Densification and forecasting of Sentinel-2 time series from multimodal SAR and Optical satellite data using deep generative models

ArXi:2605.04239v1 Announce Type: new Optical satellite image time series are extensively used in many Earth observation applications, including agriculture, climate monitoring, and land surface analysis. However, clouds and swath edges result in irregular sampling along the temporal dimension, limiting continuous monitoring. To address this issue, a growing body of work has focused on temporal densification and reconstruction of satellite image time series, with the objective of filling missing or cloud-contaminated observations within the temporal extent of the available data.