Reconstruction Of Missing Information In Satellite Imagery Using STS-CNN

Charles, Pranob Kumar; Babu, K Dinesh; Teja, Ch Siva Sai; Aditya, Somak

doi:10.2139/ssrn.3692152

Cited by 1 publication

(2 citation statements)

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“…where 𝑟 " and 𝑟 # are relative (percentage) root mean square error (RMSE) values from the linear regressions in ( 6) and (7), respectively. Using the inverse of RMSE in (10) allows 𝒙 ) to be calculated with a larger weighting for a more reliable vector between 𝒙 )," and 𝒙 ),# .…”

Section: Reconstruction Methodsmentioning

confidence: 99%

“…Bad (or missing) pixels are found not only in GEMS data but also in other satellite images. The information gaps induced by those pixels have motivated the development of methods to reconstruct Level-1 data from various space-borne instruments, including the Moderate Resolution Imaging Spectroradiometer (MODIS) [3][4][5][6][7][8], Landsat [4][5][6][7][8][9][10][11], and Gaofen-1 [12].…”

Section: Introduction Hementioning

confidence: 99%

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A Robust Bad-Pixel Radiance Reconstruction for the Geostationary Environment Monitoring Spectrometer (GEMS) – Influences on Aerosol Retrieval

Chong,

Lee,

Jung

et al. 2024

IEEE Trans. Geosci. Remote Sensing

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The Geostationary Environment Monitoring Spectrometer (GEMS), launched in February 2020, performs hourly measurements of earthshine radiances to retrieve column amounts of air pollutants over Asia. However, the charge-coupled device detector of GEMS has bad pixels that exhibit abnormal radiometric responses, which translates to a decrease in the quality of radiance measurements. Permanent bad pixels result in an information gap in the aerosol product at ~14.4-16.1ºN latitudes (e.g., in Manila, the Philippines, and Mainland Southeast Asia), which cannot be filled even with long-term observations owing to the structure of the East-West scanning mechanism of GEMS. Here, we propose a robust method to reconstruct radiances measured inaccurately by the bad pixels, based on spectral correlation induced mainly by the Fraunhofer line structures. The reconstruction aims at the bad pixels in the wavelength range of ~485-491 nm, which affects aerosol retrieval. We estimate that uncertainties in the reconstructed optical depths are ~2 orders of magnitude smaller than typical aerosol optical depths. Our results convincingly demonstrate that the reconstructed radiances effectively restore the physical distributions of visible aerosol indices, improving the determination of aerosol types. Furthermore, the reconstructed radiances enhance retrievals of aerosol layer height (ALH), holding particular significance for the long-term accumulation of ALH data over Southeast Asia using GEMS.

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Section: Reconstruction Methodsmentioning

confidence: 99%