This study focuses on the integrated remote sensing identification and research of hidden landslide dangers in Longde County, Ningxia, China. The methodology combines differential interferometric synthetic aperture radar technology (D-InSAR) and high-resolution optical remote sensing to identify and analyze potential landslide hazards. The study area, Longde County, is characterized by complex geological conditions, including loess hills and red bed hills, and experiences frequent landslides due to heavy rainfall. The research uses 85 Sentinel-1A data from 2019 to mid-2020 and high-resolution optical remote sensing images from GF-2 in 2019 to obtain surface deformation information. The integrated remote sensing identification involves interpreting the shape and deformation marks of landslide hazards, identifying disaster-bearing bodies, and expert interpretation of environmental factors. The study identified 47 suspected landslide hazards and verified 16 with an accuracy of 76.19%. The results provide a scientific and theoretical basis for monitoring and treating landslide geological disasters and play a crucial role in disaster prevention. The study highlights the effectiveness of the integrated remote sensing identification technology in complex geological conditions and suggests future research directions to improve the accuracy and applicability of the method.This study focuses on the integrated remote sensing identification and research of hidden landslide dangers in Longde County, Ningxia, China. The methodology combines differential interferometric synthetic aperture radar technology (D-InSAR) and high-resolution optical remote sensing to identify and analyze potential landslide hazards. The study area, Longde County, is characterized by complex geological conditions, including loess hills and red bed hills, and experiences frequent landslides due to heavy rainfall. The research uses 85 Sentinel-1A data from 2019 to mid-2020 and high-resolution optical remote sensing images from GF-2 in 2019 to obtain surface deformation information. The integrated remote sensing identification involves interpreting the shape and deformation marks of landslide hazards, identifying disaster-bearing bodies, and expert interpretation of environmental factors. The study identified 47 suspected landslide hazards and verified 16 with an accuracy of 76.19%. The results provide a scientific and theoretical basis for monitoring and treating landslide geological disasters and play a crucial role in disaster prevention. The study highlights the effectiveness of the integrated remote sensing identification technology in complex geological conditions and suggests future research directions to improve the accuracy and applicability of the method.