**Figure 29** displays the TOA unpolarized reflectance ratio versus the TOA total reflectance ratio at 443 nm for the situations of **Figures 27**, **28**. Reflectance ratio refers to the ratio of water reflectance just below the surface to TOA reflectance, expressed in percent. Only results for viewing zenith angles less than 60◦ (typical of satellite observations) are reported. As already mentioned, the unpolarized reflectance ratio is generally larger than the total reflectance ratio, for most viewing angles, except when aerosol optical thickness is large (i.e., 1 at 550 nm). In this case, the total reflectance ratio may be smaller by a few percent, which is practically not penalizing. Unpolarized observations in forward scattering directions, where polarization is effective, are most favorable to boost the contribution of the water signal. When chlorophyll concentration is high (i.e., 10 mgm−3 ), the unpolarized reflectance ratio is up to three times larger than the total reflectance ratio. In such productive waters, for which the water signal is usually small, using unpolarized observations would greatly facilitate atmospheric correction, which may yield to more accurate estimates of water constituents/properties.

Now when working with unpolarized reflectance, the sensitivity of the unpolarized signal backscattered by the water body (obtained after atmospheric correction) to water composition may differ from that of the total reflectance, due to changes in the polarization rate (Chami et al., 2001; Chowdhary et al., 2006; Zhai et al., 2017). This sensitivity should remain sufficient for the methodology to be effective, since the ultimate objective is not an accurate retrieval of the water signal, but an accurate retrieval of the properties that affect the water signal. **Figure 30** displays, for typical solar and viewing angles, the sensitivity of the spectral ratio of water reflectance just below the surface at 443 and 550 nm, total or unpolarized, to chlorophyll-a concentration. This ratio is typically used in OC4x and OC3M algorithms. The atmospheric and surface conditions are those from **Figure 27**. Solar and viewing zenith angles are fixed at 30◦ and relative azimuth angle is 0◦ , 90◦ , or 180◦ . In the backscattering direction, as expected, the sensitivity to chlorophyll-a concentration remains unchanged whether using unpolarized or total water reflectance. In the forward scattering direction, the sensitivity is slightly higher at low chlorophyll-a concentration when using the unpolarized component (due to the decrease in polarization rate as chlorophyll concentration increases). This illustrates the potential of working with unpolarized reflectance, at least for chlorophyll-a concentration retrieval.

In summary, one may envision using the parallel polarization component (PPR) or the unpolarized component of TOA reflectance instead of the total reflectance (the usual way) to improve atmospheric correction of satellite optical imagery and retrieve properties (optical, biogeochemical) of the water body. The methodology only requires observations in a single direction, like with current ocean-color sensors. The advantages are that the water contribution to the measured signal may be substantially enhanced for a wide range of viewing angles and that Sun glint effects may be reduced. Also, in a classic atmospheric correction scheme, the polarization information in the near infrared (even in a single direction) would help to determine a proper aerosol model. A number of issues remain to be addressed; including radiometric calibration (polarization accuracy may degrade data quality) and bio-optical relations based on unpolarized component of water reflectance (need to be established, and experimental data are lacking). Furthermore, since molecular scattering strongly polarizes incident sunlight, one wonders whether the enhancement of the water signal would remain significant after removing the molecular signal (can be computed accurately, except in the presence of Sun glint) from the polarimetric measurements.

## Benefit of Combining Multi-Angular and Polarimetric Observations

The shortcoming of conventional atmospheric correction methods in regions with complex marine and atmospheric compositions suggests an opportunity for a multi-angle polarimeter to supplement OCI remote sensing reflectance (Rrs) retrievals, particularly at short visible-UV wavelengths and in cases where the water surface in the NIR cannot be considered black, and will therefore provide significant risk reduction for meeting many PACE mission objectives. Indeed, measurements of the wavelength and angular dependence and polarization of the scattered radiance provide a determination of the aerosol physical properties, i.e., size distribution and refractive index (e.g., Hasekamp and Landgraf, 2005; Herman et al., 2005; Hasekamp et al., 2011). This information may be used to constrain the domain of possible aerosol types in a classic atmospheric correction scheme or, if sufficiently accurate, to directly compute the aerosol scattering effect. The sensitivity of polarized radiance to aerosol type has also the potential to improve inversion schemes that aim at retrieving simultaneously atmosphere and ocean properties.

Observational evidence is presented in **Figure 31** that shows retrievals of ρwEs/π (otherwise known as normalized waterleaving radiance, LwN) obtained from AirMSPI measurements collected on February 6, 2013 over a SeaPRISM site off the coast of California. AirMSPI is a multi-angle polarimeter flying on the high altitude NASA ER-2 with 20 km of atmosphere between it and the ocean below. The SeaPRISM site on an offshore platform provides spectral Lwn measured just above the ocean surface. The retrieval was accomplished using an optimization-based multipixel retrieval algorithm (Dubovik et al., 2011). The forward radiative transfer calculations were performed using a Markov chain approach developed for a coupled atmosphere/surface system (Xu et al., 2011, 2012). The water reflectance was modeled as a depolarizing Lambertian surface reflection model, plus a polarizing part modeled by the Cox-Munk model (Cox and Munk, 1954; Mishchenko et al., 1997). Atmosphere and surface properties were retrieved simultaneously, and surface reflectance was retrieved independently at each wavelength.

The comparisons between the AirMSPI retrievals with the SeaPRISM ground truth (**Figure 31**) show that LwN is retrieved much more accurately with multi-angle and polarization capability than when a more limited retrieval is made with just one angle and no polarization. Current satellite retrievals mitigate random and systematic errors over the open ocean by invoking standardized models for ocean surface spectral reflectance and aerosol scattering. However, in more complex waters, or when aerosol absorption is present at short wavelengths, traditional retrieval assumptions break down. The left-hand figure does not prescribe either an aerosol model or any surface spectral constraints, indicating that multi-angle and polarimetric imagery provides the necessary additional information in the more general scenario. The right hand panel does not invoke any of the enhanced capabilities, including expanded wavelength range, expected of the PACE OCI, and therefore suggests a worse retrieval than could actually be obtained by the radiometer alone.

Demonstrations that show the advantages of a MAP for atmospheric correction as applied to AirMSPI and POLDER are extremely promising, but still do not demonstrate how polarimeter data can be used to assist atmospheric correction of hyper-spectral radiometer measurements in complex coastal environments. These environments are located where traditional atmospheric correction assumptions do not apply, and where inherent optical properties (IOPs) can vary much more significantly and in more complex manners than in open ocean waters. In addition, OCI retrievals at UV and shortwave visible wavelengths present new challenges to atmospheric correction due to sensitivities to aerosol height in an atmospheric signal dominated by Rayleigh scattering and the interplay between scattering and gaseous absorption. For these optically complex regions and at these spectral ranges, adding information from MAP becomes most important for atmospheric correction. Efforts to acquire and make use of the appropriate demonstration data are highly recommended.

These constraints can enter the atmospheric correction procedures at a variety of levels. (1) At the simplest, they can improve our overall understanding of aerosol properties and distribution of aerosol type, which can be incorporated into heritage atmospheric correction lookup table and algorithm updates. (2) MAP based aerosol retrieval could be performed prior to OCI atmospheric correction, and direct that algorithm to a particular aerosol type and/or provide the aerosol optical depth. (3) An atmospheric correction algorithm may be performed directly with MAP observations, and after spectral and geometric interpolation, applied to OCI. (4) Both MAP and OCI observations are combined as inputs to a joint inversion. Implementation of any of these approaches would require that we overcome multi-sensor data fusion issues such as instrumental calibration, differing spatial resolution and varied geometry.

Clouds are ubiquitous in remote sensing of the ocean, so the ability to properly screen and remove "cloud contaminated" pixels is an important component of successful atmospheric correction. Such techniques must be conservative, but a "cloud free" pixel still may contain sub-pixel clouds, or be influenced by cloud adjacency effects such as shadows or other "3D" phenomena. Combined multi-angular and polarimetric observations are capable of identifying such effects, and in some cases, minimize their impact. Bréon and Goloub (1998) recognized that certain solar-view geometries observed by POLDER contained distinct cloud-bow features due to single scattering by liquid cloud drops, confirming earlier observations by Goloub et al. (1994) with airborne POLDER prototype observations. This was incorporated into POLDER cloud detection, cloud property retrieval, and cloud phase detection algorithms (Bréon and Colzy, 1999; Parol et al., 1999; Goloub et al., 2000). A later review of these capabilities by Parol et al. (2004) highlighted the utility of multiangular polarimetric observations, but also found evidence of cloud contamination in an aggregate analysis of POLDER data. For these reasons, subsequent multi-angular polarimetric instruments were designed with the recognition that they would provide valuable data in both cloud and cloud free regions (e.g., Frouin et al., 2006; Mishchenko et al., 2007; Fougnie et al., 2018; Hasekamp et al., 2019). Additionally, polarimetric observations are recognized as being less sensitive to 3D effects (e.g., Davis and Marshak, 2010) but in certain geometries capable of detecting very optically thin cirrus clouds (Sun et al., 2014). The information contained in multiangular polarimetric observations is highly sensitive to solar and observation geometry; it increases with additional measurements at angles containing the reflected cloud-bow. However, it can be large enough that simultaneous retrieval of cloud and aerosol property algorithms have been proposed for mixed cloud and cloud free pixels (Hasekamp, 2010), indicating the utility of such observations for properly cloud screened atmospheric correction.

## IMPROVEMENTS USING "SUPER-SAMPLING" IN SELECTED SPECTRAL INTERVALS

The PACE OCI will have the capability to measure TOA radiance in spectral bands 5 nm wide with sampling at finer spectral steps (2.5, 1.25, or 0.625 nm). This fine spectral sampling provides the opportunity to estimate Raman scattering by water bodies and improve retrievals of phytoplankton chlorophyll fluorescence and aerosol vertical distribution. These applications involve the development of original techniques that exploit spectral fluctuations in solar irradiance, the filling by inelastic fluorescence of oxygen absorption lines in the B-band, and the coupling between aerosol scattering and oxygen absorption in the A-band. They require or will benefit from the high spectral sampling.

## Estimation of Raman Scattering

Raman scattering occurs when an incident photon is scattered by water molecules excited into higher vibrational or rotational energy levels. The scattered photon has a frequency different from (usually lower than) that of the incident photon, i.e., the process is inelastic (kinetic energy of the incident photon is not conserved). This scattering by excitation is infrequent (1 in 10 million events), yet its contribution to diffuse water reflectance or "remote sensing" reflectance may reach 20% in the visible in clear oceanic areas (i.e., 40% of the world's oceans), as shown by Marshall and Smith (1990) and others and illustrated in **Figure 32**.

The Raman signal, therefore, needs to be known to improve the retrieval of oceanic variables (e.g., chlorophyll concentration) from "remote sensing" reflectance. The fact that Raman scattering at a given wavelength is influenced by the water optical properties at shorter wavelengths provides a second motivation to observe the Raman signal. Are the Raman scattering measurements interpretable in terms of water optical properties, and how can they complement other approaches? Measurements in the near UV, for example, should be strongly affected by CDOM absorption, while in the blue-green by chlorophyll absorption.

Raman scattering at a given wavelength is proportional to some integral of the solar irradiance at shorter wavelengths and, unlike elastic scattering by water and air constituents, its spectrum does not exhibit fluctuations in spectral solar irradiance (Fraunhofer lines and others). The approach to estimate the Raman scattering signal is thus to observe the TOA radiance at sufficiently high spectral resolution in a small wavelength interval, and to separate the part of the radiance that correlates with the solar irradiance spectrum (the elastic scattering) from the part that does not (the Raman scattering).

At the air-water interface, the spectral water-leaving radiance, Lw(λ), is the addition of an elastic scattering contribution, Lw\_elastic (λ) proportional to the solar spectral irradiance, Es(λ), and a Raman scattering contribution, Lw\_raman (λ), i.e., the integral of the excitation by the Sun at shorter wavelengths. At the TOA, the observed spectral radiance, LTOA(λ), includes the signal due to atmospheric scattering and Fresnel reflection at the interface, Latm(λ), also proportional to Es(λ). Neglecting gaseous absorption and Sun glint effects for simplicity, LTOA (λ) can be written:

$$L\_{TOA}\left(\lambda\right) = L\_{atm}\left(\lambda\right) + L\_{w\\_elastic}\left(\lambda\right)t\_{\mu}\left(\lambda,\theta\right)$$

$$+ L\_{w\\_raman}\left(\lambda\right)t\_{\mu}\left(\lambda,\theta\right)\tag{11}$$

The ability to de-correlate the elastic and inelastic scattering contributions does depend on the presence of some absorption bands in the extraterrestrial solar spectrum over the selected spectral interval. **Figure 33** shows the spectral solar irradiance, Es(λ), and the Raman "remote sensing" reflectance, Rrs\_raman(λ) (defined as Lw\_raman(λ)/Ed (λ) where Ed is the downward spectral solar irradiance at the surface, i.e., Es(λ)cos(θs)td(λ), in the range 350–550 nm at a 5 nm resolution every 1.5 nm (approximating the spectral resolution and sampling expected from OCI). The Raman signal decreases with increasing chlorophyll-a concentration and CDOM absorption and exhibits fairly large spectral variations in some regions. For some intervals, namely 398.5–412.5 nm, 436.5–452.5, 473.5–484.5 nm, and 509.5–519.5 nm, however, the Raman signal is fairly constant with wavelength and the Es variability with wavelength is sufficiently high to attempt a de-correlation of the Raman and elastic contributions to the TOA signal.

