Edited by: David Antoine, Curtin University, Australia
Reviewed by: Howard R. Gordon, University of Miami, United States; Barbara Bulgarelli, Joint Research Centre (Italy), Italy
This article was submitted to Atmospheric Science, a section of the journal Frontiers in Earth Science
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NASA’s Plankton, Aerosol, Clouds, ocean Ecosystem (PACE) satellite mission is scheduled to launch in 2022, with the Ocean Color Instrument (OCI) on board. For the first time reflected sunlight from the Earth across a broad spectrum from the ultraviolet (UV: 350 nm) to the short wave infrared (SWIR: 2260 nm) will be measured from a single instrument at 1 km spatial resolution. While seven discrete bands will represent the SWIR, the spectrum from 350 to 890 nm will be continuously covered with a spectral resolution of 5 nm. OCI will thus combine in a single instrument (and at an enhanced spatial resolution for the UV) the heritage capabilities of the Moderate resolution Imaging Spectroradiometer (MODIS) and the Ozone Monitoring Instrument (OMI), while covering the oxygen A-band (O2A). Designed for ocean color and ocean biology retrievals, OCI also enables continuation of heritage satellite aerosol products and the development of new aerosol characterization from space. In particular the combination of MODIS and OMI characteristics allows deriving aerosol height, absorption and optical depth along with a measure of particle size distribution. This is achieved by using the traditional MODIS visible-to-SWIR wavelengths to constrain spectral aerosol optical depth and particle size. Extrapolating this information to the UV channels allows retrieval of aerosol absorption and layer height. A more direct method to derive aerosol layer height makes use of O2A absorption methods, despite the relative coarseness of the nominal 5 nm spectral resolution of OCI. Altogether the PACE mission with OCI will be an unprecedented opportunity for aerosol characterization that will continue climate data records from the past decades and propel aerosol science forward toward new opportunities.
Aerosols contribute the largest uncertainties in estimating climate forcing (
The challenge to understanding these processes lies within the multiple pathways that aerosols, clouds and Earth’s energy balance intertwine (
In addition to their role in climate and hydrological processes, aerosols pose a serious global health threat. Particulate matter (PM) and aerosols that pollute ambient air are a major global cause of death and disease, having been found responsible for 3.2 million deaths per year as well as being the 9th leading risk factor for premature death globally (
An integrated approach is required to achieve sufficient understanding of the processes that aerosols affect. The approach requires modeling at all scales and observational data to inform and constrain these models.
Since the launch of Terra and the A-train constellation of satellites starting at the end of 1999, aerosol remote sensing reached a new era. The MODerate resolution Imaging Spectroradiometer (MODIS) and Multiangle Imaging SpectroRadiometer (MISR) on Terra, MODIS on Aqua, Ozone Monitoring Instrument (OMI) on Aura, Medium Resolution Imaging Spectrometer (MERIS), Sentinel-3 Ocean and Land Color Instrument (OLCI), Sentinel-2 Multi-Spectral Instrument (MSI), Polarization and Directionality of the Earth’s Reflectance (POLDER) on the Polarization and Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar (PARASOL) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) on Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations; have combined to provide an unprecedented view of the global aerosol system. Focusing on passive remote sensing from radiometers, algorithms have extracted information from spectral measurements in the visible and shortwave infrared (SWIR) portions of the spectrum (i.e., MODIS), from spectral measurements in the ultraviolet (UV) portion of the spectrum (i.e., OMI and the earlier Total Ozone Mapping Spectrometer (TOMS) instrument from which OMI draws its heritage), and from multi-angular measurements in the visible and near-infrared (NIR) [i.e., MISR, Advanced Along Track Scanning Radiometer (AATSR) and POLDER]. Algorithms have also extracted information from multi-angular measurements of polarized reflectance in the visible (POLDER), but discussion of polarimetry is outside the scope of this review and will be covered in a companion paper by
The information content of a single-look multispectral or hyperspectral sensor such as MODIS or OMI is limited, especially when neither spans the entire solar spectrum. MODIS does not measure in the UV, while OMI measures in the UV, but misses all wavelengths longer than 500 nm. In addition, OMI’s 13 km × 24 km footprint makes quantitative aerosol retrievals in all but the most cloud-free situations impossible. Both MODIS and OMI standard aerosol retrieval algorithms must rely on a series of assumptions in order to retrieve AOD and any other aerosol property. Multi-angle measurements of the same scene such as MISR or AATSR increase the information content because the instrument becomes sensitive to a wider array of aerosol properties with fewer assumptions. Even so, MISR and AATSR still are limited to determining “aerosol type,” a qualitative constraint based on a combination of size, absorption and shape. Aerosol type, while important for a wide range of applications, is not the same as determining quantitative particle properties such as size distribution, SSA or complex refractive index.
