7 Apr 2026

Vehicle and Unpiloted Aerial System Interferometric Synthetic Aperture Radar Data Collection and Processing

Abstract: Interferometric synthetic aperture radar (InSAR) systems have a wide breadth of cold regions science and engineering applications such as determining snow water storage, permafrost thaw induced subsidence and frost heave of the active layer, and ground slope and infrastructure stability in permafrost dominated regions. Here, we present project planning, data collection, and processing workflows from two L-band InSAR systems, L-band SAR (GS-L) and UAS-mounted (GLSAR). The GS-L platform is integrated on a mobile, ground-based platform while the GLSAR is integrated on an uncrewed aerial system (UAS). We describe the postprocessing steps to produce radar back-scattered power and interferograms for the analysis of subsurface and near-surface phenomena. These steps are common to all the sensors discussed in this report and include kinematic postprocessing of the sensor positions, focusing on the raw radar returns in range and azimuth to form the radar image, and calculating the interferometric phase between acquisitions. With examples from each platform, we demonstrate the utility of these InSAR sensors and discuss acquisition scenarios in which either ground-based or UAS-borne systems may deliver higher-quality information from one another.

28 Sep 2021

Incorporating Sentinel-1 SAR imagery with the MODIS MCD64A1 burned area product to improve burn date estimates and reduce burn date uncertainty in wildland fire mapping

Abstract: Wildland fires result in a unique signal detectable by multispectral remote sensing and synthetic aperture radar (SAR). However, in many regions, such as Southeast Asia, persistent cloud cover and aerosols temporarily obstruct multispectral satellite observations of burned area, including the MODIS MCD64A1 Burned Area Product (BAP). Multiple days between cloud free pre- and postburn MODIS observations result in burn date uncertainty. We incorporate cloud-penetrating, C-band SAR-with the MODIS MCD64A BAP in Southeast Asia, to exploit the strengths of each dataset to better estimate the burn date and reduce the potential burn date uncertainty range. We incorporate built-in quality control using MCD64A1 to reduce erroneous pixel updating. We test the method over part of Laos and Thailand during April 2016 and found average uncertainty reduction of 4.5 d, improving 15% of MCD64A1 pixels. A new BAP could improve monitoring temporal trends of wildland fires, air quality studies and monitoring post-fire vegetation dynamics.