Biomass is an important indicator for evaluating crop growth and yield estimation. Obtaining biomass information scientifically, quickly and accurately is of great significance for monitoring the growth status of winter wheat and yield prediction. Taking winter wheat as the research object, through correlation analysis, the wavelet energy coefficient with good correlation was selected, and the leaf area index was coupled at the same time. Based on the support vector regression algorithm, random forest algorithm, and Gaussian process regression, three algorithms were used to construct a winter wheat biomass estimation model. The verification R2 of the four growth periods were 0.55, 0.40 and 0.39; 0.75, 0.70 and 0.83; 0.84, 0.92 and 0.93; 0.84, 0.89 and 0.85, respectively. It was showed that the estimation accuracy of Gaussian process regression model was the best. Leaf area index coupled with wavelet energy coefficients, using the three algorithms to estimate biomass, the verification R2 of the four growth periods were 0.76, 0.73 and 0.77; 0.76, 0.72 and 0.84; 0.87, 0.94 and 0.94; 0.85, 0.90 and 0.91, respectively, indicating that the Gaussian process regression algorithm had the best estimation accuracy, and to a certain extent, it can overcome the canopy spectrum saturation phenomenon and improve the estimation accuracy of the model. Using wavelet energy coefficient and leaf area index as input variables combined with Gaussian process regression algorithm to establish a winter wheat biomass estimation model, which can improve the accuracy of biomass estimation and provide a scientific reference for the rapid estimation of crop parameters based on remote sensing technology.