Convergence in key month of phenology-based mangrove species classification using sentinel-2 imagery data: insights from structural and physiological indices - Yangcan Bao; Xiaofeng Lin; Mingming Jia; Zhongyong Xiao; Cuiping Wang; Jiangfu Liao; Yinghui Zhang (International Journal of Digital Earth)
Accurate classification of mangrove species is essential for sustainable conservation and precise carbon sink estimation. Integrating phenological information holds promise for enhancing species distinguishability and improving classification accuracy. However, further research is needed to clarify the roles of vegetation indices (VIs) and to identify the key phenological months for mangrove classification. In this study, a random forest algorithm (RF) and Sentinel-2 time series data were applied using the Google Earth Engine platform. Results showed that structural and physiological VIs significantly improved classification accuracy by 7–43% compared to the initial accuracy. Quantification of the variable importance using RF revealed that the structural and physiological VIs exhibited dynamic key month variations in spring and summer, but they maintained temporal stability during autumn and winter. Moreover, classification based on key phenological months reduced data redundancy and achieved higher accuracy than using all monthly VIs. The highest overall accuracy of the structural VIs achieved a stable accuracy (88 ± 4%), while the physiological VIs exhibited two-stage differentiation, with some achieving accuracies of greater than 91% and others with accuracies of less than 70%. The results of this study highlight the key phenological patterns and provide guidance for VI selection in future research.