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Fusing optical and SAR time series for LAI gap filling with multioutput Gaussian processes

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Fusing optical and SAR time series for LAI gap filling with multioutput Gaussian processes

dc.contributor.author	Pipia, Luca
dc.contributor.author	Muñoz Marí, Jordi
dc.contributor.author	Amin, Eatidal
dc.contributor.author	Belda, Santiago
dc.contributor.author	Camps-Valls, Gustau
dc.contributor.author	Verrelst, Jochem
dc.date.accessioned	2020-12-09T14:05:00Z
dc.date.available	2020-12-09T14:05:00Z
dc.date.issued	2019
dc.identifier.citation	Pipia, Luca Muñoz Marí, Jordi Amin, Eatidal Belda, Santiago Camps Valls, Gustavo Verrelst, Jochem 2019 Fusing optical and SAR time series for LAI gap filling with multioutput Gaussian processes Remote Sensing of Environment 235 1111452
dc.identifier.uri	https://hdl.handle.net/10550/76590
dc.description.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 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 2 m −2 ]) and especially over long-time gaps (R 2 of 33% vs 12%, RMSE of 0.5 vs 1.09 [m 2 m −2 ]).
dc.language.iso	eng
dc.relation.ispartof	Remote Sensing of Environment, 2019, vol. 235, p. 1111452
dc.subject	Processos estocàstics
dc.subject	Teledetecció
dc.subject	Imatges Processament
dc.title	Fusing optical and SAR time series for LAI gap filling with multioutput Gaussian processes
dc.type	journal article	es_ES
dc.date.updated	2020-12-09T14:05:01Z
dc.identifier.doi	10.1016/j.rse.2019.111452
dc.identifier.idgrec	137112
dc.rights.accessRights	open access	es_ES