Interferometric Ground Cancellation for Above Ground Biomass Estimation

A new processing technique, i.e., ground cancellation, which removes the ground signal from a pair of interferometric synthetic aperture radar (SAR) images, is used to emphasize the response from above-ground targets. This technique is of particular interest when studying forest canopies using low-f...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2020-09, Vol.58 (9), p.6410-6419
Main Authors: Mariotti d'Alessandro, Mauro, Tebaldini, Stefano, Quegan, Shaun, Soja, Maciej J., Ulander, Lars M. H., Scipal, Klaus
Format: Article
Language:English
Subjects:
Above-ground biomass (AGB)
Azimuth
Backscattering
Biomass
Carbon
Computer simulation
Errors
forest
Forestry
Interferometric synthetic aperture radar
Interferometry
Radar imaging
Regression analysis
SAR (radar)
Signal processing
Stability
Stability analysis
Synthetic aperture radar
synthetic aperture radar (SAR)
Terrain models
tomography
tropical
Tropical climate
Tropical forests
Vegetation mapping
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Summary:A new processing technique, i.e., ground cancellation, which removes the ground signal from a pair of interferometric synthetic aperture radar (SAR) images, is used to emphasize the response from above-ground targets. This technique is of particular interest when studying forest canopies using low-frequency signals able to reach the underlying ground, in which case the portion of the signal coming from the ground interferes with the recovery of information about the vegetation. We demonstrate that the power in ground-canceled P-band HV SAR data gives significantly higher correlations with above-ground biomass (AGB) than the interferometric images considered separately. In addition, a significant increase in the sensitivity of backscatter to AGB is observed. Ground-canceled power may then be modeled or regressed to estimate AGB; these possibilities are not discussed here as they will be the topic of forthcoming publications. The effectiveness of this technique is proven through simulations and analysis of real data gathered on tropical forests. The stability of the technique is analyzed under the digital terrain model and baseline control errors, and compensation strategies for these errors are presented.
ISSN:0196-2892
1558-0644
1558-0644
DOI:10.1109/TGRS.2020.2976854