Emulation of Synthetic Hyperspectral Sentinel-2-Like Reflectance Images Using Neural Networks

Hyperspectral satellite images provide highly-resolved spectral information for large areas. However, spaceborne imaging spectrometers are expensive and currently only a few hyperspectral satellites are in operation. This is a strong limitation, since hyperspectral satellite data provide vital information for numerous fields of application. To overcome this, we developed an emulator using machine learning techniques to generate a synthetic hyperspectral satellite image based on the relationship of a Sentinel-2 (S2) scene and a hyperspectral HyPlant airborne image. The proposed approach was tested on data sets recorded from an agricultural region in western Germany and the results show that a reliable hyperspectral image with the spectral resolution of HyPlant and the spatial extent of the S2 scene can be generated. We systematically tested the approach for different spatial resolutions, including and excluding the S2 spectral bands B1 (coastal aerosol band) and B10 (cirrus band), different machine learning regression algorithms and different numbers of training samples. The best performing parameters were: excluding B1 and B10 bands, resample to 20m and train the emulator with a Neural Networks (NN) with 100'000 samples. That emulator was then applied to the L2A (bottom-of-atmosphere reflectance) S2 subset, and obtained hyperspectral reflectance data were then compared to a reference HyPlant reflectance image of the same region. The synthetic hyperspectral S2-like map was generated quickly and a good agreement with the reference reflectance was achieved. To evaluate the result image we selected the band located at 760 nm due to its importance for the retrieval of solar-induced fluorescence. Goodness-of-fit results (R
²
of 0.92 and NRMSE of 3.87%) suggest that hyperspectral S2-like reflectance scenes can be produced with high accuracy. The emulator was then applied to a full S2 tile to generate a hyperspectral S2-like reflectance scene (60 Gb), which took less than one hour.