## 6 Future Research Directions
### Mitigating Decorrelation Effects in Vegetated Areas  
Selecting high-quality interferometric pairs is a crucial aspect of landslide monitoring using InSAR. While phase optimization methods, such as maximum likelihood estimation [155; 156] and eigenvalue decomposition [157; 158], are widely used, their performance in vegetated areas, where landslides often occur, might be suboptimal. Studies have shown that vegetation significantly affects InSAR coherence, with a negative correlation between vegetation density and coherence [159; 160]. In fact, several factors can influence coherence in vegetated areas, including vegetation type, posture, height, seasonal variations, and weather-induced humidity changes.  
To address this challenge, preliminary explorations have been made by scholars. One promising approach is to establish a quantitative relationship between InSAR decorrelation and vegetation characteristics, which could assist in selecting suitable SAR data before interferometric processing, thus reducing processing time. For instance, [PERSON] et al. demonstrated that the NDVI can be used to quantitatively estimate InSAR decorrelation in both co- and cross-polarization modes by constructing second-order linear models [161]. Similarly, [PERSON] et al. categorized interferometric pairs into groups with high and low vegetation coverage based on monthly FVC, applying group-specific average coherence thresholds to filter out low-quality pairs [162]. While such a method has proven to be effective, future research should account for the influence of different vegetation types and regional variations, as the quantitative relationships may need to be adapted to specific local conditions. In addition, considering the differences in the sensitivities of different polarization modes, [PERSON] et al. explored a sequential polarimetric phase optimization algorithm that incorporates multi-polarization information and demonstrated its effectiveness [163]. However, this method does not account for the differences in scattering centers of ground objects across various polarization modes, which may lead to biased phase optimization results in vegetated areas. Nevertheless, it provides a new solution for minimizing decorrelation effects in vegetated areas.  
As discussed in Section 4.2.4, long-wavelength SAR platforms exhibit better penetration capabilities, and the use of multi-orbit SAR imagery for monitoring vegetated areas has also proven effective [99; 100; 164]. However, challenges such as the limited availability of long-wavelength datasets, suboptimal temporal resolution, and suboptimal outcomes in the spatiotemporal fusion of multi-source data still remain. Future developments in SAR platforms may help address these issues. For example, the launch of satellites operating in long wavelengths, such as the recently launched L-band ALOS-4 mission [165], could facilitate the exploration of time series InSAR based on long-wavelength data. Additionally, with an increasing number of SAR satellites and the reduction in revisit times, methods for minimizing decorrelation effects in vegetated areas are expected to see satisfactory improvements.