For retrieving these quantitative particle properties from space even more information is needed, such as with a multi-wavelength, multi-angle polarimeter of sufficient accuracy, wavelength range and resolution (
New contemporary algorithms applied to the traditional sensors are enhancing aerosol retrieval capabilities despite the inherent limitations. For example, an advanced type of algorithm only now making its way toward operations is a multi-pixel algorithm called the MAIAC (
Single-view radiometers (e.g., MODIS and OMI) have provided decades of insight into the global aerosol system and new observational methods to constrain estimates of climate change (
This paper explores the potential for aerosol remote sensing and characterization in the PACE era, primarily from the OCI that has been designed to continue and even enhance heritage aerosol algorithms over both ocean and land. Section “Aerosol Remote Sensing for the PACE Mission” begins with a brief description of the PACE observatory, the two types of instruments on the observatory and the possibility for remote sensing of aerosols from the observatory. This is followed by discussion on the abilities and challenges of adapting current aerosol algorithms to OCI measurements in order to continue heritage aerosol time series beyond the lifetimes of today’s sensors and missions. Section “Advances Beyond Heritage. 1: Broad Spectral Range Retrievals From OCI” explores the potential of using OCI’s broad spectrum from the UV to the SWIR to retrieve an enhanced set of aerosol characterization products. These products include spectral AOD, a measure of particle size, SSA and aerosol layer height (ALH). Section “Advances Beyond Heritage, 2: Oxygen A-band Information Content on Aerosol Profiling” describes advances beyond heritage remote sensing by exploring the possibilities of retrieving ALH by capitalizing on OCI’s 5 nm spectral resolution through the oxygen A-band (O2A). In Section “Advances Beyond Heritage, 2: Oxygen A-band Information Content on Aerosol Profiling,” we also broach the possibility of improved aerosol profile retrievals from the combination of multiangle observations in the O2 A-band. Part 2 of this study (
The algorithm concepts presented in this paper illustrate the potential for aerosol remote sensing from OCI. These concepts are not the official, operational, set of algorithms as these will require additional vetting. The focus of this paper is OCI. For further analysis of PACE aerosol remote sensing with multi-angle and polarimetry, we refer the reader to the companion paper in this same issue (
The PACE mission offers the aerosol community new opportunities for retrieving and characterizing aerosol from space. There are two levels of opportunity based on the two types of instruments the mission intends to fly. First there is OCI (
Instrument specifications for MODIS, OMI, and OCI.
MODIS | OMI | OCI | |
---|---|---|---|
UV to NIR bands | 0.41, 0.44, 0.47, 0.49, 0.53, 0.55, 0.55, 0.66, 0.67, 0.68, 0.75, 0.85, 0.87, 0.91, 0.94, 0.94 μm | Hyperspectral continuous 0.264–0.504 μm | Continuous 0.345–0.89 μm plus discrete bands at 0.94 and 1.04 μm |
SWIR to Thermal infrared | 1.24, 1.63, 2.13, 3.7, 3.9, 3.9, 4.05, 4.46, 4.5, 1.38, 6.6, 7.2, 8.5, 9.7, 11.0, 12.0, 13.2, 13.6, 14.1 μm | None | 1.25, 1.38, 1.62, 2.13, 2.25 μm |
Swath width | 2330 km | 2600 km | 2660 km |
Global coverage | 2 days | 1 day | 1 – 2 days |
Ground pixel | 0.25, 0.5, and 1 km | 13 × 24 km | 1 km |
The second type of PACE instrument will be a pair of multi-wavelength, Mulit-Angle Polarimeters (MAPs). The PACE MAPs have the high potential to push beyond traditional limitations in characterizing aerosol retrievals with new capabilities that include retrievals of particle size distribution, complex refractive index, SSA, aerosol height distribution and simultaneous retrievals of aerosol properties with surface (ocean and land) reflectance (
Aerosol products for the PACE mission will be derived from OCI alone and from the MAPs alone. However, there is also opportunity to develop and use advanced algorithms that make use of all instruments in synergy. PACE aerosol products will at minimum, match aerosol products currently available from the OMI, MODIS and VIIRS sensors, and include the UV aerosol index (AI), spectral AOD over land and ocean, and fine mode fraction (FMF) over ocean. The FMF is a measure of the relative effect that this mode has on the reflectance at top-of-atmosphere as compared with the effect of the total aerosol of all sizes on the reflectance (
Fundamental to any aerosol retrieval is the need to identify scenes applicable for useful retrievals. This process, called masking, includes identifying and excluding scenes containing clouds, snow/ice, sun glint, and more from retrievals. In the PACE mission some masking will improve, others will be worse, while some will remain essentially the same as heritage aerosol algorithms. With the added information content for PACE, some retrievals that would have been masked for the heritage algorithms, may be able to proceed. For example, aerosol retrievals above clouds and otherwise unfavorable surfaces may be possible.
