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                <term xml:lang="en">Multiple scattering</term>
                <term xml:lang="en">Lidar attenuation coefficient</term>
                <term xml:lang="en">South China Sea</term>
                <term xml:lang="en">Hybrid method</term>
                <term xml:lang="en">Subsurface phytoplankton layer</term>
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              <p>The vertical distribution of subsurface phytoplankton in the ocean contains key information about not only ocean ecology but also water column optical properties relevant to remote sensing. The existing methods for measuring this vertical distribution are mainly field bio-optical and biogeochemical observations, which are time- and labor-intensive and do not provide sufficient spatial coverage. Passive satellite remote sensing provides global observations of phytoplankton but offers no information on subsurface vertical structure. In this study, we made the first quantitative measurements of vertical distribution of the subsurface phytoplankton layer (SPL) in the South China Sea (SCS) using airborne lidar. A total of five lidar flight experiments were conducted between 2017 and 2019 in the SCS, and approximately 2.5 terabytes of data were obtained. A hybrid retrieval method combining the Klett method for klidar and the perturbation method forβπ was developed. The lidar-retrieved chlorophyll-a concentrations and in situ data show good agreement (R2 greater than 0.8). The mean absolute percentage error for the lidar-retrieved chlorophyll-a concentrations is less than 15%. SPLs were observed both in Sanya Bay and in the open sea near Lingshui city, Hainan Province, China. The SPLs were observed at depths between 50 and 70 m in the open sea near Lingshui city, and between 5 and 30 m in Sanya Bay. The SPL depths have spatiotemporal variability, and we analyzed the possible factors (monsoon, seafloor depth, and sea surface temperature) that influence this spatiotemporal variability. The results show that lidar technology has a great potential for wide-range and long-term monitoring of SPLs, and is a good complement for discrete in situ observations and passive satellite remote sensing.</p>
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