Landslides modify the natural landscape and cause fatalities and property damage worldwide.

Quantifying landslide dynamics is challenging due to the stochastic nature of the environment.

With its large area of ~1 km2 and perennial motions at ~10–20mm per day, the

Slumgullion landslide in Colorado, USA, represents an ideal natural laboratory to better

understand landslide behavior. Here, we use hybrid remote sensing data and methods to

recover the four-dimensional surface motions during 2011–2018. We refine the boundaries of

an area of ~0.35 km2 below the crest of the prehistoric landslide. We construct a mechanical

framework to quantify the rheology, subsurface channel geometry, mass flow rate, and

spatiotemporally dependent pore-water pressure feedback through a joint analysis of displacement

and hydrometeorological measurements from ground, air and space. Our study

demonstrates the importance of remotely characterizing often inaccessible, dangerous slopes

to better understand landslides and other quasi-static mass fluxes in natural and industrial

environments, which will ultimately help reduce associated hazards.