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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRSDate not supplied

Mapping Inundation from Hurricane Florence (2018) with L-Band Synthetic Aperture Radar, Commercial Imagery, and Ancillary Data via Random Forest Classification

Mapping the extent of floodwaters following extreme rainfall aids in the distribution of resources, recovery efforts, and damage assessment practices. Development of a land cover classification system focused on mapping inundation after major hurricane events using synthetic aperture radar (SAR) data could allow for the production of near-real-time inundation mapping, enabling government and emergency response entities to get a preliminary idea of a developing situation. In response to Hurricane Florence of 2018, NASA JPL collected numerous swaths of quad-pol L-band SAR data with the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) instrument observing the record-setting river stages across North and South Carolina. The resulting fully-polarized SAR images allow for mapping of inundation extent at a high spatial resolution with a unique advantage over optical imaging stemming from the sensor’s ability to penetrate cloud cover and dense vegetation. This study seeks to determine how accurately maps of inundation can be generated from L-band SAR imagery through Random Forest classification. Once the extent of water and inundated vegetation is classified, cleanup operations are performed using fuzzy logic to reduce false detections. Estimates of water extent are then combined with datasets describing the distribution of population, buildings, and roads throughout the domain to evaluate societal impacts. Results from the Hurricane Florence case study will be discussed along with the limitations of available validation data for assessment of the classifier’s accuracy.

Alexander Melancon↗
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 NTRS2020

Residual Motion Estimation for Multi-Squint Airborne SAR

Airborne SAR data are often challenging to analyze due tothe limited accuracy of the platform motion measurements.In interferometric scenarios, even small residual motion er-rors cause undesirable artifacts in the interferometric phaseand geometric registration of image pairs. Various calibrationtechniques have been developed, often exploiting spectral di-versity in some way. The UAVSAR L-band system has imple-mented a new operating mode where multiple images are ac-quired simultaneously at different azimuth squint angles, pro-viding enhanced opportunities for motion calibration. Thispaper describes this imaging mode and the residual motioncalibration technique developed for these special data sets.

Hawkins, Brian↗
NASA NTRS2020

Assessment Of Polsar And Insar Time-Series From The 2019 Nasa Am-Pm Campaign For Above-Ground Biomass Estimation

The forthcoming launch of the NASA-ISRO Synthetic ApertureRadar (NISAR) mission will open the path to a new typeof L-band measurements constituted by dense time-series ofpolarimetric backscatter and interferometric coherence withunprecedented spatial and temporal sampling. Here, we startthe development of a theoretical framework that links L-bandbackscatter time-series with interferometric coherence measurements.The water-cloud-model (WCM) and an extendedversion of the random-motion-over-ground (RMoG) modelare adopted to express radar measurements in terms of forestabove-ground biomass and tree height. Time-series datacollected during the 2019 UAVSAR AM-PM campaign inSoutheastern United States are used to evaluate the correlationof various PolSAR- and InSAR-derived parameters withfield-measured above-ground biomass.

Lavalle, Marco↗
NASA NTRS2020

Mapping tree canopy cover and canopy height with L-band SAR using LiDAR data and Random Forests

Light detection and ranging (LiDAR) data can provide direct measurements of vegetation structures but are limited by the sparse spatial coverage. Polarimetric synthetic aperture radar (SAR) can perform large-scale high-resolution mapping without weather constraints but the information about vegetation and ground subsurface are mixed in the backscatter data. In this paper, we adopted the Random Forests algorithm to train an upscaling function using tree canopy cover (TCC) and canopy height model (CHM) derived from Goddard’s LiDAR, Hyperspectral and Thermal Imager (G-LiHT) data. The regression model is then applied to the L-band Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) data acquired during the 2017 Arctic-Boreal Vulnerability Experiment (ABoVE) airborne campaign to map the TCC and CHM over the Delta Junction area in interior Alaska.

Moghaddam, Mahta↗
NASA NTRS2020

A Regional L-band High Biomass Estimation Framework Leveraging Spaceborne Lidar and Interferometric Data to Overcome Backscatter Saturation

We propose a framework to estimate high above ground biomass (AGB) from L-band SAR imagery leveraging spaceborne lidars such as GEDI or ICESat-2 and repeat-pass coherence. Our results indicate we are able to overcome model saturation typically associated with purely backscatter methodologies. We validate our approach using lidar-derived AGB maps from the AfriSAR datasets at Mondah, Ogooue, and Lope. We apply our framework to UAVSAR and ALOS- 2 imagery to obtain 50 meter resolution biomass maps. We obtain < 60% nRMSE (in some cases much better) with negligible relative bias using a multiscale random forest model. We illustrate that the inclusion of coherence can significantly improve high AGB estimation particularly at the coastal site Mondah.