De-correlation of the two types of scattering can be accomplished by performing a linear fit of the observed spectral radiance (or equivalently reflectance) vs. the spectral solar irradiance in the intervals identified. In those relatively small spectral interval (10–15 nm wide), the spectral variation of the scattering properties, either elastic or Raman, can be assumed negligible, or, better, derived by a radiation transfer model with inputs of the optical aerosol and ocean properties as determined by the atmospheric correction algorithm. Note that the molecular atmospheric scattering contribution, which has a strong spectral dependence, can be subtracted from the TOA measurements. We have:

$$P(\lambda) = P\_0 \left(\lambda\right) + L\_{\text{w\\_elastic\\_}}(\lambda) + \left + F(\lambda) \tag{12}$$

with P0(λ) = - Latm (λ) − Lr(λ) /t u (λ, θ) where F(λ) is a spectral factor that depends on the water optical properties (since again they may not be constant over the spectral interval considered). The ordinate at the origin of the best linear fit P vs. Es or downward irradiance at the surface, Ed, gives access to Lw\_raman .

The approach and methodology are simple in principle, but to achieve useful accuracy a number of technical issues require attention: (1) Radiometric noise of the spectral measurements will result in an error on the water Raman scattering estimate that is amplified by the extrapolation of the linear fit to the ordinate axis; (2) Spectral resolution of the measurement will affect the variability of the spectral measurement as the solar spectrum is smoothed. A reduced variability of the solar spectrum will increase the error due to extrapolation; and (3) The sum of atmospheric and oceanic Raman scattering signals would be actually determined. Although the atmospheric Raman scattering is rather small (e.g., Vountas, 1998), it must be accounted for and subtracted from the water Raman scattering estimate; and (4) The Raman scattering signal can only be determined with good accuracy in some spectral intervals where the solar irradiance variability is large enough, namely around 390, 440, 475, and 515 nm, as indicated above. Interpolation/extrapolation to others wavelengths, definitively feasible, may not be straightforward since the Raman signal is not smooth spectrally.

**Figure 34** illustrates the method feasibility. The various atmospheric functions were simulated in 5 nm bands shifted by 1.5 nm resolution for the 4 suitable spectral intervals using a successive-orders-of-scattering code. Aerosols were assumed of maritime type with an optical thickness of 0.2 at 550 nm, and wind speed was 5 m s−1 . Sun and view zenith angles were 30◦ and relative azimuth angle was 90◦ . The waterleaving radiance (elastic and inelastic components) was simulated using HYDROLIGHT for Case 1 waters with a chlorophyll-a concentration, Chl-a, of 0.03 mgm−3 . Typical CDOM absorption corresponding to biogenic particles was used. No noise was introduced on the TOA reflectance and atmospheric Raman scattering was neglected. Retrievals were obtained (1) assuming F = 0 (no spectral correction), and (2) assuming F was perfectly known.

When F = 0, large errors are obtained in the determination of Lw\_raman , i.e., the methodology is not practically applicable. For example, in the 398.5–412.5 interval, the retrieved Lw\_raman value is 0.00028 instead of 0.00079 Wm−2nm−1 sr−1 when Chla is 0.03 mgm−3 . In the 436.5–452.5 nm and 473.5–484.5 nm intervals, the retrieval is completely erroneous. When using F computed assuming that Chl-a is known, the Lw\_raman estimates are very close to the prescribed values. This indicates that knowledge of Chl-a is essential to retrieve Lw\_raman accurately.

Now Chl-a can be obtained from standard band-ratio algorithms with a typical uncertainty of ±30%. The impact of such uncertainty on the Lw\_raman retrieval is still too large. For the case mentioned above, the Lw\_raman estimate would range from 0.00058 to 0.00165 Wm−2nm−1 sr−1 , which is not satisfactory. This means that Chl-a needs to be estimated more accurately for the methodology to work.

To estimate Chl-a, therefore F, more accurately, one may use the goodness of the linear fit (correlation coefficient). One expects that the linear regression will be more accurate when the spectral factor F corresponds to the actual Chl-a (see **Figure 34**). To increase sensitivity to Chl-a, especially in the presence of noise, the correlation coefficient may be computed for the 4 spectral intervals combined. This provides a practical way to determine the best F that corresponds to the observations. One could use a look-up table of F(λ, Chl-a) for each spectral interval to find the best F values that yield the best linear fit. As Raman scattering also depends on absorption by other variable constituents, the look-up table should include additional parameters (e.g., CDOM absorption coefficient). Additional theoretical and experimental work is needed, however, to demonstrate quantitatively the method's applicability to OCI imagery.

## Estimation of Chlorophyll Fluorescence

Knowledge of the solar-induced chlorophyll fluorescence of natural waters is important to understanding the physiology of phytoplankton and investigating environmental influences on primary production and food web structure (e.g., Behrenfeld et al., 2009). Chlorophyll fluorescence can be remotely sensed from space, which has been routinely accomplished by instruments like MODIS and MERIS. The retrieval algorithms (Letelier and Abbott, 1996; Gower et al., 1999; Huot et al., 2005; Behrenfeld et al., 2009) involve the subtraction of a baseline representing the shape of the water reflectance spectrum without fluorescence. In waters containing sediments and yellow substances (Case 2 waters), determination of the baseline may not be accurate, yielding unacceptable uncertainties on the fluorescence height estimates, all the more as the chlorophyll concentration or the quantum yield of fluorescence are low. It is desirable to improve accuracy in these waters, for which fluorescence, unlike blue-to-green reflectance ratios, is a good measure of chlorophyll concentration.

To improve the estimate of chlorophyll fluorescence, one may use detailed spectral measurements in the oxygen Bband centered on 687 nm. The method exploits the fact that emitted fluorescence (observed at sea level) is excited at shorter wavelengths and is not affected by the oxygen absorption, contrary to the elastic water reflectance. As the absorption lines are partially filled due to inelastic fluorescence emission, the spectral change with respect to reflected solar radiance (i.e., to the elastic component) is sensitive to the fluorescence signal. By shifting the center wavelength in a spectral interval of 10– 20 nm, one may be able to de-correlate the fluorescence signal from the elastic signal in the TOA measurements, therefore obtain a fluorescence estimate more independent from elastic scattering than the standard baseline technique. In other words, the fluorescence signal can be much more easily differentiated from the elastic signal when spectral measurements in the oxygen band are included.

Neglecting Sun glint, Raman scattering by water molecules, and assuming that in the spectral range of interest, i.e., 670– 700 nm, gaseous absorption is only due to oxygen, the radiance L ′ TOA measured from space, after correction for molecular scattering, can be expressed as:

$$\begin{aligned} L'\_{TOA} &= L\_a T\_{O2} \left( \theta\_s, \theta, H\_d \right) + L\_{w\_\text{-elastic}} t\_\text{u} \left( \theta \right) T\_{O2} \left( \theta\_s, \theta, 0 \right) \\ &+ L\_{w\_\text{-fluo}} t\_\text{u} \left( \theta \right) T\_{O2} \left( \theta, 0 \right) \tag{13} \end{aligned}$$

where Lw\_elastic is the elastic water-leaving radiance, Lw\_fluo is the radiance due to chlorophyll fluorescence, TO2 (θs , θ, Ha) is the oxygen transmittance associated with the path radiance, TO2 (θs , θ, 0) is the oxygen transmittance associated with the elastic water reflectance (affected by absorption along the Sun-to-surface path), and TO2 (θ, 0) is the oxygen transmittance associated with the fluorescence signal (only along the surfaceto-satellite path). The transmittances TO2 (θs , θ, Ha) and TO2 (θs , θ, 0)are different, the first one in particular depends significantly on the vertical distribution of the aerosols (average altitude or scale height Ha). In Equation (13), Lw\_fluo can be expressed as the product of the radiance at the peak of fluorescence emission (i.e., 685 nm) and a known spectral function, h (see, e.g., Mobley, 1994), i.e., Lw\_fluo = hL w\_fluo (685).

The variable to retrieve is Lw\_fluo(685), the fluorescence line height. This can be accomplished using a spectral optimization scheme, in which Lw\_fluo(685) is varied to obtain the best fit between the modeled and actual (measured) TOA radiance LTOA ′ . The aerosol radiance La and the aerosol optical thickness and model (affect transmittances) can be determined from measurements in the near infrared and/or shortwave infrared using standard algorithms. The oxygen transmittance TO2 (θs , θ, Ha) can be computed from an estimate of Ha (e.g., from measurements in the oxygen absorption A-band centered on 763 nm, see Dubuisson et al., 2009 and section Estimation of Aerosol Vertical Profile) or assuming that the aerosols are located at an average aerosol altitude or at the surface. It is necessary to assume that the spectral shape of the elastic waterleaving radiance, f (λ) is rather constant, or known from a model as a function of chlorophyll concentration, Chl-a, and particulate backscattering.

To demonstrate the method feasibility, Case 1 and Case 2 waters containing 0.1, 1, 5, and 30 mgm−3 of chlorophyll-a were considered. Absorption by yellow substances and sediment concentration were null for the Case 1 waters and fixed at 0.5 m−1 (440 nm) and 2 gm−3 , respectively, for the Case 2 waters. The fluorescence yield, which ranges from less than 0.01 to 0.10, was fixed at 0.05. Aerosols were of maritime type, with optical thickness of 0.2. The vertical profile of aerosol concentration was exponential with a scale height of 1 km. The TOA signal, including the coupling between oxygen absorption and aerosol scattering, was simulated with no noise using the quasi-single scattering approximation (a sufficient treatment to demonstrate feasibility). The HITRAN 2004 database was used to define spectroscopic parameters for oxygen absorption lines and compute oxygen transmittance. Absorption by water vapor was neglected. The simulations were performed for a single angular geometry, i.e., solar and viewing zenith angles of 45◦ , and a relative azimuth angle of 90◦ .

The spectral matching was accomplished on TOA reflectance, ρ ′ TOA = π ′LOA/[Es cos(θs)] rather than radiance, using a function minimization scheme (Nelder and Mead, 1965) over the range 660–720 nm. (Similar results were obtained when restricting the range to 670–700 nm.) The aerosol optical properties (optical thickness and reflectance) were assumed perfectly known. The aerosol scale height Ha was assumed to be 0.5 km. The elastic water reflectance, ρw\_elastic = πLw\_elastic/[Es cos(θs)td (θs)], was parameterized as ρw\_elastic (685) f (λ) where <f > is the average spectral function over all the water situations (Chl-a and water type). Thus the parameters used to adjust the modeled TOA reflectance, after correction for molecular scattering, were ρw\_elastic (685) and ρw\_fluo (685) = πLw\_fluo/[Es cos(θs)td (θs )].

**Figure 35** displays the results obtained for Case 1 and Case 2 waters containing 5 mgm−3 of chlorophyll-a. The retrieved and prescribed fluorescence signals agree well irrespective of water type, with differences <5% on ρw\_fluo (685). The impact of a 0.5 km uncertainty on the 1 km aerosol scale height (i.e., 50%) is small, but increased errors may occur when the difference between actual and specified scale height is larger. Accuracy remains similar for all the chlorophyll-a values in Case 1 waters, but decreases substantially with decreasing Chl-a in Case 2 waters (**Figure 36**).

Compared with the standard baseline technique using bands centered on 665, 682, and 705 nm, the spectral optimization in the spectral range of the oxygen B-band provides much better results (**Figure 36**). For Case 1 waters, the errors on estimated ρw\_fluo (685) are decreased from 15 to 20% to about 5%. For Case 2 waters, the improvement is even more dramatic: Errors are decreased from about 200% to 22% when Chl-a = 0.1 mgm−3 . The two techniques provide similar performance, however, in the presence of Case 2 when Chl-a> 5 mgm−3 , i.e., within about ±20%. Note that when Chl-a is high in Case 1 waters the influence of chlorophyll absorption at 670 nm on the elastic reflectance makes it more difficult to determine the baseline, which degrades the retrieval accuracy of the standard technique (**Figure 36**, left). These findings, based on simulations, were obtained in controlled conditions, and the documented advantages may not be as important in natural conditions, which needs to be examined in future work.

The above results, even though they demonstrate the potential of using the oxygen B-band to improve chlorophyll fluorescence estimates, were obtained without noise on the TOA signal. The acceptable level of radiometric noise needs to be evaluated to achieve useful accuracy. The wavelength range for spectral matching may be optimized for maximum sensitivity to fluorescence height and minimum sensitivity to elastic water reflectance. In this respect, one may consider differential absorption using narrow and wide spectral bands (e.g., 5 and 30 nm, respectively) centered on 687 nm. Centering the two bands on the same wavelength would reduce the impact of the elastic water signal on the estimates, but sensitivity to fluorescence height may be reduced (5 nm may be too wide for the narrow band). Errors in the retrieval of the aerosol optical thickness, reflectance, and scale height (supposed to be determined separately), need also to be included to determine realistically the expected error budget.

