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ROSES Proposals

A Global Database of High Horizontal Resolution IOPs for Validation of Remotely Sensed Ocean Color

PI: Emmanuel Boss - School of Marine Sciences, University of Maine
The activity proposed here consists of 1. Creating a consortium of practitioners interested in standardizing collection and processing of high horizontal resolution data collected using flowthrough in-line systems, 2. Sharing associated codes, 3. Providing the community with a plan for deployment, processing and computation of estimated uncertainties associated with in-line data, and 4. Creating a uniformly processed dataset for Cal/Val activities and studies of inherent optical properties (IOP) distributions throughout the world's oceans.

Such a dataset will be unique in its global extent, being ideal for validation of remote sensing product and for algorithm development for a global mission such as PACE. Critical evaluation of the in-line IOP acquisition is necessary to assign realistic uncertainties to those IOPs.

Once processing methodology is agreed upon among the collaborators, UMaine will reprocess historical in-line data collected by the collaborators and provide them to SeaBASS with the processing algorithms and source codes for future use by the ocean color community. Efforts will be made such that data generated will have sufficient details so that alternative processing could be applied without the need to reprocess the raw data.

As part of this proposal we will use the data to answer the following SCIENCE question: What are the characteristics of sub-satellite-pixel variability in IOPs in the ocean?

The utility of the in-line dataset goes well beyond the scope of this proposal and can be used to answer other science questions directly relevant to PACE (a global hyperspectral mission), such as:
  • What are the deviations of IOPs from published bio-optical relationships and how do they vary with variables such as temperature, salinity, date, distance from land and ocean depth?
  • What information is available in hyperspectral IOPs (and hence hyperspectral ocean color) in addition to that currently obtained with spectral sensors (e.g. added pigments in addition to chlorophyll a (e.g. Chase et al., 2014), size information etc.)?
As part of this proposal we will use the data to answer the following METHODOLOGICAL questions: 1. What should we acquire as discrete samples to increase the utility of the in-line systems? 2. How do in-line data compare to in-water data collected with similar sensors at the same time (e.g. are there noticeable biases in in-line data?)? Answering these methodological questions will improve in-line collection and our estimates of uncertainties for the data collected. Uncertainties are necessary to evaluate the degree of agreement between remote and in-situ estimates of IOPs as well as biogeochemical quantities.

PI Boss also proposes himself to be the IOP Science team lead.

A Net Primary Production Algorithm for Application to PACE

PI: Toby Westberry - Oregon State University
Co-I(s): Mike Behrenfeld, Oregon State University; Jason Graff, Oregon State University
The Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) Mission will deliver the first NASA-supported ocean color satellite that is designed to study aspects of the marine carbon cycle. PACE offers unprecedented improvements in technology and sampling capabilities compared to previous missions. Heritage ocean color sensors with limited bandsets have been either proof-of-concept missions (e.g., the Coastal Zone Color Scanner) or focused on a limited set of geophysical retrievals, with pigment concentration being the singularly important climate data record. Accordingly, these sensors have been insufficient for comprehensive carbon cycle studies. The science conducted by PACE data will yield a decidedly "carbon-centric" revelation in ocean color science.

A cornerstone property in marine carbon cycle science is the rate of phytoplankton net primary production (NPP). NPP in the sunlit layer of the ocean fuels marine ecosystems, providing the source material (particulate and dissolved) for trophic transfer and export to the ocean interior. Significant effort during the satellite era (~1990 - present) has been invested in estimating NPP from remote sensing measurements, but the unique and advanced observational capabilities of the PACE Mission now offer a long-awaited opportunity to significantly reduce uncertainties in global NPP estimates and thus refine our understanding of ocean carbon cycling. The importance of this opportunity is emphasized in the very first Threshold Mission Ocean Science Question (SQ-1), "What are the standing stocks, compositions, and productivity of ocean ecosystems? How and why are they changing?" To address these science questions, the PACE Threshold Ocean Mission approach explicitly calls for, "... estimates of productivity using bio-optical models, chlorophyll fluorescence, & ancillary physical properties (e.g., SST, MLD)." (see Science Traceability Matrix in final PACE Science Definition Team Report). PACE's emphasis on carbon cycle parameters quite simply requires a launch-ready algorithm for NPP that is designed to exploit the mission's unique capabilities.