OCI lacks thermal infrared (IR) observations, which in terms of clouds, will lead to a degraded cloud masking capability compared to heritage MODIS and VIIRS datasets. However, compared to heritage OMI/TOMS-alone datasets, improved spatial resolution and expanded wavelengths into the SWIR will allow for significantly improved cloud masking. The OCI 1.38 μm channel is especially important for detecting thin cirrus clouds. In addition, the 1.38 μm channel may offer possibilities for “correction” rather than blunt masking by removing the net radiative effect of the cirrus in an adjustment of the measured top-of-atmosphere radiances, and then using those previously cirrus-affected pixels to derive information about the aerosol and/or surface beneath. Furthermore, the pair of SWIR channels centered at 2.135 and 2.25 μm will provide information on cloud thermodynamic phase unavailable from previous sensors (
Masking is also typically required to remove the impact of the direct and diffuse solar radiation reflected by the sea surface and other sea surface features that can significantly enhance NIR and SWIR. Highly scattering non-aerosol targets on the ocean surface include whitecaps, foam and bubbles, sea ice, floating vegetation, high calcite waters, high sediment waters, optically shallow waters (e.g., with bright coral or sand) and regions with concentrated floating plastics (
OCI, like OMI, is a hyperspectral resolution imager with continuous spectral coverage at 5 nm spectral resolution that spans a wavelength range from the UV into the NIR. From an aerosol retrieval perspective OCI is an improvement on OMI as its spatial resolution will nominally be 1 km instead of 13 km × 24 km, and its continuous spectral range extends to 885 nm, while OMI’s ends at 500 nm. However, OCI’s spectral resolution of its contiguous channels is 10 times coarser than OMI’s in the UV and visible.
The TOMS/OMI heritage products include both the qualitative UV AI (
In addition, the wavelength pairs in the OCI oxygen A- and B- band spectral regions (∼690 and ∼765 nm) and in their adjacent continuous regions (∼680 and ∼780 nm) will allow the retrieval of the height of desert dust and carbonaceous aerosol layers using inversion schemes developed for the Earth Polychromatic Imaging Camera (EPIC) sensor (
Multiple heritage aerosol algorithms are encompassed by the MODIS/VIIRS era. Of these, the most pertinent to the discussion that follows is the Dark Target (DT) over ocean retrieval (
OCI’s hyperspectral range spans four of the MODIS bands used by the MODIS DT aerosol over ocean algorithm and further extends to include six discrete bands in the SWIR thereby spanning the range of wavelengths needed by any of the heritage aerosol algorithms, even those not specifically discussed in this paper. OCI spatial resolution of 1 km is less desirable than MODIS’ 0.5 km, and OCI is missing MODIS’ thermal IR channels used in cloud masking. Recent studies have shown that this could make a difference in maintaining continuity, but still achieve desired accuracy of key retrieved aerosol parameters (
Even though the OCI configuration spans the same parameter space of heritage MODIS algorithms, a seamless continuation of the heritage product time series is not guaranteed. Experience in porting MODIS retrievals to VIIRS has revealed the subtleties of the transfer. Issues arise in translating band spectral response functions from old sensor to new sensor, changes in gas corrections resulting from differences in spectral response function and the creation of specific LUTs for the new sensor. Other issues are introduced by differences in spatial resolution and lack of auxiliary channels used for masking (e.g., clouds). Finally, calibration of radiances from one sensor to the next may differ, introducing offsets that may either be static from pre-launch differences and also time-dependent from on-orbit corrections.
In
Differences between satellite retrieval of AOD and collocated AERONET measurements of AOD from a global data set of satellite-AERONET collocations, sorted according to AERONET AOD and binned with equal number of collocations in each bin. Red dots indicate mean of each bin, boxes enclose one standard deviation in each bin, whiskers extend to two standard deviations and blue center line of each box denotes the median.