## Estimation of Aerosol Vertical Profile

Information on the vertical aerosol profile is required for accurate atmospheric correction of satellite ocean-color imagery at short wavelengths (ultraviolet and blue) when absorbing aerosols are present (Gordon, 1997; Duforêt et al., 2007). This is due to the coupling between aerosol absorption and molecular scattering, which depends on the location of the aerosols in the vertical. Aerosol absorption reduces sun illumination and backscattering in the lower atmospheric layers, all the more as molecular scattering is large. Even if the standard atmospheric correction algorithm works well for "pure" scattering, an additional correction that depends on the aerosol vertical profile must be done at short wavelengths (see section Multiple Scattering).

The effect of aerosol absorption on the observed TOA reflectance, ρTOA, can be written as (e.g., Torres et al., 2002):

$$
\rho\_{abs} \approx -(1 - \alpha\_{0a}) \tau\_a m^\* [\rho\_w t + \rho\_r (P\_s - P\_a) / P\_s] \tag{14}
$$

where τa is the aerosol optical thickness, ω0a is the aerosol single scattering albedo, (1 − ω0a)τa is the absorption optical thickness, and Ps and Pa are surface and aerosol layer pressure levels. The observed signal, ρTOA, is reduced due to aerosol absorption, all the more as aerosols are higher in the atmosphere, absorption optical thickness is larger, and air mass is larger. Note that marine reflectance exerts some influence on ρabs.

This is illustrated in **Figure 37**, which displays ρabs as a function of wavelength (350 to 1,000 nm) and aerosol pressure level (300 to 1,000 hPa) for various aerosol models, i.e., continental (Con), urban (Urb), desert dust (DD), and biomass burning (BB). The calculations were made for a typical geometry, i.e., solar and viewing zenith angles of 30◦ , a relative azimuth angle of 90◦ , an aerosol optical thickness of 0.2 at 550 nm, and a null water reflectance. The absorption effect ρabs reaches −0.004 at 400 nm when aerosols are strongly absorbing (urban type) and located at 900 hPa (**Figure 37**, left), but may be as large as 0.02 in magnitude at that wavelength when they are located higher in the vertical, i.e., at Pa < 400 hPa (**Figure 37**, right). For this relatively small aerosol optical thickness, ρabs remains significant in the presence of less absorbing aerosols (e.g., biomass burning and desert dust types), but would increase proportionally to optical thickness (see Equation 14). Neglecting ρabs in atmospheric correction algorithms, therefore, would yield unacceptable errors on water reflectance retrievals, well above the ±0.002 accuracy requirements for clear waters. Hence, taking into account aerosol vertical structure, a key variable controlling ρabs, is needed when dealing with absorbing aerosols.

As indicated above, the effect of aerosol absorption, when aerosols are concentrated in a layer of average pressure Pa, is proportional to the absorption optical thickness of the layer and to the molecular thickness below the layer, i.e., Ps − Pa. In the more general case of an aerosol profile, Pa is replaced, in first approximation, by an equivalent mean pressure hPai, defined by the integral over pressure of the aerosol optical thickness (from top of atmosphere) normalized by the total aerosol optical thickness. Thus hPai is the vertical structure parameter to introduce for a more effective atmospheric correction in the ultraviolet and blue (details about the vertical structure do not need to be known). This equivalent pressure can be derived from spectral measurements in the oxygen A-band around 762 nm.

The envisioned methodology to retrieve hPai exploits the coupling between aerosol scattering and oxygen absorption. The TOA radiance measured in the oxygen A-band depends on the altitude of the atmospheric scatterers, especially aerosols. The higher the aerosols, the larger the reflectance, since a number of photons are backscattered to space instead of being absorbed by oxygen in the lower layers. In addition, the coupling between aerosol scattering and oxygen absorption, which enhances absorption, is less effective in this case because there are fewer molecules. Dubuisson et al. (2009) showed that the ratio of measurements in a band strongly attenuated by oxygen absorption (i.e., within the A-band) and in a band minimally attenuated (e.g., outside the A-band) is sensitive to aerosol altitude. Using a relatively large single band in the oxygen A-band (case of POLDER and MERIS), retrieval accuracy was only satisfactory when aerosol optical thickness was > 0.3.

The oxygen absorption band is a feature about 5 nm wide with two absorption maxima separated by about 3 nm. The oxygen transmission averaged at the spectral resolution of 5 nm, exhibits a larger absorption peak that at a larger spectral resolution, and some details of the spectrum are still present with a sampling every 1.25 nm, even 2.5 nm. Such sampling, possible with the PACE OCI, is expected to help retrieve more accurate information about the vertical profile of aerosol scattering. In juxtaposed 5 nm bands (or in larger spectral intervals) the spectral details would be missed, and the information about oxygen absorption would be too mixed with the out-of-band signal. An analysis of the effect of spectral resolution on the ability to use oxygen A-bands to retrieve aerosol layer height is given in Remer et al. (2019, this issue).

The inversion scheme is schematically the following. The objective is to retrieve the effective atmospheric pressure of the scattering aerosol profile, hPai. Using a single scattering approximation, and neglecting surface reflectance, the TOA reflectance, ρTOA, can be written as:

$$
\rho\_{\rm TOA}\left(\lambda\right) = \rho\_{r,O2}\left(\lambda\right) + \rho\_a\left(\lambda\right)T\_{O2}\left(\lambda, \left\right) \tag{15}
$$

Where ρr,O2 is the molecular scattering computed in the presence of oxygen absorption, ρa is the aerosol scattering reflectance interpolated from the close measurements at, for example, 748 and 865 nm, i.e., out of the absorption band, and TO2 is the oxygen transmittance at the equivalent pressure hPai. The various terms of Equation 15 depend on angular geometry (not shown for simplification). A spectral fit algorithm is then applied to retrieve hPai. The spectral fit, i.e., the retrieval accuracy, will definitively be better with measurements at 5 nm resolution every 1.5 nm in the oxygen A-band than with fewer measurements in consecutive 5 nm bands. The issues to investigate include the impact of spectral resolution and radiometric noise on the retrieval accuracy of hPai, and the influence of aerosol type and amount, and surface reflectance, which may not be null in the oxygen A-band. Note, incidentally, that the derived apparent pressure would constitute an excellent test to detect and filter out semi-transparent or small broken clouds in the absence of measurements in the thermal infrared. Effort is already underway and reported in Remer et al. (2019, this issue) to confirm that aerosol layer top pressure can be retrieved using oxygen Aband spectroscopy at the 5 nm bands of OCI for aerosol optical thickness τa ≥ 0.3 for all surfaces and ≈ 0.1 for dark surfaces. In addition these studies show that τa may also be retrieved when angular information is added with a multi-angle polarimeter.

## SIGNIFICANT ISSUES

In a seminal paper about atmospheric correction in the EOS era, Gordon (1997) discussed approximations and issues that remained to be addressed in developing and operating the 2 step multi-scattering standard algorithm. The issues at the time covered an extensive range of topics; they included whitecaps, aerosol vertical structure, appropriateness of aerosol models, absorbing aerosols, stratospheric aerosols, Earth's curvature, instrument polarization, surface roughness, in-water radiance distribution, diffuse transmittance, and radiometric calibration. Solutions (or approaches to solutions) were proposed to treat many of the issues, but some could not be handled properly, in particular those regarding aerosols. Alternative algorithms and the use of bi-directional and polarimetric information, however, have showed promise in difficult aerosol situations, either directly or indirectly (i.e., estimating aerosol optical properties separately), as demonstrated in sections Alternative Algorithms and Enhancements Using Multi-angular and/or Polarimetric Information. Some of the remaining issues are revisited here in view of new knowledge (whitecaps, Earth's sphericity), as well as others either not considered in Gordon (1997) (horizontal heterogeneity) or that came to the forefront with the new PACE capabilities (complex and large atmospheric interference in the UV).

## Adjacency Effects Evidence

Imagery of the Earth's surface (land and ocean) obtained from space at optical wavelengths is degraded due to the atmosphere (e.g., Tanré et al., 1979). One of these effects is the adjacency effect (also known as the environment or blurring effect), defined as the change in the digital number of a pixel caused by atmospheric scattering of radiance that originates outside of the sensor field-of-view. Tanré et al. (1987), among others, have provided evidence for the adjacency effect in satellite imagery of land and ocean/lakes. They showed that over water the spectral dependence of the aerosol scattering determined in the red and near infrared (where the ocean can be considered black) might differ substantially from the actual one when the environment reflectance is high (case of green vegetation). Building on previous work (Tanré et al., 1981), they proposed a theoretical formalism to describe, therefore correct the adjacency effect. Diner and Martonchik (1985) also investigated theoretically the influence of atmospheric scattering on blurring of surface details in imagery acquired from space. They predicted that blurring of a spatial boundary would be enhanced in the presence of large particles (increased forward scattering).

The adjacency effect is generally ignored in standard atmospheric correction schemes and operational processing of satellite ocean-color imagery. Yet its impact on water reflectance retrieval and derived products (e.g., chlorophyll-a concentration) may be important, especially near land, sea ice, and clouds, i.e., where the environment reflectance is much different from the target reflectance (e.g., Santer and Schmechtig, 2000). These authors reported from theoretical calculations that for a typical contrast between land and ocean reflectance of 0.3 at 865 nm and 0.07 at 670 nm, a chlorophyll-a concentration of 2 mgm−3 is underestimated by 25% at a distance of 10 km from a linear coastline when the atmospheric visibility is 23 km and by 50% when the visibility is 8 km. The underestimation becomes larger as chlorophyll-a concentration increases, with retrieved values 5 times smaller than actual values at 10 mgm−3 when the visibility is 8 km.

An example of adjacency effect is given in **Figure 38**, which displays MERIS Level 1 imagery at 865 nm over the Zuydersee in the Netherlands. Aerosol optical thickness is about 0.3. Higher reflectance is generally observed near the coast (**Figure 38**, left), which is attributed to scattering into the field-of-view of photons reflected by the contiguous vegetated land, highly reflective at 865 nm. The TOA reflectance change along a section across the Zuydersee (indicated by a black line in **Figure 38**, left) reaches about 0.02 toward the coast over a 5–10 km distance (**Figure 38**, right).

**Figure 39** displays MERIS Level 2 imagery, i.e., water reflectance at 560 nm, after standard atmospheric correction. A band of smaller marine reflectance is observed all around Corsica and Northern Sardinia (**Figure 38**, left), whereas anomalously high reflectance is observed for the waters surrounded by ice in the Beaufort Sea (**Figure 25**, right). This is likely the complex result of adjacency effects at 560 nm and at the near-infrared wavelengths used for the atmospheric correction. Depending on the environment (vegetation or ice), the coupling between surface reflection and atmospheric scattering may either decrease (Corsica/Sardinia case) or increase (Beaufort Sea case) the apparent reflectance with increasing wavelength, but this spectral dependence is not captured by determining the atmospheric signal from measurements in the near infrared and extrapolating to shorter wavelengths. Other examples have been observed along the coast of the Baltic Sea, and in Norwegian fjords.

#### Formulation

Over a homogeneous water body of reflectance ρw, the TOA reflectance ρ hom TOA can be expressed approximately as [e.g., Tanré et al., 1979; see Equation 3)]:

$$\rho\_{TOA}^{\text{hom}} \approx T\_{\text{g}}(\theta\_{\text{s}}, \theta) [\rho\_{atm} + \rho\_{\text{w}} \,\text{t}\_{d}(\theta\_{\text{s}}) \,\text{t}\_{\text{u}}(\theta)/(1 - \rho\_{\text{w}} \text{S}\_{atm})] \tag{16}$$

where Satm is the spherical albedo of the atmosphere and transmittance due to aerosol and molecule scattering and aerosol absorption. The term (1 − ρwSatm) accounts for multiple interactions between the atmosphere and the water body. Several approximations are used in Equation (16): (1) the surface is Lambertian; (2) Fresnel reflectance is neglected in the multiple interaction term, and (3) molecular absorption is decoupled from atmospheric scattering (i.e., Tg is simply a multiplicative factor).

Over a heterogeneous surface, one has to discriminate between the contribution of the target reflectance ρw viewed directly through the atmosphere and its background reflectance, ρe (Tanré et al., 1981, 1987; Santer and Schmechtig, 2000):

$$\begin{split} \rho\_{TOA}^{\text{het}} \approx T\_{\text{g}}(\theta\_{\text{s}}, \theta) \left\{ \rho\_{\text{atm}} + \, t\_{d}(\theta\_{\text{s}}) \left[ \rho\_{\text{w}} \exp(-\tau\_{\text{atm}}/\cos(\theta)) \right. \right. \\ \left. + \rho\_{\text{e}} t\_{\text{u}-d \text{jf}}(\theta) \right] / \left(1 - \rho\_{\text{e}} \text{S}\_{\text{atm}} \right) \tag{17} \end{split} \tag{17}$$

where τa is the atmospheric optical thickness for scattering and tu−dif (θ) is the diffuse component of total atmospheric transmittance due to atmospheric scattering, i.e., tu (θ) = Tu (θ) + tu−dif (θ). The approximation used in this second formulation is that the target background is homogeneous, which is of course an idealized case. Nevertheless, Equation 17 shows that the retrieval of ρw, using an atmospheric correction based on Equation 16 assuming a homogeneous scene, is affected by an error 1ρTOA (i.e., the adjacency effect), which expression can be deduced from Equations 16 and 17:

$$
\Delta \rho\_{\rm TOA} = \rho\_{\rm TOA}^{\rm hct} - \rho\_{\rm TOA}^{\rm hom} = (\rho\_\varepsilon - \rho\_\mathbf{w}) \, t\_{\rm u-dif}(\theta) T(\theta\_\mathbf{s}) \tag{18}
$$

When the background is not uniform, which is the realistic case, the many individual contributions of every pixel that surrounds the target must be considered, and ρe becomes an average reflectance hρe i:

$$
\langle \rho\_{\mathfrak{e}} \rangle = \iint f \begin{pmatrix} \mathfrak{x}, \mathfrak{y} \end{pmatrix} \rho\_{\mathfrak{e}} \begin{pmatrix} \mathfrak{x}, \mathfrak{y} \end{pmatrix} \, d\mathfrak{x} d\mathfrak{y} \tag{19}
$$

where f x, y is the atmospheric point spread function (PSF), which expresses the contribution to the measured reflectance of surface-leaving photons at horizontal coordinates x, y scattered in the field of view, and ρe x, y is the now spatially dependent environment reflectance. Given the properties of the atmospheric scattering, the PSF can be computed using a Monte Carlo code in backward mode, i.e., photons are injected in the direction of viewing and collected on the matrix of pixels at the surface (Reinersman and Carder, 1995). Eventually, the bi-directional reflectance of the pixel must be accounted for in Equation 19 (Sun glint, vegetation), which means an integration of the probability of viewing the given pixel from different altitudes.