Here we propose to deliver a state-of-the-art algorithm for estimating NPP that capitalizes on the hyperspectral retrievals of the PACE Ocean Color Instrument (OCI). Our approach focuses on three targeted advances:

  • Use of directly retrieved hyperspectral estimates of phytoplankton absorption to initiate an "absorption-based" NPP model
  • Use of retrieved hyperspectral particulate backscattering to improve estimates of phytoplankton biomass through links with the particle size distribution
  • Use of hyperspectral resolution around the chlorophyll fluorescence emission region (~650-800 nm) to refine fluorescence line height (FLH) and its use in NPP modeling

A New Semi-Analytical Ocean Color Model and Inversion Algorithm for PACE

PI: Michael Twardowski - Harbor Branch Oceanographic Institute / Florida Atlantic University
Co-I(s): Timothy Moore, HBOI/FAU
With previous NASA PACE Science Team support, Twardowski and Tonizzo (2018) (TT18) developed a new radiative transfer (RT) approximation for ocean color reflectance that includes the bidirectional reflectance distribution function (BRDF) and explicitly incorporates the volume scattering function (VSF). This model, called Zaneveld-Twardowski-Tonizzo (ZTT), has demonstrated improved performance relative to current state-of-the-art models by Morel et al. (2002) (M02) and Lee et al. (2011) that are based on simple first order approximations relating reflectance to absorption and backscattering based originally on Gordon et al. (1988). TT18 demonstrated the stronger performance was due to the greater degrees of freedom in describing the RT phenomenon relative to simple first order approximations.

The ZTT model is readily amenable to inversion within the Generalized IOP (GIOP) algorithm framework as currently implemented by NASA OBPG in SeaDAS and described in Werdell et al. (2013). Biogeochemical subcomponents of absorption and backscattering can similarly be derived. In preliminary assessments, the ZTT inversion has exhibited stronger overall performance than similar implementation configurations for GIOP. There are several additional expected advantages in application of the algorithm for the future PACE mission, as the model:
  1. is natively hyperspectral (M02 is not);
  2. enables direct assimilation of VSF data from concurrent PACE polarimetry measurements to potentially reduce uncertainties in IOP retrievals;
  3. includes the backscattering ratio, a long sought-after product of ocean color remote sensing with valuable information on particle composition;
  4. is generic to any natural water;
  5. is a single analytical and invertible expression describing the RT process for all remote sensing geometries, i.e., it includes the BRDF and eliminates BRDF normalization steps;
  6. retains native RT relationships directly linked to physically meaningful terms with more degrees of freedom than simple first order approximations;
  7. allows the uncertainties for all parameters including the VSF to be readily characterized; and
  8. can be readily enhanced by tuning one or more terms rather than developing new look-up tables from complete recomputations of full RT.
Our approach will start with characterizing uncertainties for the ZTT model for all remote sensing viewing geometries. TT18 were only able to validate the model for conventional nadir viewing radiance measurements. We will also provide a new BRDF normalization algorithm for PACE, needed for reflectance intercomparisons and mapping and will work with Marco Talone and Giuseppe Zibordi to quantify uncertainties in applying the ZTT BRDF model to the AERONET data sets.

We will assess uncertainties in applying the ZTT inversion within the GIOP framework to derive IOPs for available validation data sets and compare to current state-of-the-art inversion algorithms. We will seek to enhance the performance of ZTT inversions in GIOP by 1) compiling enhanced hyperspectral libraries of subcomponent IOPs to serve as spectral shape vectors in the algorithm and 2) by implementing a more robust, quicker, and accurate error minimization algorithm based on Particle Swarm Optimization (PSO). We will configure the ZTT inversion to incorporate concurrent VSF data from PACE polarimeters and to provide the particulate backscattering ratio as a new product. A final objective will be pursuing a polarized version of the ZTT model that may be implemented with PACE.

OUR OVERALL PROJECT GOAL: to provide IOP inversion algorithms to be implemented for the PACE mission based on the natively hyperspectral ZTT model with robustly characterized uncertainties for all IOP products.

Aerosol Absorption Retrievals from Base-Line OCI Observations: Risk Reduction for Atmospheric Correction of the PACE Mission

