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Mapping forest aboveground biomass (AGB) has become an important task,particularly for the reporting of carbon stocks and changes. AGB can be mappedusing 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 tropicalforests. Studies have mapped the rough variations in AGB by combining opticaland environmental data at regional and global scales. Nevertheless, these mapscannot represent local variations in AGB in tropical forests. In this paper, wehypothesize that the problem of misrepresenting local variations in AGB and AGBestimation with good precision occurs because of both methodological limits(signal saturation or dilution bias) and a lack of adequate calibration data inthis range of AGB values. We test this hypothesis by developing a calibratedregression model to predict variations in high AGB values (mean\textgreater{}300 Mg/ha) in French Guiana by a methodological approach forspatial extrapolation with data from the optical geoscience laser altimetersystem (GLAS), forest inventories, radar, optics, and environmental variablesfor spatial inter-and extrapolation. Given their higher point count, GLAS dataallow a wider coverage of AGB values. We find that the metrics from GLASfootprints are correlated with field AGB estimations (R 2 =0.54, RMSE=48.3Mg/ha) with no bias for high values. First, predictive models, includingremote-sensing, environmental variables and spatial correlation functions,allow us to obtain "wall-to-wall" AGB maps over French Guiana with an RMSE forthe in situ AGB estimates of ~51 Mg/ha and R${}^2$=0.48 at a 1-km grid size. Weconclude that a calibrated regression model based on GLAS with dependentenvironmental data can produce good AGB predictions even for high AGB values ifthe calibration data fit the AGB range. We also demonstrate that small temporaland spatial mismatches between field data and GLAS footprints are not a problemfor regional and global calibrated regression models because field data aim topredict large and deep tendencies in AGB variations from environmentalgradients and do not aim to represent high but stochastic and temporallylimited variations from forest dynamics. Thus, we advocate including a greatervariety of data, even if less precise and shifted, to better represent high AGBvalues in global models and to improve the fitting of these models for highvalues.

Aboveground biomass mapping in French Guiana by combining remote sensing, forest inventories and environmental data