Abstract

Multiple scattering is an inevitable effect in spaceborne oceanic lidar because of the large footprint size and the high optical density of seawater. The effective attenuation coefficient klidar in the oceanic lidar equation, which indicates the influence of the multiple scattering effect on the formation of lidar returns, is an important parameter in the retrieval of inherent optical properties (IOPs) of seawater. In this paper, the relationships between klidar of the spaceborne lidar signal and the IOPs of seawater are investigated by solving the radiative transfer equation with an improved semianalytic Monte Carlo model. Apart from the geometric loss factors, klidar is found to decrease exponentially with the increase of depth in homogeneous waters. klidar is given as an exponential function of depth and IOPs of seawater. The mean percentage errors between klidar calculated by the exponential function and the simulated ones in three typical stratified waters are within 0.5%, proving the effectiveness and applicability of this klidar-IOPs function. The results in this paper can help researchers have a better understanding of the multiple scattering effect of spaceborne lidar and improve the retrieval accuracy of the IOPs and the chlorophyll concentration of case 1 water from spaceborne lidar measurements.

© 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

1. Introduction

The ocean is a complex biological system that contains a vast number of biological species. Phytoplankton play a very important role in the material recycle and radiation budget of the marine ecosystem [1]. Satellite-based ocean color observations have been widely used to understand the distribution and variable characteristics of global phytoplankton [2]. The current ocean color measurements were made by passive sensors, which have several fundamental limitations: retrievals are highly sensitive to the atmospheric correction errors; information about the vertical distribution of ocean layers is missing; observations during nighttime, at high solar zenith angles and through thick cloud or absorbing aerosols are unavailable [3]. The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) has demonstrated advantages in day-night continuous sampling, observations through absorbing aerosols and thin cloud layers [4], capabilities of vertical profiling through the subsurface [5–7], and measurements at high-latitude regions [8]. However, CALIOP has limited spatial coverage compared to most passive sensors, and can only provide an integrated view of optical properties in the upper ocean. Specifically, CALIOP observation is limited by its coarse vertical resolution (i.e., 22.4m in the water) [9,10]. To improve the retrieval accuracy of spaceborne lidars, more information is needed to better understand factors involved in lidar functions.

Lidars obtain depth-resolved return profiles by collecting backscattered signals from molecules and particles. It is essential to establish an accurate model of the radiative transfer process. The return signal of spaceborne oceanic lidar is strongly affected by multiple scattering due to the large footprint size and the high optical density of seawater [11].To characterize the multiple-scattering radiative transfer process, an effective attenuation coefficient klidar is introduced into the oceanic lidar equation [12]. The relationships between klidar and the optical properties of water and the lidar system parameters were extensively studied. Gordon [12] found that the ratio of the radius of the spot on the sea surface viewed by the lidar receiver optics to the mean free path of photons in water is a significant parameter which is related to klidar. When this ratio is near zero, klidar is given by the beam attenuation coefficient. If the ratio is greater than 5, klidar is given by the diffuse attenuation coefficient Kd. Phillips et al. [13] proposed a slightly different assumption that the upper and lower limits of klidar is given by the beam attenuation coefficient and the absorption coefficient, respectively, depending on field of view (FOV). Lee et al. [14] and Schulien et al. [15] observed strong correlations between the in situ Kd and klidar derived from airborne lidar experiments. Collister et al. [16] exhibited the similarity between klidar and the absorption coefficient under wide FOV using shipborne lidar measurements. The aforementioned studies are based on airborne or shipborne platform, and the depth characteristics of klidar are ignored. The values of klidar at different depths in homogenous water are not constant, due to the multiple scattering effect [17,18], especially near the ocean surface. This phenomenon leads to errors in the retrieval of optical properties, which further affects the assessment accuracy of the phytoplankton biomass and primary productivity. Thus, the depth characteristics of klidar and the relationship between klidar and the optical properties of seawater need to be studied in detail.

In this paper, the potential of using an improved semianalytic Monte Carlo radiative transfer simulation model (SALMON) to accurately identify the characteristics of both homogeneous and inhomogeneous waters is investigated. The use of SALMON is proposed by Poole et al. [19] and widely applied in oceanic lidar, therefore, it would be beneficial to develop an improved SALMON that can better simulate the inhomogeneous waters in the real oceans. In Section.2, we present the principle and method of proposed SALMON. In Section 3, we use this improved model to investigate the characteristics of lidar return profiles and the corresponding klidar with analysis of the influence of the lidar system parameters on klidar and the depth characteristic of klidar for homogenous water. In addition, an exact relationship between the depth-resolved klidar and IOPs of seawater is derived based on the statistical properties of case 1 waters. In Section 4, we compare klidar calculated with our function and Kd from bio-optical model with the simulation results in three vertical stratified waters to verify the applicability of this function in real ocean environment.

2. Principle and method

2.1Return signals of oceanic lidar

The schematic diagram of the theoretical model of laser backscatter from water is illustrated in Fig. 1. The laser pulse transmits downward towards the flat horizontal water surface at the height of H. The photons are scattered and absorbed by the molecules and particles in seawater, and the backscattered photons are received by the detector forming the lidar return signal, which can be described by the lidar equation [3,20]:

P(z)=P0Mvτ2nTatm2Tsurf2A(nH+z)2βπ(z)exp(20zklidar(z)dz),
where P(z) is the power received from a depth of z, P0 is the transmitted pulse power, M is the lidar instrument constant, v is the speed of light in vacuum, τis the temporal pulse length,vt/(2n) is the depth resolution in water, Tatm is the transmission through the atmosphere, Tsurf is the Fresnel transmittance of the air-sea surface, A is the receiver area, n is the refractive index of sea water andβπis the volume scattering coefficient at a scattering angle ofπrad.

 

Fig. 1 The schematic diagram of spaceborne lidar system.

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Rewriting Eq. (1), we have the Pnorm(z) expressed by

Pnorm(z)=P(z)(nH+z)2P0M(vτ2n)Tatm2Tsurf2Aβπ(z)=exp(20zklidar(z)dz).

Thus, the effective attenuation coefficient can be written as

klidar(z)=12ddzln(P(z)norm).

klidar is an important parameter for retrieving optical properties of seawater. The IOPs considered here include the scattering coefficientb(λ)(m−1), the absorption coefficienta(λ) (m−1), and the beam attenuation coefficientc(λ)(m−1). The relation among those three parameters are expressed as

c(λ)=a(λ)+b(λ).

The IOPs of case 1 water can be parameterized in terms of the chlorophyll concentration[Chl]and wavelength [21]. The case 1 water is chlorophyll dominated water which includes almost 98% of the world's open ocean and coastal waters. Therefore, lidar remote sensing of the case 1 water plays an important role in estimating global ocean primary productivity.

According to the bio-optical model of case 1 water [21], the absorption coefficient could be expressed as

a(λ)=[aw(λ)+0.06ac(λ)[Chl]0.65][1+0.2exp(0.014(λ440))],
herein aw(λ) is the absorption of pure sea water and ac(λ)is a nondimensional statistically derived chlorophyll-specific absorption coefficient [21]. The scattering coefficient can be written as [21]
b(λ)=bw(λ)+550λ×0.3×[Chl]0.62,
where bw(λ) is the scattering coefficient of pure sea water, which is given by [22]

bw(λ)=16.06×(550/λ)4.324×1.21×104.

When [Chl] is expressed in the unit of mg/m3 and λ is in the unit of nm, the resulting a(λ) and b(λ) are in the unit of m−1. In this paper, the working wavelength is at 532 nm, which is commonly used in oceanic lidars.

The diffuse attenuation coefficient Kd, one of the apparent optical properties (AOPs) of case 1 water, is characterized by [Chl] [23]. For the wavelength of 530 nm, Kd is given by

Kd=aw+bw/2+0.04826[Chl]0.67224.

In the case of single scattering, klidar is equal to c [12,13], whereas for the multiple scattering, the express of klidar is more complicated. In this paper, an improved SALMON is used to trace the multiple scattering behavior of photons in water to get the lidar return profiles and the slope method was introduced to calculate the depth-dependent klidar.

2.2 An improved semianalytic Monte Carlo radiative transfer simulation model

Monte Carlo method is an a numerical method of solving the radiative transfer which is used in optical communication [24], biological optics [25,26] and optical remote sensing [27]. In 1981, Poole et al. [19] proposed a semianalytic Monte Carlo radiative transfer simulation model (SALMON) for atmosphere lidar, which is widely used in the current oceanic lidars. The improved SALMON proposed here includes a Gaussian beam emission model and it is applicable for both homogeneous and inhomogeneous waters.

A: Gaussian beam emission model

The beam divergence angle of lidar is always neglected in airborne lidar simulations because it is very small compared with the FOV of airborne lidar receiving system [12,13]. However, it is not negligible in the space lidar simulation, because the platform of spaceborne lidars usually has an altitude of several hundred kilometers; as a result, the spot diameter of the laser beam incident on the sea surface could be several tens of meters. A Gaussian distribution sampling model is introduced in this paper to determine the coordinates and directions of photons incident on the water surface [28].

The intensity perpendicular to the transmission direction of the Gaussian beam is expressed as

I(x,y)=12πσs2exp(x2+y22σs2),
where I is the light intensity, x and y are the coordinates on the plane particular to the transmission direction of the light ray, and σs is the root mean square (RMS) radius of the laser beam, commonly called the spot size, which is used to describe the radial distribution of the laser beam. Approximately 86.5% of the total energy of the spot is concentrated in the circle of radiusσs for a Gaussian distribution beam. In order to characterize a Gaussian beam shown in Eq. (9) in our Monte Carlo simulations, a sampling model given in Eqs. (10) and (11) is used to describe the distance from the photon to the center of light spot r and the azimuth angle φ of photons, respectively.
r=σs2ln(R1)
φ=2πR2,
where R1 and R2 are random values between 0 and 1

Therefore, the coordinates (x,y,z) of photons incident on the water surface can be expressed by

{x=rcosφy=rsinφz=0.

Because the altitude of the spaceborne lidar, H, is much greater than the Rayleigh length of the Gaussian beam, the wave front is approximately spherical near the sea surface and the normal direction of each point on the sphere is the direction (ux,uy,uz)of photons, which is given by

{ux=x/(x2+y2+H2)uy=y/(x2+y2+H2)uz=H/(x2+y2+H2).

B: Photon behavior in seawater

The SALMON model simulates the random collision and propagation in water of individual photons from the transmitted beam through a series of absorption and scattering until the photons move outside of the field of view of the lidar receiver or reach the boundaries of medium. For each segment of a photon trajectory, the distance travelled before collision and the propagation direction are determined by making random selections from cumulative probability distribution function of these parameters. These two parameters play a critical role in describing the photon behavior in seawater. The details about the definitions of these two parameters and the formation of the lidar return signals are given in the following paragraphs.

