This study investigates the risk assessment of geological landslide hazards in Longde County, Ningxia, using D-InSAR and remote sensing technologies. The research focuses on identifying hidden landslide hazards in an area with complex geology and high vegetation coverage, where traditional methods are limited. The study combines differential interferometric synthetic aperture radar (D-InSAR) with high-resolution optical remote sensing data to analyze surface deformation and identify potential landslide hazards. By analyzing 85 Sentinel-1A data from 2019 to mid-2020 and GF-2 optical remote sensing data from 2019, the study identifies 47 suspected landslide hazards and 21 field investigation points. Of these, 16 hazards were verified with an accuracy of 76.19%, confirming the effectiveness of the integrated remote sensing identification method. The study also highlights the importance of combining remote sensing data with field verification and expert interpretation to accurately identify landslide hazards. The results provide a scientific basis for future landslide monitoring and prevention, emphasizing the need for integrated remote sensing technologies in landslide hazard assessment. The study demonstrates that D-InSAR and high-resolution optical remote sensing can effectively detect surface deformation and identify landslide hazards, even in areas with dense vegetation and complex topography. The findings contribute to the understanding of landslide hazards and support the development of more effective prevention and mitigation strategies.This study investigates the risk assessment of geological landslide hazards in Longde County, Ningxia, using D-InSAR and remote sensing technologies. The research focuses on identifying hidden landslide hazards in an area with complex geology and high vegetation coverage, where traditional methods are limited. The study combines differential interferometric synthetic aperture radar (D-InSAR) with high-resolution optical remote sensing data to analyze surface deformation and identify potential landslide hazards. By analyzing 85 Sentinel-1A data from 2019 to mid-2020 and GF-2 optical remote sensing data from 2019, the study identifies 47 suspected landslide hazards and 21 field investigation points. Of these, 16 hazards were verified with an accuracy of 76.19%, confirming the effectiveness of the integrated remote sensing identification method. The study also highlights the importance of combining remote sensing data with field verification and expert interpretation to accurately identify landslide hazards. The results provide a scientific basis for future landslide monitoring and prevention, emphasizing the need for integrated remote sensing technologies in landslide hazard assessment. The study demonstrates that D-InSAR and high-resolution optical remote sensing can effectively detect surface deformation and identify landslide hazards, even in areas with dense vegetation and complex topography. The findings contribute to the understanding of landslide hazards and support the development of more effective prevention and mitigation strategies.