Mangrove Phenology From Scale, Data and Species Perspectives

TL;DR

This study uses satellite data to analyze mangrove growth patterns in China, showing how different species and datasets affect phenology observations.

Contribution

The study introduces a multi-scale satellite analysis of mangrove phenology, revealing species-specific growth patterns and dataset performance differences.

Findings

Mangrove phenology shows a unimodal annual pattern with growth from February to December.

Sentinel-2 outperforms Landsat 8 in capturing phenological signals due to higher resolution.

Species like Aegiceras corniculatum and Bruguiera gymnorhiza show continuous growth, while others have seasonal patterns.

Abstract

Land surface phenology derived from satellite observations provides an effective means of characterizing vegetation growth dynamics. Mangroves, although ecologically critical, remain understudied in phenology research due to geographic variation, inconsistent dataset selection, and limited understanding of species‐specific patterns. In this study, we investigated mangrove phenology in the northern subtropical mangrove distribution area in Zhanjiang, China, from the perspectives of spatial scale, dataset resolution, and species differences. Using harmonic analysis to reconstruct the Enhanced Vegetation Index (EVI) time series, we assessed phenological trajectories at both regional and plot levels and compared Landsat 8 with Sentinel‐2 observations. Results show that satellite‐derived mangrove phenology exhibits a clear annual unimodal pattern across scales, with growth beginning in February, peaking in July, and continuing through December. Sentinel‐2 outperformed Landsat 8 in capturing phenological signals, reflecting its superior spatial and temporal resolution. Pronounced interspecific differences were also detected: Aegiceras corniculatum and
Bruguiera gymnorhiza
exhibited continuous growth from January to December, whereas
Avicennia marina
and Rhizophora stylosa showed growth from March to December. Ground‐based litterfall data revealed strong seasonality, with peaks in July–August. Except for A. corniculatum, litterfall exhibited a significant positive correlation with satellite‐derived EVI growth patterns (p < 0.05), supporting the feasibility of species‐level phenology detection from space. Given the increasing pressures on mangrove ecosystems from climate change and human activity, these findings highlight the importance and practicality of long‐term phenology monitoring using multi‐resolution satellite observations.

This study investigates mangrove phenology in the northern subtropical zone of Zhanjiang, China, using satellite‐derived Enhanced Vegetation Index (EVI) time series. Our results reveal clear annual phenological patterns with significant variation across species and datasets, highlighting the superior sensitivity of Sentinel‐2 over Landsat 8. These findings underscore the feasibility of satellite‐based long‐term monitoring of mangrove phenology at multiple spatial scales.

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