Complex terrain is one of the main factors affecting the process of retrieving surface soil moisture using GNSS-R technology. This study evaluates the impact of complex terrain on surface soil moisture inversion using Cyclone Global Navigation Satellite System (CYGNSS) L3 SSM products, with Soil Moisture Active Passive (SMAP) SSM products as the true value. The errors in CYGNSS SSM are primarily attributed to med–high elevation and large relief. Compared with the Soil Moisture and Ocean Salinity (SMOS) and Advanced Microwave Scanning Radiometer 2 (AMSR2) SSM products, CYGNSS exhibits superior performance in terms of AD and RMSE (median AD = −0.10 m³/m³, RMSE = 0.14 m³/m³). The ubRMSE of CYGNSS (median ubRMSE = 0.094 m³/m³) outperforms SMOS, but is slightly worse than AMSR2, with the differences mainly observed in med–high elevation and large-relief regions. The three satellites complement each other in detecting complex terrain. CYGNSS errors (AD, RMSE) are higher in the rainy season than in the dry season, with greater discrepancies observed in large-relief, high-elevation regions compared to flatter, lower-elevation areas. This study provides the first comprehensive analysis of CYGNSS in such a complex region, offering valuable insights for improving the application of GNSS-R inversion technology.

Previous studies have shown that the use of appropriate ground control points (GCPs) and camera calibration models can optimize photogrammetry. However, the synergistic effects of GCPs and camera calibration models on UAV-SfM photogrammetry are still unknown. This study used camera models with varying complexities under different GCP conditions (in terms of number and quality) for UAV-SfM photogrammetry. The correlation matrix and root mean squared error (RMSE) were used to analyze the synergistic effects of GCPs and camera models. The results show that (1) without GCPs, complex camera models reduce distortion parameter correlation and improve terrain modeling accuracy by about 70%, with Model C (with F, Cx, Cy, K1–K4, and P1–P4) being the most widely applicable. (2) Increasing the number of GCPs enhances the terrain modeling accuracy more effectively than increasing the camera model complexity, reducing the RMSE by 45–70%, while the model complexity does not affect the required GCP number. (3) A strong interaction exists between the GCP quality and camera models: High-quality GCPs enhance camera model performance, while complex camera models reduce the requirement of GCP quality. This study provides both theoretical insights and practical guidance for efficient and low-cost UAV-SfM photogrammetry in different scenarios.