## 3 Results
### Determining the State of the Forest with GEDI Lidar Data  
More than 110 million lidar waveforms from NASA's GEDI mission were analyzed and linked with simulations from the forest model FORMIND. Information about the forest height was derived directly from each real GEDI waveform (here: RH95). Waveform matching between observed GEDI lidar data and forest simulations made it possible to estimate AGB, GPP, and NEE at the footprint level. The analysis was based on footprint level resolution. For visualization purposes only, the footprint-level resolution was aggregated to 1 km\({}^{2}\) and 20 \(\times\) 20 km\({}^{2}\) (Figures 3 and 4). A frequency distribution of GEDI waveform counted over the 1 km\({}^{2}\) grid showed that the individual grid cells could contain between 1 and 100 GEDI waveforms (Figure S5). Figure 2: Simulated (**a**) aboveground biomass (AGB), (**b**) gross primary productivity (GPP), and (**c**) net ecosystem exchange (NEE) over simulation time for two exemplary environmental conditions (low precipitation, low clay (LPLC) and high precipitation, high clay (HPHC)). Blue: low precipitation (1750 mm a\({}^{-1}\)), low clay content (15%), and medium PPFD (820 \(\upmu\)mol m\({}^{-2}\) ha\({}^{-1}\)). Red: high precipitation (3750 mm a\({}^{-1}\)), high clay content (0.95), and high PPFD (980 \(\upmu\)mol m\({}^{-2}\) ha\({}^{-1}\)). For both simulations, random stand-replacing disturbance events were integrated. Figure 4: Frequency distributions of canopy height (RH95) in m (**a**), mean aboveground biomass (AGB) in Mg odm per ha (**b**), mean gross primary productivity (GPP) in Mg C per ha per year (**c**), and net ecosystem exchange (NEE) in Mg C per ha per year (**d**) associated with the maps in Figure 3, with a resolution of 1 km\({}^{2}\) for the Amazon based on 110 million lidar waveforms. Figure 3: Maps of mean canopy height (RH95) in m (**a**), mean aboveground biomass (AGB) in Mg odm per ha (**b**), mean gross primary productivity (GPP) in Mg C per ha per year (**c**), and net ecosystem exchange (NEE) in Mg C per ha per year (**d**). The 1 km\({}^{2}\) resolution was aggregated a to 20 \(\times\) 20 km\({}^{2}\) resolution for visualization purposes. In our results we obtained forest heights that ranged from 0 m to a maximum of 50 m in height (Figures 3a and 4a). The mean forest height in the Amazon was 23.4 \(\pm\) 10.2 m at a spatial resolution of 1 km\({}^{2}\). After applying the data-model fusion, the estimated above-ground biomass had a mean value of 163.4 \(\pm\) 93.1 Mg odm ha\({}^{-1}\) and could reach values of up to 400 Mg odm ha\({}^{-1}\) (Figures 3b and 4b). These high aboveground biomass areas were located in the northeast, the Guiana Shield, and the central Amazon. Low aboveground biomass values were most evident in the southeast, where the Arc of Deforestation is located. Spatial variations could also be observed in the GPP distribution of the Amazon (Figures 3c and 4c). Here, values ranged from 0 to 40 Mg C ha\({}^{-1}\) a\({}^{-1}\), with a mean value of 21.1 \(\pm\) 8.7 Mg C ha\({}^{-1}\) a\({}^{-1}\). GPP was low in forested areas where aboveground biomass was also low. The productive forest areas were mainly in the central Amazon Basin. The previously described distributions of high and low values of aboveground biomass and GPP, respectively, could also be seen in the NEE distribution (Figures 3d and 4d). Negative NEE values represented carbon sources, i.e., where carbon emissions due to respiration could not be compensated for by forest carbon uptake (please note: direct carbon emissions due to deforestation were not considered). The NEE map of the Amazon showed that large areas are carbon sinks. Only regions--such as in the Andes in the west and the Negro-Branco moist forests in the north, and the southeast--where the Arc of Deforestation is located were identified as carbon sources. There, up to 2.5 Mg C ha\({}^{-1}\) carbon was found to be emitted annually.