For observations at nadir, hρei becomes (Tanré et al., 1981):

$$\langle \rho\_{\epsilon} \rangle = \iint \text{rg}\left( r \right) \rho\_{\epsilon}\left( r, \phi \right) dr d\phi = \iint \left( \frac{dF}{dr} \right) \rho\_{\epsilon}\left( r, \phi \right) dr d\phi \tag{20}$$

r is the distance to the target, rg (r) is equal to f r(x, y) , and F (r) is the integral of rg (r) between 0 and r, i.e., F (r) = R 2πr ′ g r ′ dr′ . The environment function F (r) gives the relative contribution to hρei of surface points within a radius r of the target pixel, and normalization is obtained by integrating from r = 0 to infinity, i.e., R 2πF (r) dr = 1.

Approximate analytical expressions of F (r) are given in Tanré et al. (1981) for molecular and aerosol scattering (typical continental aerosol model, scale height of 2 km), i.e.,

$$F\_m\ (r) \approx 1 - 0.93 \exp\left(-0.08r\right) - 0.07 \exp(-1.1r) \quad \text{(21a)}$$

$$F\_a(r) \approx 1 - 0.037 \exp\left(-0.2r\right) - 0.625 \exp(-1.8r) \quad \text{(21b)}$$

where r is expressed in km. In the case of aerosols characterized by a scale height Ha different from 2 km, Equation 21b is simply modified by multiplying r in the two exponentials by the weighting factor 2/Ha . For a mixture of molecules and aerosols, F(r) becomes:

$$F(r) \approx \left[ t\_{ur-dif} F\_m(r) + t\_{ua-dif} F\_a(r) \right] / \left( t\_{ur-dif} + t\_{ua-dif} \right) \tag{22}$$

where tur−dif and tua−dif are the diffuse transmittances for molecular and aerosol scattering, respectively. Equations (20) to (22) allow the computation of hρei and therefore 1ρTOA for any pixel in a scene observed from space at nadir (or for nearly vertical sightings). They are helpful to understand the variability of the adjacency effect and to estimate its impact (i.e., expected errors) on the retrieval of the target reflectance. In the case of observations at slanted angles, the adjacency effect is more complicated to formulate analytically because of the lack of symmetry in azimuth [no simple insightful expression for hρe i].

### Variability

The adjacency effect produced by molecular scattering and aerosol scattering is different and vary with F (r) and the reflectance of the background/environment. These processes act over disparate scales, i.e., about 12 km for molecular scattering and less than 1 km for aerosol scattering (equations 21a and 21b). Pixels farther away have more influence, i.e., F (r) has a larger spread, when aerosols are located higher in altitude (1/Ha dependence in the exponentials of Equation 22). This also applies to cloudy situations. Furthermore, molecular scattering varies with wavelength and increases very rapidly in the blue, but Fm remains about the same with a good approximation. Aerosol scattering is less spectrally selective, but Fa given by Equation 21b is only typical. Changes in the aerosol physical properties, e.g., size distribution, would modify its single scattering phase function and the coefficients of the exponential decrease with distance r. Note that depending on solar and viewing geometry and Sun/sensor configuration with respect to the target and its environment, Fresnel reflection by the water surface (after scattering by the atmosphere) may contribute differently to the TOA signal (Santer and Schmechtig, 2000).

**Figure 40** gives an example of adjacency effect when observing the ocean near the coast. The coastline is linear, the target is 5 km offshore, and the sensor is located above water or above land, viewing perpendicularly to the coastline at 30◦ zenith angle. The reflectance of land is 0.8 (typical value for snow) and isotropic, chlorophyll-a concentration is 1 mgm−3 , aerosols are of maritime type with scale height of 2 km and optical thickness of 0.3 at 550 nm, and wind speed is 5 m s−1 . The simulations are performed with a Monte Carlo Code operated in backward mode (Ramon et al., 2019). No assumptions are made regarding interactions between the surface and the atmosphere. TOA reflectance is higher due to the snow environment, especially at 380 nm, where atmospheric scattering is more effective (in first approximation the adjacency effect varies like τa hρei. For a Sun at zenith, the reflectance increase is 0.05 at 380 nm, 0.03 at 500 nm, and 0.01 at 800 nm. If not taken into account, these values, by acting directly and indirectly (via atmospheric correction), would yield unacceptable errors on water reflectance estimates. The effect is larger when the sensor is over land due to reduced Fresnel reflection. The degree of polarization, on the other hand, is smaller, especially when the sensor is above land.

### Correction

The TOA imagery can be corrected systematically for the adjacency effect at the Level 1b and produce a Level 1c, so that the processing of Level 2 products can be done by assuming that the surface is homogeneous (i.e., using the "large target" formalism of standard atmospheric correction schemes). This can be accomplished using a classic de-convolution algorithm, in which the de-convolution matrix is determined iteratively, since the reflectance of neighboring Level 1b pixels is affected by the adjacency effect (Reinersman and Carder, 1995; Vermote et al., 1997; Santer and Zagolski, 2008). A single iteration should normally be sufficient. In Vermote et al. (1997), for example, hρei is obtained using the surface reflectance and aerosol properties determined assuming no adjacency effects. This allows one to estimate the adjacency effect, correct the TOA signal accordingly, and then iterate. One issue for operational application, however, is processing time, i.e., de-convolution schemes need to be optimized. The simplest option, easy to implement, is to correct only the adjacency effects associated with molecular scattering. A second option, more accurate, is to correct also the effects associated with aerosol scattering. This can be done by (1) assuming background aerosols or using aerosols with average properties (eventually seasonally and regionally dependent), or (2) estimating the aerosol properties (optical thickness, type, and altitude). In the case of clouds, one can assume that they are located at the surface, or one can estimate their altitude.

Another way to deal with the adjacency effect is to develop atmospheric correction algorithms that minimize its influence. In the scheme proposed by Gross-Colzy et al. (2007a), only the principal components of the TOA signal most sensitive to the water signal are used to estimate the water reflectance, which reduces the impact of ρTOA in many situations. In the POLYMER algorithm (Steinmetz et al., 2011) the atmospheric signal is modeled as a polynomial function of wavelength with three terms, i.e., Co + C1λ −1 + C2λ −4 . The last term of the polynomial takes into account the coupling between molecular scattering and reflection by a gray surface (e.g., sea ice and clouds), which varies in λ −4 , but the polynomial may also account for other types of surface/atmosphere coupling.

Correcting systematically Level 1b imagery for the adjacency effect, either explicitly or implicitly via proper atmospheric correction schemes, is definitely recommended. For coarse resolution sensors, it may be sufficient to account for the effect due to molecular scattering (the spread function associated with aerosols is effective over a relatively small distance, typically 1–2 km). As a result, the accuracy, quality, and daily coverage of ocean-color products should be improved substantially over water surfaces contiguous to land surfaces, sea-ice, and clouds, and in the open ocean where spatial variability may be large (e.g., upwelling regions). Otherwise, non-negligible errors would affect the retrieval of water reflectance, with a significant change in spatial structure and correlation scales (may alter the interpretation of mesoscale variability patterns, see Doney et al., 2003).

## Whitecaps

#### Importance

Whitecaps generated by breaking waves, generally composed of surface foam (large bubbles separated by a thin layer of water) and underwater bubbles, have a pronounced effect on the intensity and shape of visible light diffusively reflected by the ocean surface (e.g., Frouin et al., 1996; Moore et al., 2000; Stramski and Tegowski, 2001; Terrill et al., 2001; Zhang et al., 2002; Randolph et al., 2014). They may enhance dramatically water-leaving radiance on temporal scales of seconds to minutes (Frouin et al., 1996; Randolph et al., 2017) and affect large areas such as the windy Southern oceans. This is illustrated in **Figures 41**, **42**, which display, respectively, pictures of seas with whitecaps and a time series of surface reflectance affected by whitecaps. In the presence of whitecaps, the dark ocean appears much brighter (**Figure 41**). At a local scale, the water reflectance, about 0.08 in the blue in the absence of whitecaps, is increased to 0.4–0.7 (i.e., by a factor of 5–11) depending on the breaking event, and variability is large over time scales of 5 to 10 s (**Figure 42**). The frequency and intensity of such breaking wave features over a 1-km pixel would determine the enhanced reflectance observed by the PACE sensor.

When present, the enhanced reflectance from whitecaps needs to be removed in order to produce estimates of water reflectance suitable for standard ocean-optics applications. From space, since whitecap reflectivity is high, even a small fraction of whitecaps within the instrument's elementary field of view (i.e., within a pixel) may be problematic. The pixel reflectance is affected directly, but more importantly the spectral dependence of the whitecap reflectance skews the extrapolation to the visible of the aerosol signal determined in the near infrared, which may lead to water reflectance errors much larger (i.e., by an order of magnitude) than the requirements (Frouin et al., 1996).

Whitecap corrections are conducted as part of the overall "atmospheric correction" process, even though the signal does not originate in the atmosphere and can be considered a component of the water signal. The aim is to remove the signal due to actively breaking waves (Stage A), which includes surface foam (i.e., large bubbles separated by a thin layer of water) and underwater bubbles injected in the water column. It is not designed to remove enhancements due to the quiescent or mature phase of the whitecap (Stage B), when surface foam has dissipated, but bubbles are still present in the upper layer. In practice, much of the whitecap and the bubble plume signal is removed as part of the aerosol correction, since both foam and bubbles act to enhance near infrared reflectance and the current aerosol routines (heritage atmospheric correction) cannot distinguish between different sources of near infrared reflectance. However, some problems may arise due to the spectral dependence of whitecaps (mentioned above) and the coupling between molecular and aerosol scattering processes, which may differ significantly (since the amount of aerosols is artificially increased).

## Current Approach to Whitecap Correction

The current approach is designed to remove the reflectance due to the actively breaking wave or bright white portion of the wave (Monahan, 1993) within each pixel. As part of that effort, an estimate of the fraction of the sea surface covered by whitecaps is generated for each pixel based on ancillary wind speed data. Whitecap fractions are commonly estimated using automated processing of digital photography of the sea surface (Brumer et al., 2017). However, comparisons of digital photography with radiometric approaches have shown that photographs capture primarily Stage A whitecaps and miss much of the Stage B bubble plumes that also enhance the surface reflectance. Many relations between whitecap fraction and wind speed have been developed over the last three decades for different ranges of wind speeds (Anguelova and Webster, 2006; Brumer et al., 2017). However, whitecap coverage can vary by several orders of magnitude at the same wind speed. At different locations in the world ocean, various environmental and meteorological factors act in concert but with different strengths and form a composite effect that either enhances or suppresses the effect of wind alone. These other factors include fetch and duration and the wind, water temperature, air temperature and stability of the lower atmosphere defined by the air/water temperature differential, salinity, current shear and long wave interaction, wave age, and the presence of surfactants such as organic films (reviewed in Scanlon and Ward, 2016). Hence, wind speed parameterizations should be viewed as "climatological" and cannot represent the instantaneous whitecap field required by remote sensing applications. In addition, other factors not related to wind speed could cause foam on water surfaces. High concentrations of the phytoplankton Phaeocystis globosa, for example, are associated with foam formation in coastal waters and on beaches (Armonies, 1989).

Satellite ocean color algorithms currently employ an expression for undeveloped seas based on wind speed at 10 m (U10) where fwc = 8.75 × 10−5 (U10 − 6.33)−3 (Stramska and Petelski, 2003), see **Figure 43**. This correction produces negative values below 6.33 m s−1 and is much higher than with other parameterizations at high wind speeds. To avoid overestimation, NASA implements a lower threshold of 6.33 m s−1 and an upper threshold equivalent to the whitecap fraction at 12 m s−1 in the operational code (**Figure 43**). Hence the highest fraction of the sea surface covered by whitecaps that is currently used is 1.6%. Although this range may cover the average conditions, whitecaps can occur at wind speeds as low as 3–4 m s−1 and the fractional whitecap coverage can approach 10% at wind speeds greater than 12 m s−1 (Brumer et al., 2017).

