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

    James H. Churnside

    Downloaded From: on 14 May 2021 Terms of Use:

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

    James H. Churnside* NOAA Earth System Research Laboratory, 325 Broadway, Boulder, Colorado 80305,

    United States

    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 subsur- face 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. [DOI: 10 .1117/1.JRS.9.095989]

    Keywords: ocean color; remote sensing; thin-plankton layers; lidar; chlorophyll.

    Paper 15262P received Apr. 10, 2015; accepted for publication Oct. 9, 2015; published online Nov. 6, 2015.

    1 Introduction

    There has been a significant effort to describe passive remote sensor signals for case 1 waters with a semianalytic approach.1–8 For case 1 waters, the optical properties are assumed to be dominated by pure water contributions and the effects of chlorophyll-containing phytoplankton. In the semianalytic approach, analytic relationships between optical properties of the water are combined with empirical dependencies on chlorophyll concentration. In these studies, the optical properties are assumed to be homogeneous, determined by the value of chlorophyll concentra- tion at the surface.

    More recently, the approach has been extended to include case 2 waters,9–13 generally coastal waters, where the effects of suspended and dissolved materials that are not related to chlorophyll on the optical properties are important. In these investigations, the main motivation was the estimation of chlorophyll concentration from passive remote sensing signals in coastal and estua- rine waters.

    Other work has focused on deriving additional information from passive remote sensors. One area of investigation has been to obtain information about phytoplankton cell size.14–17

    Another is to identify phytoplankton functional groups.18–21 A third area is to detect harmful algal blooms.22–25

    There have been several investigations into the effects of nonuniform vertical distribution of chlorophyll on passive remote sensing signals. Sathyendranath and Platt26 investigated the effects on the blue-green ratio and found relative errors in excess of 100% in estimates of photic depth and total chlorophyll concentration in the photic zone. Gordon27 used a Monte-Carlo sim- ulation to test the hypothesis that the reflectance of a stratified ocean is the same as that of a

    *Address all correspondence to: James H. Churnside, E-mail:

    Journal of Applied Remote Sensing 095989-1 Vol. 9, 2015

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  • homogeneous ocean with a constant chlorophyll concentration equal to the depth-weighted aver- age of the actual profile, and found that the hypothesis was accurate to within 25% for the cases tested. Stramska and Stramski28 used a sophisticated radiative transfer model to investigate the effects of a nonuniform depth profile, reporting differences from the uniform profile greater than 70%, with the larger differences produced by lower surface chlorophyll concentrations. Pitarch et al.29 considered retrieval of the chlorophyll profile using passive remote sensing with some success.

    Approximate models of active remote sensor signals have also been developed.30 The most successful is probably the quasisingle-scattering approximation,2,31–33 which has been shown to provide good agreement with measured values.34 This model has recently been used to obtain an expression for the lidar extinction-to-backscatter ratio for lidar inversions35 that can provide the depth profile of phytoplankton directly.

    Roughly, half of the global photosynthesis takes place in the upper ocean, and inversion models have been developed to quantify this production using satellite data.36–38 These models are generally based on surface chlorophyll concentration from ocean color and sea-surface tem- perature from thermal radiometers. These estimates do not always agree with in situ estimates of productivity, and stratification has been suggested as one possible reason for the discrepancy.38

    Stratification is important, because phytoplankton are often seen to occur in thin (3 to 5 m or less) layers associated at the pycnocline at the base of the surface mixed layer.39–41 Most of the observations have been in coastal regions, but thin layers have also been observed in the open ocean.39 These layers can contain a significant fraction of the total chlorophyll in the water col- umn,42,43 affecting not only primary production, but also the grazing success of zooplankton and higher-trophic levels.

    In this work, we consider a semianalytic bio-optical model to describe passive and active remote sensor signals. To illustrate the effects of stratification, a layer of relatively high-chloro- phyll concentration is embedded within a lower-background concentration. The effects of such a layer on primary productivity and on satellite estimates of primary productivity are estimated.

    2 Semianalytic Bio-Optical Model

    In the semianalytic ocean color model of Gordon et al.,1


    R Q

    ¼ 0.0949 bb aþ bb

    þ 0.0794 �

    bb aþ bb

    � 2

    ; (1)

    where R is the irradiance reflectance just below the surface, Q is the ratio of upwelling radiance to upwelling irradiance, bb is the integral of the scattering coefficient over all possible scattering directions where the scattering angle is greater than 0.5π, and a is the absorption coefficient. This equation is simplified by assuming that the second term is small. Gordon et al.1 continue with the approximation for the diffuse attenuation coefficient

    EQ-TARGET;temp:intralink-;e002;116;252Kd ¼ 1.054ðaþ bbÞ

    cosðθsÞ ; (2)

    where θs is the solar zenith angle. They use a nominal solar zenith angle of 25 deg, with the assertion that the resulting error is less than 10% for angles between 0 deg and 34 deg. Rather than take this approach, we will consider a zenith angle of 0 deg; the correction for other zenith angles is straightforward and will be included in the estimates of primary productivity. The result of these approximations is


    R Q

    ¼ 0.10 bb Kd

    : (3)

    For passive remote sensing, we are interested in the remote sensing reflectance, Rrs, defined as the ratio of the upwelling radiance to the downwelling irradiance just above the surface. Mobley44 has shown that Rrs can be approximated by 0.54R∕Q, so we have

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

    Journal of Applied Remote Sensing 095989-2 Vol. 9, 2015

    Downloaded From: on 14 May 2021 Terms of Use:

  • EQ-TARGET;temp:intralink-;e004;116;735Rrs ¼ 0.054 bb Kd

    : (4)

    We expect this approximation to be valid for values less than about 0.01 sr−1. Equation (4) was developed under the assumption that the optical properties of the water are constant with depth. If they are not, it should be possible to estimate the remote sensing reflectance, to the same level of approximation, by the expression

    EQ-TARGET;temp:intralink-;e005;116;653Rrs ¼ 0.11 Z


    bbðzÞ exp � −2

    Z z


    Kdðz 0Þdz 0 � dz: (5)

    This equation was developed to have the expected exponential attenuation with depth and to reduce to Eq. (4) when bb and Kd are constant.

    We can relate the remote sensing reflectance to chlorophyll concentration, C, using the model described by Morel and Maritorena.3 In this model, we have