We present a low-cost method to create high-precision, spatially explicit reference maps of large-scale forest aboveground biomass (AGB) to provide a scientific basis for quantitative assessment of forest management decisions involving, for example, forest-destroying carbon emissions. However, due to the limited availability of the sample lands and other space restrictions, the prediction accuracy of current AGB reference maps is associated with deviations, whose source may be related to the poorly understood effects of the spatial heterogeneity of multiple environmental factors (such as topography, soil, and forest structures) on the spatial distribution of AGB. To address these problems, we propose a method that combines machine learning and spatial statistics to determine the influence of biased samples by finding multiple environmental factors of regional covariates through the model algorithm. Subsequently, we consider an empirical case to quantitatively assess the prediction accuracy of the AGB reference map of an Eucalyptus plantation in Nanjing, China. In this case study, the biased AGB analysis data of 90 parse trees are selected and combined with regional forest resource inventory data to build three main machine-learning methods (support vector machine, random forest, and artificial neural network) and a spatial statistical analysis integration technology (a PBSHDE model). The results show that the evaluation indexes of the prediction accuracy (i.e., RMSE, MAE, and MRE) of the method that combines machine learning (random forest) and the PBSHDE model are significantly better than those for machine learning or the PBSHDE model individually. We conclude that the combination of spatial statistical analysis and machine learning can improve the accuracy AGB mapping in regions with biased samples and thus provide accurate predictions from AGB reference maps. Our research methods and conclusions may provide references for AGB remote-sensing mapping and simulation of ecological processes of different types of forests in various countries and tropical regions of the world.