## 2 Materials and Methods
### Individual-Based Forest Model Formind  
FORMIND is an individual-based forest gap model developed in the late 1990s to simulate forest dynamics in high spatial resolution patches. In addition, physiological processes at the tree level were simulated [24; 25; 26; 27; 28]. The main processes of FORMIND are tree growth, tree mortality, recruitment and tree competition. In this study, we applied FORMIND to the tropical forest in the Amazon [15; 26] where the parametrization was chosen as in [PERSON] et al. and [PERSON] et al. [3; 15].  
The forest model represents tree diversity by grouping tree species into three plant functional types (PFTs) [29] in the tradition of global vegetation models (PFT grouping from [PERSON] et al. [15; 16; 26] and [PERSON] et al. [3]). These three PFTs represent different successful stages: shade-intolerant pioneer species, shade-tolerant climax species and medium shade-tolerant tree species. It has been shown in several studies that this grouping could sufficiently describe forest dynamics and changes in species group compositions [15; 26; 30; 31]. In addition, the parameters of the dominant PFT with the climax tree species were regionally adjusted. It was found that the mortality parameters of the forest model for shade-tolerant late-successful trees could be adjusted to better match forest inventory data and reproduce local measurements of aboveground forest biomass, mean wood density and basal area [26]. The regional mortality parameters depend on local characteristics such as climate conditions (e.g., precipitation) and soil properties (e.g., clay content), which were derived from global maps [26].  
We then determined forest parameters like the aboveground biomass, net primary productivity, gross primary productivity and leaf area index of trees. In order to implement the FORMIND model for the complete Amazon rainforest, the Amazon was split into environmental regions and we performed forest simulations for the whole Amazon in high-resolution including simulations of lidar waveforms [26]. This well-established workflow, which combines FORMIND and GEDI data using waveform matching, determines the current forest condition in the Amazon region [3; 16]. For the waveform matching (Figure 1), each real GEDI waveform was compared with hundreds of simulated lidar waveforms in the respective environmental region to determine the state of the forest. The simulated waveforms with the 50 best overlaps were used to determine the current forest condition because these waveforms matched best with the real GEDI data. Then, for the best matching forest stands it was possible to calculate forest attributes like the aboveground biomass, net ecosystem exchange, gross primary productivity, stem size distribution and leaf area index.  
Figure 1: A lidar profile from the GEDI mission (blue) is compared with hundreds of simulated lidar profiles derived from a forest model (red). The forest model simulates site-specific forest dynamics and the corresponding lidar profile for each simulated forest stand. The real GEDI lidar measurements act as a filter to select the current forest state from all possible forest states. More details on this approach can be found in [3; 16].  
With this model-data fusion, we analyzed different forest properties at the footprint level of a GEDI shoot. The forest properties investigated were aboveground biomass (AGB in Mg organic dry mass (odm)), net primary productivity (NPP), gross primary productivity (GPP) and leaf area index (LAI). All attributes were calculated at the individual tree level and then summed up for all trees within the footprint. GPP is the sum of the gross primary productivities of all simulated trees within the footprint. NPP is the difference between GPP and maintenance respiration of trees. LAI is defined as the leaf area per ground surface (does not include grass and bushes).