If all reflectance measurements made by OCI are well-calibrated, we anticipate similar magnitude differences between heritage aerosol products and OCI aerosol products based on heritage algorithms as is illustrated in
Irrespective of the challenges described above, OCI-alone can still advance aerosol characterization beyond heritage expectations by allowing for new algorithms that make use of the information content available from a broad spectrum spanning the UV to the SWIR. This is essentially merging the capabilities of OMI and MODIS into a single sensor with common geometry and spatial resolution, instantaneous viewing and the same calibration. This configuration will permit retrieval of aerosol loading (spectral AOD), a measure of particle size (FMF), a measure of aerosol absorption (SSA), and ALH. Thus, OCI should be able to retrieve four parameters to characterize aerosol, while OMI and MODIS heritage algorithms separately could each only retrieve two of the four parameters, while assuming values for the remaining two. Aerosol layer in this context is an optically effective scale height. Other satellite sensors past and present have also measured reflectances from the UV to the SWIR. These include the Thermal And Near infrared Sensor for Carbon Observation – Cloud and Aerosol Imager (TANSO-CAI) with four wavelengths (380, 674, 870, and 1620 nm) at 1 km spatial resolution (eoPortal directory: GOSAT) and the Tropospheric Ozone Monitoring Instrument; (TropOMI) with three spectral regions (270–495 nm, 710–775 nm, and 2305–2385 nm) at 7 km spatial resolution (
Reflectances in the UV spectrum (e.g., those used in the OMI aerosol algorithms) are primarily sensitive to three aerosol characteristics: aerosol loading (AOD), aerosol absorption, and height of the aerosol layer. Unfortunately, heritage measurements in the UV provide only two pieces of independent information. Thus, retrievals are unconstrained, requiring assumptions to constrain one of the three aerosol parameters in order to retrieve the other two. The traditional tactic taken and applied to TOMS and OMI aerosol algorithms is to constrain aerosol height with assumptions based on climatology and then retrieve loading and absorption in the form of AOD and SSA. The retrievals, especially SSA, are also susceptible to assumptions of (i) surface reflectances, particularly in extremely oligotrophic oceans, and (ii) aerosol particle properties used by the retrieval, including particle size and most importantly the spectral signature of absorption through the UV and blue regions of the spectrum.
Meanwhile, aerosol retrievals based on the visible-NIR-SWIR spectrum (e.g., those applied in the MODIS and VIIRS aerosol algorithms) are primarily sensitive to only two aerosol characteristics: aerosol loading (AOD) and a measure of relative particle size based on spectral AOD. In addition to spectral AOD, particle size can be represented by a parameter denoted as FMF, which is usually thought of in terms of the ratio of fine mode AOD at 550 nm to total AOD at 550 nm. The retrieval in the visible-NIR-SWIR is less sensitive to ALH or aerosol absorption. Some MODIS algorithms that make use of the blue wavelengths retrieve and report SSA, but those products have not been validated.
Making use of the complementary capabilities of OMI and MODIS was an early goal of both science teams. Soon after the OMI-alone aerosol retrieval had been validated, attempts were made to join OMI’s sensitivity to absorption and layer height with MODIS’ sensitivity to loading and size. With both instruments being in the A-Train, and measuring less than 8-minutes apart, it was theoretically possible to create such a combined OMI-MODIS retrieval. However, the endeavor to merge Level 1 radiance data from the two instruments faced significant challenges in terms of non-uniform calibration, spatial resolution and viewing geometry. Instead, a retrieval was devised that used Level 2 retrieval products from each instrument. MODIS spectral AOD was extrapolated into the UV, thereby constraining the UV AOD and allowing OMI to return a constrained SSA and ALH that compared well to independent observations (
The EPIC sensor on Deep Space Climate Observatory (DSCOVR) currently deployed on the L1 point (
Here we present a novel, OCI prototype algorithm developed specifically for a single instrument spanning the complete range from the UV to the SWIR and therefore moves beyond the
The OCI prototype algorithm begins first by identifying applicable ocean scenes for retrieval and discarding non-applicable scenes with clouds/cirrus, ice, glint or suspended sediments in the water. The algorithm then ingests reflectances of the identified ocean scene in six wavelengths (550, 650, 880, 1240, 1640, and 2110 nm), as well as ancillary data in the form of total column ozone and surface wind speed. The water leaving radiance is assumed null at all wavelengths, except at 550 nm, where it is assumed equal to 0.005. The measured reflectances are compared with modeled top-of-atmosphere (TOA) reflectances, and the best fit between satellite-measured and modeled TOA spectral reflectances is used to determine the aerosol characteristics and aerosol loading (AOD) of the scene. Modeled TOA reflectances are computed for a combination of one fine mode model (out of four) and one coarse mode model (out of five). In all nine models aerosols are assumed to be spherical particles with minimal absorption. The latter assumption is justified by the fact that the algorithm at these wavelengths is not sensitive to particle absorption. Thus the DT retrieval returns the AOD, a choice of a fine mode and a coarse mode, each defined with a specific size distribution, and also the relative weighting between fine and coarse modes, resulting in a bimodal size distribution.