Measurement of the spectral reflectance of whitecaps is challenging due to the many types and stages of whitecaps, the rapid time scale on the order of seconds, determining the contribution of foam and background water, and potential contamination from reflectance of sun and skylight. Reflectance of whitecaps varies with the type of breaking wave (e.g., rolling breakers and plunging breakers) and the layers of foam produced. From historic data (Whitlock et al., 1982; Koepke, 1984), an age-averaged effective reflectance, ρwc, of 22% is used in the NASA standard whitecap algorithm for visible wavelengths up to 550 nm. Frouin et al. (1996) studied the visible and near infrared reflectance of sea foam found in the turbulent surf zone. Sea foam reflectance was found to monotonically decrease into the near infrared wavelengths due to enhanced water absorption in these wavelengths. The spectral dependence was confirmed from aircraft measurements of whitecaps in the open ocean (Nicolas et al., 2001). From this work, a reduction factor is applied for red and near infrared wavelengths such that whitecap reflectance is 0.889, 0.760, and 0.645 at 670, 765, and 865 nm respectively and interpolated between these values. Whitecap reflectance can be approximated for wavelengths between 555 and 865 nm as: ρwc (λ > 555) = 0.22(−1.162 × 10−3λ + 1.653). For MODIS bands, this translates to whitecap reflectance of 22% from 412 to 555 nm and 19.3, 19.0, 17.2, and 14.1% at 667, 678, 748, and 869.5 nm, respectively.

Following the discussions above, the standard reflectance for actively breaking waves, ρwc, is multiplied by the estimated fraction of the sea surface covered by whitecaps, fwc, to generate the within-pixel reflectance attributable to whitecaps. An important distinction must be made at this point. Whitecap reflectance is not treated as an "augmented" reflectance above the background reflectance, as is commonly interpreted. Because whitecaps are so bright, the signal is believed to dwarf the contributions from water reflectance and surface reflectance (skylight and glint). The estimate ρwc represents the total upwelling signal above the sea surface of a breaking wave, Eu(0+), normalized to the downwelling irradiance incident on the sea surface, Ed(0+). Hence, the ρwc used in this context is considered invariant of the water reflectance and sky conditions. However, the actual enhancements due to whitecap reflectance can be influenced by many other factors including the intensity of the wave breaking and the concentrations of optical constituents in the water.

This whitecap contribution to reflectance is propagated through the atmosphere to estimate the enhancement ρ TOA wc at the satellite. Assuming that whitecap reflectance is isotropic, we have:

$$
\rho\_{\rm wc}^{TOA} = f\_{\rm wc} \, \rho\_{\rm wc} t\_d(\theta\_s) t\_u(\theta) \tag{23}
$$

The at-sensor signal due to whitecaps (ρ TOA wc ) is then subtracted from the TOA reflectance measured at the satellite. This effectively accounts for the reflectance from the area of the sea surface that is covered by whitecaps. The whitecap-free area should also be weighted by (1-fwc) following Gordon (1997). Ignoring this weighting term is generally not important in the currently implementation of the whitecap correction where whitecap fractions are <2%, but area-weighting the whitecapfree sea surface would be important if higher whitecap fractions were modeled like in the Southern Ocean or for applications to high spatial resolution satellites.

#### Future Possibilities

Several issues must be considered going forward into the PACE era. The algorithm discussed above is a very crude and inaccurate representation of the impact of foam and bubbles on water-leaving radiance. Data collected to date show that wind speed can only roughly approximate the whitecap coverage in a climatological sense and cannot provide accurate realtime estimates of fractional whitecap coverage. The current wind speed thresholds used in the whitecap approach provide a minimal and largely inaccurate estimate of whitecap fraction that can be an order of magnitude lower or higher in the environment. Furthermore, use of a single reflectance value for all whitecaps does not represent the large amount of variability in the types of breaking waves and foam that can occur on the sea surface. Reflectance of whitecaps is variable based on the strength of the wave breaking and is not necessarily invariant of water reflectance signal. The bubbles plume or Stage B also serves to enhance reflectance substantially above background reflectance and this component is not explicitly treated in the model. Finally, the PACE mission is proposed to be hyperspectral from the ultraviolet (350 nm) to near-infrared (885 nm) and have shortwave infrared bands at 940, 1,038, 1,250, 1,378, 1,615, 2,130, 2,260 nm. The current simplified spectral model of whitecaps will need modification to account for the spectral variability of whitecap reflectance in the near and shortwave infrared (see below).

The PACE mission aims to provide a better separation of atmosphere and ocean processes. Currently, much of the whitecap and bubble correction, particularly during high wind conditions, is inaccurately modeled as an aerosol. Whitecaps and bubble serve to enhance reflectance across the near and short wave infrared and cannot be easily separated using the current aerosol routines. Aerosol modeling assumes that water-leaving radiance at two near infrared wavebands are zero or can be estimated with sufficient accuracy using a bio-optical model. The presence of foam and bubbles is not explicit to this formulation and can lead to errors in retrievals when incorrectly treated as spectrally dependent backscattering from waterborne hydrosols or from airborne aerosols.

With more spectral information, the contribution of whitecaps may be measurable in the hyper-spectral signal and removed as part of the data processing scheme without relying on wind speed measurements and single reflectance values (see Dierssen, 2019, this issue). Measured reflectances of whitecaps and generated foam reveal identifiable spectral features from visible to short wave infrared (**Figure 44**). These new values are largely consistent with the diminishing reflectance in NIR wavebands measured by Frouin et al. (1996), but also reveal many features in the signal that cannot be dealt with as a simple linear decrease into the near infrared. Reflectance dips occur particularly at 750, 980, and 1,150 nm that have enhanced liquid water absorption, a result of multiple scattering in and around the bubbles and foam.

A potential approach for PACE and other missions is to identify spectral features associated with whitecaps that are unique from atmospheric and oceanic spectral properties. Water absorbs differently based on whether it is in vapor, liquid or solid form. As discussed above, unique peaks and dips occur in the near infrared portion of the reflectance spectrum that are associated with weak absorbing features of liquid water. The measured spectrum of intense foam and bubbles at the sea surface associated with the wake of a ship with a peak reflectance of 40% in visible wavelengths, As derived in Dierssen (2019, this issue), the general shape of this reflectance can be modeled from the logarithm of water absorption.

The overall shape of whitecap reflectance is directly related to liquid water absorption from visible through shortwave infrared. Some divergence at ultraviolet and violet wavelengths is apparent and likely due to colored substances such as absorption due to colored dissolved organic matter in these coastal waters. However, the overall shape can be used to assess the contribution of whitecaps into the near infrared and shortwave infrared. Parameterizations based on other physical considerations (e.g., vertical structure) should be contemplated to allow a direct estimation of the whitecap effect on water reflectance.

One approach would be to measure the depth of a liquid water absorption feature (e.g., 980 nm) to estimate the "effective" contribution of whitecaps to the total pixel reflectance. If we presume that the water reflectance does not contain this liquid water feature, then the line-depth of the water-absorption feature at 980 nm, for example, can be related to a linear mixing of unaffected sea surface with different intensities and areas with foam and bubble plumes. If the background water is highly scattering due to sediments or floating vegetation, for example, then this water absorption feature may also be present in the background signal and such water types would need to be identified prior to assessing the whitecap contribution (Dierssen, 2019, this issue). Candidate algorithms to model the whitecap factor, Awc, from the hyper-spectral radiance rather than the area-weighted whitecap fraction derived from wind speed are provided in Dierssen (2019, this issue). The whitecap factor is the fraction of a standard whitecap reflectance that accounts for enhancements in spectral reflectance of the sea surface above the background reflectance. Since Awc is optically derived, it is better suited for atmospheric correction techniques because it specifically incorporates different levels of foam and bubbles associated with breaking waves and requires no implicit spatial scale.

In summary, decades of data have shown that wind speed models will not be able to predict the whitecap fraction with sufficient accuracy to use in daily remote sensing imagery. Use of other ancillary parameters such as fetch, air, and water temperature, and currents may have utility in further refining estimates of whitecap fraction. In the least, the Stramska and Petelski (2003) model should be replaced with a more recent parameterization that better fits both high and low wind speed conditions (e.g., Brumer et al., 2017). However, it is unlikely that the whitecap fraction and particularly the associated bubble plume will be predictable from wind speed data for any given image to within an order of magnitude (see scatter of data points in **Figure 43** above).

We should continue to refine spectral models of foam and bubbles to understand their impact on upwelling irradiance at the pixel level. As part of that effort, more hyperspectral datasets are critical that provide a means to estimate reflectance of whitecaps over time and space under a wide variety of water types and whitecap conditions. New methods that seek to use the measured spectral information in near infrared wavelengths to estimate the contribution of whitecaps directly will provide a means to separate whitecaps from aerosols and other conditions where near infrared reflectance is non-negligible. Preliminary results have been conducted at the sea surface and more research is warranted to model the transmission of hyper-spectral reflectance from whitecaps through the atmosphere. Moreover, uncertainties need to be assessed when aerosol products are derived from at-sensor radiance that is "contaminated" by whitecaps and bubble plumes. Continuing to propagate such errors impacts the selection of spectral aerosol model and magnitude of aerosols, as well as impacts the spectral shape and magnitude of retrieved waterleaving radiance and associated ocean color products. Until correction methods with sufficient accuracy are found and tested widely across ocean provinces, we recommend that PACE images collected under high wind conditions, which may contain significant whitecap coverage, be flagged such that users will recognize higher uncertainty in the water reflectance and aerosol retrievals.

Finally, breaking waves on the ocean surface are areas of significant importance to air-sea interaction. Whitecaps foster climate-relevant physical and chemical processes in the ocean, including the production of sea salt aerosols, mixing processes, and the exchange of gas (e.g., CO2, CH4, DMS, water vapor) and heat with the atmosphere (Monahan and Spillane, 1984; Woolf, 1997; Asher and Wanninkhof, 1998; Woolf et al., 2007). Wave breaking generates turbulent kinetic energy in the surface ocean, driving upper ocean mixing, and transferring energy to currents and longer waves (Cavaleri et al., 2007). Visible manifestations of breaking waves (i.e., foam and bubbles) are related to the energy injected into the surface ocean. Using imagery to assess whitecaps directly will open up new science directions.

## Earth's Curvature

It was mentioned in section From Multi-Spectral to Hyper-Spectral Remote Sensing that assuming a plane-parallel atmosphere might introduce significant atmospheric correction errors for Sun zenith angles > 70◦ . This was also the conclusion of Ding and Gordon (1995), who suggested that for those angles a sufficient treatment would be to compute the molecular reflectance with a spherical-shell atmosphere RT code. It is important to emphasize, however, that the effect of Earth's curvature is not only to increase intensity at grazing Sun zenith angles due to the smaller attenuation of the direct solar beam, but also to lower intensity at low Sun zenith angles due to a smaller illumination volume (e.g., Chowdhary et al., 2019, this issue; Ramon et al., 2019). This second effect is generally understated, but critical for accurate atmospheric correction. **Figure 45** displays, as an example, Monte Carlo simulations of molecular reflectance, ρr , and aerosol reflectance, ρa, at 446, 558, 672, and 867 nm as a function of Sun zenith angle for a view zenith angle of 15◦ and a relative azimuth angle of 90◦ . Aerosols are of maritime type with optical thickness 0.1 at 550 nm. In the 0–75◦ Sun zenith angle range, differences between plane-parallel and spherical-shell calculations are relatively small (0.3–0.5%) for ρr , and reach 1.5% for ρa. However, since molecular scattering may be large and dominate the TOA signal, especially in the blue and UV, even a small fraction of 1% error in ρr would affect the water reflectance retrieval significantly (i.e., with resulting uncertainty above requirements). On the other hand, the impact of 1.5% differences in ρa is comparatively inconsequential for the aerosol loading considered (relatively small aerosol signal). But one expects non-negligible effects for higher view zenith angle and aerosol optical thickness. Thus Earth's curvature should be taken into account in generating at least Rayleigh LUTs (it is also recommended to use spherical-shell RT code for aerosol LUTs), and this for all solar and viewing geometries, not only large Sun zenith angles.

## Multiple Scattering

Although the atmosphere is relatively thin optically compared with the water body, multiple scattering effects are not negligible in the atmosphere, especially at wavelengths for which scattering is effective (blue and UV). These effects are taken into account in atmospheric correction schemes, but their dependence on aerosol altitude is often neglected (case of the heritage algorithm in particular). It is generally assumed that aerosol altitude has a significant effect on the TOA signal only when aerosols are absorbing, which was discussed in section Estimation of Aerosol Vertical Profile. The coupling between scattering by molecules and aerosols, however, depends on their relative vertical distribution.