PI: Lorraine Remer - Joint Center for Earth Systems Technology, University of Maryland Baltimore County
Aerosol absorption is a key aerosol parameter required for quantification of direct aerosol radiative forcing and has been linked to changes in atmospheric circulations and large scale precipitation patterns and to cloud macrophysical properties over large regions. Thus, a satellite aerosol product that quantifies absorbing aerosols will make a significant contribution to climate science. Absorbing aerosols also confuse atmospheric correction over oceans and interfere with determination of ocean water-leaving radiances, even at low aerosol loading. Thus, both the atmospheric and oceanic communities are united by their need to identify and quantify absorbing aerosols over the global oceans. The Pre-Aerosols-Clouds-Ecosystem (PACE) mission will attempt to push oceanographic science a full step forward by defining the Ocean Color Instrument (OCI) with hyperspectral capability from the ultraviolet (UV) to the near-infrared (NIR), and with additional wavelengths in the shortwave infrared (SWIR). We propose here that the basic PACE OCI instrument, with no enhancement, will also make a significant improved contribution to aerosol science. OCI is an exciting instrument for aerosol scientists because it will be the first truly broad spectrum U.S. instrument, effectively combining the aerosol-retrieval capabilities of MODIS and OMI, but on the same instrument, at the same spatial resolution. Sensitivity tests tell us that we should be able to retrieve 3 pieces of aerosol information from this configuration: nominally loading, absorption and either particle size or layer height. These published sensitivity tests are far from complete and they do not cover the wide range of circumstances that are required to identify optimal wavelength configurations and retrieval assumptions necessary to prepare for producing an aerosol product from OCI. Here we propose a series of theoretical studies to determine the uncertainties involved in 1) identifying absorbing aerosol at low aerosol optical depth (AOD) for the purposes of atmospheric correction, and 2) retrieving aerosol information including AOD and absorption at moderate to high AOD. Our focus is on the over ocean retrievals, where the new broad spectrum OCI offers enhanced possibilities, but this will be overlaid on a global (ocean and land) product that would represent adapting existing OMI and MODIS algorithms to OCI radiances. We are offering a perspective towards atmospheric correction that is aerosol-centered, proposed by investigators who are undisputed experts in aerosol retrieval from an OCI type of sensor. This work represents the major theoretical exploration and defining of uncertainties necessary for producing an operational product from satellite data. In addition, the PI (Remer) is volunteering to serve in a leadership capacity on the PACE Science Team.

Atmospheric Correction for Retrieval of Ocean Spectra from Space (ACROSS)

PI: Jacek Chowdhary - Columbia University and NASA Goddard Institute for Space Studies
The 2013 ROSES A.25 solicitation calls for studies on atmospheric correction in support of the PACE (Pre-Aerosol, Cloud, ocean Ecosystem) mission. To address this call, we will examine the capacity of PACE-like observations for atmospheric correction by (i) applying statistical methods to compute data information content, and (ii) inverting synthetic and real PACE-like observations into ocean spectra. In step (i), we will focus on the instrument options proposed in the PACE SDT (Science Definition Team) report. Specifically, we will consider the base instrument option OCI (Ocean Color Imager, which includes the 350, 865, 1240, 1640, and 2130 nm channels for atmospheric correction), and elements of instrument options OCI/OG (i.e. OCI base option augmented by a 820 nm band and an Oxygen A band), OCI/+ (i.e. OCI base option augmented by 940, 1378, and 2250 nm bands), OCI-3M (i.e. OCI base option plus a multi-angle multispectral multi-polarization, or 3M, imager), and OCI/A-3M (i.e. OCI/+ option plus a 3M imager). In step (ii), we will apply optimization techniques to extract ocean spectra from various data sets. Specifically, we will apply these techniques to synthetic data created for instrument options with a range of atmospheric-correction performances, as identified in step (i). In addition, we will apply these techniques to three existing measurement sets: the 2011 HOPE-COAST (Hands On Project Experience-Coastal and Ocean Airborne Science Testbed) and the 2013 OCEANIA/ACOCO (Ocean Color Ecosystem Assessments with Novel Instruments and Aircraft/Atmospheric Correction Over Coastal Oceans) measurements acquired in Monterey Bay (which include measurements of inherent optical properties and of chlorophyll), and coinciding HICO (Hyperspectral Imager for the Coastal Ocean) and CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) measurements acquired across the globe. These measurement sets incorporate aspects of the instrument options proposed for PACE and contain data to validate atmospheric correction and/or ocean color spectra retrievals.

Using statistical methods to examine the information content and inversion of ocean color remote sensing observations is a complex undertaking that requires a multidisciplinary team. Dr. Chowdhary has worked on ocean and aerosol retrievals from observations by the RSP (Research Scanning Polarimeter) instrument, which is an airborne version of the Aerosol Polarimeter Sensor (APS) onboard the failed 2011 NASA/Glory mission. He wrote a radiative transfer program for polarized underwater light, was a member of the Glory ST (Science Team) and PACE SDT, and is PI of ACOCO. Dr. Alexandrov has written cloud inversion algorithms for RSP, and was also a member of the Glory ST. Dr. Knobelspiesse has worked on inverting coincident RSP and Lidar data, has done information content analyses for different satellite instrument options in support of Glory ST and PACE SDT reports, and was a member of the Glory ST. Dr. van Diedenhoven has expertise in simulating and analyzing hyper-spectral and polarimetry data and the Oxygen A-band, as well as in information content analyses. Dr. Cairns was a member of the Glory ST and the PACE SDT, and has developed techniques for the efficient implementation of inversion algorithms. Dr. Kudela has worked extensively on ocean color retrievals and modeling of underwater light field to describe biological-physical coupling in coastal waters. He is the PI of the coastal ocean project that collaborated with OCEANIA. Dr. Guild has expertise in coastal ecosystem science, and was PI of HOPECOAST and OCEANIA. She is now leading the HQ2O (High-Quality Optical Observations: Improving Atmospheric Correction and Remote Sensing of Water Quality in the Coastal Zone) project with Dr. Kudela. Dr. Palacios has expertise in discriminating phytoplankton types from ocean color, and is processing and analyzing coastal data from the HOPE-COAST and OCEANIA campaigns for the HQ2O project.

