We combine Aqua MODIS and Aura Ozone Monitoring Instrument (OMI) data with a radiative transfer model to estimate the penetration depths of solar UV radiation into global ocean. The penetration depths of DNA damaging UV irradiance (10% of the irradiance at the surface) vary from a few meters in areas of productive waters to about 30m in the clearest waters.
Scientific significance, societal relevance, and relationships to future missions:
Assessment of the UV effects on aquatic ecosystems, e.g., damage to the phytoplankton deoxyribonucleic acid (DNA), requires an estimate of the in-water hyperspectral radiation field. The paper describes a prototype of satellite algorithm for computing the hyperspectral surface and in-water UV solar radiation for the Ocean Color Instrument (OCI), which is the primary sensor of the NASA’s Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission.
The OCI is high spatial resolution (~1km) spectrometer, which has ultraviolet A (UV-A) channels longer than ~340 nm.
We estimate the penetration depth of solar UV radiation in ocean waters on a global scale by combining satellite data of cloud/surface UV reflectivity and ozone from the Ozone Monitoring Instrument (OMI) on board NASA’s Earth Observing System (EOS) Aura spacecraft and in-water chlorophyll concentration from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board NASA’s EOS Aqua satellite with the radiative transfer computations in the atmosphere-ocean system. A dominant factor defining the UV penetration depths is chlorophyll concentration. There are other constituents in waters that absorb in addition to chlorophyll; the absorption from these constituents can be related to that of chlorophyll in open ocean (Case I) waters using an inherent optical properties model.
The DNA damage penetration depths of UV irradiance (10% of the irradiance at the surface) vary from a few meters in areas of productive waters to about 30-35 meters in the clearest waters. To significantly enhance the computational efficiency of the algorithm, we developed a machine learning approach based on artificial neural network based on the full physical algorithm. The machine learning algorithm shows a very good performance and considerably less computing time in predicting the UV penetration depths.
Vasilkov A. P., Krotkov, N. A., Haffner, D., Fasnacht, Z., Joiner, J., Estimates of hyperspectral surface and underwater UV planar and scalar irradiances from OMI measurements and radiative transfer calculations, Remote Sensing, 2022, 14, 2278. https://doi.org/10.3390/rs14092278 , published May 9, 2022