## 4 Discussion
### The Influence of Different GEDI Algorithm Setting Groups for the Carbon Storage Estimation Accuracy  
The estimation results of forest canopy height and biomass using spaceborne LiDAR can be affected by adverse conditions such as low-energy ground reflection or high background noise. For different canopy and ground scenes, at the same time, in order to reduce those unfavorable conditions and consider the difference between daytime and night, GEDI set six different combinations of noise threshold, signal threshold, start threshold, and end threshold for users to choose, as shown in Table 5. Different setting groups achieved the purpose of controlling the waveform length by changing the waveform signal smoothing width, signal start threshold, and signal end threshold [49]. Setting group 1 was typically used for most cases, while other setting groups were designed to provide accurate information when observation conditions were not good.  
In order to obtain a more accurate forest canopy height, [PERSON] et al. [50] obtained the final forest canopy height by removing the maximum and minimum values in the six algorithms and then by averaging them. Compared with airborne LiDAR data, it showed that different GEDI algorithm setting groups in different environments will affect the measurement accuracy of GEDI. [PERSON] et al. [51] compared different algorithms for varying vegetation coverage and selected the optimal algorithm based on different degrees of coverage. The results indicated that algorithm four worked best for plant density less than 0.2, algorithm two for vegetation coverage above 0.8, and algorithm one is more accurate for the remaining coverage than the others. [PERSON] et al. [52] verified six groups of data with airborne data, and the precision of six algorithm sets of elevation data inversion of max canopy height of GEDI L2A was analyzed. At the same time, the accuracy of estimating forest biomass was calculated for seven algorithm groups of L4A biomass products. The results showed that among the six algorithm settings of GEDI L2A, the lowest estimation accuracy was that of algorithm five, and the highest accuracy was that of algorithm four. When using L4A data to evaluate biomass, the algorithm two group had the highest accuracy among the seven algorithm groups.  
The different algorithms had a certain influence on GEDI accuracy under different environments. Although GEDI L2B did not involve specific algorithm setting groups, some parameters (such as rg, pgap_thea, rv, and rx_enrgy) in L2B were designed by the selected L2A algorithm or others corresponding to different L2A processing algorithms. To compensate for the lack of airborne LiDAR data in verifying the accuracy of GEDI algorithm setting groups, therefore, this study selected the L2B parameters under the default algorithm for Kriging interpolation to obtain surface information. Future research will focus on analyzing the accuracy of various GEDI algorithm groups when estimating carbon storage in Shangri-La according to different forest types, surface slopes, and vegetation coverage, and to find the algorithm group with the highest estimation precision under different conditions.