Method and system for optical and microwave synergistic retrieval of aboveground biomass

A system, comprising:

• a first module, configured to select a plurality of sample plots;
• measure tree height (H) of each sample plot using a laser hypsometer and tree diameter at breast height (DBH) using a tape;
• determine a center of each sample plot using a GPS;
• derive above-ground biomass (AGB) of vegetation of each individual tree in each sample plot by using species-specific allometric equations with inputs of the tree diameter at breast height and the tree height;
• add up the AGB of all individual trees in each sample plot thereby obtaining a total AGB;
• a second module, configured to pre-process LiDAR data, optical remote sensing data and microwave remote sensing data covering a research region, to yield crown height model (CHM) data, surface reflectance data and a backscattering coefficient, respectively;
• a third module, configured to extract LiDAR variables based on the CHM data;
• based on the surface reflectance data, calculate bands between multispectral images using algorithm of band math, and extract a plurality of optical characteristic vegetation indexes;
• and extract a plurality of microwave characteristic variables based on the backscattering coefficient;
• a fourth module, configured to establish a multiple stepwise linear regression model of biomass with the measured AGB in the sample plots as a dependent variable and the extracted LiDAR variables as independent variables, bring the LiDAR data into the linear regression model, and obtain a biomass estimation value of a coverage region of the LiDAR data;
• a fifth module, configured to take the biomass estimation value of the coverage region of the LiDAR data as a training set and a verification sample set, and select samples for modeling and verification by a stratified random sampling method;
• a sixth module, configured to screen out optimal optical characteristic variables and optimal microwave characteristic variables by a variable screening method;
• and
• a seventh module, configured to put the optimal optical characteristic variables, the optimal microwave characteristic variables and a combination of the optimal optical characteristic variables and the optimal microwave characteristic variables to a plurality of prediction models respectively, thereby constructing an optical model, a microwave model, and an optical and microwave synergistic model of AGB retrieval;
• model and verify according to the samples;
• and select an optimal model for biomass retrieval.