Global navigation satellite system reflectometry (GNSS-R) uses the reflection characteristics of navigation satellite signals reflected from the earth’s surface to provide an innovative tool for remote sensing, especially for monitoring surface and atmospheric environmental variables, such as wind speed, soil moisture, vegetation, and sea ice parameters. This paper focuses on the current application and future potential of spaceborne GNSS-R in vegetation remote sensing and the retrieval of inland water environmental and physical parameters. This paper reviews the technical progress of GNSS-R in detail, from early feasibility studies to multiple application examples at this stage, from the United Kingdom Disaster Monitoring Constellation (UK-DMC) satellite in 2003 to other recent GNSS-R missions. These cases demonstrate the unique advantages of GNSS-R in terms of global coverage, low cost, and real-time monitoring. This paper explores the application of GNSS-R technology in vegetation parameters and inland water monitoring, especially its potential in vegetation parameters and surface water monitoring applications. The article also mentioned that the accuracy and efficiency of parameter retrieval can be significantly improved by improving models and algorithms, such as using neural networks and data fusion technology. Finally, the article points out the future direction of spaceborne GNSS-R technology in vegetation remote sensing and the retrieval of inland water environment and physical parameters, including expanding its application areas to a broader range of environmental monitoring and resource management. It emphasized its essential role in monitoring the global ecosystem and monitoring water resources.

Landslide susceptibility mapping (LSM) is widely used for identifying potential landslide-prone areas. However, many existing approaches rely on global models that assume spatial stationarity, which limits their ability to capture spatially heterogeneous relationships in complex mountainous regions. To address this issue, this study improved landslide susceptibility evaluation by accounting for spatial heterogeneity using a Geographically Weighted Random Forest (GWRF) model. By allowing the influence of conditioning factors to vary spatially, the proposed method provides a more adaptive representation of landslide susceptibility compared to conventional global models. The GWRF-based evaluation results were compared with those obtained from Random Forest (RF) and XGBoost models to examine relative performance. The study was conducted in Yingjiang County, a landslide-prone mountainous area, using multiple landslide conditioning factors, including topographic and anthropogenic variables such as slope and distance to roads. Landslide susceptibility maps were generated, and the evaluation results were supported by InSAR-derived deformation data, field investigations, and UAV observations. The results indicate that the GWRF model achieved superior overall susceptibility evaluation performance compared to the RF and XGBoost models, with an AUC value of 0.922. Furthermore, compared to global models, the GWRF model revealed more detailed spatial patterns of landslide susceptibility, particularly in high-susceptibility zones. Areas classified as highly susceptible by the GWRF model also demonstrated greater consistency with observed deformation features. These findings highlight the importance of considering spatial heterogeneity in landslide susceptibility evaluation and demonstrate that the proposed GWRF approach is applicable for regional-scale susceptibility assessment in complex mountainous environments.