Accurate and up-to-date mapping of Eucalyptus plantations is essential for assessing their effects on soil, hydrology, and biodiversity. However, no proper approaches are available for extracting such data on Eucalyptus. This study proposes a uniform procedure for mapping Eucalyptus plantations based on fused medium-high spatial resolution satellite datasets. To develop this procedure, a total of 810 variables were extracted from the fused images of an experimental site, the Gaofeng Forest Farm in Guangxi, China. Six key variables were determined by using the Z-statistic and random forest methods. A total of 123 scenarios were then designed based on the key variables and three decision tree classifiers. Potential robust variables were determined through a comparative analysis of accuracy assessments from all scenarios. All the designed procedures were tested at a second site, Xingye County, Guangxi, using the same data processing methodology. The uniform procedure was finally determined through a comparative analysis of accuracy assessments from all the scenarios at both sites. The proposed procedure was then applied to a third study site, Yunxiao County, Fujian, China, to examine its transferability. Results from the Gaofeng site showed that the first short-wave infrared band (SWIR1) and the normalized difference index with near-infrared and green bands (NDNG) were two independent spectral-based variables for Eucalyptus delineation. Scenarios comprising mean texture with a window size of 15 (MEAN₁₅), homogeneity with a window size of 5 (HOM₅), or ratio of number of pixels where homogeneity based on window size 5 is greater than threshold T to the total number of pixels in the target segment polygon (HOM₅R_T) performed significantly greater than scenarios without these variables (α = 0.05). Scenarios with HOM₅R_T performed significantly greater than those with HOM₅ (α = 0.05), and scenarios with random forest (RF) classifier performed greater than those with the other two classifiers. The results from the Xingye site were highly consistent with those from the Gaofeng site. Thus, the final uniform procedure containing these four optimal variables (SWIR1, NDNG, MEAN₁₅, and HOM₅R_T) and RF classifier was determined. An additional independent test at the Yunxiao site showed an overall accuracy of 96.01% and a high spatial agreement between the delineated Eucalyptus and observed distributions, indicating that the proposed procedure has good transferability. This research implies that the proposed procedure is robust and can be used in other subtropical regions to map Eucalyptus distributions.