Hyperspectral imaging provides detailed spectral information and holds
significant potential for monitoring of greenhouse gases (GHGs). Cependant, its
application is constrained by limited spatial coverage and infrequent revisit
times. In contrast, multispectral imaging offers broader spatial and temporal
coverage but often lacks the spectral detail that can enhance GHG detection. To
address these challenges, this study proposes a spectral transformer model that
synthesizes hyperspectral data from multispectral inputs. The model is
pre-trained via a band-wise masked autoencoder and subsequently fine-tuned on
spatio-temporally aligned multispectral-hyperspectral image pairs. The
resulting synthetic hyperspectral data retain the spatial and temporal benefits
of multispectral imagery and improve GHG prediction accuracy relative to using
multispectral data alone. This approach effectively bridges the trade-off
between spectral resolution and coverage, highlighting its potential to advance
atmospheric monitoring by combining the strengths of hyperspectral and
multispectral systems with self-supervised deep learning.

Cet article explore les excursions dans le temps et leurs implications.

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