Rubber (Hevea brasiliensis) tree cultivation is being continuously expanded northward by replacing evergreen forests and swidden-related regenerated vegetation across the uplands of mainland Southeast Asia (MSEA), e.g., Laos, a landlocked mountainous country. The non-native tree establishment in the northern tropical edge, or the non-traditional suitable planting area, provides stable supplies of natural latex, yet also leads to severe ecological degradation and environmental effects in water conservation, soil quality, rainforest fragmentation and biodiversity. Rubber plantations in the northern part of MSEA are normally characterized by periodic deciduous during the dry season, along with a lengthy defoliation-foliation duration, because of seasonal variations in temperature and precipitation. It thus lays a phenological and physiological base for dynamics monitoring with common multispectral (e.g., near-infrared and short-wave infrared bands) satellites, particularly Landsat. However, whether Sentinel-2 red-edge based algorithms are suitable for discriminating rubber plantations is not yet exclusively reported. Here, we developed a red-edge spectral indices (RESI) method through the normalization of three red-edge bands and applied it to identify and map rubber plantations in Luang Namtha Province of northern Laos, where a rubber boom begun in the mid-2000s. The RESI algorithm highlights the sensitivity of red-edge bands to the changes in moisture content and canopy density of rubber plantations. The area of mature rubber plantations was estimated to be 771.2 km² in this province bordering southwest China in 2018, which was nearly twice as much as that of 2011, with the overall accuracy and kappa coefficient up to 92.50% and 0.91, respectively. Our phenology-based RESI approach not only indicates that Sentinel-2 imagery holds significant potential for monitoring rubber plantations, but also improves the remotely-sensed methods of rubber boom mapping via introducing the red-edge channel.