This paper explores further the precision estimate of biomass by making improvement on the way biomass models are constructed and parameters identified. To that end, a unified model is set up to estimate the biomass by constructing a group of product-type base of p-dimensional continuous function space through Chebyshev polynomial combination. This model has the following characteristics: (1) It can overcome the experientialism, instability, limited applicability and poor adaptability to biomass-affecting factors ridden in conventional models in biomass estimate. (2）It has wider and more stable applicability given that this model is applicable to any of the biomass-influencing factors. (3）It can determine on the factors that affect the biomass and the size of the order according to actual needs and required accuracy of estimate in different cases. (4）This model works in a way similar to number interpolation between the interval \[-1,1\], the higher order the variables go, the more points should be inserted, the more realistic the estimate becomes. The above interpolation process in estimation abides by the same principle by which tree growth are measured based on the tree trunk analysis. Moreover, it constitutes an equally important task to find best method in identifying parameters for each estimate model. So far, the most commonly used method in parameter identification in term of biomass estimate is classic least squares algorithm. However, because of its inherent defects, classic least squares algorithm is bounded in accuracy and application. Though the partial least-squares algorithm of modern multivariate statistical analysis can somewhat overcome the shortcomings of traditional least-square, it is still not perfect in abstracting constituents. In view of that problem, this thesis has made improvement on modern multivariate statistical analysis to increase the accuracy of calculation. As a result, not only are the shortcomings of the partial least-square overcome, but the accuracy in estimate is raised substantially. By comparing three methods in calculating biomass in two cases, it proved that the unified model in biomass estimate, together with improved partial least square algorithm can render the most accurate result so much so that the biomass-estimate error formed a straight line closely along the zero axis.