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

Abstract

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. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

Spaceborne Lidar in the Study of Marine Systems

“…Analysis of SABOR data indicated that surface-weighted ocean-color-like properties yielded estimates of water column-integrated net primary production that consistently underestimated values calculated with vertically resolved data, with errors of up to 54% (Schulien et al 2017). The vertical plankton structure during SABOR was modest at best, and previous estimates of net primary production errors associated with a wider range in vertical structure indicate that such errors can exceed 100% (Platt & Sathyendranath 1988, Hill & Zimmerman 2010, Churnside 2015. These findings demonstrate the importance of plankton vertical structure for accurate assessments of ocean plankton stocks, productivity, and carbon cycling.…”

“…The result is that more than 92% of the ocean color signal emanates from the first optical depth (10-m geometric depth if the diffuse attenuation coefficient = 0.1 m −1 ), and 71% comes from the first half of the first optical depth (5 m for the same case). This limitation of ocean color can result in significant errors in important water column-integrated ocean properties, such as chlorophyll concentration (Sathyendranath & Platt 1989, Stramska & Stramski 2005 or net primary production (Platt & Sathyendranath 1988, Churnside 2015, Jacox et al 2015.…”

“…Churnside et al (2014) employed a parameterization based on chlorophyll concentration to estimate the ratio of attenuation to backscatter, thereby reducing the retrieval of β P and K L to solving for a single unknown, as is done for vertically resolved atmospheric aerosol retrievals (Fernald 1984). Churnside (2015) extended this bio-optical approach with an iterative scheme that also enabled the retrieval of chlorophyll concentration. This technique was then used to study plankton layers in the Arctic (Churnside & Marchbanks 2015) and vertical distributions of primary production (Churnside 2016).…”