%0 Journal Article
%A Major, David
%A Horváth, Zsolt
%A Kröber, Felix
%A Augustin, Hannah
%A Sudmanns, Martin
%A Ševčík, Petr
%A Baraldi, Andrea
%A Berg, Astrid
%A Cornel, Daniel
%A Tiede, Dirk
%T A holistic approach for multi-spectral Sentinel-2 super-resolution and spectral evaluation
%J International journal of remote sensing
%V 46
%N 20
%@ 0143-1161
%C London
%I Taylor & Francis
%M FZJ-2025-04463
%P 7437 - 7464
%D 2025
%X Images provided by the European Copernicus Sentinel-2 satellites are valuable and easily accessible sources of remote sensing data for tasks across various fields. These data have a high spectral and temporal resolution, but a rather low spatial resolution, limiting their applicability for many tasks. In agricultural tasks, such as crop monitoring of small land parcels, the use of these data for fine-scale analysis is contingent upon the enhancement of spatial resolution while maintaining spectral fidelity. In this work, we propose a comprehensive single-image super-resolution reconstruction workflow that ensures both properties and is divided into two parts. First, a deep learning-based super-resolution reconstruction approach is applied to improve the spatial resolution of multi-spectral Sentinel-2 images to 2.5 m. For this purpose, a novel method is applied to achieve super-resolution of multiple spectral bands where associated real-word reference data is only partially available. It learns to increase the spatial resolution while preserving spectral accuracy of 10 m bands using high-resolution data from an auxiliary satellite with spectral correspondence, and 20 m bands without reference data using synthetic Sentinel-2 pairs. Second, the suitability of the method to subsequent agricultural tasks is evaluated by measuring the discrepancy between the super-resolved and reference data through a novel spectral knowledge-based validation method. This method leverages mappings of reflectances to spectral categories that enable assessing the spectral fidelity of super-resolved outputs, which is complementary to existing image quality assessment metrics, but with greater depth. The promising spectral validation results suggest that our super-resolution reconstruction pipeline has a great potential for agricultural applications.
%F PUB:(DE-HGF)16
%9 Journal Article
%R 10.1080/01431161.2025.2549132
%U https://juser.fz-juelich.de/record/1047697