The distance travelled before collision is the step size (s) in dimensionless unit and defined as the integration of the attenuation coefficient over the photon pathway [26]. In the SALMON model, s is calculated by ln(R)/c, where R is a random value between 0 and 1. The larger the attenuation coefficient c, the shorter distance the photon traveled before it is scattered or absorbed. For the stratified inhomogeneous waters, the IOPs are assigned separately for each layer. If the photon passes through more than one layer, c will change along the pathway. Therefore, the actual step size in layer i±1could be calculated as si±1=(sidi)ci±1/ci,where d is the geometrical distance along the ray in layer i where the scattering took place. The sign indicates the photon propagates up (-) or down ( + ). The photon travels until the initial step size has been consumed (si<di).

The propagation direction of the photon after each collision can be expressed by the scattering angle θ and the azimuth angleα. The value of αis distributed uniformly between 0 and 2π, and the distribution of angle θ is modeled by the scattering phase function [24]. Both the pure water molecules and particles contribute to the total scattering phase functionβ˜ [27], i.e.,

β˜=ηβ˜w+(1η)β˜p.
the subscripts w and p represent the water molecules and the particles, respectively. The probability that the photon is scattered by molecule is calculated through η=bw/(bw+bp)=bw/b. And the phase function for water molecules is determined by Rayleigh scattering [22], i.e.,
β˜w(cosθ)=0.06225(1+0.835cos2θ),
On the other hand, the probability that the photon is scattered by particle is expressed as 1-η. The scattering property of particles is generally characterized by Henyey-Greenstein (HG) phase function [29], i.e.,
β˜p(cosθ)=β˜HG(g,cosθ)=14π1g2(1+g22gcosθ)3/2,
herein, g is the asymmetry parameter controlling the relative amounts of forward and backward scattering in β˜HG and is equal to the average of the cosine of the scattering angle over the angular distribution. The HG phase function allows a simple computation of the radiation transfer equation (RTE). However, it only imitates the forward-directed peak of the Petzold’s experimental phase function and describes the backward-directed peak inadequately [24]. Therefore, a weighted sum of HG phase function, called the two-term Henyey-Greenstein (TTHG), is used here to solve the issue, which is given by Kattawar [30]:
β˜p(cosθ)=β˜TTHG(cosθ,α,g1,g2)=αβ˜HG(cosθ,g1)+(1α)β˜HG(cosθ,g2),
where g1 and g2 are the asymmetry factors for the forward-directed and backward-directed HG phase functions, respectively. α(0α1) is the relative contributions of the two HG phase functions to the TTHG phase function. The average-particle phase function derived from three measurements of the volume scattering function from Bahama Islands, San Pedro Channel and San Diego Harbor by Petzold shows that the average value of the particle backscatter fraction Bpis 0.0183 [31]. Hence, we set α=0.9843,g1=0.9809and g2=0.6921, which gives Bp=0.0183 according to the exact relationships between α,g1,g2andBpgiven by Haltrin [32].

In the process of tracking the photons, each photon is conceived as a large packet of identical photons traveling in the same direction. The photon packet has an initial weight w=1. After each collision, the current weight is multiplied by the single scattering albedo ω0, which is given by ω0=b/c. A roulette procedure is employed when the weight w is below the threshold of 10-4. The expected value of the fraction of photons collected by the lidar receiver without further interactions at each collision point in layer i is given by

E=β(θ)4πA(nH+z)2exp(j=1ic(j)d(j))TatmTsurf,
whered(j)is the depth in layer j and θ is the angle between the scattering direction and the direction from photon towards the receiver. The expected value of the fraction of photons reaching the detector is added to the signal; then the packet is reduced by the same value. The remaining photon packet proceeds to the next interaction. All the calculations in this paper are executed with more than 1011 photon packets to ensure a low level of standard deviation of the statistical estimates.

3. Results

3.1 Influence of the lidar system parameters on klidar

The FOV of the lidar receiver is an important parameter that influences the order of scattering in the lidar return signals. For the airborne lidars, the FOV ranges from tens of milliradians to several hundred milliradians depending on different detection purpose. For a spaceborne lidar, smaller FOV is preferred in order to reduce the impact of the background noise from sun light. The FOV of the lidar receiving system should be larger than the divergence angle of the transmitting laser beam due to the divergence of the Gaussian beam. Besides, the altitude of satellite platform also affects the multiple scattering signal. In this paper, we analyze four satellite orbits of the actual spaceborne atmospheric lidar systems: 705 km for CALIOP on the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite [9], 550 km for the Japanese Experimental Lidar in Space Equipment (ELISE) [33], 415 km for the Cloud-Aerosol Transport System (CATS), which is a lidar remote sensing instrument on the International Space Station (ISS) [34], as well as 400 km for the Earth Clouds, Aerosol and Radiation Explorer (EarthCARE) satellite to be launched in 2021 [35]. In these simulations, the divergence angle of the laser beam is assumed to be 0.1 mrad and the diameter of the telescope is 1.5 m [9]. The depth resolution of the signals is set to be 0.3 m. The ocean is assumed to have a depth of 50 m with a black bottom, considering the effective attenuation optical depth of three, and a small chlorophyll concentration of 0.35 mg/m3 that is uniformly distributed from the surface to the bottom. See Table 1 for the values of input parameters of the calculations.

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Table 1. The values of input parameters of the calculations

The klidar for different system parameters, such as FOV of the receiving telescope and orbit altitude, are compared in Fig. 2. The orbit altitude in Fig. 2(a) is 400 km, and the FOV is in the range of 0.1 to 0.5 mrad, then the corresponding footprint of the receiver is in the range of 20 to 100 m and the diameter of laser spot on the ocean surface is 40 m. Figure 2(a) shows that klidar at different optical depths decreases with the increase of FOV, but remains almost constant when the FOV is larger than a critical value, indicating that the lidar receives the full-multiple scattering signals. For the orbit altitude of 400 km, FOV of 0.4 mrad is wide enough to satisfy the full-multiple scattering statement at optical depth (τo,τo=c×z) from 1 to 5. Figure 2(b) presents the klidar for four orbit altitudes, namely 400 km, 415 km, 550 km and 705 km, with FOV equals to 0.4 mrad. The result indicates that klidar of the full-multiple scattering return signals is independent of H. According to our simulation, this result is still valid even when the divergence angle of the incident beam increases to 0.2 mrad because the beam divergence in water is mainly due to the multiple scattering. Therefore, klidar of spaceborne lidar is only related to the IOPs of water, and system parameters for the full-multiple scattering statement are adopted in the following sections.

 

Fig. 2 Influence of FOV of the lidar receiving system and orbit altitude on klidar. (a) klidar of different optical depths under different FOV with the orbit altitude of 400 km, (b) klidar for different orbit altitudes with the FOV of 0.4 mrad. Here, τO is the optical depth with = 0.233m-1.

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3.2 Relationships between IOPs and klidar

A. Characteristics of lidar return signals for homogeneous case 1 waters

The effective attenuation coefficient klidar in lidar equation is the reflection of the IOPs of seawater. Determining the value of klidar is beneficial to improve the retrieve accuracy of IOPs from lidar equation. In terms of the orbit altitude of 400 km, the characteristics of lidar return signals from homogeneous case 1 water with various chlorophyll concentrations are studied. The normalized lidar return signal power and the associated effective attenuation coefficients of eight groups of seawaters with [Chl] between 0 to 2 mg/m3, which covers most open ocean and productive coastal environment, are provided in Fig. 3. The normalized lidar return profiles in Fig. 3(a) shows that the linearity of the lidar return signal decreases with the increase of [Chl]. For seawaters with high [Chl], klidar is not constant even if the IOPs are uniform in the vertical direction [Fig. 3(b)]. klidar decreases with the increase of depth at first, then it becomes more stable. The full range of the nonlinearity in klidar depends on the sensitivity of the receiving system. If this nonlinearity in klidar is ignored in homogeneous waters, it may cause error in the retrieval of lidar signal.

 

Fig. 3 (a) Depth profiles of the normalized lidar return signals Pnorm(z) and (b) the corresponding effective attenuation coefficients klidar(z) for [Chl] between 0 to 2 mg/m3.

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Figure 4 illustrates the statistical estimates of the normalized lidar return signal power with different order of scattering and the percentage for each order of scattering in the total scattering signal for [Chl] = 0.35 mg/m3. The signal of the first order of scattering decreases exponentially with the increase of depth, and the slope of the signal is c. The presence of multiple scattering deforms the waveform of the total lidar return signal, and the rapid increase of high-order scattering signal causes the decrease of klidar. It is shown in Fig. 4(b), that the percentage of high-order scattering in the total signal increases with depth. When the depth is larger than about 10 meters, signal with scattering order more than 1 becomes dominant in the total signal power. Due to the absorption of water, the intensity of the high-order scattering signal declines and klidar shows a characteristic of stability and invariability.

 

Fig. 4 (a) The normalized lidar return signal power with different order of scattering Pnorm(z) and (b) the percentage of each order of scattering (n is the order of scattering) for [Chl] = 0.35 mg/m3. The number represents the order of scattering and ‘total’ depicts the full multiple scattering signal.

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The nonuniformity of klidar caused by multiple scattering is inevitable and it increases the uncertainty of the retrieval. However, in the real ocean environment, the IOPs is not homogenously distributed. Therefore, it is necessary to make a further research of the relationship between the klidar(z) and IOPs of water.

B. The relationships between the klidar(z) and IOPs of water

Based on the results shown in Fig. 3(b), an exponential formula is proposed to describe the depth characteristic of klidar, which is given by

klidar(z)=m×exp(n×z)+p.

The fitting results of the effective attenuation coefficients for 16 types of case 1 waters with [Chl] ranging from 0.005 to 2 mg/m3 are listed in Table 2. R2 is the criterion for the goodness of fit. All the R2 values for the fitting results of each group of seawater are greater than 0.99, which confirms the validity of Eq. (19). As shown in Eq. (19), klidar will be given by is an asymptotic parameter p, when z is large enough to make the exponential term close to zero. The fitting results show that the asymptotic value for each klidar is a little bit smaller than Kd.

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Table 2. Fitting results of klidar for 16 types of case 1 waters.

The slope and the asymptotic value of klidar in Eq. (19) are determined by m, n and p, respectively. Moreover, klidar involves the IOPs of seawater, indicating these three parameters are related to the IOPs of seawater.

The influence of the IOPs on the qualitative behavior of klidar is analyzed using a series of calculations with one variable at a time (Fig. 5). Figure 5(a) shows that the curves of klidar for differentω0 (with b = 0.2 m−1) are parallel to each other, which means that the absorption coefficient a only affects the parameter p. Figure 5(b) shows that the scattering coefficient b influences both the slope and the asymptotic value of klidar. Figure 5(c) shows that the phase function mainly affects the slope of the klidar but has little influence on the asymptotic value of klidar. The dependence of klidar on the phase function and b can be summarized by a backscattering coefficient bb, which is obtained by integrating the phase function in the range from π/2 to π.

