  Mapping forest aboveground biomass (AGB) has become an important task,
particularly for the reporting of carbon stocks and changes. AGB can be mapped
using synthetic aperture radar data (SAR) or passive optical data. However,
these data are insensitive to high AGB levels (\textgreater{}150 Mg/ha, and
\textgreater{}300 Mg/ha for P-band), which are commonly found in tropical
forests. Studies have mapped the rough variations in AGB by combining optical
and environmental data at regional and global scales. Nevertheless, these maps
cannot represent local variations in AGB in tropical forests. In this paper, we
hypothesize that the problem of misrepresenting local variations in AGB and AGB
estimation with good precision occurs because of both methodological limits
(signal saturation or dilution bias) and a lack of adequate calibration data in
this range of AGB values. We test this hypothesis by developing a calibrated
regression model to predict variations in high AGB values (mean
\textgreater{}300 Mg/ha) in French Guiana by a methodological approach for
spatial extrapolation with data from the optical geoscience laser altimeter
system (GLAS), forest inventories, radar, optics, and environmental variables
for spatial inter-and extrapolation. Given their higher point count, GLAS data
allow a wider coverage of AGB values. We find that the metrics from GLAS
footprints are correlated with field AGB estimations (R 2 =0.54, RMSE=48.3
Mg/ha) with no bias for high values. First, predictive models, including
remote-sensing, environmental variables and spatial correlation functions,
allow us to obtain "wall-to-wall" AGB maps over French Guiana with an RMSE for
the in situ AGB estimates of ~51 Mg/ha and R${}^2$=0.48 at a 1-km grid size. We
conclude that a calibrated regression model based on GLAS with dependent
environmental data can produce good AGB predictions even for high AGB values if
the calibration data fit the AGB range. We also demonstrate that small temporal
and spatial mismatches between field data and GLAS footprints are not a problem
for regional and global calibrated regression models because field data aim to
predict large and deep tendencies in AGB variations from environmental
gradients and do not aim to represent high but stochastic and temporally
limited variations from forest dynamics. Thus, we advocate including a greater
variety of data, even if less precise and shifted, to better represent high AGB
values in global models and to improve the fitting of these models for high
values.
