Evaluating P-Band TomoSAR for Biomass Retrieval in Boreal Forest
Journal article, 2021

P-band synthetic aperture radar (SAR) is sensitive to above-ground biomass (AGB) but retrieval accuracy has been shown to deteriorate in topographic areas. In boreal forest, the signal penetrates through the canopy to interact with the ground producing variations in backscatter depending on ground topography, forest structure, and soil moisture. Tomographic processing of multiple SAR images Tomographic SAR (TomoSAR) provides information about the vertical backscatter distribution. This article evaluates the use of P-band TomoSAR data to improve AGB retrievals from backscattered intensity by suppressing the backscattered signal from the ground. This approach can be used even when the tomographic resolution is insufficient to resolve the vertical backscatter profile. The analysis is based on P-band data from two campaigns: BioSAR-1 (2007) in Remingstorp, southern Sweden, and BioSAR-2 (2008) in Krycklan (KR), northern Sweden. BioSAR airborne data were also processed to correspond as closely as possible to future BIOMASS TomoSAR acquisitions, with BioSAR-2-based results shown. A power law AGB model using volumetric HV polarized backscatter performs best in KR, with training residual root mean-squared error (RMSE) of 30%-36% (27-33 t/ha) for airborne data and 38%-39% for simulated BIOMASS data. Airborne TomoSAR data suggest that both vertical and horizontal tomographic resolution are of importance and that it is possible to greatly reduce AGB retrieval bias when compared with airborne P-band SAR backscatter intensity-based retrievals. A lack of significant ground slopes in Remningstorp reduces the benefit of using TomoSAR data which performs similar to retrievals based solely on P-band SAR backscatter intensity.




boreal forest


Erik Blomberg

Chalmers, Space, Earth and Environment, Microwave and Optical Remote Sensing

Lars Ulander

Geoscience and Remote Sensing

S. Tebaldini

Polytechnic University of Milan

L. Ferro-Famil

University of Rennes 1

IEEE Transactions on Geoscience and Remote Sensing

0196-2892 (ISSN)

Vol. 59 5 3793-3804 9204469

Subject Categories

Remote Sensing


Signal Processing



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