 

Fig. 5 The influence of IOPs on klidar for (a) b=0.2m-1 and w0=0.3(0.1)0.6, (b) a=0.2m-1 and w0=0.3(0.1)0.6, and (c) [Chl] = 0.35 mg/m3 and g1 = 0.9509(0.01)0.9809.

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In summary, the relationships among the parameters m, n, p and IOPs can be derived based on the fitting results in Table 1 (Fig. 6). Both m and n show a linear relation with bb, and p is a linear function of a and bb. The linear functions can be expressed by

 

Fig. 6 Relationships between the parameters (a) m, (b) n, and (c) p and IOPs.

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m=4.8907bb0.0004n=4.2506bb0.0055.p=a+0.3582bb0.0042

klidar for the phytoplankton bloom case 1 water with [Chl] of 10 mg/m3 and 20 mg/m3 are also calculated, and the fitting results are coincident with the fitted curves in Fig. 6, which broadens the application range of Eq. (20). It is easy to obtain the relationship of IOPs and depth resolved klidar by substituting Eq. (20) into Eq. (19). For convenience, this function is termed and referred to as klidar-IOPs model in the following sections. This model depends on the IOPs’ bio-optical model and the scattering phase function of particle, especially the backscattering properties of particle. Since the scattering properties are variable in real waters, the goodness of fit of this klidar-IOPs model will be sensitive to particles with very strong or weak backward scattering.

4. Applicability of the relationship between IOPs and depth resolved klidar in real seawaters

In the real ocean environment, the distribution of optical properties is normally inhomogeneous, so the validity of the klidar-IOPs model needs to be tested in the practical seawaters. We build a model of inhomogeneous water by defining multiple parallel thin layers, which have the same optical properties in the horizontal direction but different in the vertical direction. The thickness of each layer is assumed to be the same as the resolution of the lidar return profiles. According to Uitz et al. [36], the vertical distribution of chlorophyll a concentration in the global open ocean conforms to the Gaussian distribution given by

C(ζ)=Cbsζ+Cmaxexp{[(ζζmax)/Δζ]2},
ζ is the dimensionless depth obtained by dividing the geometrical depth, z, by the euphotic depth, Zeu. C(ζ) is the dimensionless chlorophyll a concentration defined as the ratio of the chlorophyll a concentration (Chla), to the average concentration within the euphotic layer Chla¯Zeu. Cbis the background chlorophyll concentration, which decreases linearly with slope s starting from the surface value [Chla]surf. Cmaxrepresents the maximum concentration, ζmaxis the depth of the maximum concentration andΔζdenotes the width of the peak.

Uitz et al. [36] classified the stratified case 1 waters into nine trophic categories, named from S1 to S9, with respect to the Chlorophyll a concentration within the surface layer [Chla]surf. In order to examine the relationship between IOPs and depth resolved klidar derived in Section 3 in actual seawaters, three trophic categories, with low [Chla]surf(<0.04 mg/m3), medium [Chla]surf(0.2-0.3 mg/m3), and high [Chla]surf(2.2-4 mg/m3), namely S1, S5 and S9, are selected based on the experimental and statistical results of stratified waters. These three waters can represent the trophic states of most open ocean waters in the world. The parameters in Eq. (21) of these three typical trophic class of stratified waters are outlined in Table 3. The distributions of IOPs and Chla of these three waters are shown in Fig. 7.

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Table 3. Values of the parameters to be used in Eq. (21) for S1, S5 and S9, respectively [36].

 

Fig. 7 The inherent optical properties (IOPs) and chlorophyll a concentration (Chla) for (a) S1 water, (b) S5 water and (c) S9 water.

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Comparisons of the Kd computed by the bio-optical model given as Eq. (8) and klidar obtained from the calculation model proposed in this paper with simulated effective attenuation coefficients for S1, S5 and S9 waters are shown in Fig. 8. The lidar is expected to penetrate about 50 m in S1 water, 40 m in S5 water and 20 m in S9 water, with the effective detection depth of three optical thicknesses. For all these three waters, the effective attenuation coefficient klidar of simulating return signals is found to be different from Kd (Fig. 8). The values ofklidarcwhich is the attenuation coefficient calculated using the klidar-IOPs model are very close to the simulated ones.

 

Fig. 8 The inherent optical properties (IOPs) and attenuation coefficients for (a) S1 water, (b) S5 water and (c) S9 water, respectively. klidar represents the effective attenuation coefficient of simulating return signals, and klidar-c is the attenuation coefficient calculated using the klidar-IOPs model shown in Eqs. (19) and (20).

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To quantify the accuracy of klidar-IOPs model, a mean percentage error within the effective detection depth range is defined, which is expressed by

δ=i=1i=n|k(zi)klidar(zi)|/klidar(zi)n×100%
where the k is the parameter to be evaluated, n is the number of depth intervals in the effective detection range. The mean percentage errors between klidar-c and klidar are 0.22%, 0.11% and 0.34% for S1, S5 and S9 waters, respectively. To make a primary comparation between the klidar-IOPs model and Kd from the bio-optical model, mean percentage errors between Kd and klidar are given which are 3.07%, 4.05% and 6.24% for S1, S5 and S9 waters, respectively. The accuracy of the klidar-c is more than one order of magnitude higher than that of Kd. It indicates that the klidar-IOPs model proposed in this paper can precisely describe the relationships between the lidar effective attenuation coefficient and the IOPs of seawater in real ocean environment, which is an improvement of the lidar measurement of IOPs.

5. Conclusions

In this paper, the multiple scattering effect on the spaceborne oceanic lidar returns is investigated with an improved SALMON, which includes a Gaussian beam emission model and is applicable for both homogeneous and inhomogeneous waters. The effective attenuation coefficient decays exponentially with the increase of depth in water according to the simulations of lidar return signal from case 1 waters, because high order scattering signals increase with depth and predominate the total signal at shallow depth. An exponential function is proposed to describe the depth characteristic of klidar and the parameters in this exponential function is related to IOPs. The exact function of klidar, depth and the IOPs of seawater is given and examined using real ocean waters simulated by a model of inhomogeneous water.

Comparison of the simulated and calculated results of three typical stratified case 1 waters shows that the errors between klidar-c and klidar are less than 0.5%. The better agreement shows that the exponential klidar-IOPs function is feasible for oceanic lidar measurement in theory. Using this function, one can estimate the inherent optical properties and retrieve the chlorophyll concentration of case 1 water, which improves the accuracy of spaceborne lidar data retrieval and assessing global phytoplankton biomass and primary productivity.

Funding

National Key Research and Development Program of China (2016YFC1400902, 2016YFC1400905, 2016YFC0200700); National Natural Science Foundation of China (NSFC) (41775023, 61475141).

Acknowledgments

The authors thank Prof. Heather Bouman in the Department of Earth Sciences, University of Oxford for providing the knowledge of chlorophyll concentration.

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2. C. R. McClain, “A decade of satellite ocean color observations,” Annu. Rev. Mar. Sci. 1(1), 19–42 (2009). [CrossRef]   [PubMed]  

3. C. A. Hostetler, M. J. Behrenfeld, Y. Hu, J. W. Hair, and J. A. Schulien, “Spaceborne Lidar in the Study of Marine Systems,” Ann. Rev. Mar. Sci. 10, 121–147 (2017).

4. M. J. Behrenfeld, Y. Hu, C. A. Hostetler, G. Dall’Olmo, S. D. Rodier, J. W. Hair, and C. R. Trepte, “Space‐based lidar measurements of global ocean carbon stocks,” Geophys. Res. Lett. 40(16), 4355–4360 (2013). [CrossRef]  

5. X. Lu, Y. Hu, J. Pelon, C. Trepte, K. Liu, S. Rodier, S. Zeng, P. Lucker, R. Verhappen, J. Wilson, C. Audouy, C. Ferrier, S. Haouchine, B. Hunt, and B. Getzewich, “Retrieval of ocean subsurface particulate backscattering coefficient from space-borne CALIOP lidar measurements,” Opt. Express 24(25), 29001–29008 (2016). [CrossRef]   [PubMed]  

6. X. Lu, Y. Hu, C. Trepte, S. Zeng, and J. H. Churnside, “Ocean subsurface studies with the CALIPSO spaceborne lidar,” J. Geophys. Res. Oceans 119(7), 4305–4317 (2015). [CrossRef]  

7. J. H. Churnside, B. J. Mccarty, and X. Lu, “Subsurface Ocean Signals from an Orbiting Polarization Lidar,” Remote Sens. 5(7), 3457–3475 (2013). [CrossRef]  

8. M. J. Behrenfeld, Y. Hu, R. T. O’Malley, E. S. Boss, C. A. Hostetler, D. A. Siegel, J. L. Sarmiento, J. Schulien, J. W. Hair, and X. Lu, “Annual boom-bust cycles of polar phytoplankton biomass revealed by space-based lidar,” Nat. Geosci. 10, 118 (2016).

9. D. M. Winker, M. A. Vaughan, A. Omar, Y. Hu, K. A. Powell, Z. Liu, W. H. Hunt, and S. A. Young, “Overview of the CALIPSO Mission and CALIOP Data Processing Algorithms,” J. Atmos. Ocean. Tech. 26(11), 2310–2323 (2009). [CrossRef]  

10. L. Dong, L. Qun, B. Jian, and Z. Yupeng, “Data processing algorithms of the space-borne lidar CALIOP: a review,” Infr. Laser Eng. 46(12), 1202001 (2017). [CrossRef]  

11. G. M. Krekov, M. M. Krekova, and V. S. Shamanaev, “Laser sensing of a subsurface oceanic layer. I. Effect of the atmosphere and wind-driven sea waves,” Appl. Opt. 37(9), 1589–1595 (1998). [CrossRef]   [PubMed]  

12. H. R. Gordon, “Interpretation of airborne oceanic lidar: effects of multiple scattering,” Appl. Opt. 21(16), 2996–3001 (1982). [CrossRef]   [PubMed]  

13. D. Phillips and B. Koerber, “A Theoretical Study of an Airborne Laser Technique for determining Sea Water Turbidity,” Aust. J. Phys. 37(1), 75 (1984). [CrossRef]  

14. J. H. Lee, J. H. Churnside, R. D. Marchbanks, P. L. Donaghay, and J. M. Sullivan, “Oceanographic lidar profiles compared with estimates from in situ optical measurements,” Appl. Opt. 52(4), 786–794 (2013). [CrossRef]   [PubMed]  

15. J. A. Schulien, M. J. Behrenfeld, J. W. Hair, C. A. Hostetler, and M. S. Twardowski, “Vertically- resolved phytoplankton carbon and net primary production from a high spectral resolution lidar,” Opt. Express 25(12), 13577–13587 (2017). [CrossRef]   [PubMed]  