Atmospheric Correction Over Bright Water Targets with Non-Negligible Radiances in the Near Infrared

PI: Heidi Dierssen - Marine Sciences/Geography, University of Connecticut
Many scientists working with ocean color satellite imagery are required to conduct independent or partial atmospheric correction due to high backscattering in the Near Infrared (NIR). The standard atmospheric correction algorithms typically interpret the enhanced NIR from whitecaps, coccolithophores, cyanobacteria, floating vegetation, suspended sediments, and the benthos as enhanced scattering by aerosols. This creates both omission and commission errors such that the derived water-leaving reflectance and backscattering products are decreased and the aerosol products are increased in magnitude. If the PACE mission aims to derive climate quality aerosol concentrations and ocean biogeochemistry metrics, then better methods must be developed for dealing with water with non-negligible NIR and partitioning top of the atmosphere reflectance into the appropriate streams (aerosol, whitecap, glint). Having published on a variety of bright water targets over the last 15 years, I propose to be considered for the PACE Atmospheric Correction Science Team to bridge the gap between the atmospheric and water column approaches. I currently serve on the International Ocean Colour Coordinating Group and have served on a standing committee of the Space Studies Board, as well as several strategic working groups and satellite project teams. As part of this effort, I also propose to conduct targeted field measurements to provide better estimates of elevated reflectance due to whitecaps, foam and bubbles. Modeling enhanced reflectance due to whitecaps and bubbles requires a more complex treatment than a single windspeed parameterization, which cannot capture the orders of magnitude variability between the parameters. Here, we propose to conduct local measurements of the enhancement in reflectance due to whitecaps from the ultraviolet through the short wave infrared (SWIR). Coincident measurements of bubble entrainment will be conducted, as well as host of physical and bio-optical parameters. These data will be combined with available satellite imagery to evaluate partitioning whitecaps and bubbles from various atmospheric correction schemes. Correctly estimating whitecaps could also be an important climate relevant science parameter for those studying air-sea gas exchange, generation of sea spray aerosols and potentially applicable for estimating mixed layer depth for primary productivity models. Data collected in conjunction with this field effort, and other relevant project we and others have gathered on regions prone to elevated non-negligible NIR will be compiled in an archive of coincident satellite top of the atmosphere reflectance and high quality field measurements of water leaving reflectance. Such a database can be used to evaluate approaches across a variety of challenging bright water targets where common algorithms fail.

Bayesian Methodology for Atmospheric Correction of PACE Ocean-Color Imagery

PI: Robert Frouin - Ocean Optics , Scripps Institution of Oceanography
The PACE mission will carry into space a spectrometer measuring at 5 nm resolution in the UV to NIR and at lower resolution in spectral bands in the NIR and SWIR and, eventually, a multispectral, multi-angle polarimeter measuring in the UV to SWIR. These instruments have great potential for improving estimates of marine reflectance in the post-EOS era. In view of this, the proposal objectives are as follows. The first objective is to evaluate, using the Bayesian approach to inverse problems, the gain in marine reflectance accuracy expected by 1) including observations in the UV and SWIR and 2) further including polarimetric and directional observations in selected spectral bands. This for the PACE threshold aggregate bands with respect to the standard MODIS set of bands used to generate ocean color products. The second objective is to assess, also in a Bayesian context, the utility of hyper-spectral information for improving atmospheric correction in the aggregate bands, and to quantify the accuracy of the atmospheric correction at 5 nm resolution for separating ocean constituents and characterizing phytoplankton communities. To achieve these objectives, the TOA signal measured by the PACE spectrometer and the eventual polarimeter will be simulated for a variety of realistic atmospheric and oceanic conditions. Typical prior distributions for the aerosol, water reflectance, and surface parameters, suitable for utilization at a global scale, will be used, as well as noise distributions. The noise will encapsulate all the sources of uncertainties in the radiative transfer (RT) modeling and include sensor noise. The inverse models will be constructed based on several considerations, i.e., computational cost, convenience to approximate the conditional covariance (a second order quantity), and detection of abnormal values (due to limitations of the forward model). Ways to improve performance by specifying prior distributions from independent information about regional and temporal variability (e.g., from output of numerical transport models) will be investigated, and practical implementation of the Bayesian methodology will be outlined for routine application.

