A paper discusses a calibration method suited to correct variations of UAVSAR (unmanned aerial vehicle synthetic aperture radar) backscatter with topography. To use radar backscatter to estimate forest biomass on terrain with slopes, it is necessary to remove the effect of topography. The remaining signal should be related to biomass. The hybrid approach uses the radar line of sight to project an oversampled version of the Digital Elevation Model into radar coordinates for summation. Terrain topography has a major impact on the radar backscatter. Slopes facing the radar appear very bright while slopes facing away appear darker.
The calibration method presented here uses a combination of two types of corrections: homomorphic and heteromorphic. In the first case, the terrain is assumed homogeneous within the radar pixel to be corrected, while the second method assumes the terrain is rough.
The calibration process only uses the files delivered in a standard UAVSAR scene download. The method compensates for average residual aircraft motion while still accurately modeling the illuminated area over moderately steep terrain.
This work was done by Marc Simard and Scott Hensley of Caltech, and Bryan V. Riel of the University of Texas for NASA’s Jet Propulsion Laboratory.
The software used in this innovation is available for commercial licensing. Please contact Dan Broderick at
This Brief includes a Technical Support Package (TSP).
Implementation of a Terrain Radiometric Correction for UAVSAR
(reference NPO-47891) is currently available for download from the TSP library.
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Overview
The document is a Technical Support Package from NASA's Jet Propulsion Laboratory (JPL) detailing the implementation of a Terrain Radiometric Correction for UAVSAR (Uninhabited Aerial Vehicle Synthetic Aperture Radar). It emphasizes the necessity of calibrating UAVSAR backscatter data, which can be distorted by geometric and radiometric factors due to the underlying topography and radar viewing geometry.
The calibration process utilizes specific files from a standard UAVSAR scene download, including an annotation file, a reference Digital Elevation Model (DEM), and multi-looked intensity images for each polarization. Key parameters extracted from the annotation file include image dimensions, peg parameters (latitude, longitude, and heading of the nadir point), and various flight parameters such as average aircraft altitude, pitch, yaw, and electronic steering angle. The calibration algorithm processes each pixel in the map, estimating the illuminated area and mapping it to the slant range plane. It also computes the local incidence angle to correct backscatter for incidence angle effects.
The document presents sample results from a UAVSAR scene acquired over the White Mountain National Forest in New Hampshire, showcasing the calibration's effectiveness in estimating illuminated areas. The analysis compared the performance of the calibration method against traditional techniques, revealing that the conventional sine of the incidence angle calibration performed poorly in moderately steep areas, while the new projection angle method provided more accurate results.
The conclusion highlights the calibration method's suitability for correcting UAVSAR backscatter, particularly in compensating for average residual aircraft motion and accurately modeling illuminated areas over moderately steep terrain. It notes that residual aircraft motion can significantly alter illuminated area estimates, making it crucial for absolute radiometric calibration.
Overall, the document underscores the importance of accurate calibration in enhancing the reliability of UAVSAR data for various scientific applications, including environmental monitoring and resource management. The research was conducted under the auspices of NASA, with acknowledgment of government sponsorship, and aims to make the results of aerospace-related developments accessible for broader technological, scientific, and commercial applications.
