Bio-optical model to describe remote sensing signals from a stratified ocean

Abstract. We use a bio-optical model of the optical properties of natural seawater to investigate the effects of subsurface chlorophyll layers on passive and active remote sensors. A thin layer of enhanced chlorophyll concentration reduces the remote sensing reflectance in the blue, while having little effect in the green. As a result, the chlorophyll concentration inferred from ocean color instruments will fall between the background concentration and the concentration in the layer, depending on the concentrations and the depth of the layer. For lidar, an iterative inversion algorithm is described that can reproduce the chlorophyll profile within the limits of the model. The model is extended to estimate column-integrated primary productivity, demonstrating that layers can contribute significantly to overall productivity. This contribution also depends on the chlorophyll concentrations and the depth of the layer. Using passive remote sensing alone to estimate primary productivity can lead to significant underestimation in the presence of subsurface plankton layers. Active remote sensing is not affected by this bias.

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