  The availability of satellite optical information is often hampered by the
natural presence of clouds, which can be problematic for many applications.
Persistent clouds over agricultural fields can mask key stages of crop growth,
leading to unreliable yield predictions. Synthetic Aperture Radar (SAR)
provides all-weather imagery which can potentially overcome this limitation,
but given its high and distinct sensitivity to different surface properties,
the fusion of SAR and optical data still remains an open challenge. In this
work, we propose the use of Multi-Output Gaussian Process (MOGP) regression, a
machine learning technique that learns automatically the statistical
relationships among multisensor time series, to detect vegetated areas over
which the synergy between SAR-optical imageries is profitable. For this
purpose, we use the Sentinel-1 Radar Vegetation Index (RVI) and Sentinel-2 Leaf
Area Index (LAI) time series over a study area in north west of the Iberian
peninsula. Through a physical interpretation of MOGP trained models, we show
its ability to provide estimations of LAI even over cloudy periods using the
information shared with RVI, which guarantees the solution keeps always tied to
real measurements. Results demonstrate the advantage of MOGP especially for
long data gaps, where optical-based methods notoriously fail. The
leave-one-image-out assessment technique applied to the whole vegetation cover
shows MOGP predictions improve standard GP estimations over short-time gaps
(R$^2$ of 74\% vs 68\%, RMSE of 0.4 vs 0.44 $[m^2m^{-2}]$) and especially over
long-time gaps (R$^2$ of 33\% vs 12\%, RMSE of 0.5 vs 1.09 $[m^2m^{-2}]$).
