Extending satellite ocean color remote sensing to the near-blue ultraviolet bands

Ultraviolet (UV) radiation has a profound impact on marine life, but historically and even currently, most ocean color satellites cannot provide radiance measurements in the UV, and thus UV penetration, in the global ocean. We develop a system (termed as UVISRdl) in this study, based on deep learning, to estimate remote sensing reflectance (R-rs) at 360, 380, and 400 nm (collectively termed as near-blue UV bands, nbUV) from R-rs in the visible bands that are obtained by ocean color satellites. This system is tested using both synthetic and field-measured data that cover a wide range and large number of values, with the resulted coefficient of determination close to 1.0 and bias close to 0 between UVISRdl estimated and known R-rs(nbUV). These results indicate excellent predictability of R-rs(nbUV) from Rrs(visible) via UVISRdl. The system was further applied to VIIRS (the Visible Infrared Imaging Radiometer Suite) data with the estimated R-rs(nbUV) evaluated using matchup field measurements, and obtained a mean absolute relative difference (MARD) at 360 nm of similar to 14% for oceanic waters and similar to 50% for coastal waters. These results are equivalent to those reported in the literature for satellite R-rs(visible) in oceanic and coastal waters. Examples of the global distribution of R-rs(nbUV), and subsequently the diffuse attenuation coefficient at the nbUV bands (K-d(nbUV)), are generated after applying UVISRdl to R-rs(visible) from the VIIRS data. The system lays the groundwork to generate decade-long R-rs(nbUV) and K-d(nbUV) from satellite ocean color data, which will be useful and important for both ocean color remote sensing and biogeochemical studies.