Abstract
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. To improve the network performance over extremely turbid coastal oceans and inland waters, the networks are retrained with a training dataset including ocean color data from the Bohai Sea, Baltic Sea, and La Plata River Estuary, covering water types from clear open oceans to moderately turbid and highly turbid waters. The evaluation results show that the super-resolved Kd(490) image is much sharper than the original one, and has more detailed fine spatial structures. A similar enhancement of finer structures is also found in the super-resolved Chl-a images. Chl-a filaments are much sharper and thinner in the super-resolved image, and some of the very fine spatial features that are not shown in the original images appear in the super-resolved Chl-a imageries. The networks are also applied to four other coastal and inland water regions. The results show that super-resolution occurs mainly on pixels of Chl-a and Kd(490) features, especially on the feature edges and locations with a large spatial gradient. The biases between the original M-band images and super-resolved high-resolution images are small for both Chl-a and Kd(490) in moderately to extremely turbid coastal oceans and inland waters, indicating that the super-resolution process does not change the mean values of the original images.
Highlights
The biases between the original M-band images and super-resolved high-resolution images are small for both Chl-a and Kd (490) in moderately to extremely turbid coastal oceans and inland waters, indicating that the super-resolution process does not change the mean values of the original images
Chl-a data are the primary product for all the follow on satellite ocean color missions [26], such as the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) [27], the Moderate Resolution Imaging Spectroradiometer (MODIS) [28,29], the Medium-Resolution Imaging Spectrometer (MERIS) [30], Visible Infrared Imaging Radiometer Suite (VIIRS) [13,15], the Ocean and Land Colour Instrument (OLCI) [31], and the Second-Generation Global Imager [32]
VIIRS ocean color data over the Bohai Sea and Baltic Sea were used to train the networks for superresolution of coastal ocean color images [16]
Summary
Chl-a data are the primary product for all the follow on satellite ocean color missions [26], such as the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) [27], the Moderate Resolution Imaging Spectroradiometer (MODIS) [28,29], the Medium-Resolution Imaging Spectrometer (MERIS) [30], VIIRS [13,15], the Ocean and Land Colour Instrument (OLCI) [31], and the Second-Generation Global Imager [32]. We further develop and evaluate high-spatial resolution Chl-a and Kd (490) products based on super-resolved nLw (λ) images of the VIIRS five M-bands. Liu and Wang (2021) [16] trained and evaluated neural networks for super-resolving VIIRS M-bands nLw (λ) using ocean color data from the Bohai Sea and Baltic Sea separately, and found that the networks trained from these two regions are equivalent.
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