## 4 Results and Analysis
### LAI Retrieval in Saihanba National Forest Park  
#### 4.2.1 GLAS LAI vs. TRAC LAI  
Figure 6a presents the comparison between the GLAS LAI (i.e., the result of LAD integration from the canopy top to the ground) and the TRAC LAI. There was moderate consistency between them, with a determination coefficient (R\({}^{2}\)) = 0.79 and root mean square error (RMSE) = 0.83. However, the GLAS LAI was higher than the TRAC LAI (bias = 0.68), which may be because TRAC works. In the measurement process, the TRAC is on a transect approximately 1 m above the ground, and the sensor on the TRAC is upward; therefore, the vegetation near the ground (i.e., understory vegetation) may be ignored. To prove our conjecture, we chose a set of comparative data, and the corresponding plot photos are shown in Figure 6a. Compared to the LAI derived from the footprint with almost no understory vegetation, the LAI from the footprint with dense understory vegetation has a considerable bias with the TRAC LAI. Therefore, the difference between ground observations and satellite observations was the main reason for inconsistencies in the comparison. To ensure consistency between the observations, we recalculated the GLAS LAI by integrating the LAD from the canopy top to the layer 1 m above the ground. We then obtained improved validation results (Figure 6b), with R\({}^{2}\) = 0.79, RMSE = 0.49, and bias = 0.17. From Figure 6b, we can find that there were some visually obvious outliers in the validation results. It has been well documented by previous studies that slope and SNR (signal-to-noise ratio = i_maxRecAmp/i_sDevNsObj) have a great influence on the accuracy of lidar measurements [48; 49; 50; 51; 52; 53]. Some studies concluded that GLAS observations with slopes of less than 15 and SNR values greater than 60 could accurately represent the vegetation structure [27; 54]. Therefore, we analyzed the role of slope and SNR in our method to determine whether they caused these outliers. The distribution of the slope and SNR values of the GLAS footprints in Saihanba National Forest Park, which provide by SRTM data and GLAS product, are shown in Figure 7a,b. We found four footprints with slopes greater than 15 and five footprints with SNR values less than 60. Among these potentially problematic GLAS observations, three observations not only had large slopes (i.e., slope > 15) but also low SNR values (i.e., SNR < 60); that is, six of 50 GLAS observations might have been problematic. After removing these six potentially problematic GLAS observations, the validation accuracy was improved, with R\({}^{2}\) = 0.85, RMSE = 0.35, and bias = 0.10 (Figure 7c). The above result indicated that the uncertainty caused by the slope and  
Figure 6: (**a**) Scatter plot of the GLAS LAI and tracing radiation and architecture of canopies (TRAC)-measured effective LAI. The red circles are different typical examples that were used to compare and describe the difference in affecting the inconsistency between satellite retrievals and ground observations, most likely caused by the understory. The photos pointed by the arrows show the situation of vegetation within the GLAS footprints. (**b**) Scatter plot of the recalculated GLAS LAI (i.e., an integration of leaf area density (LAD) from the canopy top to the layer 1 m above the ground) and TRAC-measured effective LAI. SNR were probably two primary error sources in the LAI estimation using the method proposed in this study. We further explored how these two factors affected our method. Here, we selected typical GLAS observations with large slopes and low SNRs for further analysis. Figure 8 presents an example of GLAS observations with complex terrain (i.e., slope = 21\({}^{\circ}\)). Notably, the echo energy from the canopy was mixed with the echo energy from the bare ground on the top of the mountain, which resulted in considerable canopy echo energy (Figure 8b). In addition, the obvious scattering phenomenon on the slope area led to the echo energy of the last Gaussian component being very small (Figure 8b).