The investigation will provide a Bayesian methodology for atmospheric correction of the PACE spectrometer data. The methodology makes it possible to incorporate known constraints of the marine reflectance (i.e., correlation between components) and to account for the varied sources of uncertainty (i.e., measurement noise, RT modeling errors). Importantly, it allows the construction of reliable multi-dimensional confidence domains of the retrieved marine reflectance. Specifically, the mean and covariance of the posterior distribution are computed. These quantities provide, for each pixel, an estimate of the marine reflectance and a measure of its uncertainty. Situations for which observation and forward model are incompatible are also identified. Thus the methodology will offer the means to analyze and interpret PACE ocean-color imagery in view of confidence limits and model adequacy, on a pixel-by-pixel basis.

By evaluating via theoretical studies the accuracy of the atmospheric correction of PACE ocean-color radiometry and the expected improvements with respect to current ocean color sensors, by identifying optimum sets of spectral bands, and by providing an inverse methodology adapted to the problem, which can be viewed as a generalization of the standard algorithm, the investigation responds directly to the PACE Science Team Announcement of Opportunity, which seeks methods and approaches that will maximize the new capabilities of the PACE mission for understanding global ocean ecology in a changing climate.

Cloud Products from the PACE Ocean Color Imager

PI: Kerry Meyer - Climate and Radiation Branch, Laboratory for Atmospheres, NASA Goddard Space Flight Center
Co-I(s): Steven Platnick, NASA GSFC; Odele Coddington, University of Colorado; Robert Holz, University of Wisconsin - Madison; Steven Ackerman, University of Wisconsin - Madison
The PACE mission represents the next generation of ocean remote sensing, and will also provide an opportunity to extend key aerosol and cloud property climate data records. The primary PACE instrument, the hyperspectral Ocean Color Imager (OCI), promises well calibrated, wide-swath observations of the Earth having both high spatial and 5-nm spectral resolution from 350nm to 885nm with an additional seven discrete narrowband shortwave infrared channels between 940 and 2260nm. Secondary instruments include two contributed polarimeters. Core PACE mission requirements include OCI-only retrieval products of the following key cloud properties: cloudy/clear sky discrimination (cloud masking), cloud top pressure/altitude, and cloud optical thickness and particle effective size.

We propose to develop the algorithms for the key cloud products to satisfy the OCI-only mission requirements. Because the information content for cloud optical properties is not appreciably increased by hyperspectral observations in the visible and near infrared, our retrieval algorithm for cloud optical thickness and particle effective size will follow a multi-channel approach with MODIS-VIIRS continuity cloud product (CLDPROP) heritage. Similarly, our cloud masking algorithm will also follow heritage multispectral shortwave approaches (e.g., components of the MODIS-VIIRS CLDMSK). We will develop new algorithms for cloud top pressure/altitude and thermodynamic phase because OCI lacks the thermal infrared channels that have been used in heritage algorithms to retrieve these quantities. Instead, our new algorithm approach for cloud top pressure/altitude will rely on spectral channels in and near the O2-A band and water vapor absorption bands (940, 1378nm), and our new algorithm approach for thermodynamic phase discrimination will be a combination of multiple SWIR spectral channels having differential liquid/ice absorption (1615, 2130, 2260nm). Moreover, while the focus of this proposal is on cloud products from OCI alone, we also propose to utilize the information from the contributed polarimeters in quality assurance and assessments of the OCI-only retrievals, to the extent possible (e.g., effective size, thermodynamic phase discrimination).

Deciphering Sargassum Physics, Biology, and Physiology through PACE Measurements: Implications to Ocean Ecology, Biogeochemistry, and Management Decision Support

