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Mapping forest resources and carbon is important for improving forestmanagement and meeting the objectives of storing carbon and preserving theenvironment. Spaceborne remote sensing approaches have considerable potentialto support forest height monitoring by providing repeated observations at highspatial resolution over large areas. This study uses a machine learningapproach that was previously developed to produce local maps of forestparameters (basal area, height, diameter, etc.). The aim of this paper is topresent the extension of the approach to much larger scales such as the Frenchnational coverage. We used the GEDI Lidar mission as reference height data, andthe satellite images from Sentinel-1, Sentinel-2 and ALOS-2 PALSA-2 to estimateforest height and produce a map of France for the year 2020. The height map isthen derived into volume and aboveground biomass (AGB) using allometricequations. The validation of the height map with local maps from ALS data showsan accuracy close to the state of the art, with a mean absolute error (MAE) of4.3 m. Validation on inventory plots representative of French forests shows anMAE of 3.7 m for the height. Estimates are slightly better for coniferous thanfor broadleaved forests. Volume and AGB maps derived from height shows MAEs of75 tons/ha and 93 m${}^3$/ha respectively. The results aggregated bysylvo-ecoregion and forest types (owner and species) are further improved, withMAEs of 23 tons/ha and 30 m${}^3$/ha. The precision of these maps allows tomonitor forests locally, as well as helping to analyze forest resources andcarbon on a territorial scale or on specific types of forests by combining themaps with geolocated information (administrative area, species, type of owner,protected areas, environmental conditions, etc.). Height, volume and AGB mapsproduced in this study are made freely available.

Estimation of forest height and biomass from open-access multi-sensor  satellite imagery and GEDI Lidar data: high-resolution maps of metropolitan  France