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As involving too many remotely sensed characteristics in modeling may cause details redundancy, correlation analysis amongst aspect variables and forest biomass is needed, and feature variables with solid correlations with biomass are screened. On this paper, the Pearson correlation coefficient approach was utilized to perform correlation analysis. Amongst the element variables attained depending on C-band GF-three, Sentinel-1 and L-band ALOS-2 facts, the twin-polarization decomposition parameters had the best sensitivity to forest biomass, followed by the texture characteristic of backscatter and the backscatter coefficient. The backscatter coefficient and texture element with the L-band ALOS-two were being more delicate to biomass than These of the C-band because of the penetration capacity of L-band wavelengths.

In the outcome of estimating forest aboveground biomass making use of nine different types of facts individually, the CNN-LSTM model achieved the top brings about 8 forms of facts, plus the accuracies have been all much better than Individuals with the RF product as well as CNN model. Thus, the possible of CNN-LSTM in estimating aboveground biomass is very good and justifies abide by-up analysis.

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Editors find a small range of content just lately revealed within the journal they feel is going to be particularly

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