Improving Mangrove Height and Above‐Ground Biomass Estimates in Small Island Developing States Using Multi‐Sensor Satellite Data: Case Study of Mauritius

National-scale monitoring of mangrove forests, including their spatial structure (e.g., canopy height and above-ground biomass), is necessary for the implementation of global agreements related to climate change and biodiversity conservation. Field measurements of mangrove structural parameters, however, are costly to collect. Here, we investigated the use of freely available data from multiple satellite sensors to monitor mangrove height and above-ground biomass at the national scale in Mauritius. L-band synthetic aperture radar data, optical multispectral data, and an existing global tree canopy height map were used as the main inputs to a regression model for estimating mangrove canopy height, while satellite LIDAR height measurements (Global Ecosystem Dynamics Inves-tigation (GEDI) data) were used to calibrate and validate the models. Based on 5-fold cross-validation, random forest regression achieved an R2 value of 0.46 and root-mean-square error (RMSE) of 4.45m. The main factor limiting higher model accuracy was likely the sparsity of satellite LIDAR data points in man-grove areas of Mauritius (n = 65), and this may be an issue in other SIDS also due to the limited geographic coverage of the LIDAR data. Still, our approach outperformed the existing global tree canopy height map (RMSE = 5.33) and a linear regression modeling approach (R2 = 0.34, RMSE = 4.85m).