[2605.04239] Densification and forecasting of Sentinel-2 time series from multimodal SAR and Optical satellite data using deep generative models
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Computer Science > Computer Vision and Pattern Recognition
arXiv:2605.04239 (cs)
[Submitted on 5 May 2026]
Title:Densification and forecasting of Sentinel-2 time series from multimodal SAR and Optical satellite data using deep generative models
Authors:Véronique Defonte, Dawa Derksen, Alexandre Constantin, Bastien Nespoulous
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Abstract: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. While these approaches improve temporal continuity, they are inherently restricted to the reconstruction of the gaps within the observed time periods, and do not address the prediction of future observations. This work proposes a probabilistic deep learning framework for the densification and forecasting of Sentinel-2 time series by generating optical images at arbitrary past or future dates. The approach leverages multimodal satellite data by jointly exploiting Sentinel-2 optical and Sentinel-1 SAR observations. Unlike most existing works, we propose to focus on the uncertainty of the generated images. Experimental results demonstrate effective densification and forecasting, on sparse and temporally misaligned time series.
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2605.04239 [cs.CV]
(or arXiv:2605.04239v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2605.04239
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arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Véronique Defonte [view email] [v1] Tue, 5 May 2026 19:27:29 UTC (18,323 KB)
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