This is illustrated in **Figure 46**, which displays the coupling term of the aerosol reflectance at 443 nm for two very weakly absorbing models, i.e., Maritime with 98% humidity (M98) and Tropospheric with 70% humidity (T70). These models are part of the Shettle and Fenn (1979) suite used in early two-step atmospheric correction algorithms (e.g., Gordon and Wang, 1994; Gordon, 1997). Aerosol optical thickness is 0.1 at 865 nm, and aerosol concentration decreases exponentially with increasing altitude with scale height from 2 to 8 km. Sun zenith angle is 36.2◦ , and the results are presented as a function of viewing zenith angle (0 to 75◦ ) in the principal plane of the Sun. Note that the coupling term can be negative since computed as the difference between the total atmospheric reflectance and the molecular component (i.e., with no aerosols) and the aerosol component (i.e., with no molecules) (Deschamps et al., 1983). It would always be positive if the molecular and aerosol components were computed for molecules only but in the presence of aerosols and for aerosols only but in the presence of molecules (Antoine and Morel, 1999). For some remote sensing geometries the effect of aerosol scale height reaches 0.002 (M98) and over 0.01 (T70) in amplitude, which in the case of T70 is one order of magnitude larger than the required accuracy on water reflectance. Thus it is important to include the effect of aerosol altitude when correcting the TOA signal for atmospheric effects, even when aerosols are little or not absorbing. This can be done using PACE observations at 5 nm resolution in the oxygen A-band, especially using the sub-5nm spectral sampling capabilities of the OCI, which allow an estimate of aerosol altitude or scale height (see section Estimation of Aerosol Vertical Profile).

## UV Observations

Observing in the UV is important to separate absorption by CDOM and phytoplankton pigments, to distinguish hydrosol types (e.g., sediments from organic particles) in optically complex waters, to allow the discrimination of functional, taxonomic, and harmful algal groups, and to improve atmospheric correction in the presence of absorbing aerosols. Hence measurements in the UV (from 350 nm) constitute a key PACE mission requirement. Retrieving accurately the water signal in the UV, however, presents a number of specific challenges that originate chiefly from the large optical thickness of the atmosphere. These challenges are emphasized in the following; see also Chowdhary et al. (2019, this issue) for a discussion of the UV regime in the context of RT modeling for atmospheric correction.

(1) The water reflectance contribution to the TOA signal is small in the UV, often much smaller than in the visible, especially when CDOM absorption is strong. This can be exploited in some situations (i.e., when the water signal is negligible) to constrain the aerosol signal estimated from NIR to SWIR wavelengths in a classic atmospheric correction scheme, but in general the water reflectance retrieval is more difficult. In addition, errors in estimating the spectral shape and magnitude of reflected sky radiance off of wind-roughened seas are enhanced in the UV. Currently this component is modeled as part of the Rayleigh LUTs for wind-roughened surfaces and is not validated with field data. A further complication is that aerosol and CDOM absorption may act similarly, i.e., their effects on the TOA signal may not be easy to separate. Also the steep decrease of solar irradiance due to absorption bands in this spectral range makes radiometric calibration difficult.

(2) Multiple scattering and coupling processes between molecular scattering and aerosol absorption are effective, and the resulting effect strongly depends on the relative vertical distribution of molecules and aerosols. This significantly complicates the atmospheric correction problem, for which knowledge of aerosol altitude becomes necessary. This will be possible with OCI (see section Estimation of Aerosol Vertical Profile and Remer et al., 2019, this issue). In one-step deterministic and statistical schemes, however, aerosol altitude can be variable, i.e., taken into account in determining the best solution.

(3) The influence of the environment, and in general spatial heterogeneities in the atmosphere, surface, and water body properties, is substantial (since depending on atmospheric transmittance, which itself depends exponentially on optical thickness see section Adjacency Effects and **Figure 40**, left), and the "large target" formalism may not yield acceptable water reflectance retrievals in coastal oceanic regions and water bodies surrounded by land (lakes, ponds, rivers).

(4) The spherical albedo of the atmosphere becomes large (e.g., 0.37 at 350 nm for a clear atmosphere) and neglecting the multiple interactions between the water body and the atmosphere may introduce errors of up to 2% (case of oligotrophic waters) on water reflectance estimates.

(5) Errors in the spectral dependence of aerosol scattering in the NIR and SWIR, by propagating through the standard algorithm, may translate into large errors in the UV (since further away than the visible). This and the complex RT processes in the UV make it difficult to achieve via "system" vicarious calibration (Franz et al., 2007) radiometric accuracy within a small fraction of 1%.

**Figure 47** displays various components of the TOA signal and their relative importance for typical conditions, i.e., a WMO maritime atmosphere (aerosol optical thickness of 0.2 at 550 nm) over waters with chlorophyll-a concentration of 0.1 mgm−3 and CDOM absorption of 0.02 m−1 at 440 nm. Spectral range is 350–400 nm, Sun and view zenith angles are 30 and 15◦ , respectively, relative azimuth angle is 90◦ , aerosol scale height is 2 km, and wind speed is 7 m s−1 . The water signal represents only 3 to 5% of the TOA signal and 34 to 42% of the TOA signal corrected for molecular scattering effects, with the lower values at 350 nm and the higher values at 400 nm. For waters with the same chlorophyll concentration but zero CDOM absorption, however, the contribution of the water signal would be larger, i.e., 8 to 12% and about 62%, respectively. For smaller aerosols, e.g., tropospheric type, the relative importance of the water signal would be reduced at shorter wavelengths. The small contribution of the water signal to the TOA signal stresses the importance of accurate radiometric calibration and modeling of molecular effects. In the case of **Figure 47**, an error as small as 0.1% on the TOA reflectance would already translate into a 3.3% error on the water reflectance at 350 nm.

**Figure 48** displays the absorption effect of a continental aerosol with scale height of 2, 5, and 8 km on the TOA signal and the effect of CDOM absorption (0.01, 0.02, and 0.1 m−1 at 440 nm) on the water signal observed at TOA. The absorption effect is the difference between the atmospheric reflectance at TOA in the presence of aerosol absorption and without absorption (see section Estimation of Aerosol Vertical Profile and Equation 14). Chlorophyll-a concentration is 1 mgm−3 . Spectral range, angular geometry, wind speed, and aerosol optical thickness are the same as in **Figure 47**. For the CDOM absorption effect, i.e., the difference between the water signal with and without CDOM absorption, aerosol scale height (slightly affects the atmospheric transmittance) is 2 km. The spectral dependence of the aerosol and CDOM absorption effects is comparable, making distinction of the two signals difficult in some one-step atmospheric correction schemes (e.g., spectral-matching). For example, 0.01 m−1 of CDOM absorption has nearly the same effect as aerosol absorption when scale height is 2 km, i.e., about −0.002 over 50 nm.

## STRATEGY FOR ATMOSPHERIC CORRECTION

The standard, 2-step approach to atmospheric correction of satellite ocean-color imagery has been used operationally by space agencies since the CZCS was launched in 1978 to process data from major large-scale ocean color missions into water reflectance. The approach has proved to be robust, sufficiently accurate, and useful for many applications, scientific and societal (IOCCG, 2008). Its chief advantage over a variety of alternatives, as pointed out in sections Heritage Atmospheric Correction Algorithm and Alternative Algorithms, is that essentially minimal assumptions about water reflectance, the signal to be retrieved, are made. Algorithms have evolved and improved to account for new capabilities (e.g., observations in the shortwave infrared to deal with turbid waters) and take advantage of new knowledge (e.g., aerosol optical properties, whitecap reflectance), but have followed the same principle of using a spectral region where the water body is not or little reflecting to isolate the perturbing influence of the atmosphere and surface. In view of the success of this heritage approach, and the fact that it has been selected and applied systematically to correct imagery from most ocean color sensors, it is highly desirable to continue its usage in current and future processing lines, in particular for the PACE OCI. The adaptation to hyper-spectral OCI would address the challenges associated with observing in the ultraviolet and dealing with strong gaseous absorption bands; see section From Multi-Spectral to Hyper-Spectral Remote Sensing. Observing in the UV, even with hyper-spectral capability, continues to be an open question and area of active research, but does not negate the overall benefit of proceeding with a heritage 2-step retrieval for OCI. This continuity would maintain a certain level of consistency across sensors from different missions, allowing for a long-term record of water reflectance relatively free of algorithm-related discrepancies and biases. Furthermore, new methodologies may not be applicable to some sensors, complicating the generation of accurate long-term time series.

The heritage algorithm, however, has important limitations, as discussed in section Procedures. In particular it cannot handle properly situations of absorbing aerosols (i.e., dust, biomass burning and pollution particles), which are encountered over large oceanic regions and the coastal zone, reducing the spatiotemporal coverage of ocean color products, therefore their utility for scientific investigations and operational oceanography. It also does not work in Sun glint regions and in the presence of clouds, resulting in a typical 10–15% daily spatial coverage. To resolve these issues, one needs to move to further explore and implement alternative algorithms, deterministic or statistical, such as those described in section Alternative Algorithms. These algorithms exploit observations in the entire set of available spectral bands and eventually include, depending on the sensor, bidirectional and polarization information, either directly or indirectly (e.g., to constrain the set of possible aerosol models). So far only a few new algorithms have been checked thoroughly and applied systematically (i.e., at the scale of a satellite mission), notably the Steinmetz et al. (2011) spectral matching POLYMER algorithm and the Schroeder et al. (2007) neural network algorithm. Neural network atmospheric correction including sun glint correction was part of MERIS standard processing for Case 2 waters during the whole lifetime of the mission. Most of the advanced algorithms have been only tested on a few images, and they need to be further evaluated theoretically and experimentally and examined for robustness, reliability, and general applicability in an operational context. Their potential, however, as demonstrated in the examples presented in section Alternative Algorithms, is undoubtedly great to improve atmospheric correction in difficult situations (i.e., absorbing aerosols, turbid waters, Sun glint, whitecaps, land proximity, sea ice, thin clouds), offering the opportunity to generate more accurate ocean color products with wider coverage (i.e., less gaps) that will allow a better description of ocean properties (IOPs, concentrations) and biogeochemical phenomena.

In view of the above, a suitable strategy for atmospheric correction during the PACE era would be to continue performing atmospheric correction with the standard, 2-step heritage algorithm, adapted/adjusted as necessary, but at the same time applying the best advanced algorithm that fits the characteristics and capabilities of a given sensor, or a suitable combination of sensors. In the case of PACE, it is expected (by mission design) that OCI and polarimeters will be used synergistically. The selected methodology may be different, indeed, for a sensor that only measures spectrally total TOA radiance and a sensor that also measures directionally polarized TOA radiance. Whenever possible satellite data from previous missions would be reprocessed with the new algorithm. An elaborate flagging system, although not easy to define and validate, is necessary to warn the user about doubtful algorithm performance. The quality of the atmospheric correction processors, therefore selection of the best one, could be assessed following procedures developed by Müller et al. (2015a,b) for the ESA Ocean Color-Climate change Initiative (OC-CCI), which include in a straightforward fashion not only comparisons with in situ measurements, but also analysis of image quality and processor behavior along scan line (e.g., spatial and temporal homogeneity and consistency in water reflectance retrieval). Since individual advanced algorithms may lead to improved products (or may perform similarly) in some situations, or have advantageous features, their implementation in data analyzing systems such as SeaDAS would provide the user community with best options depending on the study or application. This strategy would generate two separate water reflectance products, which is not a problem as long as respective advantages and limitations are understood and uncertainties specified, preferentially on a pixel-by-pixel basis. Thus we would end up with a continuity product and an advanced product that fully exploits the mission capabilities, which for PACE will combine hyper-spectral radiometry and multi-angle polarimetry. The two products could be merged optimally, at the Level 3 highest resolution possible, i.e., native resolution, taking into account their uncertainties (bias and standard deviation). Various merging procedures exist (see IOCCG, 2007 for a review), and choosing the best procedure will depend critically on the representation of algorithm performance and uncertainties (expected to be better and smaller, respectively, for the advanced algorithm).

## CONCLUSIONS

Substantial progress in atmospheric correction of ocean-color imagery has been made since the proof-of-concept CZCS mission that demonstrated the feasibility of retrieving water reflectance from space and led to the first generation of operational oceancolor sensors (SeaWiFS, MODIS, MERIS, VIIRS, etc). Compared with CZCS, the new sensors have higher radiometric sensitivity and they measure in more and better-defined spectral bands in the visible. They also have spectral bands in the near infrared and (for some sensors) shortwave infrared, facilitating the removal of atmosphere and surface effects. This has generated a flurry of activities aimed at developing efficient atmospheric correction algorithms that exploit the new capabilities, which has contributed to a better understanding of the problem and resulted in significant improvements and new avenues.

Since the first attempts, the approach to atmospheric correction has essentially remained a 2-step process, in which the perturbing effects of the atmosphere and surface are determined in spectral bands where the signal from the water body can be considered negligible (i.e., in the near infrared and/or shortwave infrared) and propagated or extrapolated to the shorter spectral bands. Definite improvements to algorithms based on this approach have been made, notably in the specification of the aerosol models (based on AERONET measurements), the correction of gaseous absorption (e.g., nitrous oxide influence in the blue), and the processing of imagery over optically complex waters, where the water-leaving signal in the near infrared (or even SWIR) is not negligible and must be estimated through iterative bio-optical modeling. A number of approximations and issues have been identified and studied, such as whitecaps, Sun glint, aerosol vertical structure, polarization, sea surface roughness, Earth curvature, in-water radiance angular distribution, diffuse transmittance, absorbing atmospheric gases, absorbing aerosols, stratospheric aerosols, optically thin clouds, highly turbid waters, and adjacency effects. For some of these issues, satisfactory solutions have been proposed and implemented operationally, but not for others (e.g., absorbing aerosols, whitecaps, adjacency effects). In the absence of satisfactory solutions for a given observing condition, the operational approach has been to allow the atmospheric correction to fail, or to flag the water reflectance retrievals as suspect quality. This has resulted in large data gaps in some regions, preventing the efficient utilization of ocean color products.