16. B. L. Collister, R. C. Zimmerman, C. I. Sukenik, V. J. Hill, and W. M. Balch, “Remote sensing of optical characteristics and particle distributions of the upper ocean using shipboard lidar,” Remote Sens. Environ. 215, 85–96 (2018). [CrossRef]  

17. R. E. Walker and J. W. McLean, “Lidar equations for turbid media with pulse stretching,” Appl. Opt. 38(12), 2384–2397 (1999). [CrossRef]   [PubMed]  

18. V. I. Feygels, “Mathematical modeling of input signals for oceanographic lidar systems,” Proc. SPIE 5155, 30–39 (2003). [CrossRef]  

19. L. R. Poole, D. D. Venable, and J. W. Campbell, “Semianalytic Monte Carlo radiative transfer model for oceanographic lidar systems,” Appl. Opt. 20(20), 3653–3656 (1981). [CrossRef]   [PubMed]  

20. J. H. Churnside, “Review of profiling oceanographic lidar,” Opt. Eng. 53(5), 051405 (2014). [CrossRef]  

21. A. Morel, “Light and marine photosynthesis: a spectral model with geochemical and climatological implications,” Prog. Oceanogr. 26(3), 263–306 (1991). [CrossRef]  

22. C. D. Mobley, Light and Water: Radiative Transfer in Natural Waters (Academic Press, 1994).

23. A. Morel and S. Maritorena, “Bio‐optical properties of oceanic waters: A reappraisal,” J. Geophys. Res. Oceans 106(C4), 7163–7180 (2001). [CrossRef]  

24. C. Gabriel, M. A. Khalighi, P. Léon, S. Bourennane, and V. Rigaud, “Monte-Carlo-Based Channel Characterization for Underwater Optical Communication Systems,” J. Opt. Commun. Netw. 5(1), 1–12 (2013). [CrossRef]  

25. B. H. Hokr, J. N. Bixler, G. Elpers, B. Zollars, R. J. Thomas, V. V. Yakovlev, and M. O. Scully, “Modeling focusing Gaussian beams in a turbid medium with Monte Carlo simulations,” Opt. Express 23(7), 8699–8705 (2015). [CrossRef]   [PubMed]  

26. L. Wang, S. L. Jacques, and L. Zheng, “MCML—Monte Carlo modeling of light transport in multi-layered tissues,” Comput. Meth. Prog. Bio. 47, 131-146 (1995).

27. K. I. Gjerstad, J. J. Stamnes, B. Hamre, J. K. Lotsberg, B. Yan, and K. Stamnes, “Monte Carlo and discrete-ordinate simulations of irradiances in the coupled atmosphere-ocean system,” Appl. Opt. 42(15), 2609–2622 (2003). [CrossRef]   [PubMed]  

28. M. Xia, K. Yang, Y. Zheng, and J. Rao, “Influence of Wavy Sea Surface on Airborne Lidar Underwater Beam Quality with Monte Carlo Method,” Chin. J. Lasers 35(2), 178–182 (2008). [CrossRef]  

29. L. G. Henyey and J. L. Greenstein, “Diffuse radiation in the Galaxy,” Astrophys. J. 93, 70–83 (1941).

30. G. W. Kattawar, “A three-parameter analytic phase function for multiple scattering calculations,” J. Quant. Spectrosc. Ra. 15(9), 839–849 (1975). [CrossRef]  

31. C. D. Mobley, L. K. Sundman, and E. Boss, “Phase function effects on oceanic light fields,” Appl. Opt. 41(6), 1035–1050 (2002). [CrossRef]   [PubMed]  

32. V. I. Haltrin, “One-parameter two-term Henyey-Greenstein phase function for light scattering in seawater,” Appl. Opt. 41(6), 1022–1028 (2002). [CrossRef]   [PubMed]  

33. Z. Liu, P. Voelger, and N. Sugimoto, “Simulations of the observation of clouds and aerosols with the Experimental Lidar in Space Equipment system,” Appl. Opt. 39(18), 3120–3137 (2000). [CrossRef]   [PubMed]  

34. “Official website of Cloud-Aerosol Transport System(CATS),NASA”, retrieved https://cats.gsfc.nasa.gov/.

35. “Earth Clouds, Aerosol and Radiation Explorer (EarthCARE), ESA”, retrieved https://earth.esa.int/web/guest/missions/esa-future-missions/earthcare.

36. J. Uitz, H. Claustre, A. Morel, and S. B. Hooker, “Vertical distribution of phytoplankton communities in open ocean: An assessment based on surface chlorophyll,” Jo. Geophys. Res. Oceans 111, C08005 (2006).

References

  • View by:
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  • |

  1. J. H. Churnside, “LIDAR detection of plankton in the ocean,” in IEEE International Geoscience and Remote Sensing Symposium (2007), 3174–3177.
    [Crossref]
  2. C. R. McClain, “A decade of satellite ocean color observations,” Annu. Rev. Mar. Sci. 1(1), 19–42 (2009).
    [Crossref] [PubMed]
  3. C. A. Hostetler, M. J. Behrenfeld, Y. Hu, J. W. Hair, and J. A. Schulien, “Spaceborne Lidar in the Study of Marine Systems,” Ann. Rev. Mar. Sci. 10, 121–147 (2017).
  4. M. J. Behrenfeld, Y. Hu, C. A. Hostetler, G. Dall’Olmo, S. D. Rodier, J. W. Hair, and C. R. Trepte, “Space‐based lidar measurements of global ocean carbon stocks,” Geophys. Res. Lett. 40(16), 4355–4360 (2013).
    [Crossref]
  5. X. Lu, Y. Hu, J. Pelon, C. Trepte, K. Liu, S. Rodier, S. Zeng, P. Lucker, R. Verhappen, J. Wilson, C. Audouy, C. Ferrier, S. Haouchine, B. Hunt, and B. Getzewich, “Retrieval of ocean subsurface particulate backscattering coefficient from space-borne CALIOP lidar measurements,” Opt. Express 24(25), 29001–29008 (2016).
    [Crossref] [PubMed]
  6. X. Lu, Y. Hu, C. Trepte, S. Zeng, and J. H. Churnside, “Ocean subsurface studies with the CALIPSO spaceborne lidar,” J. Geophys. Res. Oceans 119(7), 4305–4317 (2015).
    [Crossref]
  7. J. H. Churnside, B. J. Mccarty, and X. Lu, “Subsurface Ocean Signals from an Orbiting Polarization Lidar,” Remote Sens. 5(7), 3457–3475 (2013).
    [Crossref]
  8. M. J. Behrenfeld, Y. Hu, R. T. O’Malley, E. S. Boss, C. A. Hostetler, D. A. Siegel, J. L. Sarmiento, J. Schulien, J. W. Hair, and X. Lu, “Annual boom-bust cycles of polar phytoplankton biomass revealed by space-based lidar,” Nat. Geosci. 10, 118 (2016).
  9. D. M. Winker, M. A. Vaughan, A. Omar, Y. Hu, K. A. Powell, Z. Liu, W. H. Hunt, and S. A. Young, “Overview of the CALIPSO Mission and CALIOP Data Processing Algorithms,” J. Atmos. Ocean. Tech. 26(11), 2310–2323 (2009).
    [Crossref]
  10. L. Dong, L. Qun, B. Jian, and Z. Yupeng, “Data processing algorithms of the space-borne lidar CALIOP: a review,” Infr. Laser Eng. 46(12), 1202001 (2017).
    [Crossref]
  11. G. M. Krekov, M. M. Krekova, and V. S. Shamanaev, “Laser sensing of a subsurface oceanic layer. I. Effect of the atmosphere and wind-driven sea waves,” Appl. Opt. 37(9), 1589–1595 (1998).
    [Crossref] [PubMed]
  12. H. R. Gordon, “Interpretation of airborne oceanic lidar: effects of multiple scattering,” Appl. Opt. 21(16), 2996–3001 (1982).
    [Crossref] [PubMed]
  13. D. Phillips and B. Koerber, “A Theoretical Study of an Airborne Laser Technique for determining Sea Water Turbidity,” Aust. J. Phys. 37(1), 75 (1984).
    [Crossref]
  14. J. H. Lee, J. H. Churnside, R. D. Marchbanks, P. L. Donaghay, and J. M. Sullivan, “Oceanographic lidar profiles compared with estimates from in situ optical measurements,” Appl. Opt. 52(4), 786–794 (2013).
    [Crossref] [PubMed]
  15. J. A. Schulien, M. J. Behrenfeld, J. W. Hair, C. A. Hostetler, and M. S. Twardowski, “Vertically- resolved phytoplankton carbon and net primary production from a high spectral resolution lidar,” Opt. Express 25(12), 13577–13587 (2017).
    [Crossref] [PubMed]
  16. B. L. Collister, R. C. Zimmerman, C. I. Sukenik, V. J. Hill, and W. M. Balch, “Remote sensing of optical characteristics and particle distributions of the upper ocean using shipboard lidar,” Remote Sens. Environ. 215, 85–96 (2018).
    [Crossref]
  17. R. E. Walker and J. W. McLean, “Lidar equations for turbid media with pulse stretching,” Appl. Opt. 38(12), 2384–2397 (1999).
    [Crossref] [PubMed]
  18. V. I. Feygels, “Mathematical modeling of input signals for oceanographic lidar systems,” Proc. SPIE 5155, 30–39 (2003).
    [Crossref]
  19. L. R. Poole, D. D. Venable, and J. W. Campbell, “Semianalytic Monte Carlo radiative transfer model for oceanographic lidar systems,” Appl. Opt. 20(20), 3653–3656 (1981).
    [Crossref] [PubMed]
  20. J. H. Churnside, “Review of profiling oceanographic lidar,” Opt. Eng. 53(5), 051405 (2014).
    [Crossref]
  21. A. Morel, “Light and marine photosynthesis: a spectral model with geochemical and climatological implications,” Prog. Oceanogr. 26(3), 263–306 (1991).
    [Crossref]
  22. C. D. Mobley, Light and Water: Radiative Transfer in Natural Waters (Academic Press, 1994).
  23. A. Morel and S. Maritorena, “Bio‐optical properties of oceanic waters: A reappraisal,” J. Geophys. Res. Oceans 106(C4), 7163–7180 (2001).
    [Crossref]
  24. C. Gabriel, M. A. Khalighi, P. Léon, S. Bourennane, and V. Rigaud, “Monte-Carlo-Based Channel Characterization for Underwater Optical Communication Systems,” J. Opt. Commun. Netw. 5(1), 1–12 (2013).
    [Crossref]
  25. B. H. Hokr, J. N. Bixler, G. Elpers, B. Zollars, R. J. Thomas, V. V. Yakovlev, and M. O. Scully, “Modeling focusing Gaussian beams in a turbid medium with Monte Carlo simulations,” Opt. Express 23(7), 8699–8705 (2015).
    [Crossref] [PubMed]
  26. L. Wang, S. L. Jacques, and L. Zheng, “MCML—Monte Carlo modeling of light transport in multi-layered tissues,” Comput. Meth. Prog. Bio. 47, 131-146 (1995).
  27. K. I. Gjerstad, J. J. Stamnes, B. Hamre, J. K. Lotsberg, B. Yan, and K. Stamnes, “Monte Carlo and discrete-ordinate simulations of irradiances in the coupled atmosphere-ocean system,” Appl. Opt. 42(15), 2609–2622 (2003).
    [Crossref] [PubMed]
  28. M. Xia, K. Yang, Y. Zheng, and J. Rao, “Influence of Wavy Sea Surface on Airborne Lidar Underwater Beam Quality with Monte Carlo Method,” Chin. J. Lasers 35(2), 178–182 (2008).
    [Crossref]
  29. L. G. Henyey and J. L. Greenstein, “Diffuse radiation in the Galaxy,” Astrophys. J. 93, 70–83 (1941).
  30. G. W. Kattawar, “A three-parameter analytic phase function for multiple scattering calculations,” J. Quant. Spectrosc. Ra. 15(9), 839–849 (1975).
    [Crossref]
  31. C. D. Mobley, L. K. Sundman, and E. Boss, “Phase function effects on oceanic light fields,” Appl. Opt. 41(6), 1035–1050 (2002).
    [Crossref] [PubMed]
  32. V. I. Haltrin, “One-parameter two-term Henyey-Greenstein phase function for light scattering in seawater,” Appl. Opt. 41(6), 1022–1028 (2002).
    [Crossref] [PubMed]
  33. Z. Liu, P. Voelger, and N. Sugimoto, “Simulations of the observation of clouds and aerosols with the Experimental Lidar in Space Equipment system,” Appl. Opt. 39(18), 3120–3137 (2000).
    [Crossref] [PubMed]
  34. “Official website of Cloud-Aerosol Transport System(CATS),NASA”, retrieved https://cats.gsfc.nasa.gov/ .
  35. “Earth Clouds, Aerosol and Radiation Explorer (EarthCARE), ESA”, retrieved https://earth.esa.int/web/guest/missions/esa-future-missions/earthcare .
  36. J. Uitz, H. Claustre, A. Morel, and S. B. Hooker, “Vertical distribution of phytoplankton communities in open ocean: An assessment based on surface chlorophyll,” Jo. Geophys. Res. Oceans 111, C08005 (2006).

