Investigating mangrove canopy phenology in coastal areas of China using time series Sentinel-1/2 images

Abstract

Mangrove forests with high vegetation productivity play crucial roles in the coastal blue carbon ecosystem. Accurate monitoring of mangrove canopy phenology is essential to improve the survival rate of restoration plantings and maintain the health and sustainability of mangrove ecosystems. Remote sensing technology has been commonly used for monitoring vegetation phenology, while there remain knowledge gaps in exploring the remotely-sensed phenology of mangrove forests in coastal China. In this study, we investigated the phenological characteristics of two typical mangrove sites in coastal China using remote sensing indices based on Sentinel-1 and Sentinel-2 time series during 2016–2020, compared the performances of mangrove phenology detection across different remote sensing indices and spatial scales, and explored the influences of environmental factors based on meteorological data. The results demonstrated that enhanced vegetation index (EVI), red-edge band index (NDRE2), phenology index (NDPI), and radar vegetation index (RVI) were efficient in characterizing mangrove phenological trajectories. Sentinel-2 data can precisely describe the phenological characteristics of mangroves and has the highest correlation with ground-observed phenology data (r = −0.581), when compared to Landsat-8 and MODIS data. The meteorological factors of precipitation, humidity, and wind speed mainly led to the differences in mangrove phenology across the two study sites. This finding can improve our understanding of the phenological characteristics of mangrove forests in coastal China, which facilitates local governments to develop appropriate mangrove restoration and management policies.

Keywords

• Mangrove
• Phenology
• Remote sensing indices
• Sentinel-1/2
• Time series
• China