Spectrotemporal fusion : generation of frequent hyperspectral satellite imagery

Abstract

Recent advances in remote sensing technology have facilitated the emergence of high-quality hyperspectral satellite sensors with spatial resolutions comparable to well-established multispectral platforms like Landsat series and Sentinel-2. However, most hyperspectral satellite datasets suffer from limited temporal resolution, hindering the effective monitoring of rapid changes on the Earth's surface. To address this issue, we proposed an innovative fusion strategy named spectrotemporal fusion (SpecTF). Through SpecTF, high-frequency temporal information from multispectral images (MSIs) and narrow-band spectral information from hyperspectral images (HSIs) can be blended for applications that require high resolutions in both temporal and spectral domains. SpecTF first leverages a limited number of historical HSI-MSI pairs to learn the cross-sensor spectral mapping and then fuses this spectral mapping with broad-band time series to reconstruct narrow-band ones. The performance of SpecTF was evaluated using typical satellite datasets across six sites and a suite of field measurements. The average root mean square error (RMSE) and spectral angle of SpecTF are 0.0224 ± 0.0142 and 3.3734 ± 1.5476°, respectively, which represent a 24.83 % and 33.23 % reduction in error compared to the second-best method. The experimental results demonstrate that the synthetic frequent narrow-band products exhibit satisfactory quality and improved accuracy of land surface parameter retrieval compared to real broad-band observations.