2018 (1)

B. L. Collister, R. C. Zimmerman, C. I. Sukenik, V. J. Hill, and W. M. Balch, “Remote sensing of optical characteristics and particle distributions of the upper ocean using shipboard lidar,” Remote Sens. Environ. 215, 85–96 (2018).
[Crossref]

2017 (3)

J. A. Schulien, M. J. Behrenfeld, J. W. Hair, C. A. Hostetler, and M. S. Twardowski, “Vertically- resolved phytoplankton carbon and net primary production from a high spectral resolution lidar,” Opt. Express 25(12), 13577–13587 (2017).
[Crossref] [PubMed]

C. A. Hostetler, M. J. Behrenfeld, Y. Hu, J. W. Hair, and J. A. Schulien, “Spaceborne Lidar in the Study of Marine Systems,” Ann. Rev. Mar. Sci. 10, 121–147 (2017).

L. Dong, L. Qun, B. Jian, and Z. Yupeng, “Data processing algorithms of the space-borne lidar CALIOP: a review,” Infr. Laser Eng. 46(12), 1202001 (2017).
[Crossref]

2016 (2)

M. J. Behrenfeld, Y. Hu, R. T. O’Malley, E. S. Boss, C. A. Hostetler, D. A. Siegel, J. L. Sarmiento, J. Schulien, J. W. Hair, and X. Lu, “Annual boom-bust cycles of polar phytoplankton biomass revealed by space-based lidar,” Nat. Geosci. 10, 118 (2016).

X. Lu, Y. Hu, J. Pelon, C. Trepte, K. Liu, S. Rodier, S. Zeng, P. Lucker, R. Verhappen, J. Wilson, C. Audouy, C. Ferrier, S. Haouchine, B. Hunt, and B. Getzewich, “Retrieval of ocean subsurface particulate backscattering coefficient from space-borne CALIOP lidar measurements,” Opt. Express 24(25), 29001–29008 (2016).
[Crossref] [PubMed]

2015 (2)

X. Lu, Y. Hu, C. Trepte, S. Zeng, and J. H. Churnside, “Ocean subsurface studies with the CALIPSO spaceborne lidar,” J. Geophys. Res. Oceans 119(7), 4305–4317 (2015).
[Crossref]

B. H. Hokr, J. N. Bixler, G. Elpers, B. Zollars, R. J. Thomas, V. V. Yakovlev, and M. O. Scully, “Modeling focusing Gaussian beams in a turbid medium with Monte Carlo simulations,” Opt. Express 23(7), 8699–8705 (2015).
[Crossref] [PubMed]

2014 (1)

J. H. Churnside, “Review of profiling oceanographic lidar,” Opt. Eng. 53(5), 051405 (2014).
[Crossref]

2013 (4)

C. Gabriel, M. A. Khalighi, P. Léon, S. Bourennane, and V. Rigaud, “Monte-Carlo-Based Channel Characterization for Underwater Optical Communication Systems,” J. Opt. Commun. Netw. 5(1), 1–12 (2013).
[Crossref]

J. H. Churnside, B. J. Mccarty, and X. Lu, “Subsurface Ocean Signals from an Orbiting Polarization Lidar,” Remote Sens. 5(7), 3457–3475 (2013).
[Crossref]

M. J. Behrenfeld, Y. Hu, C. A. Hostetler, G. Dall’Olmo, S. D. Rodier, J. W. Hair, and C. R. Trepte, “Space‐based lidar measurements of global ocean carbon stocks,” Geophys. Res. Lett. 40(16), 4355–4360 (2013).
[Crossref]

J. H. Lee, J. H. Churnside, R. D. Marchbanks, P. L. Donaghay, and J. M. Sullivan, “Oceanographic lidar profiles compared with estimates from in situ optical measurements,” Appl. Opt. 52(4), 786–794 (2013).
[Crossref] [PubMed]

2009 (2)

C. R. McClain, “A decade of satellite ocean color observations,” Annu. Rev. Mar. Sci. 1(1), 19–42 (2009).
[Crossref] [PubMed]

D. M. Winker, M. A. Vaughan, A. Omar, Y. Hu, K. A. Powell, Z. Liu, W. H. Hunt, and S. A. Young, “Overview of the CALIPSO Mission and CALIOP Data Processing Algorithms,” J. Atmos. Ocean. Tech. 26(11), 2310–2323 (2009).
[Crossref]

2008 (1)

M. Xia, K. Yang, Y. Zheng, and J. Rao, “Influence of Wavy Sea Surface on Airborne Lidar Underwater Beam Quality with Monte Carlo Method,” Chin. J. Lasers 35(2), 178–182 (2008).
[Crossref]

2006 (1)

J. Uitz, H. Claustre, A. Morel, and S. B. Hooker, “Vertical distribution of phytoplankton communities in open ocean: An assessment based on surface chlorophyll,” Jo. Geophys. Res. Oceans 111, C08005 (2006).

2003 (2)

2002 (2)

2001 (1)

A. Morel and S. Maritorena, “Bio‐optical properties of oceanic waters: A reappraisal,” J. Geophys. Res. Oceans 106(C4), 7163–7180 (2001).
[Crossref]

2000 (1)

1999 (1)

1998 (1)

1995 (1)

L. Wang, S. L. Jacques, and L. Zheng, “MCML—Monte Carlo modeling of light transport in multi-layered tissues,” Comput. Meth. Prog. Bio. 47, 131-146 (1995).

1991 (1)

A. Morel, “Light and marine photosynthesis: a spectral model with geochemical and climatological implications,” Prog. Oceanogr. 26(3), 263–306 (1991).
[Crossref]

1984 (1)

D. Phillips and B. Koerber, “A Theoretical Study of an Airborne Laser Technique for determining Sea Water Turbidity,” Aust. J. Phys. 37(1), 75 (1984).
[Crossref]

1982 (1)

1981 (1)

1975 (1)

G. W. Kattawar, “A three-parameter analytic phase function for multiple scattering calculations,” J. Quant. Spectrosc. Ra. 15(9), 839–849 (1975).
[Crossref]

1941 (1)

L. G. Henyey and J. L. Greenstein, “Diffuse radiation in the Galaxy,” Astrophys. J. 93, 70–83 (1941).

Audouy, C.

Balch, W. M.

B. L. Collister, R. C. Zimmerman, C. I. Sukenik, V. J. Hill, and W. M. Balch, “Remote sensing of optical characteristics and particle distributions of the upper ocean using shipboard lidar,” Remote Sens. Environ. 215, 85–96 (2018).
[Crossref]

Behrenfeld, M. J.

C. A. Hostetler, M. J. Behrenfeld, Y. Hu, J. W. Hair, and J. A. Schulien, “Spaceborne Lidar in the Study of Marine Systems,” Ann. Rev. Mar. Sci. 10, 121–147 (2017).

J. A. Schulien, M. J. Behrenfeld, J. W. Hair, C. A. Hostetler, and M. S. Twardowski, “Vertically- resolved phytoplankton carbon and net primary production from a high spectral resolution lidar,” Opt. Express 25(12), 13577–13587 (2017).
[Crossref] [PubMed]

M. J. Behrenfeld, Y. Hu, R. T. O’Malley, E. S. Boss, C. A. Hostetler, D. A. Siegel, J. L. Sarmiento, J. Schulien, J. W. Hair, and X. Lu, “Annual boom-bust cycles of polar phytoplankton biomass revealed by space-based lidar,” Nat. Geosci. 10, 118 (2016).

M. J. Behrenfeld, Y. Hu, C. A. Hostetler, G. Dall’Olmo, S. D. Rodier, J. W. Hair, and C. R. Trepte, “Space‐based lidar measurements of global ocean carbon stocks,” Geophys. Res. Lett. 40(16), 4355–4360 (2013).
[Crossref]

Bixler, J. N.

Boss, E.

Boss, E. S.

M. J. Behrenfeld, Y. Hu, R. T. O’Malley, E. S. Boss, C. A. Hostetler, D. A. Siegel, J. L. Sarmiento, J. Schulien, J. W. Hair, and X. Lu, “Annual boom-bust cycles of polar phytoplankton biomass revealed by space-based lidar,” Nat. Geosci. 10, 118 (2016).

Bourennane, S.

Campbell, J. W.

Churnside, J. H.