PI: Chuanmin Hu - Optical Oceanography, University of South Florida
Co-I(s): Brian Lapointe, Florida Atlantic University; Gustavo Jorge Goni, NOAA AOML
Pelagic Sargassum macroalgae can be both an ecologically important habitat in the ocean and a nuisance on the beach. Recent efforts, mainly funded by NASA's Ocean Biology and Biogeochemistry program and Ecological Forecast program, led to the initial understanding of Sargassum biology, abundance distributions, and development of a decision-making tool to track large Sargassum mats in near real-time (see Sargassum Watch System or SaWS, https://optics.marine.usf.edu/projects/saws.html), with SaWS and its associated monthly bulletins being used extensively and routinely by many stakeholders including governmental agencies, environmental groups, private sectors, and the general public. The discovery of the great Atlantic Sargassum belt extending from the west Africa to the Gulf of Mexico, which has been recurrent since 2011, suggests a possible oceanic regime shift where recurrent Sargassum blooms may become the new normal in future years. Other macroalgae blooms have also been reported in other parts of the oceans, emphasizing the importance of studying macroalgae in general. The goal of this project is to improve our understanding of Sargassum physics, biology, and physiology in support of PACE mission goals, with the following objectives:
  1. Develop PACE-compatible algorithms to characterize Sargassum physics, including color tones, depth, areal density, and biomass density
  2. Develop PACE-compatible algorithms to understand Sargassum biology, including Sargassum pigment composition, carbon, nitrogen, phosphorous, and growth rate
  3. Understand Sargassum physiology through characterizing its Sun-induced fluorescence (SIF)
  4. Work with the NASA PACE SDS team to implement core algorithms to prepare for PACE mission
  5. Demonstrate the potentials of using PACE-analog data and PACE-compatible algorithms in improving SaWS in order to benefit the user communities
  6. Explore the possibility of using PACE-analog data to study other types of floating macroalgae (e.g., Ulva) and microalgae (e.g., Noctiluca scintillas, Trichodesmium).
The project will be conducted through field and laboratory measurements, bio-optical modeling, numerical modeling, sensitivity analysis, and algorithm development with particular emphasis on the hyperspectral capacity of PACE. Although the project is mainly focused on science, it has significant implications on management applications. Indeed, based on the currently NASA funded efforts, SaWS is at approximately Application Readiness Level (ARL) 7, with an anticipated ARL 9 by the project end (2021). The proposed work will not change the ARL of SaWS, but will add significant values to improve SaWS, thus contributing directly to the ultimate goal of NASA Applied Science. Furthermore, because of its vast distributions and abundance at scales way beyond previous knowledge, Sargassum is closely related to the Surface Biology and Geology (SBG) theme (subject: macroalgae) from the most recent decadal survey, and remote sensing study of Sargassum has significant implications to both ocean ecology and biogeochemistry (e.g., carbon and nutrient cycles). We expect the following project outcome to showcase PACE capacity in understanding the role of pelagic Sargassum macroalgae in ocean ecology and biogeochemistry:
  1. Improved understanding of Sargassum physics, biology, and physiology
  2. PACE-compatible algorithms to characterize Sargassum physics, biology, and physiology, which can be implemented by NASA SDS and improved upon the launch of PACE. The primary product will be Sargassum biomass density, while the second product will be Sargassum growth rate and/or fluorescence efficiency.
  3. Demonstration of PACE-unique products to enhance SaWS
  4. Technical reports, publications, and a sustained system to facilitate the use of NASA PACE data and other data to help make management and research decisions.

Derivation of Inherent Optical Properties from Satellite Top of Atmosphere Measurements in Optically Complex Waters

PI: Susanne Craig - Universities Space Research Association, NASA Goddard Space Flight Center
The inherent optical properties (IOPs) of a water body can serve as robust proxies for many important ecological and biogeochemical processes that are of fundamental importance to the Earth system. IOPs can be derived from measurements of satellite ocean color, and many successful derivation methods now exist, and provide a powerful means of synoptically monitoring these processes and their response to a changing climate. However, in waters such as the coastal ocean and inland water bodies, accurate retrieval of IOPs is often hampered by factors including difficulties in removing the contributions of the atmosphere from the satellite signal, and poor performance of standard ocean color algorithms due to the complex relationships amongst the water constituents.

The objective of this project, therefore, is to develop an approach to derive accurate estimates of IOPs from top of atmosphere (TOA) satellite radiance, thereby bypassing the difficulties often associated with atmospheric correction procedures. This is of particular relevance to coastal and inland water bodies where retrieval of robust ocean color products is notoriously challenging, and is frequently hampered by difficulties in achieving accurate atmospheric correction. The approach may be used for all waters, but most importantly, offers a means to accurately estimate IOPs from ocean color in scenarios where it may otherwise not be possible.

The proposed objectives will be achieved using an approach already proven for both in situ hyperspectral and satellite multispectral measurements. Using a combination of existing satellite and aircraft hyperspectral ocean color measurements and a custom generated TOA synthetic dataset, we will perform rigorous statistical model evaluation, sensitivity analyses to investigate variable oceanic and atmospheric effects on model skill, and finally, will develop operational implementation strategies. These activities will allow the model to be fully developed for hyperspectral TOA applications and a determination of the best model type - regional, water type or global. The end product will be a set of methodologies to provide an accurate means of deriving hyperspectral IOPs in the most challenging scenarios and will represent a significant advance in our ability to fully exploit remote sensing of the planets most important and vulnerable water bodies. The proposed research directly addresses the requirements of the PACE mission to achieve accurate hyperspectral IOP estimates, and insight into the processes for which they are proxies, in critical coastal ocean and inland water bodies - areas particularly susceptible to the impacts of climate change and anthropogenic perturbation. This is entirely in keeping with the broader NASA Earth Science Research Program to acquire new insights into the Earth system.