Alternative approaches, deterministic and statistical, which aim at inverting the top-of-atmosphere signal (i.e., retrieving the water reflectance) in a single step, have been developed with varied degrees of success. The information in all available spectral bands is generally used, which is advantageous in some situations (e.g., absorbing aerosols), but the main drawback is that water reflectance, i.e., the signal to retrieve, is often constrained by a model (which may not represent well the environmental conditions of a given observation). Some algorithms have been able to handle situations of Sun glint and thin clouds, increasing significantly the daily coverage of ocean color products. All the proposed methods, however, have been based on multispectral observations, the information generally available; few attempts have been made to exploit the polarized and/or directional properties of reflected sunlight (available for some sensors, e.g., POLDER and MISR), but the potential of these properties to improve atmospheric correction (e.g., to enhance the contribution of water reflectance, to deal effectively with Sun glint, and to determine absorption effects) has been recognized and, to some extent, demonstrated. The improvements revealed in the selected examples, however, need to be confirmed (e.g., by analyzing other situations) before drawing final conclusions.

The PACE mission, which will carry into space a spectrometer measuring at 5 nm resolution in the UV to NIR with additional spectral bands in the SWIR and two multi-angle polarimeters, has great potential for improving estimates of water reflectance in the post-EOS era. The improved instrument capabilities offer opportunities, but retrieving a continuous water reflectance spectrum at 5 nm resolution in the UV to NIR, a chief mission objective, poses new challenges. First, measurements in the UV, where molecular scattering is effective (and may dominate the signal), are strongly affected by the vertical structure of atmospheric scatterers, and adjacency effects are large. Second, correcting measurements in spectral regions where gaseous absorption is strong may be difficult when absorbers are located in lower atmospheric levels (e.g., water vapor) due to coupling between scattering and absorption. Observing in the UV to SWIR, however, is adapted to the problem since observations in the UV are sensitive to absorbing aerosols, those in the SWIR to coarse aerosols (and they allow a better separation of the atmospheric/surface signal), and those in the oxygen-A band (763 nm) to aerosol altitude, a parameter controlling aerosol absorption effects. This is especially useful for inversion schemes that incorporate all the available information to retrieve water reflectance. Polarized and multi-angular measurements, sensitive to aerosol type, increase the information content in the inversion process and, therefore, are expected to yield more accurate retrievals, but the possibilities and improvements have yet to be fully investigated.

During the PACE era one may envision an approach to atmospheric correction that is based primarily on the heritage 2-step algorithm, taking into account and extending past accomplishments, yet exploiting as much as possible new capabilities (observations in the UV and in oxygen-A band) and multi-angular polarimetry (e.g., to constrain the aerosol model ensemble or estimate directly aerosol absorption effects). It should be clear that polarized and directional measurements are essential here, due to the inherent limitations of the heritage algorithm, in particular its inability to handle absorbing aerosols, which may affect large oceanic areas. This strategy, aimed at ensuring continuity with previous ocean-color missions, should be complemented by the development and implementation of more sophisticated inversion schemes (deterministic, Bayesian) that, by the nature of their construction, their robustness, and their generalization capabilities, have the undeniable potential to become the new standard in the near future.

Despite the progress made and the major improvements in atmospheric correction expected during PACE, important gaps/issues still remain to be filled/tackled. They include (1) improving the accuracy of whitecap corrections, for which coverage and optical properties are highly variable and difficult to model, (2) accounting for Earth curvature effects in atmospheric radiative transfer, which may be significant even at small zenith angles, (3) correcting for adjacency effects (influential near land, sea ice, and clouds) and cloud shadows, (4) accounting for the coupling between scattering and absorption (especially important in the UV where the molecular scattering signal is large), (5) modeling accurately water reflectance, including polarization (too little is known about the polarization properties of hydrosols), and (6) acquiring a sufficiently representative data set of water reflectance in the UV to SWIR to describe the variety of water bodies (such prior information is needed in Bayesian approaches). Dedicated efforts, experimental as well as theoretical, are in order to gather the necessary information and resolve inadequacies. Ideas and solutions exist and have been put forward to address the unsolved issues, thanks especially to the new capabilities provided by PACE, which will mark the beginning of a new era of accurate ocean-color radiometry from space.

## AUTHOR CONTRIBUTIONS

This work was a statement of findings by the Atmospheric Correction subgroup of the first NASA PACE Science Team (Principal Investigators and Associates). All the authors contributed to the conception and organization of the study, the analysis of the results, and the writing of the sections. They critically revised the manuscript and helped to bring about the final version. RF developed the overall structure of the manuscript and arranged the contents of the sections. He also led the writing of sections Introduction, Improvements Using Super-sampling in Selected Spectral Intervals, Significant Issues, Strategy for Atmospheric Correction, and Conclusions. LR, EB, OD, and AO contributed to the section Introduction and provided invaluable comments to the other sections. BF, AI, KK, ZA, and B-CG wrote the section Heritage Atmospheric Correction Algorithm. BC, JC, OD, XH, DT, OK, AD, RF, and P-WZ wrote the section Alternative Algorithms. LG, BP, FS, RF, and DR provided results for that section. KK, OK, LR, LG, OC, AO, and AD wrote or contributed to the section Enhancements Using Multi-Angular and/or Polarimetric Information. JT, P-YD, LR, OK, OC, AO, and AD provided useful insight for the section Improvements Using Super-sampling in Selected Spectral Intervals. HD, DR, P-YD, JT, and JC contributed to the section Significant Issues. BF, AI, and LR helped to work out the atmospheric correction strategy (section Strategy for Atmospheric Correction) and to form the conclusions of the study (section Conclusions).

## FUNDING

The National Aeronautics and Space Administration's Ocean Biology and Biogeochemistry Program, managed by Dr. Paula Bontempi, provided funding for the study under solicitation NNH13ZDA001N-PACEST.

## ACKNOWLEDGMENTS

The PRISM flight over Santa Monica Bay was enabled by a PRISM AITT grant from NASA ESTO. We thank other sponsors including: the NASA Earth Science Division for the AVIRIS-NG instrument and the data analysis program Utilization of Airborne Visible/Infrared Imaging Spectrometer Next Generation Data from an Airborne Campaign in India NNH16ZDA001N-AVRSNG, managed by Dr. Woody Turner;

## REFERENCES

the Jet Propulsion Laboratory and California Institute of Technology President and Director's Fund; the Jet Propulsion Laboratory 20 Research and Technology Development Program; and the NASA Center Innovation Fund managed in conjunction with the Jet Propulsion Laboratory Office of the Chief Scientist and Technologist. RF acknowledges the technical support of Mr. John McPherson, Scripps Institution of Oceanography. A portion of this research took place at the Jet Propulsion Laboratory, California Institute of Technology.

## SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/feart. 2019.00145/full#supplementary-material

consistent properties of clouds, aerosols, and surfaces. Remote Sens. Environ. 97, 495–518. doi: 10.1016/j.rse.2005.06.006

ocean colour images over turbid waters. Cont. Shelf Res. 25, 539–555. doi: 10.1016/j.csr.2004.10.007

in atmospheric correction processors. Remote Sens. Environ. 162, 257–270. doi: 10.1016/j.rse.2015.01.033

aerosol optical properties and chlorophyll concentrations. Appl. Opt. 42:939. doi: 10.1364/AO.42.000939

**Conflict of Interest Statement:** LG is employed by company Pixstart, and DR and FS are employed by company HYGEOS.

The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2019 Frouin, Franz, Ibrahim, Knobelspiesse, Ahmad, Cairns, Chowdhary, Dierssen, Tan, Dubovik, Huang, Davis, Kalashnikova, Thompson, Remer, Boss, Coddington, Deschamps, Gao, Gross, Hasekamp, Omar, Pelletier, Ramon, Steinmetz and Zhai. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

## APPENDIX A: METHODOLOGY FOR INFORMATION CONTENT ASSESSMENT

The information content assessment described in section Information Content Assessment uses a Bayesian approach with Gaussian distributions as described in Rodgers (2000), and implemented for aerosol remote sensing by Knobelspiesse et al. (2012) and references therein. This method estimates retrieval uncertainty given an observational configuration and uncertainty with the Equation:

$$
\hat{\mathbf{S}}^{-1} = \mathbf{K}^T \mathbf{S}\_\mathbf{e} \mathbf{K} + \mathbf{S}\_a^{-1}
$$

where **S**ˆ is the retrieval error covariance matrix, **S**ǫ is the observation error covariance matrix, **Sa** is the a priori error covariance matrix, **K** is the Jacobian (forward model sensitivity) matrix, T denotes the transpose, and −**1** denotes the inverse. The observation error covariance matrix represents measurement uncertainty and is square, with the dimension of the number of measurements [m x m], made with each observation. The retrieval error covariance matrix, **S**ˆ, has a similar structure, but represents the uncertainty in parameters retrieved from the data and has the dimension of the number of retrieved parameters [n × n]. Essentially, it is the projection of observational uncertainties into state (parameter) space. The Jacobian matrix, **K**, expresses the sensitivity of the atmosphere/ocean radiative transfer model to changes in the parameters to be retrieved, and has the dimension [m x n]. Radiative transfer simulations, indicated by the function **F** (**x**) = **y** (where **x** is a vector atmospheric and surface optical parameters, often called the state space, and **y** is the measurement vector), are used to estimate the Jacobian matrix

$$K\_{i,j}(\mathbf{x}) = \frac{\partial F\_i(\mathbf{x})}{\partial \mathbf{x}\_j} \approx \frac{F\_i\left(\mathbf{x'}\right) - F\_i(\mathbf{x})}{\mathbf{x'}\_j - \mathbf{x}\_j}$$

where the partial derivative of the radiative transfer model for the simulated set of parameters, **x**, is computed for each observation, **i**, and each parameter, **j**. We approximate the Jacobian using the forward difference technique, where the radiative transfer model is rerun with a perturbed parameter **x** ′ **j** , and the forward model is assumed linear over that perturbation.

This method provides the means to relate measurement characteristics to expected retrieval success. Instrument capability is defined by the contents of the measurement vector, **y**, and the measurement uncertainty defined in **S**ǫ . The a priori error covariance matrix, **Sa**, describes what we know about state parameters **x** prior to a measurement. The Jacobian, **K**, is the product of a radiative transfer model and thus computationally expensive to generate. It is, however, crucial since it links parameter to measurement space. In practice, we generate a Jacobian matrix for a very large measurement vector, encompassing all possible measurement spectral sensitivities, geometries, and polarimetric states, and then make a subset corresponding to the instrument system in question. Since the atmosphere/ocean system is highly nonlinear, we also need to repeat this analysis for a variety of states, and express the results as an aggregate of many states. For example, we might expect the ability to determine ocean properties decreases as the amount of aerosol loading increases, so we must test with a variety of aerosol amounts.

The averaging kernel matrix, **A** (also known as the model resolution matrix), is a useful reformulation that is an identity matrix for perfect retrieval ability. It is calculated as:

$$\boldsymbol{A} = \left[\boldsymbol{K}^T \mathbf{S}\_\mathbf{e} \mathbf{K} + \mathbf{S}\_a^{-1}\right]^{-1} \boldsymbol{K}^T \mathbf{S}\_\mathbf{e} \mathbf{K}$$

where a null matrix indicates no ability to determine the state parameters beyond what is already known from the a priori matrix. The Degrees of Freedom for Signal is a scalar, representation of measurement system capability, calculated as DFS=trace(**A**), and has the property 0 ≤ DFS ≤ n. We use DFS to compactly show system capability for many simulated states.

We used a forward model that defines the measurement state with ocean and atmospheric physical and optical properties. However, PACE SDT requirements were expressed as the water reflectance [ρ**w**], which is instead a byproduct of the model. We must therefore project the results **S**ˆ into this space. To do so, we first determine the Jacobian matrix, **J**, for water reflectance in the same manner as **K**:

$$J\_{l\boldsymbol{j}}(\boldsymbol{\mathfrak{x}}) = \frac{\partial \boldsymbol{M}\_l(\boldsymbol{\mathfrak{x}})}{\partial \boldsymbol{\mathfrak{x}}\_{\boldsymbol{j}}} \approx \frac{\boldsymbol{M}\_l\left(\boldsymbol{\mathfrak{x}}^{'}\right) - \boldsymbol{M}\_l(\boldsymbol{\mathfrak{x}})}{\boldsymbol{\mathfrak{x}}^{'}\_{\boldsymbol{j}} - \boldsymbol{\mathfrak{x}}\_{\boldsymbol{j}}}.$$

where λ corresponds to each wavelength in [ρ**w**], and **M** is the forward model component that produces the water reflectance. To project the retrieval error covariance matrix we therefore use

$$\hat{\mathbf{S}}\_{b}^{-1} = \boldsymbol{J}^{T} \mathbf{K}^{T} \mathbf{S}\_{\epsilon} \mathbf{K} \mathbf{J} + \boldsymbol{J}^{T} \mathbf{S}\_{a}^{-1} \boldsymbol{J}^{T}$$

Where **S**ˆ b is the byproduct error covariance matrix. The indirect nature of this matrix may mean that it is has an implicit regularization. Comparison of **S**ˆ band the projection of the a priori error covariance matrix **J TS** −1 a **J** can provide clues to the intermediate step constraints.

section Information Content Assessment contains an implementation of these techniques for several prototypical MAP designs for a variety of geophysical conditions. In practice, this means we construct Jacobian matrices to include all possible measurement spectral channels and geometries, and subset the Jacobian to represent the measurement system of interest. This is repeated for a variety of geophysical cases, and results presented in aggregate. In section Information Content Assessment we show the DFS as an overall metric of measurement capability, and **S**ˆ b for water reflectance to show atmospheric correction capability.

