## 2 Materials and Methods
### Data Processing
#### 2.3.1 Carbon Sinks and Aerosol LiDAR Data Processing  
Footprint Screening  
The accurate estimation of tree height using satellite-borne large-footprint LiDAR data requires rigorous quality control, as atmospheric interference (e.g., clouds, haze) and non-forested laser returns can significantly compromise measurement fidelity. To ensure data integrity, low-quality waveforms were filtered using quality assessment fields embedded in the CASAL L2 product, retaining only observations with a signal-to-noise ratio (\(SNR\)) \(>\) 5. The CASAL system operates in two gain modes (Fgain and Vgain); we exclusively analyzed Fgain-mode footprints due to their superior SNR characteristics. Footprints were geolocated using IndexNum and BLH fields, with non-forest returns excluded through spatial filtering. Only footprints meeting all quality criteria (flag \(>\) 0, rec_peaks \(>\) 1, \begin{table}
\begin{tabular}{c c} \hline
**No.** & **Dataset ID** \\ \hline
1 & CM1 CASAL A1 20221029 0000001309 L20000232387 \\
2 & CM1\_CASAL-A1\_20221103\_000001386 L20000231223 \\
3 & CM1\_CASAL-A1\_20221213\_0000001993 L20000231221 \\
4 & CM1\_CASAL-A1\_20320102\_0000002297 L20000337144 \\
5 & CM1\_CASAL-A1\_20230117\_0000002525 L20000337174 \\
6 & CM1\_CASAL-A1\_20230206\_0000002829\_120000337152 \\
7 & CM1\_CASAL-A1\_20230313\_0000003361\_120000337159 \\
8 & CM1\_CASAL-A1\_20230318\_000003438 L20000337137 \\
9 & CM1\_CASAL-A1\_20230323\_000003513\_120000337167 \\ \hline \end{tabular}
\end{table} 
Table 2: List of CASAL datasets used in this study. and \(SNR>5\)) were retained, ensuring valid echoes with detectable waveform peaks and sufficient signal quality. \(SNR\) was computed using Equation (1) [32] as follows:  
\[SNR=10\log_{10}\frac{DN_{\text{max}}-N_{\text{are}}}{\sigma} \tag{1}\]  
where \(DN_{\text{max}}\) denotes the maximum waveform amplitude, \(N_{\text{are}}\) represents the mean background noise value, and \(\sigma\) indicates the standard deviation of background noise. The fields involved in the screening criteria are detailed in Table 3. After applying these filters, a total of 18,321 valid laser footprints were retained for analysis. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Directory** & **Description** \\ \hline IndexNum & Lasern/IndexNum & Laser waveform sequence number \\ BLH & Lasern/Location\_Fgain/BLH & Geodetic coordinates: B: Latitude L: Longitude H: Elevation \\ Flag & Location\_Fgain/Flag & Data quality flag \\ rec\_peaks & Rec\_waveform/Wf\_Fgain/Characters/rec\_peaks & Number of Gaussian components in fitted \\ rec\_Thr & Rec\_waveform/Wf\_Fgain/Characters/rec\_Thr & Waveform \\ rec\_noise & Rec\_waveform/Wf\_Fgain/Characters/rec\_noise & Waveform \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Description of main parameters in CASAL LiDAR products. Figure 3: Workflow for accuracy assessment of CASAL products using airborne laser scanning (ALS) data. Waveform Feature Parameter Extraction  
Forest canopy height is typically derived by subtracting the ground elevation (corresponding to the last waveform peak) from the maximum elevation detected in the full waveform, with the resulting difference representing the maximum canopy height [33; 34; 35; 36]. Using the extracted threshold value (rec_Thr) from the dataset, we identified the waveform signal boundaries by (1) defining SigBeg as the first position where backward signal strength from the initial data frame exceeds rec_Thr and (2) defining SigEnd as the first position where the forward signal strength from the terminal data frame surpasses rec_Thr. The relative height (RH) metric represents the vertical distance from the cumulative energy percentage (0% to 100%) of the full waveform to the ground echo position [26; 37]. It is commonly used to characterize the vertical structure of forest canopies and serves as a critical parameter in CASAL LiDAR data processing and analysis. However, in forested areas, the ground echo is often difficult to identify.