## 3 Applications of L-Band SAR Data in Forestry
### Forest Height  
[PERSON] successfully estimated and verified the potential of L-band, P-band and X-band to estimate tree height in temperate forests by using airborne SAR sensors combined with constraints [6; 7]. [PERSON] et al. used the improved RMoG model and ALOS-1 data to invert forest height with the estimation error reduced by 27.73% and 8.57% [PERSON] et al. used UAV SAR technology to estimate the tree height of tropical forests with a root mean square error (RMSE) of 4.21 m [8]. [PERSON] et al. estimated tree height by using L-band SAR data and generated a digital terrain model (DTM) and digital surface model (DSM) validated by using UAV LiDAR data [9]. [PERSON] et al. proposed a new algorithm based on a mean set to increase phase, and combined it with the polarization characteristics of the VE-RVoG optimized set, developed to improve the estimation of forest height, and obtained an RMSE of 2.91 m and a correlation coefficient of 0.909 [10]. [PERSON] et al. improved the accuracy of forest height estimation by using the new airborne PollnSAR data-processing strategy; the RMSE was significantly reduced, to 1.02 m, with a decrease of 12.86%, providing a feasible solution for forest height estimation with X-band waves [11]. Based on L-band single-baseline pooled SAR interferometric simulation data, [PERSON] et al. proposed a standard scale optimization model suitable for various densities, successfully overcoming the failure of traditional methods in low-density regions, and effectively realized the estimation of forest height [12].  
Moreover, [PERSON] et al. used UAV SAR multi-baseline L-band data from the AfriSAR project to show better accuracy in forest height estimation, providing an improved method for estimating structural parameters of tropical rain forests [13]. [PERSON] et al. conducted an estimation experiment using L-band multi-baseline fully polarized data from the AfriSAR project in the Lope Pongara pilot area and proposed a depth-based error-correction method that improved the accuracy of forest height estimation and demonstrated potential applications [14] of machine learning-interference feature prediction. [PERSON] et al. used the simulated L-band SAR data and combined it with the improved three-stage method to derive forest height with a significantly improved accuracy [15]. Integrating the TF-RVoG method based on time-frequency analysis and the improved single-baseline data decompo  
Figure 5: Publications by journal in the field of using L-band SAR data and dealing with forest canopy penetration.  
sition method significantly improved the estimation accuracy of the forest canopy height model, with the RMSE dropping to 2.54 m [16]. [PERSON] et al. combined ICESat-2 data and tree age information to propose a method to estimate the change in palm tree height in Peninsular Malaysia, and successfully produced a comprehensive map of tree height change from 2001 to 2020 [17]. The verification results showed that the estimated height was highly consistent with the actual height. Providing a spatially explicit tool with great potential for quantifying plantation stocks, [PERSON] and [PERSON] et al. used ALOS-2 L-band data to retrieve confer tree height in Saihanba, Hebei Province and obtained an R\({}^{2}\) of 0.67 between the SAR-based estimated and the LiDAR-based conifer tree height [18]. These studies show that L-band SAR data have great potential in forest height estimation, but the estimation accuracy cannot meet the needs of forestry production.