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Sentinel-2 and Planetscope Data Fusion into Daily 3 M Images for Leaf Area Index Monitoring

Remote-sensing applications are limited by the tradeoff between spatial and temporal resolutions. Monitoring the dynamics of Leaf Area Index (LAI) from space is a key attribute to estimate crop types and their phenology over large areas, and in characterising spatial variations within growers' fields. This paper proposes a new method to fuse a time-series of Sentinel-2 and CubeSat imagery into daily RGB-NIR surface reflectance and subsequently LAI datasets at 3 m resolution. The results of this study, which focused on monitoring wheat LAI, show that this method is effective (RMSE of 0.58-0.76) for high spatio-temporal monitoring of field-crops.