Retrieval of timber volume and snow water equivalent over a Finnish boreal forest from airborne polarimetric Synthetic Aperture Radar (original) (raw)

Abstract

Airborne polarimetric Synthetic Aperture Radar (SAR) is used for estimating stem volume of a Finnish boreal forest by comparing different empirical models. Its capability for retrieval of snow water equivalent is then explored. Fully polarimetric Land C-band data were acquired over a Finnish test site in March and May 1995. The information content was explored qualitatively by inspecting polarimetric colour composites, and by applying decomposition algorithms to the polarimetric covariance matrices at individual frequencies. Three families of quantitative models were fitted to estimate stem volume: 1) F1P1 models, using a single frequency and a single polarisation; 2) F2P1 models, using the difference between HV polarisation at C-and L-band related to stem volume; 3) F1P4 models, based on a single frequency and the full polarimetric information, selected by stepwise multiple regression with stem volume; Stem volume estimates from SAR are compared with digital stem volume data by the Finnish Forest Research Institute. Prior information about the stem volume distribution addresses the saturation problem of the microwave response. The L-band F1P4 models in March and May 1995 have the smallest root mean square (rms) errors, around 22 m 3 /ha. Three multiple regression models to retrieve snow water equivalent from backscatter are presented: 1) EU model, an explorative, uncorrected multiple regression model; 2) EC model, an explorative, stem volume corrected multiple regression model; 3) CC model, a statistically conservative, stem volume corrected multiple regression model. The accuracy of snow water equivalent estimates was improved significantly by a simple linear correction for stem volume. The statistically conservative CC model showed that only L-band in HH polarisation explained a significant (P<0.05) proportion of snow water equivalent (r 2 =0.51). The explorative EC model resulted in r 2 =0.68 (P>0.05). Conclusions are: 1) Decomposition algorithms of the polarimetric covariance matrix result in information on scattering mechanisms in the vegetation canopy and on the ground, so being potentially of great value for land cover mapping; 2) Satellite polarimetric SARs, for example those to be flown on Envisat and ALOS, will be able to estimate stem volume on continental and global scales; 3) L-band SAR has a potential for snow cover mapping and runoff prediction.

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