Journal
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Volume 59, Issue 8, Pages 6386-6396Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2020.3026944
Keywords
Atmospheric temperature; high-spectral infrared observations; improved atmospheric sounding in the infrared (IASI); neural networks (NNs); retrieval uncertainties; satellite remote sensing
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Neural networks are increasingly popular for interpreting remote sensing observations, providing flexibility and accuracy in retrieving various geophysical variables in the atmosphere, land, and ocean. Future work will focus on developing simpler methods for estimating retrieval uncertainties in neural network inversion schemes.
Neural networks (NNs) are becoming increasingly more popular to interpret remote sensing observations in many contexts. Their flexibility and accuracy have been a true advantage for the retrieval of many geophysical variables in the atmosphere, land, and ocean, over the last three decades. Uncertainty of the retrieved products is important to assess the quality of the retrievals but also to combine products a posteriori, combine them in a complex way to study for instance the water cycle, or assimilate them in numerical weather prediction (NWP) centers. However, no easy solution has been proposed so far to estimate the retrieval uncertainties of NN inversion schemes. A simple, pragmatic, and easy-to-implement scheme based on the input space clustering is proposed here to perform this task. Tests are conducted using an application aiming at retrieving atmospheric profiles based on improved atmospheric sounding in the infrared (IASI) high-spectral resolution observations in the infrared.
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