Synthetic aperture radar (SAR) features have 2 been demonstrated that they have the potentiality to improve 3 forest above ground biomass (AGB) estimation accuracy, 4 especially including polarimetric information. Genetic 5 algorithms (GAs) have been successfully implemented in optimal 6 feature identification, while support vector regression (SVR) has 7 great robustness in parameter estimation. The use of combined 8 GAs and SVR can improve the accuracy of forest AGB 9 estimation through simultaneously identifying the optimal SAR 10 features and selecting the SVR model parameters. In this paper, 11 14 SAR polarimetric features were extracted from C-band and 12 L-band full-polarization SAR images and worked as input SAR 13 features, respectively. C-band data was acquired on GaoFen-3 14 mission, we also call it GF-3 image. L-band data was ALOS-2 15 PALSAR-2 data. Both feature subsets from GF-3 and ALOS-2 16 PALSAR-2 and SVR hyper parameters used in the forest AGB 17 estimation were optimized by a GA processing, where 8 different 18 settings of 3 kinds of parameters, as 512 kind of different 19 combinations were applied for SVR hyper parameters searching 20 field. The results of GA-SVR performance using the two datasets 21 were presented and compared with two traditional methods: the 22 algorithm of GA feature selection companied with default SVR 23 parameters (GA +Default SVR), and the algorithm of GA feature 24 selection companied with grid searching for SVR parameter 25 selection (GA+Grid SVR). The results showed that the proposed 26 GA-SVR algorithm improved the forest AGB estimation 27 accuracy with cross-validation coefficient (CVC) of 80.21% for 28 GF-3 and 71.41% for ALOS-2 PALSAR-2 data.

Forest biomass plays an essential role in forest carbon reservoir studies, biodiversity protection, forest management, and climate change mitigation actions. Synthetic Aperture Radar (SAR), especially the polarimetric SAR with the capability of identifying different aspects of forest structure, shows great potential in the accurate estimation of total and component forest above-ground biomass (AGB), including stem, bark, branch, and leaf biomass. This study aims to fully explore the potential of polarimetric parameters at the C- and L-bands to achieve high estimation accuracy and improve the estimation of AGB saturation levels. In this study, the backscattering coefficients at different polarimetric channels and polarimetric parameters extracted from Freeman2, Yamaguchi3, H-A-Alpha, and Target Scattering Vector Model (TSVM) decomposition methods were optimized by a random forest algorithm, first, and then inputted into linear regression models to estimate the total forest AGB and biomass components of two test sites in China. The results showed that polarimetric observations had great potential in total and component AGB estimation in the two test sites; the best performances were for leaves at test site I, with R2 = 0.637 and RMSE = 1.27 t/hm2. The estimation of biomass components at both test sites showed obvious saturation phenomenon estimation according to their scatter plots. The results obtained at both test sites demonstrated the potential of polarimetric parameters in total and component biomass estimation.

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