Deriving VIIRS High-Spatial Resolution Water Property Data over Coastal and Inland Waters Using Deep Convolutional Neural Network

Liu, Xiaoming; Wang, Menghua

doi:10.3390/rs13101944

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Deriving VIIRS High-Spatial Resolution Water Property Data over Coastal and Inland Waters Using Deep Convolutional Neural Network

2021
By Liu, Xiaoming ; Wang, Menghua
Source: Remote Sens. 2021, 13(10), 1944

[PDF-4.49 MB]

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Journal Title:

Remote Sensing
Personal Author:

Liu, Xiaoming ; Wang, Menghua

Liu, Xiaoming ; Wang, Menghua Less -
NOAA Program & Office:

NESDIS (National Environmental Satellite, Data, and Information Service) ; STAR (Center for Satellite Applications and Research) ; JPSS (Joint Polar Satellite System Program Office) ; CIRA (Cooperative Institute for Research in the Atmosphere)

NESDIS (National Environmental Satellite, Data, and Information Service) ; STAR (Center for Satellite Applications and Research) ; JPSS (Joint Polar Satellite System Program Office) ; CIRA (Cooperative Institute for Research in the Atmosphere) Less -
Description:

The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) satellite has been a reliable source of ocean color data products, including five moderate (M) bands and one imagery (I) band normalized water-leaving radiance spectra nLw(λ). The spatial resolutions of the M-band and I-band nLw(λ) are 750 m and 375 m, respectively. With the technique of convolutional neural network (CNN), the M-band nLw(λ) imagery can be super-resolved from 750 m to 375 m spatial resolution by leveraging the high spatial resolution features of I1-band nLw(λ) data. However, it is also important to enhance the spatial resolution of VIIRS-derived chlorophyll-a (Chl-a) concentration and the water diffuse attenuation coefficient at the wavelength of 490 nm (Kd(490)), as well as other biological and biogeochemical products. In this study, we describe our effort to derive high-resolution Kd(490) and Chl-a data based on super-resolved nLw(λ) images at the VIIRS five M-bands.
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Chlorophyll-a Convolutional Neural Network Super-resolution VIIRS; Ocean Color
Source:

Remote Sens. 2021, 13(10), 1944
DOI:

https://doi.org/10.3390/rs13101944
Document Type:

Journal Article
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CC BY
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Submitted
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urn:sha256:17b0b834066163a5300240120f8609b196132366b7491c06ae687cb66014ef33
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https://repository.library.noaa.gov/view/noaa/41835/noaa_41835_DS1.pdf
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Deriving VIIRS High-Spatial Resolution Water Property Data over Coastal and Inland Waters Using Deep Convolutional Neural Network

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