Developing a PACE Hyperspectral Bio-Optical Algorithm Framework for Detection of Freshwater Harmful Algal Blooms

PI: Robert Shuchman - Michigan Tech Research Institute
Co-I(s): Michael Sayers, Michigan Tech Research Institute, Michigan Technological University; Gary Fahnenstiel, Michigan Tech Research Institute, Michigan Technological University; Timothy Moore, Harbor Branch Oceanographic Institute, Florida Atlantic University
We propose to develop bio-optical algorithms and products using hyperspectral data under program element 2.4 to generate an improved application for the detection of freshwater cyanobacteria that are often toxic, belonging to a larger family of algal conditions referred to as harmful algal blooms (HABs). We propose to develop a new detection application for freshwater cyanobacteria HABs (CHABs) by integrating information from several different approaches, some of which expand upon current operational algorithms but which are not based on hyperspectral data. We will produce a suite of advanced bio-optical products, including from semi-analytic algorithms (SAAs). We believe that hyperspectral remote sensing will improve the ability of water quality algorithms to quantify CHABs. For the SAAs, we propose to develop separate products based on optical model inversion incorporating cyanobacteria properties. Cyanobacteria have unique absorption and scattering properties, which make them difficult to invert using standard SAAs models, but our approach will use these characteristics to create a unique identification of cyanobacteria populations, among other phytoplankton groups that are typically also present before, during and after CHAB events. Toxicity is often highly variable within CHABs is not directly detectable with optical methods, however, we note that CHAB surface scums, which we will detect, are often toxic. The main application question we are addressing is: can we improve CHAB detection and assessment including early stages with hyperspectral data? We believe that a combination of approaches based on hyperspectral data will provide necessary discrimination to monitor CHAB populations, as well as other phytoplankton groups with many applications to freshwater quality. Thus, our ultimate application product will be a comprehensive CHAB assessment that includes confidence levels tied to bio-optical algorithm uncertainties. Our specific objectives are to:
Develop new adaptive CHAB indices based on present approaches but modified by dynamically selecting optimal bands per pixel depending on locations of spectral features expanding algorithm sensitivity and dynamic range.
Develop novel adaptive semi-analytic bio-optical models that use spectral libraries for inherent optical properties that themselves are associated with different algal groups and physiological conditions.
Develop a CHAB detection product application with confidence levels based on the combination of algorithm outputs and criteria established from objectives 1 and 2. Generate demonstration products from the proposed scheme using PACE analog hyperspectral in situ and remote sensing data over the case study areas.
We will develop and test this hyperspectral CHAB detection framework with a large in situ optical database collected in Lake Erie, Saginaw Bay and Green Bay – three areas of the Great Lakes that experience annual CHAB events. This new framework will improve the quality of information obtained from satellites, such as that being disseminated through the HAB Tracker by NOAA's Great Lakes Environmental Research Laboratories (GLERL). The optical database contains hyperspectral measurements previously collected in the study region by the project team, and from NOAA GLERL's operational field monitoring programs that include routine water sampling, cell counts, optical profiles and radiometry. We will apply the new algorithms to hyperspectral imagery collected from aircraft and HICO, which had a dedicated Lake Erie sensing mission. The available data over the proposed study period are not costed to this proposal. We believe this is one of the most complete bio-optical data sets available in the world for a freshwater lake system, collected in an area with recurring CHABs. This site and data set is very compelling for developing and testing hyperspectral algorithms for CHABs, and we believe this proposed work is very responsive to element 2.4.

Development of Datasets and Algorithms for Hyperspectral IOP Products from the PACE Ocean Color Measurements

