Since the launch of the Google Earth Engine (GEE) cloud platform in 2010, it has been widely used, leading to a wealth of valuable information. However, the potential of GEE for forest resource management has not been fully exploited. To extract dominant woody plant species, GEE combined Sentinel-1 (S1) and Sentinel-2 (S2) data with the addition of the National Forest Resources Inventory (NFRI) and topographic data, resulting in a 10 m resolution multimodal geospatial dataset for subtropical forests in southeast China. Spectral and texture features, red-edge bands, and vegetation indices of S1 and S2 data were computed. A hierarchical model obtained information on forest distribution and area and the dominant woody plant species. The results suggest that combining data sources from the S1 winter and S2 yearly ranges enhances accuracy in forest distribution and area extraction compared to using either data source independently. Similarly, for dominant woody species recognition, using S1 winter and S2 data across all four seasons was accurate. Including terrain factors and removing spatial correlation from NFRI sample points further improved the recognition accuracy. The optimal forest extraction achieved an overall accuracy (OA) of 97.4% and a map-level image classification efficacy (MICE) of 96.7%. OA and MICE were 83.6% and 80.7% for dominant species extraction, respectively. The high accuracy and efficacy values indicate that the hierarchical recognition model based on multimodal remote sensing data performed extremely well for extracting information about dominant woody plant species. Visualizing the results using the GEE application allows for an intuitive display of forest and species distribution, offering significant convenience for forest resource monitoring.