Controlled Evaluation of Sentinel-2 Annual Compositing Strategies for Deep Learning-Based Mangrove Mapping in China

Abstract

Accurate national-scale mangrove mapping remains challenging because mangroves occur in narrow intertidal belts affected by tidal variation, residual clouds, water background effects, and spectral confusion with adjacent vegetation. This study evaluated five Sentinel-2 annual compositing rules for 10 m mangrove mapping in China: MAX-KNDVI, MAX-EVI, a negative-NDWI-based composite, MAX-MFI, and Median compositing. A single shared ResNet-34 U-Net model was trained using pooled patches from the five rule-specific composites and was then applied separately to each composite. Labelled sample locations, validation data, probability threshold, and post-processing settings were kept consistent to focus the comparison on the compositing rule. Using 695 labelled patch locations and 10,354 spatially independent validation points from 29 coastal regions, the Median-based output achieved the most balanced performance among the tested rules, with an overall accuracy of 90.1% and a Kappa coefficient of 0.801. Compared with GMW v3.0, HGMF_2020, and LREIS_v2_2020, the Median-based output showed higher agreement with the independent validation samples and fewer omissions in selected fragmented coastal zones. Applying the Median configuration to annual Sentinel-2 composites from 2019 to 2023 indicated an increase in mapped mangrove area in China from 22,731.76 ha to 24,631.62 ha. These results show that annual compositing-rule selection is an important source of performance variation in Sentinel-2 deep learning-based mangrove mapping and should be considered explicitly in national-scale coastal wetland monitoring.