The first step, as described above, is unchanged from the traditional DT algorithm first developed for MODIS. To the first order, because of the selected wavelengths, the DT algorithm is sensitive to aerosol loading and to particle size distribution but insensitive to absorption variation (
The creation of a second step, an appendage to the traditional DT algorithm that uses the returns from the first step, provides quantitative measures of aerosol absorption from measured UV reflectances. The other inputs are OCI-measured reflectances at 354 and 388 nm. In this second step we use expanded LUTs of the nine traditional models to include the two new UV wavelengths, and in addition allow the imaginary part of the refractive index to vary between four distinct values in these two new wavelengths. Size distribution and the real part of the refractive index are spectrally extrapolated to the UV from the original values assigned to each of the nine models for visible and SWIR wavelengths, and are kept constant as those models’ imaginary parts of the refractive index are allowed to vary between four different values. These four imaginary parts of the refractive index represent four different models representative of different degrees of aerosol absorption. These are referred to as “absorption models,” even though one is purposely non-absorbing. The range of the imaginary part of the refractive index varies from 0.000 to 0.011. Thus the prototype algorithm LUT consists of 36 models: nine original DT models, each expanded into four possible absorption models in the UV spectrum.
The LUT of step 2 consists of top-of-atmosphere reflectances at 354 and 388 nm for a variety of geometries, a variety of AODs at 0.55 μm extrapolated to the UV wavelengths using the extinction optical properties of each of the 36 models and for four ALHs.: 1.5, 3, 6, and 10 km. Thus for a specific geometry and a specific AOD at 0.55 μm determined from step 1, there are computed reflectances from 144 possible situations (36 models × 4 layer heights). Note that the expansion into multiple absorption properties and layer heights for each size distribution only affects the computed top-of-atmosphere reflectance in the two UV wavelengths. There is no change in the LUT values of the original six wavelength bands of the first step. As such, step 1 and step 2 are only linked because the outputs of step 1 become the inputs to step 2. Step 1 already has determined the size distribution that best fits the observations, choosing one fine mode and one coarse mode from the nine possible models and combining them into a bimodal distribution with a retrieved reflectance weighting parameter (FMF). This retrieved bimodal size distribution and the retrieved spectral AOD are the outputs from step 1 that are input into step 2. These inputs from step 1 constrain the step 2 retrieval in the UV into just 16 possibilities (4 absorption levels × 4 layer heights) out of the original 144.
In the traditional UV aerosol retrievals (
Examples of the proto-type OCI algorithm described in the text applied to custom-built collocated MODIS-OMI granules where the OMI reflectances are mapped to the MODIS granule at the MODIS spatial resolution, although the OMI reflectances maintain their original geometric meta data. The
We tested the algorithm through a series of experiments in which we simulate TOA spectral reflectance from forward calculations using the aerosol models described above, and then retrieve from these calculated reflectances using the proto-algorithm. Doing such an experiment for a range of AOD at 550 nm (0–3.0) and doubling the normalized water leaving radiance to 0.010 from the UV to 0.66 μm, we found the algorithm retrieved the correct input parameters in 100% of the cases when AOD > 0.30, but only 56% of the cases when the AOD was less. This suggests that we can expect little sensitivity to ocean color as long as AOD at 550 nm exceeds about 0.30. When AOD is less than this threshold, preliminary results obtained from theoretical studies suggest that absorbing aerosol can still be identified and separated from non-absorbing marine aerosol, even if the absorption cannot be quantified. Identifying absorbing aerosol depends on spectral signatures in the UV region that differ from spectral signatures of chlorophyll in Case I (open ocean) waters.
One of the expected products from this prototype algorithm is an optically effective aerosol layer scale height. Retrieving this parameter should be possible, given that the AOD and aerosol model are sufficiently constrained by Step 1 of the algorithm. However, this algorithm will not be the only means of obtaining height from PACE instruments. As will be shown in Section “Advances Beyond Heritage, 2: Oxygen A-band Information Content on Aerosol Profiling,” there is sufficient information in the OCI channels that cover the O2A to constrain aerosol height independently. In addition one of the MAPs on PACE will be measuring the polarized signal in the UV and deep blue wavelengths. ALH can also be retrieved from the polarized signal in these wavelengths (
Validation of the prototype retrieval demonstrated in
Part of the difficulty in proving this retrieval with the merged MODIS-OMI data sets is the original problem of possible inconsistent calibration, spatial resolution and geometry. The work done so far demonstrates feasibility and identifies/quantifies sensitivities. The exact retrieval algorithm to be applied to actual OCI observations may take advantage of advanced computer power and two decades of experience and need not follow the MODIS or OMI heritage procedures, as was presented here. Although any OCI aerosol algorithm will make use of the same information content and be challenged by the similar uncertainties.