## Developing a Community of Practice for Applied Uses of Future PACE Data to Address Marine Food Security Challenges

Stephanie Schollaert Uz1 \*, Grace E. Kim1,2, Antonio Mannino1 , P. Jeremy Werdell1 and Maria Tzortziou3

1 NASA Goddard Space Flight Center, Greenbelt, MD, United States, 2 Universities Space Research Association, Columbia, MD, United States, 3 The City College of New York, New York, NY, United States

Edited by: Irina Alexandrovna Mironova, Saint Petersburg State University, Russia

#### Reviewed by:

Yoav Lehahn, University of Haifa, Israel Tim J. Malthus, CSIRO Oceans and Atmosphere (O&A), Australia

> \*Correspondence: Stephanie Schollaert Uz<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Atmospheric Science, a section of the journal Frontiers in Earth Science

Received: 09 December 2018 Accepted: 17 October 2019 Published: 05 November 2019

#### Citation:

Schollaert Uz S, Kim GE, Mannino A, Werdell PJ and Tzortziou M (2019) Developing a Community of Practice for Applied Uses of Future PACE Data to Address Marine Food Security Challenges. Front. Earth Sci. 7:283. doi: 10.3389/feart.2019.00283 The Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission will include a hyperspectral imaging radiometer to advance ecosystem monitoring beyond heritage retrievals of the concentration of surface chlorophyll and other traditional ocean color variables, offering potential for novel science and applications. PACE is the first NASA ocean color mission to occur under the agency's new and evolving effort to directly engage practical end users prior to satellite launch to increase adoption of this freely available data toward societal challenges. Here we describe early efforts to engage a community of practice around marine food-related resource management, business decisions, and policy analysis. Obviously one satellite cannot meet diverse end user needs at all scales and locations, but understanding downstream needs helps in the assessment of information gaps and planning how to optimize the unique strengths of PACE data in combination with the strengths of other satellite retrievals, in situ measurements, and models. Higher spectral resolution data from PACE can be fused with information from satellites with higher spatial or temporal resolution, plus other information, to enable identification and tracking of new marine biological indicators to guide sustainable management. Accounting for the needs of applied researchers as well as non-traditional users of satellite data early in the PACE mission process will ultimately serve to broaden the base of informed users and facilitate faster adoption of the most advanced science and technology toward the challenge of mitigating food insecurity.

Keywords: satellite ocean color applications, PACE mission user community, phytoplankton, fisheries, aquaculture, societal impact

## INTRODUCTION

Seafood is an important source of protein for much of the world's population. Wild catches cannot match growing demand and their sustainability is in question. Aquaculture is an increasingly important industry to feed the world's population. According to the Food and Agriculture Organization of the United Nations (FAO), as of 2017 more than 200 nations and \$152 Billion per year are involved in the international trade of fish, shellfish, and fish products, as the most traded food product for developing countries at twice the value of the coffee trade (FAO, 2018).

However, fisheries face significant threats from environmental and human pressures. There is a need for sustainable and local seafood. Aquaculture has been increasing to supply this demand, yet coastal eutrophication and harmful algal blooms imperil the safety of this food source. Warming, acidification, deoxygenation, and pollution are a few of the environmental stressors that cause habitat shift or loss and undermine the sustainability of the fishing industry. Around the world, illegal, unreported, and unregulated fishing also jeopardizes food security, economic security, and human security. The latest global assessment by the Food and Agriculture Organization of the United Nations indicates that the percentage of fisheries that are unsustainably overfished has increased to one third (World Bank, 2017; FAO, 2018). Fragile states with limited capabilities to enforce their Exclusive Economic Zones need actionable information to evaluate their natural resources. Freely available remotely sensed data from satellites combined with in situ sensors provide environmental insights that can help resource managers and other operators respond to these challenges.

Global aquatic ecosystems have been regularly monitored from space during the past four decades by polar-orbiting, multispectral ocean color radiometers, transforming our understanding of processes that support life in the ocean (McClain, 2009). U.S. examples include the NASA Coastal Zone Color Scanner (CZCS; 1978–1986), Sea-viewing Wide Fieldof-view Sensor (SeaWiFS; 1997–2010), Moderate Resolution Imaging Spectroradiometer (MODIS; 1999-present on Terra and 2002-present on Aqua), and Visible Infrared Imaging Radiometer System (VIIRS; 2012-present on Suomi NPP and 2018-present on NOAA-20). These satellite-borne sensors were designed to provide daily, global views of the open ocean where sampling opportunities are infrequent and expensive. In Europe, the Ocean and Land Color Imager (OLCI) was launched onboard Sentinel 3A and 3B in 2016 and 2018, respectively, with another two Sentinel 3 satellites planned to ensure continuity and consistency of services (Donlon et al., 2012; Groom et al., 2019). Additional ocean color sensors have been successfully launched into polar orbit by India, Japan, and China, and into geostationary orbit by South Korea (Groom et al., 2019).

NASA is currently developing the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission, with its launch scheduled for late 2022. PACE has several important technological advancements that will expand its use beyond the heritage instruments listed above, enabling it to enhance and extend current biological and water quality parameters measured from space with new spectral information (Werdell et al., 2019). The primary instrument on the observatory, the Ocean Color Instrument (OCI), consists of two spectrometers that continuously span the ultraviolet (UV) and orange and orange to near-infrared (NIR) spectral regions, as well as seven additional detectors to collect measurements at discrete NIR and shortwave infrared (SWIR) bands (**Figure 1**). Although not the focus of this manuscript, note that OCI is complemented on the observatory by two multi-angle polarimeters, the Spectropolarimeter for Planetary Exploration (SPEXone) (Hasekamp et al., 2019) and the Hyper Angular Rainbow Polarimeter (HARP2) (Martins et al., 2018). These instruments will provide additional aerosol and cloud data products relative to what is possible with OCI alone. **Table 1** provides additional details on characteristics of OCI and the PACE observatory. SPEXone and HARP2 are scheduled for delivery to NASA in the 3rd and 4th quarter of 2020 when integration and testing of the spacecraft begins. Testing and integration of the OCI flight unit will begin in 2020 and continue until delivery in the 3rd quarter of 2021 when integration and testing with the observatory begins. The PACE observatory has a scheduled launch window between November 2022 and March 2023. Public distribution of PACE science data products will begin immediately following official commissioning of the observatory, which is scheduled to occur 60 days after launch. As with most scientific satellites, delays in the delivery of the instruments or in the observatory integration and testing are possible and may result in postponement of the launch, which would in turn delay the availability of PACE science data.

The mission of NASA is to explore through a synthesis of science and technology. Traditionally, NASA Earth Science designs new Earth observing satellites based upon science research questions and then NOAA and the private sector adopt the results of the evolving technology and apply it to practical applications around fisheries and aquaculture. NOAA resource managers, researchers and private businesses have been using satellite data to assist with finding fish for decades, and especially since the launch of SeaWiFS in 1997 (e.g., Maul et al., 1984; Friedl et al., 2006; Wilson et al., 2008, 2009; Wall et al., 2009; Wilson, 2011). NASA is currently undergoing a paradigm shift to consider such practical applications of its satellite data in coordination with science research questions during the initial concept and pre-formulation of a mission before it becomes formalized (National Academies of Sciences Engineering and Medicine [NASEM], 2018). Although the international community has already been considering practical applications in their satellite design process (e.g., ESA's Copernicus Program), PACE is the first NASA ocean color mission that endeavors to engage operational stakeholders during the design-build phases of a mission prior to launch (**Figure 2**). Our objective is to prepare a community of practice that can immediately apply the advanced data from PACE as soon as it is available to maximize the nation's return on investments of this space-based asset. In this manuscript, we describe several early efforts to survey the fisheries and aquaculture communities, among others, about their current uses of ocean color data and how future PACE science data streams could help fill gaps. In addition to regularly attending international meetings to discuss capabilities and needs with scientists and users from around the world, we have undertaken a few targeted activities to foster the exchange of ideas and requirements between satellite data providers and stakeholders. The end goal is to increase access to satellite products that can guide more informed decisions by resource managers, the fishing industry, as well as artisanal and recreational fishers.

The activities and outcomes we describe in this manuscript are complementary to previous efforts to ensure the continuity of high quality ocean color satellite data for basic and applied research uses. For example, in 2011 an international committee of experts outlined minimum requirements to sustain

global ocean color measurements for research and operational applications, and options to minimize the risk of a data gap (National Research Council [NRC], 2011). NOAA routinely conducts needs assessments targeted to various thematic and regional communities, including fisheries and water quality issues, and looks for ways to connect satellite data providers to practical end users. In 2007, the International Ocean Colour Coordinating Group (IOCCG) established a working group on the operational use of ocean color (IOCCG, 2008). In 2014, the IOCCG established a working group to develop a strategy for incorporating current and future satellite missions into global near-coastal and inland water quality monitoring efforts. Through breakout sessions at international conferences and their 2018 report (IOCCG, 2018), these working groups identified user needs and requirements in the context of existing observing capabilities, and offered suggestions for improving links between data providers and end users, as have other efforts (e.g., Nahorniak et al., 2013). We aim to accomplish similar objectives with an eye toward the role of new spectral capabilities by PACE through the activities described here.

## SATELLITE OCEAN COLOR PROVIDES APPLICATIONS-RELEVANT DATA RECORDS

Heritage satellite OCIs observe the spectral radiance emanating from the top-of-the-atmosphere at discrete visible, NIR, and SWIR wavelengths. Atmospheric correction algorithms are applied to remove the contribution of the atmosphere and reflection off of the sea surface (e.g., foam and whitecaps) from the total signal to produce estimates of remote sensing

reflectances [Rrs(λ); sr−1 ], the light exiting the water normalized to the downwelling irradiance incident on the sea surface (Mobley et al., 2016). Bio-optical algorithms are then applied to the Rrs(λ) to derive geophysical properties of interest, such as surface concentrations of chlorophyll-a (Chl-a; mg m−3 ) (O'Reilly et al., 1998). SeaWiFS, for example, collected data at six visible and two NIR wavelengths, where both NIR wavelengths were used for atmospheric correction and four of the six visible wavelengths were used to derive Chl-a. Other applicationsrelevant geophysical parameters derived from satellite ocean color include indicators of harmful algal blooms (HABs), water clarity, turbidity, sediments and detritus, shallow submerged and floating aquatic vegetation, surface oil slicks, and other variables estimated or inferred through regional correlations between field measurements and remotely sensed proxies (e.g., Stumpf et al., 2003; Hu et al., 2004; Stumpf and Tomlinson, 2005; Hu et al., 2009).

With respect to food security, the most relevant capability of a satellite OCI is its ability to monitor phytoplankton community structure (IOCCG, 2014). Understanding spatial and temporal changes in such structure provides insight into aquatic ecosystem health, water quality and related human health issues, as in the case of HABs, and ecological forecasting of aquatic resources including fisheries production. While ocean color methods for identifying different phytoplankton groups exist (e.g., Bracher et al., 2017; Mouw et al., 2017), most remain confounded by the limited number of wavelengths available on heritage instruments. Increased spectral resolution in the visible region offers improved characterization of spectral phytoplankton pigment absorption and particulate backscattering, both of which provide highly useful metrics for discriminating between phytoplankton groups and plankton size composition (Lubac et al., 2008; Bracher et al., 2009; Torrecilla et al., 2011; Catlett and Siegel, 2018). Several studies identify needs for hyperspectral measurements of at least 5 nm resolution to support classification of phytoplankton community structure (Lee et al., 2007; Vandermeulen et al., 2017). One promise of PACE is the utility of OCI's hyperspectral capability for the improved assessment of phytoplankton biomass, composition, and production (**Table 1**). Advances in such routine, global assessments will benefit ecosystem-based models, which aid in managing fisheries and coastal resources as well as policy-making related to monitoring of water clarity and detection and identification of the spatial extent and durations of HABs.

## END USER ENGAGEMENT ACTIVITIES AROUND FISHERIES AND AQUACULTURE

In this section, we describe preliminary efforts to engage a diverse group of potential PACE data users from ocean color experts to novices and non-traditional user groups. The following sections explore these themes for attendees at our workshops, with a few specific case studies of current end users, the ways they utilize remote sensing technologies now, and their data needs related to remote sensing. All workshops were hosted in Washington, DC or Greenbelt, MD through the Applied Sciences activities of the Earth Science Division at NASA Goddard. The workshops

were promoted through routine communication channels (e.g., websites, monthly e-mailed newsletter, social media) with free and open registration by anyone interested in attending. The majority of participants in these activities and those who responded to our pre- and post-workshop surveys described their line of work as research, resource management, and public health. Other fields represented included policy/regulatory, remote sensing product development, commercial business, and applied remote sensing training and education. Casting a wide net of data users and potential users serves to deepen our awareness of broader needs beyond the traditional ocean color research community and also informs new groups about the possible applications of this freely available, public information resource.