Multi-Source Remote Sensing for Mangrove Mapping in South China: Sensor-Configuration Responses and Cross-Regional Model Applicability

Mangrove mapping increasingly integrates multisource remote sensing, yet the geographically transferable value of additional sensors remains uncertain. This study evaluated Sentinel-2 multispectral imagery, Sentinel-1 and GF-3 SAR, GF-5 AHSI hyperspectral observations, terrain variables, and sensor-independent coastal-context information across five coastal regions of southern China. Five nested feature configurations (D1–D5) were tested with Random Forest, XGBoost, DeepLabV3, and SegFormer under true leave-one-study-area-out validation on 349,898 common 10 m coordinates; deep models were replicated across three random seeds. Model-averaged F1-scores were 0.9025, 0.9031, 0.9123, 0.9134, and 0.9220 for D1–D5, respectively, but differences were not statistically supported across held-out regions (Friedman χ2 = 0.48, p = 0.9754). Sentinel-1, GF-3, and their interaction showed small, geographically inconsistent contrasts. GF-5 effects were model- and preprocessing-dependent, and cross-scene normalization altered performance in contrasting directions. Gain and grouped-permutation analyses indicated model-specific predictor use and sensitivity rather than independent sensor contribution. Overall, multisource benefits depended on sensor configuration, model representation, geographic domain, and preprocessing conditions, supporting context-specific sensor selection and geographically independent validation.