OCI offers two new capabilities that permit advancing aerosol retrievals beyond heritage. In Section “Advances Beyond Heritage. 1: Broad Spectral Range Retrievals from OCI” we explored the first of these capabilities: the broad spectrum from the UV through the SWIR at a common 1 km spatial resolution and in a single instrument. In Section “Advances Beyond Heritage, 2: Oxygen A-band Information Content on Aerosol Profiling” we explore the second of these capabilities: the continuous 5 nm spectral resolution through the O2A (758–778 nm) region. This capability through the O2A can be mined from the single-view OCI measurements to retrieve ALH, but if it can be measured from a multi-angle instrument, such as the PACE MAPs, information on aerosol layering in the atmosphere increases. Both of these situations will be addressed in this section.
Radiance measurements in the O2A are sensitive to the vertical distribution of atmospheric scatterers, which in principle allows for the retrieval of aerosol height from future instruments with hyperspectral capability. OCI has continuous spectral measurements through the O2A and, while much coarser than an ideal (quasi-line-by-line) hyperspectral coverage such as by Orbiting Carbon Observatory (OCO)-2/3 sensors (
The ability to use oxygen absorption for the retrieval of aerosol height from passive sensors has been described extensively in the literature (
OCI, at a nominal resolution of 5 nm, will have 3 or 4 spectral bands in the O2A, but only at a single viewing angle. One of the proposed PACE MAPs, Spectro-Polarimeter for Planetary Exploration-1 (SPEXone) (
To begin we examine whether or not OCI will be able to discriminate ALH. The upper-left panel of
When this study was performed, the rational hypothesis is that we will have no control over how OCI will sample the O2A spectral region.
In the lower-left panel of
This impact of aerosol height on the measured signal will naturally be stronger as the AOD and SSA increase, with the usual tradeoff between the two aerosol optical properties (scattering and absorption). In short, if AOD and SSA are reasonably well quantified from other spectral regions/channels of OCI and/or MAP instruments, we anticipate workable retrieval accuracy for ALH from the O2A, at least sufficient to discriminate between low altitude (background and/or near sources) and high altitude (long-range transported) aerosol scenarios.
To rigorously quantify the retrievability of parameterized aerosol profile using O2A spectroscopy, supplemented or not with simultaneous multi-angle observations, we use
The result of these sensitivity studies shows that the top pressure, hence altitude, of an aerosol layer can be retrieved using O2A spectroscopy at the relatively modest spectral resolution (5 nm) of the OCI sensor that is planned to launch on PACE. The often-stated lower limit of ≈0.3 in AOD at 550 nm may be relaxed to ≈0.1, as long as the underlying surface is very dark, to achieve an uncertainty of ≈0.15 km on aerosol layer height.
Now the question is whether or not DOAS applied to a multi-angle instrument improves on OCI’s abilities.
Returning to
Moving on to
What happens to the above DoF assessment if the spectrometer is supplemented by a multi-angle radiometer with non-polarized O2A band channels? We assume an in-band channel as shown at the bottom of the upper left-hand panel of
The bottom line of this theoretical exploration is that PACE will be able to discriminate different altitudes of ALH, but will not be able to profile that height, even by combining OCI with multi-angle measurements.
Oxygen A-band techniques have already been adapted to other sensors currently taking data, and results validated. For example,
Scatterplots of EPIC retrieved aerosol layer height (ALH) versus CALIOP extinction-weighted ALH.
PACE offers multi-tiered opportunities for aerosol remote sensing. At the most basic level, OCI will allow continuation of heritage aerosol retrieval algorithms developed for and applied to MODIS, VIIRS and OMI. Continuation aerosol algorithms include the MODIS/VIIRS DT and Deep Blue family of algorithms and products, the OMI UV AI, the OMI UV-derived aerosol products, and new algorithms such as MODIS MAIAC. There are no foreseen barriers to porting these algorithms directly to OCI and we anticipate comparable validation results over both ocean and land surfaces. However, experience in porting aerosol retrievals from MODIS to VIIRS, or OMI to EPIC, suggest points of difficulty that will need to be addressed. Shifts in wavelengths will require new gas corrections and re-calculation of any empirical spectral relationships in the algorithm. Algorithms that depend on acquisition of statistics of surface characteristics will require time to acquire and build these new LUTs. Absence of thermal IR channels may affect cloud masking and introduce differences from heritage sensor results. Furthermore, consistency of sensor characterization will be a significant issue. Homogenizing calibration from existing sensors to new sensors, such as OCI will be necessary in order to maintain consistency across aerosol Climate Data Records. We recommend planning for collocations between OCI and other satellite sensors in the overall mission strategy. All of these recommendations are supported by first-hand experience in the transfer from MODIS to VIIRS, or in OMI to EPIC.
