Fusing optical and SAR time series for LAI gap fillingwith multioutput Gaussian processes.

ABSTRACT: 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 imager

SUBMITTER: Pipia L 

PROVIDER: S-EPMC7613333 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature