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Research article - Peer-reviewed, 2013

Model-Based Biomass Estimation of a Hemi-Boreal Forest from Multitemporal TanDEM-X Acquisitions

Askne, J.I.H.; Fransson, Johan; Santoro, M.; Soja, M.J.; Ulander, L.M.H.


Above-ground forest biomass is a significant variable in the terrestrial carbon budget, but is still estimated with relatively large uncertainty. Remote sensing methods can improve the characterization of the spatial distribution and estimation accuracy of biomass; in this respect, it is important to examine the potential offered by new sensors. To assess the contribution of the TanDEM-X mission, eighteen interferometric Synthetic Aperture Radar (SAR) image pairs acquired over the hemi-boreal test site of Remningstorp in Sweden were investigated. Three models were used for interpretation of TanDEM-X signatures and above-ground biomass retrieval: Interferometric Water Cloud Model (IWCM), Random Volume over Ground (RVoG) model, and a simple model based on penetration depth (PD). All use an allometric expression to relate above-ground biomass to forest height measured by TanDEM-X. The retrieval was assessed on 201 forest stands with a minimum size of 1 ha, and ranging from 6 to 267 Mg/ha (mean biomass of 105 Mg/ha) equally divided into a model training dataset and a validation test dataset. Biomass retrieved using the IWCM resulted in a Root Mean Square Error (RMSE) between 17% and 33%, depending on acquisition date and image acquisition geometry (angle of incidence, interferometric baseline, and orbit type). The RMSE in the case of the RVoG and the PD models were slightly higher. A multitemporal estimate of the above-ground biomass using all eighteen acquisitions resulted in an RMSE of 16% with R2 = 0.93. These results prove the capability of TanDEM-X interferometric data to estimate forest aboveground biomass in the boreal zone.


TanDEM-X; InSAR; forestry; boreal; biomass estimation; model-based; allometry

Published in

Remote Sensing
2013, volume: 5, number: 11, pages: 5574-5597
Publisher: MDPI AG

Authors' information

Askne, J.I.H.
Fransson, Johan
Swedish University of Agricultural Sciences, Department of Forest Resource Management
Santoro, M.
Soja, M.J.
Ulander, L.M.H.
Swedish Defence Research Agency (FOI)

Associated SLU-program


UKÄ Subject classification

Environmental Sciences
Remote Sensing
Forest Science

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