Liao, Tien-hao↗
NASA NTRS2021

Comparison of SAR and CYNSS surface water extent metrics over the Yucatan Lake wetland site

Wetlands have a major role in the carbon cycle, outgassing large quantities of carbon dioxide and methane through processes that are directly and strongly influenced by the duration and timing of inundation. Therefore, understanding the seasonal pattern of inundation can be a component for regional to global scale carbon models. Measurement of inundation extent also establishes a benchmark for the current status of wetland areas, useful in assessing the future impacts of climate change. The incorporation of frequent measurements of inundation extent into large-scale hydrological models would permit the evaluation of more detailed seasonal and longer-term floodplain dynamics and their associated management implications.The Cyclone GNSS (CYGNSS) constellation of satellites launched in 2016, and carries receivers capable of receiving data from L-band GNSS reflections. Delay Doppler maps (DDM) are generated on board and telemetered to the ground, along with a small number of raw data takes that can be used for special processing on the ground for evaluation purposes. It has been previously shown that these data can be sensitive to inundation.The NASA ISRO Synthetic Aperture Radar, currently planned for launch in January 2023, has both an L-band and S-band SAR for earth imaging. The L-band SAR, which will image the Earth's land mass twice every 12 days, has a requirement for measuring wetland inundation extent at the 1 ha scale. One of the sites that will be used to validate this requirement is Yucatan Lake, Louisiana. This oxbow lake and surrounding area located adjacent to the Mississippi river experiences periodic and extensive flooding in the surrounding forest areas.In 2019, NASA's UAVSAR fully polarimetric airborne L-band SAR conducted a flight campaign to image a dozen sites in the SE USA at approximately 12-day intervals and both in the morning and evening, to simulate the type of data NISAR will obtain. One site imaged during this campaign was the Yucatan Lake area, spanning water stages from low to high flood conditions.It has been demonstrated previously that GNSS reflectometry such as that measured by CYGNSS may be used to characterize surface inundation. It has also been known for decades that L-band SAR may be used to characterize not only the presence of open water, but also the presence of subcanopy inundation in forested areas. In this paper we will present results comparing data from these two types of instruments.

Lavalle, Marco↗
NASA NTRSDate not supplied

An Introduction to NASA DEVELOP & Project Applications of Airborne Data

DEVELOP projects often explore how NASA airborne data can aid partners in understanding the feasibility of using EO to support decisions. The use of NASA airborne EO has been a strong capacity building tool for many DEVELOP participants. A key to success in the DEVELOP projects was NASA scientists with expert knowledge of the airborne sensor data. NASA scientists will have to continue in this role until the community knowledge/capacity is built up as for many of the spaceborne EO data. NASA airborne sensors used in the last decade include AirSWOT, AVIRIS, AVIRIS-NG, HyTES, and UAVSAR.

Airborne remote sensing↗
NASA NTRS2022

Time-Series Ratio Algorithm for Nisar Soil Moisture Retrieval

The NASA ISRO Synthetic Aperture Radar (NISAR) mission is currently under development and will provide global L-band radar observations that will be helpful for various soil moisture applications. The final NISAR soil moisture product will have 200m spatial resolution with 12-day exact revisit time. A time-series ratio algorithm was implemented using NISAR simulated UAVSAR data collected during the SMAPVEX12 field experiment. In this paper, the performance of the time series ratio algorithm was assessed using in situ observations. Performance of the soil moisture retrieval algorithm was also assessed for dual polarization and quad-polarization observations modes.

Jeonghwan Park↗
NASA NTRSDate not supplied

The NASA Disasters Response Coordination System’s (DRCS) Response to the 2024 Hurricane Season

The National Aeronautics and Space Administration (NASA) Disasters Response Coordination System (DRCS) leverages the best available science and expertise to aid federal, state, local, and non-governmental organization (NGO) partners in addressing identified needs during a disaster response. During the 2024 hurricane season, the DRCS activated for seven hurricanes/tropical storms, providing openly available geospatial data through NASA’s Disasters Mapping Portal. Hurricanes Helene and Milton were major hurricanes that impacted the southeastern United States within weeks of one another. Impacts from Helene and Milton to the region included inland flooding, record coastal storm surge, over a thousand landslides, and regional power and telecommunications outages. In response to Helene and Milton, DRCS provided actionable information and products to stakeholders, including Synthetic Aperture Radar (SAR) analysis for landslide and flood detection, Black Marble nighttime lights products for assessing power outages, and Normalized Difference Vegetation Index (NDVI) analysis for post-event vegetation change detection. NASA deployed an Uninhibited Aerial Vehicle SAR (UAVSAR) instrument to collect data on flood extent, providing crucial information on affected communities. Additionally, astronauts aboard the International Space Station (ISS) collected hand-held photography along the paths of Hurricane Helene and Milton to aid in response efforts. Here, we summarize the DRCS responses to Helene and Milton, in particular highlighting the information and products that were provided by the DRCS and how they were utilized by partners to address immediate response needs.

Earth observations↗