remote sensing Article Incorporating the Plant Phenological Trajectory into Mangrove Species Mapping with Dense Time Series Sentinel-2 Imagery and the Google Earth Engine Platform Huiying Li 1,2, Mingming Jia 1,3,4,* , Rong Zhang 1, Yongxing Ren 1,5 and Xin Wen 1,5 1 Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China;
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[email protected] (X.W.) 2 School of Management Engineering, Qingdao University of Technology, Qingdao 266520, China 3 State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China 4 National Earth System Science Data Center, Beijing 100101, China 5 College of Earth Sciences, Jilin University, Changchun 130061, China * Correspondence:
[email protected] Received: 12 September 2019; Accepted: 22 October 2019; Published: 24 October 2019 Abstract: 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.