenced by vegetation. The upcoming L-band (~24 cm) NISAR mission will provide more accurate estimates of damage, with the longer wavelength, expanding the applications of SAR to assist in damage assessment caused by severe thunderstorms.

Hannah G Pankratz↗