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The response of JERS-1 and ERS-1 synthetic aperture radar (SAR) to the forest stem volume (biomass) was investigated by employing a digital stem volume map and weather information. The stem volume map was produced from the National Forest Inventory sample plot data together with a LANDSAT thematic mapper (TM) image. A new indirect inversion method was developed and tested to estimate the forest blockwise stem volume from JERS-1 and/or ERS-1 SAR images. The method is based on using a semiempirical backscatter model for inversion. The model presumes that backscatter from a forest canopy is determined by the stem volume, soil moisture, and vegetation moisture. The area of interest is divided into a training and test area. In this study, the training area was 10% of the test site, while the remaining 90% was used for testing the method. The inversion algorithm is carried out in the following three steps. 1) For the training area, the soil and vegetation moisture parameters are estimated from the backscattering coefficients and stem volume (must be known for training areas) with the semiempirical backscatter model. 2) For the area of interest, the stem volume is estimated from the moisture parameters and backscattering coefficients with the semiempirical backscattering model. 3) If several SAR images are used, the stem volume estimates are combined with a multiple linear regression. The regression equation is defined using the stem volume estimates for the training area. The results for the stem volume estimation using L-band and/or C-band SAR data showed promising accuracies: the relative retrieval rms error varied from 30 to 5% as the size of the forest area varied from 5 to 30000 ha.
Kurvonen et al. (Fri,) studied this question.