LAUSR

Abstract Sentinel-2 satellite is a new generation of multi-spectral remote sensing technique with high spatial, temporal and spectral resolution. Especially, Sentinel-2 incorporates three red-edge bands with central wavelength at 705, 740 and 783 nm, which are very sensitive to vegetation changing, heath and variations. Unfortunately, their spatial resolution is only 20 m, which is lower comparably. This spatial resolution brings difficulties for mining the potential of Sentinel-2 image in vegetation monitoring. Therefore, we focus on enhancing the spatial resolution of Sentinel-2 red edge band images to 10m using the SupReME algorithm. Furthermore, the summer corn canopy leaf area index (LAI), leaves chlorophyll content (LCC) and canopy chlorophyll content (CCC) were retrieved by the linear and physical models for the corn growth monitoring purpose. The results showed that the spatial resolution of Sentinel-2 images had been enhanced to 10m from original 20m, and the estimation accuracy (EA) was over 97% for pixels planted by summer corn. Moreover, the accuracy of summer corn canopy LAI, LCC and CCC was improved respectively using enhanced Sentinel-2 images by SupReME method. During these three parameters retrieval, the red-edge bands or SWIR bands were introduced into optimal cost function and vegetation index which the accuracy of these models was high. The SupReME algorithm provides a valuable way for Sentinel-2 images enhancement, which is of great potential to mining Sentinel-2 images and multitude its application.