X. Lu, Y. Hu, C. Trepte, S. Zeng, and J. H. Churnside, “Ocean subsurface studies with the CALIPSO spaceborne lidar,” J. Geophys. Res. Oceans 119(7), 4305–4317 (2015).
[Crossref]

J. H. Churnside, “Review of profiling oceanographic lidar,” Opt. Eng. 53(5), 051405 (2014).
[Crossref]

J. H. Churnside, B. J. Mccarty, and X. Lu, “Subsurface Ocean Signals from an Orbiting Polarization Lidar,” Remote Sens. 5(7), 3457–3475 (2013).
[Crossref]

J. H. Lee, J. H. Churnside, R. D. Marchbanks, P. L. Donaghay, and J. M. Sullivan, “Oceanographic lidar profiles compared with estimates from in situ optical measurements,” Appl. Opt. 52(4), 786–794 (2013).
[Crossref] [PubMed]

J. H. Churnside, “LIDAR detection of plankton in the ocean,” in IEEE International Geoscience and Remote Sensing Symposium (2007), 3174–3177.
[Crossref]

Claustre, H.

J. Uitz, H. Claustre, A. Morel, and S. B. Hooker, “Vertical distribution of phytoplankton communities in open ocean: An assessment based on surface chlorophyll,” Jo. Geophys. Res. Oceans 111, C08005 (2006).

Collister, B. L.

B. L. Collister, R. C. Zimmerman, C. I. Sukenik, V. J. Hill, and W. M. Balch, “Remote sensing of optical characteristics and particle distributions of the upper ocean using shipboard lidar,” Remote Sens. Environ. 215, 85–96 (2018).
[Crossref]

Dall’Olmo, G.

M. J. Behrenfeld, Y. Hu, C. A. Hostetler, G. Dall’Olmo, S. D. Rodier, J. W. Hair, and C. R. Trepte, “Space‐based lidar measurements of global ocean carbon stocks,” Geophys. Res. Lett. 40(16), 4355–4360 (2013).
[Crossref]

Donaghay, P. L.

Dong, L.

L. Dong, L. Qun, B. Jian, and Z. Yupeng, “Data processing algorithms of the space-borne lidar CALIOP: a review,” Infr. Laser Eng. 46(12), 1202001 (2017).
[Crossref]

Elpers, G.

Ferrier, C.

Feygels, V. I.

V. I. Feygels, “Mathematical modeling of input signals for oceanographic lidar systems,” Proc. SPIE 5155, 30–39 (2003).
[Crossref]

Gabriel, C.

Getzewich, B.

Gjerstad, K. I.

Gordon, H. R.

Greenstein, J. L.

L. G. Henyey and J. L. Greenstein, “Diffuse radiation in the Galaxy,” Astrophys. J. 93, 70–83 (1941).

Hair, J. W.

J. A. Schulien, M. J. Behrenfeld, J. W. Hair, C. A. Hostetler, and M. S. Twardowski, “Vertically- resolved phytoplankton carbon and net primary production from a high spectral resolution lidar,” Opt. Express 25(12), 13577–13587 (2017).
[Crossref] [PubMed]

C. A. Hostetler, M. J. Behrenfeld, Y. Hu, J. W. Hair, and J. A. Schulien, “Spaceborne Lidar in the Study of Marine Systems,” Ann. Rev. Mar. Sci. 10, 121–147 (2017).

M. J. Behrenfeld, Y. Hu, R. T. O’Malley, E. S. Boss, C. A. Hostetler, D. A. Siegel, J. L. Sarmiento, J. Schulien, J. W. Hair, and X. Lu, “Annual boom-bust cycles of polar phytoplankton biomass revealed by space-based lidar,” Nat. Geosci. 10, 118 (2016).

M. J. Behrenfeld, Y. Hu, C. A. Hostetler, G. Dall’Olmo, S. D. Rodier, J. W. Hair, and C. R. Trepte, “Space‐based lidar measurements of global ocean carbon stocks,” Geophys. Res. Lett. 40(16), 4355–4360 (2013).
[Crossref]

Haltrin, V. I.

Hamre, B.

Haouchine, S.

Henyey, L. G.

L. G. Henyey and J. L. Greenstein, “Diffuse radiation in the Galaxy,” Astrophys. J. 93, 70–83 (1941).

Hill, V. J.

B. L. Collister, R. C. Zimmerman, C. I. Sukenik, V. J. Hill, and W. M. Balch, “Remote sensing of optical characteristics and particle distributions of the upper ocean using shipboard lidar,” Remote Sens. Environ. 215, 85–96 (2018).
[Crossref]

Hokr, B. H.

Hooker, S. B.

J. Uitz, H. Claustre, A. Morel, and S. B. Hooker, “Vertical distribution of phytoplankton communities in open ocean: An assessment based on surface chlorophyll,” Jo. Geophys. Res. Oceans 111, C08005 (2006).

Hostetler, C. A.

C. A. Hostetler, M. J. Behrenfeld, Y. Hu, J. W. Hair, and J. A. Schulien, “Spaceborne Lidar in the Study of Marine Systems,” Ann. Rev. Mar. Sci. 10, 121–147 (2017).

J. A. Schulien, M. J. Behrenfeld, J. W. Hair, C. A. Hostetler, and M. S. Twardowski, “Vertically- resolved phytoplankton carbon and net primary production from a high spectral resolution lidar,” Opt. Express 25(12), 13577–13587 (2017).
[Crossref] [PubMed]

M. J. Behrenfeld, Y. Hu, R. T. O’Malley, E. S. Boss, C. A. Hostetler, D. A. Siegel, J. L. Sarmiento, J. Schulien, J. W. Hair, and X. Lu, “Annual boom-bust cycles of polar phytoplankton biomass revealed by space-based lidar,” Nat. Geosci. 10, 118 (2016).

M. J. Behrenfeld, Y. Hu, C. A. Hostetler, G. Dall’Olmo, S. D. Rodier, J. W. Hair, and C. R. Trepte, “Space‐based lidar measurements of global ocean carbon stocks,” Geophys. Res. Lett. 40(16), 4355–4360 (2013).
[Crossref]

Hu, Y.

C. A. Hostetler, M. J. Behrenfeld, Y. Hu, J. W. Hair, and J. A. Schulien, “Spaceborne Lidar in the Study of Marine Systems,” Ann. Rev. Mar. Sci. 10, 121–147 (2017).

X. Lu, Y. Hu, J. Pelon, C. Trepte, K. Liu, S. Rodier, S. Zeng, P. Lucker, R. Verhappen, J. Wilson, C. Audouy, C. Ferrier, S. Haouchine, B. Hunt, and B. Getzewich, “Retrieval of ocean subsurface particulate backscattering coefficient from space-borne CALIOP lidar measurements,” Opt. Express 24(25), 29001–29008 (2016).
[Crossref] [PubMed]

M. J. Behrenfeld, Y. Hu, R. T. O’Malley, E. S. Boss, C. A. Hostetler, D. A. Siegel, J. L. Sarmiento, J. Schulien, J. W. Hair, and X. Lu, “Annual boom-bust cycles of polar phytoplankton biomass revealed by space-based lidar,” Nat. Geosci. 10, 118 (2016).

X. Lu, Y. Hu, C. Trepte, S. Zeng, and J. H. Churnside, “Ocean subsurface studies with the CALIPSO spaceborne lidar,” J. Geophys. Res. Oceans 119(7), 4305–4317 (2015).
[Crossref]

M. J. Behrenfeld, Y. Hu, C. A. Hostetler, G. Dall’Olmo, S. D. Rodier, J. W. Hair, and C. R. Trepte, “Space‐based lidar measurements of global ocean carbon stocks,” Geophys. Res. Lett. 40(16), 4355–4360 (2013).
[Crossref]

D. M. Winker, M. A. Vaughan, A. Omar, Y. Hu, K. A. Powell, Z. Liu, W. H. Hunt, and S. A. Young, “Overview of the CALIPSO Mission and CALIOP Data Processing Algorithms,” J. Atmos. Ocean. Tech. 26(11), 2310–2323 (2009).
[Crossref]

Hunt, B.

Hunt, W. H.

D. M. Winker, M. A. Vaughan, A. Omar, Y. Hu, K. A. Powell, Z. Liu, W. H. Hunt, and S. A. Young, “Overview of the CALIPSO Mission and CALIOP Data Processing Algorithms,” J. Atmos. Ocean. Tech. 26(11), 2310–2323 (2009).
[Crossref]

Jacques, S. L.

L. Wang, S. L. Jacques, and L. Zheng, “MCML—Monte Carlo modeling of light transport in multi-layered tissues,” Comput. Meth. Prog. Bio. 47, 131-146 (1995).

Jian, B.

L. Dong, L. Qun, B. Jian, and Z. Yupeng, “Data processing algorithms of the space-borne lidar CALIOP: a review,” Infr. Laser Eng. 46(12), 1202001 (2017).
[Crossref]

Kattawar, G. W.

G. W. Kattawar, “A three-parameter analytic phase function for multiple scattering calculations,” J. Quant. Spectrosc. Ra. 15(9), 839–849 (1975).
[Crossref]

Khalighi, M. A.

Koerber, B.

D. Phillips and B. Koerber, “A Theoretical Study of an Airborne Laser Technique for determining Sea Water Turbidity,” Aust. J. Phys. 37(1), 75 (1984).
[Crossref]

Krekov, G. M.

Krekova, M. M.

Lee, J. H.

Léon, P.

Liu, K.

Liu, Z.

D. M. Winker, M. A. Vaughan, A. Omar, Y. Hu, K. A. Powell, Z. Liu, W. H. Hunt, and S. A. Young, “Overview of the CALIPSO Mission and CALIOP Data Processing Algorithms,” J. Atmos. Ocean. Tech. 26(11), 2310–2323 (2009).
[Crossref]

Z. Liu, P. Voelger, and N. Sugimoto, “Simulations of the observation of clouds and aerosols with the Experimental Lidar in Space Equipment system,” Appl. Opt. 39(18), 3120–3137 (2000).
[Crossref] [PubMed]

Lotsberg, J. K.

Lu, X.

X. Lu, Y. Hu, J. Pelon, C. Trepte, K. Liu, S. Rodier, S. Zeng, P. Lucker, R. Verhappen, J. Wilson, C. Audouy, C. Ferrier, S. Haouchine, B. Hunt, and B. Getzewich, “Retrieval of ocean subsurface particulate backscattering coefficient from space-borne CALIOP lidar measurements,” Opt. Express 24(25), 29001–29008 (2016).
[Crossref] [PubMed]

M. J. Behrenfeld, Y. Hu, R. T. O’Malley, E. S. Boss, C. A. Hostetler, D. A. Siegel, J. L. Sarmiento, J. Schulien, J. W. Hair, and X. Lu, “Annual boom-bust cycles of polar phytoplankton biomass revealed by space-based lidar,” Nat. Geosci. 10, 118 (2016).

