  Mapping forest resources and carbon is important for improving forest
management and meeting the objectives of storing carbon and preserving the
environment. Spaceborne remote sensing approaches have considerable potential
to support forest height monitoring by providing repeated observations at high
spatial resolution over large areas. This study uses a machine learning
approach that was previously developed to produce local maps of forest
parameters (basal area, height, diameter, etc.). The aim of this paper is to
present the extension of the approach to much larger scales such as the French
national coverage. We used the GEDI Lidar mission as reference height data, and
the satellite images from Sentinel-1, Sentinel-2 and ALOS-2 PALSA-2 to estimate
forest height and produce a map of France for the year 2020. The height map is
then derived into volume and aboveground biomass (AGB) using allometric
equations. The validation of the height map with local maps from ALS data shows
an accuracy close to the state of the art, with a mean absolute error (MAE) of
4.3 m. Validation on inventory plots representative of French forests shows an
MAE of 3.7 m for the height. Estimates are slightly better for coniferous than
for broadleaved forests. Volume and AGB maps derived from height shows MAEs of
75 tons/ha and 93 m${}^3$/ha respectively. The results aggregated by
sylvo-ecoregion and forest types (owner and species) are further improved, with
MAEs of 23 tons/ha and 30 m${}^3$/ha. The precision of these maps allows to
monitor forests locally, as well as helping to analyze forest resources and
carbon on a territorial scale or on specific types of forests by combining the
maps with geolocated information (administrative area, species, type of owner,
protected areas, environmental conditions, etc.). Height, volume and AGB maps
produced in this study are made freely available.
