Predictions of Biomass Change in a Hemi-Boreal Forest Based on Multi-Polarization L- and P-Band SAR Backscatter
Journal article, 2020

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.

Author

Ivan Huuva

Swedish University of Agricultural Sciences (SLU)

Henrik J. Persson

Swedish University of Agricultural Sciences (SLU)

Maciej J. Soja

MJ Soja Consulting

University of Tasmania

Joergen Wallerman

Geoscience and Remote Sensing

Lars Ulander

Geoscience and Remote Sensing

Johan E. S. Fransson

Swedish University of Agricultural Sciences (SLU)

Canadian journal of remote sensing

0703-8992 (ISSN) 17127971 (eISSN)

Vol. 46 6 661-680

Subject Categories

Remote Sensing

Geophysics

Physical Geography

DOI

10.1080/07038992.2020.1838891

More information

Latest update

4/8/2022 9