Information on mangrove species composition and distribution is key to studying functions of mangrove ecosystems and securing sustainable mangrove conservation. Even though remote sensing technology is developing rapidly currently, mapping mangrove forests at the species level based on freely accessible images is still a great challenge. This study built a Sentinel-2 normalized difference vegetation index (NDVI) time series (from 2017-01-01 to 2018-12-31) to represent phenological trajectories of mangrove species and then demonstrated the feasibility of phenology-based mangrove species classification using the random forest algorithm in the Google Earth Engine platform. It was found that (i) in Zhangjiang estuary, the phenological trajectories (NDVI time series) of different mangrove species have great differences; (ii) the overall accuracy and Kappa confidence of the classification map is 84% and 0.84, respectively; and (iii) Months in late winter and early spring play critical roles in mangrove species mapping. This is the first study to use phonological signatures in discriminating mangrove species. The methodology presented can be used as a practical guideline for the mapping of mangrove or other vegetation species in other regions. However, future work should pay attention to various phenological trajectories of mangrove species in different locations. mangrove forests is essential for the accurate formulation of future studies and management of mangrove ecosystems [8][9][10].Remote sensing has served as a sustainable tool in the mapping and monitoring of mangrove forests for decades, primarily because of the logistical and practical difficulties involved in field surveys of the muddy environments [11,12]. Before the launch of the high resolution satellite sensors, it was impossible to accurately discriminate mangrove species with traditional medium resolution satellite data [13]. Recently, with the development of commercial sensors (high spatial resolution, hyperspectral, and active remote sensor), many studies have employed single or combined airborne or satellite imagery to map mangrove species [2,3,8]. However, so far, the mapping of mangrove species with freely accessible imagery still remains a challenge, as mangrove species often exhibit similar spectral signatures and spatial textures [10].The aforementioned issues indicate the need to explore more substantial features to improve the detection of mangrove species from remote sensing data. A phenological trajectory of plants can be acquired from the time series of remote sensing images through delineating the temporal variation in spectrum during the growing period [14]. To date, remotely sensed plant phenology has been widely used to conduct vegetation discrimination, but most of the studied vegetation has been crops or inland forests [15,16]. Although mangroves are evergreen plants, different mangrove species have different phenophase peaks [17]. However, to date, no studies have dealt with mangrove phenological trajectories in remote sensing-based ...