【Objective】 Forest aboveground biomass (AGB) is an important indicator for evaluating forest ecosystem health status and carbon sink potential. Accurate and quick mapping regional forest AGB has become intensively researched in forest ecosystem status assessment and global climate change studies in recent years. The major objective of this study was to develop a framework for improving the mapping accuracy of AGB in a subtropical forested area with complex terrain. 【Method】 Spectral features, textural indices, backscattering coefficients, and topographical variables were derived from Landsat 5 TM, ALOS-1 PALSAR-1 data and STRM DEM. Next, in tandem with national forest inventory plot measurements, a random forest/Co-Kriging framework that combines the advantages of random forest (RF) and a geostatistical approach was proposed to map AGB in northern Guangdong Province. 【Result】 The experimental results showed that the ordinary Kriging (OK) and Co-Kriging (CK) were able to predict the distribution of the RF-predicted AGB residuals. The predicted structured components of the residuals adding onto the RF predictions could improve the mapping accuracy of AGB to some extent. After the validation of the independent 20% dataset, the determination coefficient between the predictions and the observations increased from 0.46 (RF) to 0.51 (RFOK) and to 0.57 (RFCK). The root mean square error decreased from 32.48 to 31.58 and to 29.80 t/hm² accordingly. The mean absolute error decreased from 27.28 to 26.63 and to 25.12 t/hm². Overall, co-Kriging, which considers elevation as a co-variable, was better than ordinary Kriging in predicting AGB residuals. 【Conclusion】 The RFCK framework provides an accurate and reliable method to map subtropical AGB with complex topography. The resulting AGB maps contribute to targeted forest resource management and promote forest carbon sequestration and sustainable forest management under global warming scenarios.