Landslides are mass movements of rock or soil down a slope, which may cause economic loss, damage to natural resources and frequent fatalities. To support risk management, landslide dating methods can provide useful knowledge about the date of the landslide and the frequency of occurrences, and thus potential triggers. Remote sensing techniques provide opportunities for landslide dating and are especially valuable in remote areas. However, the use of optical remote sensing is frequently hampered by cloud cover, decreasing the success rate and accuracy of dating. Here, we propose a landslide dating framework that combines the advantages of optical and SAR remote sensing satellites, because optical monitoring provides spectral changes on the ground and microwave observations provide information on surface changes due to loss of coherence. Our method combines Sentinel-1 and -2 satellite data, and is designed for cases wherein the landslide causes vegetation decrease and terrain deformation resulting in changing Normalized Difference Vegetation Index (NDVI) and SAR backscatter values. This landslide dating framework was tested and evaluated against 60 published landslides across the world. We show that the mean accuracy of landslide dating reaches 23 days when using combined Sentinel-1 and -2 imagery, which is a pronounced improvement compared to using only optical Sentinel-2 images resulting in an accuracy of 51 days. This study highlights that a combination of optical and SAR remote sensing monitoring is a promising technique for dating landslides, especially in remote areas where monitoring equipment is limited or which are frequently covered by clouds. Our method contributes to identifying failure mechanism by providing reliable date ranges of landslide occurrence, assessing landslide hazard and constructing landslide early warning systems.