Landslide rheology governs the deformation and flow behavior of sliding masses. As rheology strongly varies as a function of the composition and environment of landslides, a wide range of viscosities have been suggested based on very limited experimental or observational constraints. Here, we introduce a novel method to quantify the landslide rheology from remote sensing data. We focus on an ideal natural laboratory, the Slumgullion landslide, Colorado, which has moved at tens of millimeters per day for centuries. A joint analysis of Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) Interferometric Synthetic Aperture Radar (InSAR)‐derived surface displacements and Light Detection and Ranging (LiDAR) Digital Elevation Model (DEM)‐derived landslide thickness at its frontal toe allows us to invert for the intrinsic viscosity (109–1011.5 Pa·s under different degrees of plasticity) based on the Bingham plastic model. Detailed displacement measurements also elucidate local variations in magnitude and orientation. Our method presents the capability of remote sensing data to understand the rheology of quasi‐static debris slides in general. Plain Language Summary Landslides are common geological hazards that may lead to casualties and damage. Landslides also present as a surface process that reshapes mountainous landscapes around the world. The soil and rock materials, water content, and the associated vegetation and organic matter contribute to a variety of landslide mechanical properties and rheology, which characterize the slide/flow approximation of the mass wasting process and thus the landslide speed. To date, the determination of the landslide rheology has mainly relied on analyzing samples in the lab. However, many landslides are inaccessible for sample collection, and the lab environment subject to isolated and small samples can hardly be compared to the intact landslide in nature. Taking advantage of high‐resolution airborne remote sensing data sets at the Slumgullion debris slide in Colorado, we extract the landslide surface displacements and topography. Incorporating our observations, we consider a classic model in fluid mechanism to infer the rheological parameters. We also identify the spatiotemporally variable landslide movements. A joint analysis and interdisciplinary approach incorporating remote sensing and basic physics can help us better determine the landslide dynamics and mitigate the risks to humans.