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Heavy rainfall events in mountainous areas can trigger thousands of destructive landslides, which pose a risk to people and infrastructure and significantly affect the landscape. Landslide locations are typically mapped using optical satellite imagery, but in some regions their timings are often poorly constrained due to persistent cloud cover. Physical and empirical models that provide insights on the processes behind the triggered landsliding require information on both the spatial extent and timing of landslides. Here we demonstrate that Sentinel-1 SAR amplitude time series can be used to constrain landslide timing to within a few days and present three methods to accomplish this based on time series of: (i) the difference in amplitude between the landslide and its surroundings, (ii) the spatial variability of amplitude between pixels within the landslide, and (iii) geometric shadows cast within the landslide. We test these methods on three inventories of landslides of known timing, covering various settings and triggers, and demonstrate that, when used in combination, our methods allow 20 % of landslides to be timed with an accuracy of 80 %. This will allow multi-temporal landslide inventories to be generated for long rainfall events such as the Indian summer monsoon, which triggers large numbers of landslides every year and has until now been limited to annual-scale analysis.