Accurate estimation of forest aboveground biomass is important for global carbon budgets and ecosystem change studies.
Most algorithms for regional or global aboveground biomass estimation using optical and microwave remote sensing
data are based on empirical regression and non-parametric training methods, which require large amount of ground
measurements for training and are lacking of explicit interaction mechanisms between electromagnetic wave and
vegetation. In this study, we proposed an optical/microwave synergy method based on a coherent polarimetric SAR
model to estimate woody biomass. The study area is sparse deciduous forest dominated by birch with understory of
shrubs and herbs in Daxing’anling, China. HJ-1, Radarsat-2 images, and field LAI were collected during May to August
in 2013, tree biophysical parameters were measured at the field campaign during August to September in 2012. The
effects of understory and wet ground were evaluated by introducing the NDVI derived from HJ-1 image and rain rate.
Field measured LAI was used as an input to the SAR model to define the scattering and attenuation of the green canopy
to the total backscatter. Finally, an logarithmic equation between the backscatter coefficient of direct forest scattering
mechanism and woody biomass was generated (R2=0.582). The retrieval results were validated with the ground biomass
measurements (RMSE=29.01ton/ha). The results indicated the synergy of optical and microwave remote sensing data
based on SAR model has the potential to improve the accuracy of woody biomass estimation.
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