## 5 Discussion  
Prior to this study, the relative suitabilities of DAP and ALS data for canopy cover and LAI estimation have not been sufficiently compared. We collected DAP and ALS data of 166 tropical forest plots as well as ground-based forest inventory measurements. A CHM-based canopy cover estimation method was used based on DAP data whereas an echo index-based canopy cover estimation was used for ALS data. Both DAP- and ALS-derived LAIs were determined based on gap probability theory (also known as the Beer-Lambert law) with nadir gap fraction observation (i.e., the opposite of canopy cover). Our results indicate significant differences in the canopy cover and LAI estimation over tropical forests using ALS and DAP data, as well as different correlations with field-measured AGB. Figure 4: Comparison between airborne and satellite-retrieved leaf area index (LAI), with ALS-based LAI against Sentinel-2-based LAI (**a**), and with DAP-based LAI against Sentinel-2-based LAI (**b**). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Coefficients of Determination** & **LAI\({}_{\text{ALS}}\)-AGB** & **LAI\({}_{\text{DAP}}\)-AGB** \\ \hline Mixed coniferous and broad-leaved forest & 0.77 & 0.96 \\ Mixed broad-leaved forest & 0.39 & 0.65 \\ Other coniferous trees & 0.67 & 0.55 \\ Eucalyptus robusta [PERSON] & 0.67 & 0.63 \\ Acacia confusa [PERSON]. & 0.30 & 0.24 \\ Hevea brasiliensis & 0.03 & 0.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Coefficients of determination (R\({}^{2}\)) of fitted curves between field-measured aboveground biomass (AGB) and the estimated LAI using ALS and DAP methods under different forest types. Almost all DAP-based canopy cover estimates were larger than those using ALS-based methods, which was in accordance with findings from other studies [3; 15; 29]. Generally, ALS-based canopy variable estimation was recognized as a benchmarking method when no ground-based measurements were apparent [39], hence the DAP-based method tended to overestimate. This overestimation can be attributed to the characteristics of the DAP data themselves (including flight settings and camera configurations), the complexity of the forest structure, and the illumination environment [27; 29]. DAP data mainly characterize the outer canopy envelope where the shadows and occlusions from surrounding trees can negatively influence the completeness of SfM point clouds. Additionally, the grid size also impacts the canopy cover estimation during the rasterization of SfM point clouds. Both the neglect of small canopy gaps and the large grid size led to the overestimation of canopy cover [32]. Compared to the CHM-based estimation using DAP data, ALS data enable the echo index (also known as light penetration index) to represent canopy cover using the echo-number-based method (i.e., the method using LiDAR echo number to derive an echo index) [33]. FCI is a widely used proxy of canopy cover although many other echo indices have been proposed in the last decade. It should be noted that FCI tends to only characterize between-crown gaps because the within-crown gaps are generally smaller than the pulse footprint, and hence may lead to overestimation [23]. Much explicit evidence indicates that effective canopy cover is overestimated in most forest stands and the estimates show a saturation effect in dense forests [40]. As we defined the canopy cover without considering the within-crown gaps, the FCI seems more adequate than other reported echo indices, such as SCI and WCI [14; 41]. Our comparison demonstrated that the DAP data failed to estimate canopy cover with the same level of accuracy as the ALS data. The further investigation based on multiple forest types verified this finding. The airborne LAI estimation accuracy is influenced by the correctness of gap fraction observations, the representativeness of leaf angle distribution, and the characterization of clumping effect regardless of wood elements (e.g., branches).