X. Lu, Y. Hu, C. Trepte, S. Zeng, and J. H. Churnside, “Ocean subsurface studies with the CALIPSO spaceborne lidar,” J. Geophys. Res. Oceans 119(7), 4305–4317 (2015).
[Crossref]

J. H. Churnside, B. J. Mccarty, and X. Lu, “Subsurface Ocean Signals from an Orbiting Polarization Lidar,” Remote Sens. 5(7), 3457–3475 (2013).
[Crossref]

Lucker, P.

Marchbanks, R. D.

Maritorena, S.

A. Morel and S. Maritorena, “Bio‐optical properties of oceanic waters: A reappraisal,” J. Geophys. Res. Oceans 106(C4), 7163–7180 (2001).
[Crossref]

Mccarty, B. J.

J. H. Churnside, B. J. Mccarty, and X. Lu, “Subsurface Ocean Signals from an Orbiting Polarization Lidar,” Remote Sens. 5(7), 3457–3475 (2013).
[Crossref]

McClain, C. R.

C. R. McClain, “A decade of satellite ocean color observations,” Annu. Rev. Mar. Sci. 1(1), 19–42 (2009).
[Crossref] [PubMed]

McLean, J. W.

Mobley, C. D.

Morel, A.

J. Uitz, H. Claustre, A. Morel, and S. B. Hooker, “Vertical distribution of phytoplankton communities in open ocean: An assessment based on surface chlorophyll,” Jo. Geophys. Res. Oceans 111, C08005 (2006).

A. Morel and S. Maritorena, “Bio‐optical properties of oceanic waters: A reappraisal,” J. Geophys. Res. Oceans 106(C4), 7163–7180 (2001).
[Crossref]

A. Morel, “Light and marine photosynthesis: a spectral model with geochemical and climatological implications,” Prog. Oceanogr. 26(3), 263–306 (1991).
[Crossref]

O’Malley, R. T.

M. J. Behrenfeld, Y. Hu, R. T. O’Malley, E. S. Boss, C. A. Hostetler, D. A. Siegel, J. L. Sarmiento, J. Schulien, J. W. Hair, and X. Lu, “Annual boom-bust cycles of polar phytoplankton biomass revealed by space-based lidar,” Nat. Geosci. 10, 118 (2016).

Omar, A.

D. M. Winker, M. A. Vaughan, A. Omar, Y. Hu, K. A. Powell, Z. Liu, W. H. Hunt, and S. A. Young, “Overview of the CALIPSO Mission and CALIOP Data Processing Algorithms,” J. Atmos. Ocean. Tech. 26(11), 2310–2323 (2009).
[Crossref]

Pelon, J.

Phillips, D.

D. Phillips and B. Koerber, “A Theoretical Study of an Airborne Laser Technique for determining Sea Water Turbidity,” Aust. J. Phys. 37(1), 75 (1984).
[Crossref]

Poole, L. R.

Powell, K. A.

D. M. Winker, M. A. Vaughan, A. Omar, Y. Hu, K. A. Powell, Z. Liu, W. H. Hunt, and S. A. Young, “Overview of the CALIPSO Mission and CALIOP Data Processing Algorithms,” J. Atmos. Ocean. Tech. 26(11), 2310–2323 (2009).
[Crossref]

Qun, L.

L. Dong, L. Qun, B. Jian, and Z. Yupeng, “Data processing algorithms of the space-borne lidar CALIOP: a review,” Infr. Laser Eng. 46(12), 1202001 (2017).
[Crossref]

Rao, J.

M. Xia, K. Yang, Y. Zheng, and J. Rao, “Influence of Wavy Sea Surface on Airborne Lidar Underwater Beam Quality with Monte Carlo Method,” Chin. J. Lasers 35(2), 178–182 (2008).
[Crossref]

Rigaud, V.

Rodier, S.

Rodier, S. D.

M. J. Behrenfeld, Y. Hu, C. A. Hostetler, G. Dall’Olmo, S. D. Rodier, J. W. Hair, and C. R. Trepte, “Space‐based lidar measurements of global ocean carbon stocks,” Geophys. Res. Lett. 40(16), 4355–4360 (2013).
[Crossref]

Sarmiento, J. L.

M. J. Behrenfeld, Y. Hu, R. T. O’Malley, E. S. Boss, C. A. Hostetler, D. A. Siegel, J. L. Sarmiento, J. Schulien, J. W. Hair, and X. Lu, “Annual boom-bust cycles of polar phytoplankton biomass revealed by space-based lidar,” Nat. Geosci. 10, 118 (2016).

Schulien, J.

M. J. Behrenfeld, Y. Hu, R. T. O’Malley, E. S. Boss, C. A. Hostetler, D. A. Siegel, J. L. Sarmiento, J. Schulien, J. W. Hair, and X. Lu, “Annual boom-bust cycles of polar phytoplankton biomass revealed by space-based lidar,” Nat. Geosci. 10, 118 (2016).

Schulien, J. A.

Scully, M. O.

Shamanaev, V. S.

Siegel, D. A.

M. J. Behrenfeld, Y. Hu, R. T. O’Malley, E. S. Boss, C. A. Hostetler, D. A. Siegel, J. L. Sarmiento, J. Schulien, J. W. Hair, and X. Lu, “Annual boom-bust cycles of polar phytoplankton biomass revealed by space-based lidar,” Nat. Geosci. 10, 118 (2016).

Stamnes, J. J.

Stamnes, K.

Sugimoto, N.

Sukenik, C. I.

B. L. Collister, R. C. Zimmerman, C. I. Sukenik, V. J. Hill, and W. M. Balch, “Remote sensing of optical characteristics and particle distributions of the upper ocean using shipboard lidar,” Remote Sens. Environ. 215, 85–96 (2018).
[Crossref]

Sullivan, J. M.

Sundman, L. K.

Thomas, R. J.

Trepte, C.

Trepte, C. R.

M. J. Behrenfeld, Y. Hu, C. A. Hostetler, G. Dall’Olmo, S. D. Rodier, J. W. Hair, and C. R. Trepte, “Space‐based lidar measurements of global ocean carbon stocks,” Geophys. Res. Lett. 40(16), 4355–4360 (2013).
[Crossref]

Twardowski, M. S.

Uitz, J.

J. Uitz, H. Claustre, A. Morel, and S. B. Hooker, “Vertical distribution of phytoplankton communities in open ocean: An assessment based on surface chlorophyll,” Jo. Geophys. Res. Oceans 111, C08005 (2006).

Vaughan, M. A.

D. M. Winker, M. A. Vaughan, A. Omar, Y. Hu, K. A. Powell, Z. Liu, W. H. Hunt, and S. A. Young, “Overview of the CALIPSO Mission and CALIOP Data Processing Algorithms,” J. Atmos. Ocean. Tech. 26(11), 2310–2323 (2009).
[Crossref]

Venable, D. D.

Verhappen, R.

Voelger, P.

Walker, R. E.

Wang, L.

L. Wang, S. L. Jacques, and L. Zheng, “MCML—Monte Carlo modeling of light transport in multi-layered tissues,” Comput. Meth. Prog. Bio. 47, 131-146 (1995).

Wilson, J.

Winker, D. M.

D. M. Winker, M. A. Vaughan, A. Omar, Y. Hu, K. A. Powell, Z. Liu, W. H. Hunt, and S. A. Young, “Overview of the CALIPSO Mission and CALIOP Data Processing Algorithms,” J. Atmos. Ocean. Tech. 26(11), 2310–2323 (2009).
[Crossref]

Xia, M.

M. Xia, K. Yang, Y. Zheng, and J. Rao, “Influence of Wavy Sea Surface on Airborne Lidar Underwater Beam Quality with Monte Carlo Method,” Chin. J. Lasers 35(2), 178–182 (2008).
[Crossref]

Yakovlev, V. V.

Yan, B.

Yang, K.

M. Xia, K. Yang, Y. Zheng, and J. Rao, “Influence of Wavy Sea Surface on Airborne Lidar Underwater Beam Quality with Monte Carlo Method,” Chin. J. Lasers 35(2), 178–182 (2008).
[Crossref]

Young, S. A.

D. M. Winker, M. A. Vaughan, A. Omar, Y. Hu, K. A. Powell, Z. Liu, W. H. Hunt, and S. A. Young, “Overview of the CALIPSO Mission and CALIOP Data Processing Algorithms,” J. Atmos. Ocean. Tech. 26(11), 2310–2323 (2009).
[Crossref]

Yupeng, Z.

L. Dong, L. Qun, B. Jian, and Z. Yupeng, “Data processing algorithms of the space-borne lidar CALIOP: a review,” Infr. Laser Eng. 46(12), 1202001 (2017).
[Crossref]

Zeng, S.

Zheng, L.

L. Wang, S. L. Jacques, and L. Zheng, “MCML—Monte Carlo modeling of light transport in multi-layered tissues,” Comput. Meth. Prog. Bio. 47, 131-146 (1995).

Zheng, Y.

M. Xia, K. Yang, Y. Zheng, and J. Rao, “Influence of Wavy Sea Surface on Airborne Lidar Underwater Beam Quality with Monte Carlo Method,” Chin. J. Lasers 35(2), 178–182 (2008).
[Crossref]

Zimmerman, R. C.

B. L. Collister, R. C. Zimmerman, C. I. Sukenik, V. J. Hill, and W. M. Balch, “Remote sensing of optical characteristics and particle distributions of the upper ocean using shipboard lidar,” Remote Sens. Environ. 215, 85–96 (2018).
[Crossref]

Zollars, B.

Ann. Rev. Mar. Sci. (1)

C. A. Hostetler, M. J. Behrenfeld, Y. Hu, J. W. Hair, and J. A. Schulien, “Spaceborne Lidar in the Study of Marine Systems,” Ann. Rev. Mar. Sci. 10, 121–147 (2017).

