## 4 Results and Analysis
### LAI Retrieval in Heilongjiang Province  
We derived a total of 4071 GLAS LAI over Heilongjiang Province. Among these GLAS LAI values, approximately 35% were derived from the GLAS observations with large slope (i.e., slope \(>\) 15\({}^{\circ}\)) or low SNR (e.g., SNR \(<\) 60). According to the analysis results in Section 4.2.1, we thought that 35% of the inversion results might have uncertainty. To ensure the accuracy of the analysis, we only used the remaining 65% of inversion results for analysis. Figure 12 shows the distribution of GLAS LAI after removing these the 35% of values that were potentially problematic results, which totaled 2628 GLAS LAI values, with a mean value of 2.64 and a mean square error of 1.15.  
The vertical LAI integrations from 0-4 m, 4-8 m, and 8-18 m were also mapped at the scale of GLAS footprints (~65 m) (Figure 13). We noted that the LAIs at different height levels performed differently; in particular, the LAI integrated from 0-4 m was relatively large, which indicated that there was understory vegetation in the forest of Heilongjiang Province. In addition, we also found that in some cases, although the total LAI (e.g., LAI \(\approx\) 4) was the same, the LAIs at different height levels had different contributions to the total, as seen in Figure 14.  
Figure 10: Examples of the LAD derived from GLAS data and the corresponding observed forest photos.  
Figure 11: Evaluation of LAI variation in 10 years using Moderate Resolution Imaging Spectroradiometer (MODIS) LAI product. (**a**) is the column plot displaying the variation between MODIS LAI of 2018 and 2006. (**b**) is the scatter plot displaying the correlation between MODIS LAI of 2018 and 2006.  
Figure 12: Histogram of effective LAI from GLAS data over Heilongjiang Province.  
Figure 13: Vertical LAI integration maps from 0 to 4 m, 4 to 8 m, 8 to 18 m.  
GLAS LAI vs. MODIS LAI  
The GLAS and MODIS LAI were found to correlate poorly (Figure 15). The overall correlation coefficient was only sqrt (0.003), although the comparison of the two gave relatively low RMSE (1.69) and bias (0.89). The differences between the GLAS and MODIS LAI values were highly skewed because of large positive differences at low and middle LAIs (Figure 15b). The main reason for the poor correlation was likely attributed to the differing characteristics of the two LAI products. In particular, the MODIS LAI retrieval algorithm accounted for vegetation clumping through 3-D radiative transfer modeling [55; 56] and was supposed to provide the true LAI. In contrast, our GLAS method did not consider the clumping effect and derives only the effective LAI. Therefore, the MODIS LAI tended to be higher than the GLAS LAI. Interestingly, for the LAIs higher than 4, the pattern was reversed with higher values for GLAS (Figure 15a). This reversal was due to the saturation of MODIS LAI at approximately 5.5 (Figure 15a). However, the GLAS LAI estimates showed no signs of saturation, even up to 8 (Figure 15a). Overall, the comparison clearly highlighted the unique value or even potential superiority of lidar data for measuring LAI for dense forests--an expected finding consistent with the majority of lidar studies over forests of medium to high biomass [57].