University Of Tasmania
152730-Predictions of biomass change in a Hemi-Boreal Forest based on multi-polarization L- and P-Band SAR backscatter.pdf (3.94 MB)
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Predictions of biomass change in a Hemi-Boreal Forest based on multi-polarization L- and P-Band SAR backscatter

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journal contribution
posted on 2023-05-21, 12:42 authored by Huuva, I, Persson, HJ, Maciej Soja, Wallerman, J, Ulander, LMH, Fransson, JES
Above-ground biomass change accumulated during four growth seasons in a hemi-boreal forest was predicted using airborne L- and P-band synthetic aperture radar (SAR) backscatter. The radar data were collected in the BioSAR 2007 and BioSAR 2010 campaigns over the Remningstorp test site in southern Sweden. Regression models for biomass change were developed from biomass maps created using airborne LiDAR data and field measurements. To facilitate training and prediction on image pairs acquired at different dates, a backscatter offset correction method for L-band data was developed and evaluated. The correction, based on the HV/VV backscatter ratio, facilitated predictions across image pairs almost identical to those obtained using data from the same image pair for both training and prediction. For P-band, previous positive results using an offset correction based on the HH/VV ratio were validated. The best L-band model achieved a root mean square error (RMSE) of 21 t/ha, and the best P-band model achieved an RMSE of 19 t/ha. Those accuracies are similar to that of the LiDAR-based biomass change of 18 t/ha. The limitation of using LiDAR-based data for training was considered. The findings demonstrate potential for improved biomass change predictions from L-band backscatter despite varying environmental conditions and calibration uncertainties.


Publication title

Canadian Journal of Remote Sensing










School of Geography, Planning and Spatial Sciences


Canadian Aeronautics Space Inst

Place of publication

1685 Russell Rd, Unit 1-R, Ottawa, Canada, On, K1G 0N1

Rights statement

©2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License. Attribution 4.0 International (CC BY 4.0)(,

Repository Status

  • Open

Socio-economic Objectives

Expanding knowledge in engineering

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