Annu. Rev. Mar. Sci. (1)

C. R. McClain, “A decade of satellite ocean color observations,” Annu. Rev. Mar. Sci. 1(1), 19–42 (2009).
[Crossref] [PubMed]

Appl. Opt. (9)

G. M. Krekov, M. M. Krekova, and V. S. Shamanaev, “Laser sensing of a subsurface oceanic layer. I. Effect of the atmosphere and wind-driven sea waves,” Appl. Opt. 37(9), 1589–1595 (1998).
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H. R. Gordon, “Interpretation of airborne oceanic lidar: effects of multiple scattering,” Appl. Opt. 21(16), 2996–3001 (1982).
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J. H. Lee, J. H. Churnside, R. D. Marchbanks, P. L. Donaghay, and J. M. Sullivan, “Oceanographic lidar profiles compared with estimates from in situ optical measurements,” Appl. Opt. 52(4), 786–794 (2013).
[Crossref] [PubMed]

L. R. Poole, D. D. Venable, and J. W. Campbell, “Semianalytic Monte Carlo radiative transfer model for oceanographic lidar systems,” Appl. Opt. 20(20), 3653–3656 (1981).
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R. E. Walker and J. W. McLean, “Lidar equations for turbid media with pulse stretching,” Appl. Opt. 38(12), 2384–2397 (1999).
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K. I. Gjerstad, J. J. Stamnes, B. Hamre, J. K. Lotsberg, B. Yan, and K. Stamnes, “Monte Carlo and discrete-ordinate simulations of irradiances in the coupled atmosphere-ocean system,” Appl. Opt. 42(15), 2609–2622 (2003).
[Crossref] [PubMed]

C. D. Mobley, L. K. Sundman, and E. Boss, “Phase function effects on oceanic light fields,” Appl. Opt. 41(6), 1035–1050 (2002).
[Crossref] [PubMed]

V. I. Haltrin, “One-parameter two-term Henyey-Greenstein phase function for light scattering in seawater,” Appl. Opt. 41(6), 1022–1028 (2002).
[Crossref] [PubMed]

Z. Liu, P. Voelger, and N. Sugimoto, “Simulations of the observation of clouds and aerosols with the Experimental Lidar in Space Equipment system,” Appl. Opt. 39(18), 3120–3137 (2000).
[Crossref] [PubMed]

Astrophys. J. (1)

L. G. Henyey and J. L. Greenstein, “Diffuse radiation in the Galaxy,” Astrophys. J. 93, 70–83 (1941).

Aust. J. Phys. (1)

D. Phillips and B. Koerber, “A Theoretical Study of an Airborne Laser Technique for determining Sea Water Turbidity,” Aust. J. Phys. 37(1), 75 (1984).
[Crossref]

Chin. J. Lasers (1)

M. Xia, K. Yang, Y. Zheng, and J. Rao, “Influence of Wavy Sea Surface on Airborne Lidar Underwater Beam Quality with Monte Carlo Method,” Chin. J. Lasers 35(2), 178–182 (2008).
[Crossref]

Comput. Meth. Prog. Bio. (1)

L. Wang, S. L. Jacques, and L. Zheng, “MCML—Monte Carlo modeling of light transport in multi-layered tissues,” Comput. Meth. Prog. Bio. 47, 131-146 (1995).

Geophys. Res. Lett. (1)

M. J. Behrenfeld, Y. Hu, C. A. Hostetler, G. Dall’Olmo, S. D. Rodier, J. W. Hair, and C. R. Trepte, “Space‐based lidar measurements of global ocean carbon stocks,” Geophys. Res. Lett. 40(16), 4355–4360 (2013).
[Crossref]

Infr. Laser Eng. (1)

L. Dong, L. Qun, B. Jian, and Z. Yupeng, “Data processing algorithms of the space-borne lidar CALIOP: a review,” Infr. Laser Eng. 46(12), 1202001 (2017).
[Crossref]

J. Atmos. Ocean. Tech. (1)

D. M. Winker, M. A. Vaughan, A. Omar, Y. Hu, K. A. Powell, Z. Liu, W. H. Hunt, and S. A. Young, “Overview of the CALIPSO Mission and CALIOP Data Processing Algorithms,” J. Atmos. Ocean. Tech. 26(11), 2310–2323 (2009).
[Crossref]

J. Geophys. Res. Oceans (2)

X. Lu, Y. Hu, C. Trepte, S. Zeng, and J. H. Churnside, “Ocean subsurface studies with the CALIPSO spaceborne lidar,” J. Geophys. Res. Oceans 119(7), 4305–4317 (2015).
[Crossref]

A. Morel and S. Maritorena, “Bio‐optical properties of oceanic waters: A reappraisal,” J. Geophys. Res. Oceans 106(C4), 7163–7180 (2001).
[Crossref]

J. Opt. Commun. Netw. (1)

J. Quant. Spectrosc. Ra. (1)

G. W. Kattawar, “A three-parameter analytic phase function for multiple scattering calculations,” J. Quant. Spectrosc. Ra. 15(9), 839–849 (1975).
[Crossref]

Jo. Geophys. Res. Oceans (1)

J. Uitz, H. Claustre, A. Morel, and S. B. Hooker, “Vertical distribution of phytoplankton communities in open ocean: An assessment based on surface chlorophyll,” Jo. Geophys. Res. Oceans 111, C08005 (2006).

Nat. Geosci. (1)

M. J. Behrenfeld, Y. Hu, R. T. O’Malley, E. S. Boss, C. A. Hostetler, D. A. Siegel, J. L. Sarmiento, J. Schulien, J. W. Hair, and X. Lu, “Annual boom-bust cycles of polar phytoplankton biomass revealed by space-based lidar,” Nat. Geosci. 10, 118 (2016).

Opt. Eng. (1)

J. H. Churnside, “Review of profiling oceanographic lidar,” Opt. Eng. 53(5), 051405 (2014).
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Opt. Express (3)

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A. Morel, “Light and marine photosynthesis: a spectral model with geochemical and climatological implications,” Prog. Oceanogr. 26(3), 263–306 (1991).
[Crossref]

Remote Sens. (1)

J. H. Churnside, B. J. Mccarty, and X. Lu, “Subsurface Ocean Signals from an Orbiting Polarization Lidar,” Remote Sens. 5(7), 3457–3475 (2013).
[Crossref]

Remote Sens. Environ. (1)

B. L. Collister, R. C. Zimmerman, C. I. Sukenik, V. J. Hill, and W. M. Balch, “Remote sensing of optical characteristics and particle distributions of the upper ocean using shipboard lidar,” Remote Sens. Environ. 215, 85–96 (2018).
[Crossref]

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C. D. Mobley, Light and Water: Radiative Transfer in Natural Waters (Academic Press, 1994).

J. H. Churnside, “LIDAR detection of plankton in the ocean,” in IEEE International Geoscience and Remote Sensing Symposium (2007), 3174–3177.
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Figures (8)

Fig. 1
Fig. 1 The schematic diagram of spaceborne lidar system.
Fig. 2
Fig. 2 Influence of FOV of the lidar receiving system and orbit altitude on klidar. (a) klidar of different optical depths under different FOV with the orbit altitude of 400 km, (b) klidar for different orbit altitudes with the FOV of 0.4 mrad. Here, τ O is the optical depth with = 0.233m-1.
Fig. 3
Fig. 3 (a) Depth profiles of the normalized lidar return signals Pnorm(z) and (b) the corresponding effective attenuation coefficients klidar(z) for [Chl] between 0 to 2 mg/m3.
Fig. 4
Fig. 4 (a) The normalized lidar return signal power with different order of scattering Pnorm(z) and (b) the percentage of each order of scattering (n is the order of scattering) for [Chl] = 0.35 mg/m3. The number represents the order of scattering and ‘total’ depicts the full multiple scattering signal.
Fig. 5
Fig. 5 The influence of IOPs on klidar for (a) b=0.2m-1 and w0=0.3(0.1)0.6, (b) a=0.2m-1 and w0=0.3(0.1)0.6, and (c) [Chl] = 0.35 mg/m3 and g1 = 0.9509(0.01)0.9809.
Fig. 6
Fig. 6 Relationships between the parameters (a) m, (b) n, and (c) p and IOPs.
Fig. 7
Fig. 7 The inherent optical properties (IOPs) and chlorophyll a concentration (Chla) for (a) S1 water, (b) S5 water and (c) S9 water.
Fig. 8
Fig. 8 The inherent optical properties (IOPs) and attenuation coefficients for (a) S1 water, (b) S5 water and (c) S9 water, respectively. klidar represents the effective attenuation coefficient of simulating return signals, and klidar-c is the attenuation coefficient calculated using the klidar-IOPs model shown in Eqs. (19) and (20).

Tables (3)

Tables Icon

Table 1 The values of input parameters of the calculations

Tables Icon

Table 2 Fitting results of klidar for 16 types of case 1 waters.

Tables Icon

Table 3 Values of the parameters to be used in Eq. (21) for S1, S5 and S9, respectively [36].

Equations (22)

Equations on this page are rendered with MathJax. Learn more.

P ( z ) = P 0 M v τ 2 n T a t m 2 T s u r f 2 A ( n H + z ) 2 β π ( z ) exp ( 2 0 z k l i d a r ( z ) d z ) ,
P n o r m ( z ) = P ( z ) ( n H + z ) 2 P 0 M ( v τ 2 n ) T a t m 2 T s u r f 2 A β π ( z ) = exp ( 2 0 z k l i d a r ( z ) d z ) .
k l i d a r ( z ) = 1 2 d d z ln ( P ( z ) n o r m ) .
c ( λ ) = a ( λ ) + b ( λ ) .
a ( λ ) = [ a w ( λ ) + 0.06 a c ( λ ) [ Chl ] 0.65 ] [ 1 + 0.2 exp ( 0.014 ( λ 440 ) ) ] ,
b ( λ ) = b w ( λ ) + 550 λ × 0.3 × [ Chl ] 0.62 ,
b w ( λ ) = 16.06 × ( 550 / λ ) 4.324 × 1.21 × 10 4 .
K d = a w + b w / 2 + 0.04826 [ Chl ] 0.67224 .
I ( x , y ) = 1 2 π σ s 2 exp ( x 2 + y 2 2 σ s 2 ) ,
r = σ s 2 ln ( R 1 )
φ = 2 π R 2 ,
{ x = r cos φ y = r sin φ z = 0 .
{ u x = x / ( x 2 + y 2 + H 2 ) u y = y / ( x 2 + y 2 + H 2 ) u z = H / ( x 2 + y 2 + H 2 ) .
β ˜ = η β ˜ w + ( 1 η ) β ˜ p .
β ˜ w ( cos θ ) = 0.06225 ( 1 + 0.835 cos 2 θ ) ,
β ˜ p ( cos θ ) = β ˜ H G ( g , cos θ ) = 1 4 π 1 g 2 ( 1 + g 2 2 g cos θ ) 3 / 2 ,
β ˜ p ( cos θ ) = β ˜ T T H G ( cos θ , α , g 1 , g 2 ) = α β ˜ H G ( cos θ , g 1 ) + ( 1 α ) β ˜ H G ( cos θ , g 2 ) ,
E = β ( θ ) 4 π A ( n H + z ) 2 exp ( j = 1 i c ( j ) d ( j ) ) T a t m T s u r f ,
k l i d a r ( z ) = m × exp ( n × z ) + p .
m = 4 .8907 b b 0.00 04 n = 4.2506 b b 0.00 55 . p = a + 0. 3582 b b 0.00 4 2
C ( ζ ) = C b s ζ + C max exp { [ ( ζ ζ max ) / Δ ζ ] 2 } ,
δ = i = 1 i = n | k ( z i ) k l i d a r ( z i ) | / k l i d a r ( z i ) n × 100 %

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