In addition, OCI alone offers a step up from current sensors in terms of its broad spectrum and 5 nm spectral resolution. These additional abilities show promise of obtaining new information on aerosol absorption and ALH. OCI is equivalent to merging MODIS and OMI into a single instrument and extending OMI’s spectral range into the SWIR, co-registered and with common calibration and common moderate spatial resolution. For the first time we will have UV channels available for aerosol retrievals at the same resolution as the visible-SWIR channels. Note that OMI’s spatial resolution is 13 × 24 km, and the Ozone Mapping and Profiling Suite (OMPS) is 50 km, whereas OCI’s spatial resolution is 1 km. Using the broad-spectrum retrieval we will be able to characterize aerosol absorption, layer height, AOD and a measure of particle size across the broad OCI swath, over ocean. Meanwhile, OCI’s spectral resolution of 5 nm is sufficient to resolve the signal through the oxygen-A band, and will permit independent retrievals of ALH. Independent retrievals of aerosol height from the UV and oxygen-A band offer an opportunity for redundancy. However, these advances are only possible when aerosol loading is sufficiently high, so that the aerosol signal overwhelms ocean variability. This is more important to the UV to SWIR retrieval than it is when applying O2A algorithms. We also see that oxygen-A band retrievals can be applied to multi-angle measurements that increase confidence in the OCI-alone retrieval of ALH, but still do not allow for profiling of aerosol layers.
A potential limitation to applying the UV proto-algorithm to the aerosol over global oceans is the requirement of moderate to high aerosol loading (AOD at 550 nm > 0.30). We need this requirement to be assured that the atmosphere dominates the OCI-measured signal in the UV and is not affected by variable conditions in the ocean. In testing, we found that the retrieval correctly identified input aerosol absorption 100% of the time despite perturbation to ocean spectral reflectance when AOD > 0.30, but only 56% of the time when the AOD was less. The fraction of retrievals that meet this threshold and would be available for retrieval is small, globally. According to the first 5–7 years of MODIS aerosol product over ocean, only about 10% of the 1 degree grid squares met this requirement (
Heritage and new aerosol algorithms can be developed from PACE sensor measurements for both ocean and land surfaces, and in some cases over clouds, as well. The PACE mission will be able to produce global aerosol products. However, because of the high interest in the mission’s ocean objectives, we comment now specifically on the aerosol over ocean retrieval. Heritage aerosol algorithms have served us well and “heritage plus” algorithms will continue to serve us well when applied to OCI measurements over open ocean in Case 1 waters. However, in coastal regions, where traditional assumptions of the ocean spectral signal breaks down, backscattering from suspended hydrosol particles (e.g., phytoplankton and sediment) in unanticipated spectral ranges can be misinterpreted as aerosols, leading to overestimation of AOD at low aerosol loading and interfering with new retrievals making use of the UV and hyperspectral capabilities. Furthermore, adjacency effects between water and shore also add to the complexity in near-shore regions (
There exists strong synergy between atmosphere and ocean retrievals, for PACE. Products derived from each sensor and from each discipline will provide essential information to each other’s algorithms, constraining aerosol characteristics and ocean properties for each other, either in near real time, statistically over time, or for reprocessing. The aerosol loading limitations of the above described methods prevents their direct use for atmospheric correction over most of the world’s oceans, but does not prevent exploration of more sophisticated methods that might retrieve aerosol and ocean properties together. Simultaneous retrievals of aerosol and ocean properties, or aerosol and land properties are possible. We did not deliberate on the aerosol/land retrievals, but we did consider the possibilities of aerosol/ocean retrievals. These possibilities are discussed more fully in other papers appearing in this special issue (
Currently there is a severe lack of appropriate space or field data to use as a test bed for algorithm development. Hyperspectral data to simulate OCI observations can be provided by spaceborne HICO or AVIRIS, but neither cover the full spectral range of OCI, and both sensors tend to target scenes when and where aerosol loading is minimal. High altitude hyperspectral imagery that extends into the UV, and is complemented by bands in the SWIR, similar to the OCI design will be essential for preparation of the PACE mission. Such imagery should target aerosol events and various cloudy scenes, over land and ocean, as well as oceanic targets. These images will aid PACE cloud, atmospheric correction and Inherent Optical Property algorithm development, as well as aerosol algorithms. We note that maintaining calibration of such an instrument, especially in the deep blue and UV part of the spectrum is currently challenging, with commonly used sensors such as AVIRIS declaring known issues in this spectral range. Not having reliable full spectrum test data is a serious technical gap in preparing for the PACE mission. Furthermore, such instruments are rarely deployed with airborne MAPs. Existing data in cloud free conditions for with both types of instruments are extremely rare (
The PACE mission will be an unprecedented opportunity for aerosol characterization. The combination of the broad-spectrum hyperspectral radiometer and multi-angle polarimeters, used independently or synergistically, will be able to continue heritage global aerosol remote sensing while opening up new opportunities for aerosol characterization on several levels. In addition, the PACE mission unites aerosol, cloud and ocean communities as they work towards meeting intertwined technical and research challenges. We anticipate PACE leading a surge of new understanding that crosses traditional atmosphere-ocean boundaries, leading to new science and societal benefits.
