From plot to region: canopy height-mediated mangrove aboveground biomass mapping by synergizing UAV-LiDAR and multi-source satellite data

Abstract

Mangroves are critical coastal blue carbon ecosystems, yet regional-scale mapping of aboveground biomass (AGB) remains challenging because of the limited availability of field measurements. To address this limitation, we developed a hierarchical structural-spectral upscaling framework in the Guangxi Shankou National Mangrove Nature Reserve, China. UAV-LiDAR-derived canopy height was first used as a structural reference to train three machine-learning models for regional canopy height retrieval using Sentinel-1, Sentinel-2, and SDGSAT-1 observations. XGBoost achieved the best performance on the held-out test set (R 2 = 0.80, RMSE = 0.81 m) and was subsequently used to generate a continuous 10-m canopy height product. Satellite-retrieved canopy height was then integrated with multispectral vegetation indices to develop a structural-spectral log-linear AGB model based on 32 field plots. Leave-one-out cross-validation showed that canopy height provided the dominant predictive contribution (R 2 = 0.63, RMSE = 20.23 Mg ha -1 ), while the addition of the mean enhanced vegetation index (EVI) further improved performance to R 2 = 0.68 and RMSE = 18.72 Mg ha -1 , outperforming both strucutural-only and spectral-only models. Regional mapping estimated total mangrove AGB at 44,112 Mg, with a mean density of 49.20 Mg ha -1 . Higher biomass was concentrated in interior stands, whereas lower biomass occurred predominantly along seaward edges and marginal zones. Biomass trajectories also differed among geomorphic settings, with increasing, decreasing, and unimodal patterns observed along different coastal-distance gradients. Overall, the proposed framework provides a practical pathway for extending localized UAV-LiDAR structural information to regional mangrove AGB mapping while reducing dependence on wall-to-wall active sensing, thereby supporting regional blue carbon assessment and spatially targeted conservation and management of climate-vulnerable mangrove ecosystems.