This work represents part of the overall consensus statement agreed to by the first NASA PACE Science Team and includes contributions from the Principal Investigators of the Atmospheric Correction subgroup of that Science Team, along with their associates involved with the aerosol characterization focus of that subgroup. All authors of this manuscript were part of the discussions of that subgroup and Science Team from 2014 to 2018. In addition, LR was the Deputy Lead of the Science Team and Leader of the subgroup. She organized this particular manuscript and consolidated the input from all authors. In addition she led the effort in creating the prototype broad-spectrum algorithm. SM, RL, and OT all contributed directly in obtaining the results of the heritage and broad-spectrum algorithms and writing of those sections. AD along with OK led the O2A work presented herein. KK and JC were active participants in commenting on early versions of this manuscript and helped to clarify several sections. ZA, EB, BC, OC, HD, DD, BF, RF, B-CG, AI, JM, AO, FX, and P-WZ were active participants during the years of discussion that resulted in this manuscript, and each has made contributions, edited, or commented on this manuscript.
AI was employed by Science Application International Inc. 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.
Portions of this work were carried out by OK, AD, and DD at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. JPL work was supported by the PACE science team grant, under Paula Bontempi.
Advanced Along Track Scanning Radiometer
Advanced Baseline Imager
Aerosol Robotics Network
Aerosol Index
Airborne Multiangle SpectroPolarimetric Imager
aerosol layer height
aerosol optical depth
Airborne Visible InfraRed Imaging Spectrometer
bidirectional reflection function
Cloud-Aerosol Lidar with Orthogonal Polarization
Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations
cloud condensation nuclei
Cloud Physics Lidar
differential optical absorption spectroscopy
degrees of freedom
Deep Space Climate Observatory
Dark Target
Earth Polychromatic Imaging Camera
fine mode fraction
Geostationary Operational Environmental Satellite
Hyperspectral Imager for Coastal Ocean
ice nuclei
InfraRed
Look Up Table
MODIS Collection 6
Multi-Angle Implementation of Atmospheric Correction
multi-angle imaging polarimeters
Medium Resolution Imaging Spectrometer
Multi-angle Imaging SpectroRadiometer
MODIS-Like applied to MODIS inputs
MODIS-Like applied to VIIRS inputs
Moderate Resolution Imaging Spectroradiometer
Multi-Spectral Instrument
National Aeronautics Space Administration
Near InfraRed
oxygen A-band
Ocean Color Instrument
Orbiting Carbon Observatory
Ocean and Land Color Instrument
Ozone Monitoring Instrument
Ozone Mapping and Profiling Suite
Plankton, Aerosols, Clouds, Ocean Ecosystems
Polarization and Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar
particulate matter
Polarization and Directionality of the Earth’s Reflectance
Root Mean Square Error
Sea and Land Surface Temperature Radiometer
Spectro-Polarimeter for Planetary Exploration-1
single scattering albedo
Short Wave Infra Red
Thermal And Near infrared Sensor for Carbon Observation - Cloud and Aerosol Imager
top of atmosphere
Total Ozone Mapping Spectrometer
Tropospheric Ozone Monitoring Instrument
ultraviolet
Visible Infrared Imaging Radiometer Suite.
At the time of finalizing this report, it has since been decided that OCI’s on-board spectral sampling at 2.5 nm will be used to optimize the information content of the A-band channels.