PI: ZhongPing Lee - School for the Environment, University of Massachusetts, Boston
Inherent optical properties (IOPs) play a key role in modulating the color of oceanic and coastal waters, and provide the critical link to infer the concentrations of constituents in the upper water column. In the recent decade, various algorithms, both empirical and semi-analytical, have been developed for the retrieval of IOPs from ocean color, which is measured by the spectrum of remote-sensing reflectance (Rrs, sr-1). These algorithms, in particular the algebraic algorithm (QAA) and the spectral optimization algorithms (e.g., GSM, GIOP), have been implemented to retrieve various IOPs from Rrs measured by SeaWiFS and MODIS, thus providing prototype IOP products at a few bands for the global oceans. The quality of these products, however, depends on the validity of the spectral shapes of the IOPs (SSIOP) used in these semi-analytical algorithms, but the determination of the SSIOP from remote sensing is far from mature. More importantly, the PACE mission will provide hyperspectral Rrs of the global oceans, thus the derivation of hyperspectral IOP products will demand accurate estimation of hyperspectral SSIOP. The improvement of the SSIOP estimation and the determination of hyperspectral IOP algorithms for PACE will depend critically on a robust hyperspectral Rrs-IOPs dataset, but there is no such a dataset yet for the community to use. To fill this void, with an ultimate goal to maximize the IOP products from the PACE hyperspectral measurements, we propose to 1) compile a hyperspectral Rrs-IOPs dataset from field measurements; 2) improve the estimation of SSIOP from ocean color; 3) revise the QAA and HOPE (a hyperspectral optimization algorithm) to take advantage of the hyperspectral and UV measurements offered by PACE, with a goal to expand the current IOP products to include information beyond chlorophyll-a (e.g., the absorption coefficients of chlorophyll-b,-c, and phycocyanin); and 4) test and evaluate these semi-analytical algorithms with HICO measurements. Outcomes from this effort will be fourfold: 1) a hyperspectral Rrs-IOPs dataset with closure for the community to use, 2) improved estimation of SSIOP from ocean color to benefit all semi-analytical algorithms, 3) revised QAA and HOPE to derive hyperspectral IOP products, and 4) experience with HICO in processing and storing hyperspectral image products. These results will provide desired tools and knowledge for processing hyperspectral measurements by PACE, and contribute to "consensus and communityendorsed paths forward for the PACE sensor(s).

Development of Robust Spectral Derivative Algorithms for Phytoplankton Pigment Concentrations on Local to Global Scales

PI: David Siegel - Department of Geography, UC Santa Barbara
Co-I(s): Stéphone Maritorena, Earth Research Institute, UC Santa Barbara
The Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission will provide unprecedented high spectral resolution ocean color observations with the potential to discover new insights into phytoplankton community dynamics on local to global scales. However, for many reasons, satellite ocean color algorithms designed to take advantage of hyperspectral data have lagged behind the development and application of multispectral algorithms. Here we propose to further develop and validate spectral derivative methods for quantifying the concentrations of marker phytoplankton pigments on local and on global scales, extending the approach of Catlett and Siegel [2018; JGR]. Spectral derivative methods have the advantage that they are, to some degree, insensitive to large spectral scale (>>100 nm) signals associated with issues of instrument calibration and atmospheric correction, as well as colored dissolved organic matter and detrital ocean optical properties. On the other hand, spectral phytoplankton absorption and backscatter features are found on spectral scales of ~10 to 100 nm. Spectral derivative methods have the potential to take advantage of this spectral gap in ocean optical properties so that concentrations of phytoplankton biomarker pigments can be accurately quantified.

Our goal is to develop and validate robust spectral derivative algorithms for phytoplankton biomarker pigments for NASA's upcoming PACE mission. We will develop these models using two data sets already in hand: 1) a synthesis of global scale observations data mined from the data repositories and the literature and 2) local scale observations from the Plumes and Blooms (PnB) program from the coastal waters of the Santa Barbara Channel, California. Statistical analyses of high performance liquid chromatography (HPLC) phytoplankton pigment concentrations show both similarities and differences in the groupings of pigments (cf., diatoms, picoplankton, etc.) derived for the two data sets due to the large degree of covariation among the pigments and the scales over which the data are assembled (which relates to the uniqueness of their information content). This suggests that there should be differences in global and local biomarker pigment algorithms created from these two data sets. Further, available hyperspectral observations come from a wide range of sources, sites, sampling protocols, analytical methods, and uncertainties. Thus, care needs to be taken in merging and applying these data to build useful and robust spectral derivative ocean color algorithms for biomarker pigment concentrations and their uncertainties.

Our work will contribute to the advancement of ocean color sciences beyond the development of robust, pre-launch phytoplankton biomarker pigment concentration algorithms for the PACE mission. First, we will create procedures for synthesizing hyperspectral field data that will come from a variety of data sources of varying quality. These protocols should be of great value to the PACE Science Team. Second, our approach will develop spectral derivative methods for both remote sensing reflectance and phytoplankton absorption, providing a test of the validity and a demonstration of the potential of the phytoplankton spectral gap hypothesis. Last, the simultaneous development of ocean color algorithms from the two data sets (global vs. local) will contribute to our understanding of how to maximize the information retrieved from satellite ocean color algorithms when switching one's focus from global to local scales.

Evaluation of UV Atmospheric Correction in the Presence of Absorbing Aerosols, and Quantification of Enhancements Provided by Multiangle, Polarimetric and Oxygen A-Band Observations

PI: Olga Kalashnikova - Earth Science Division, NASA Jet Propulsion Laboratory
Satellite remote sensing of ocean color is an invaluable tool for assessing the productivity of marine ecosystems and monitoring changes resulting from climatic or environmental influences. Yet water-leaving radiance comprises less than 10% of the signal measured from space, making correction for absorption and scattering by the intervening atmosphere imperative. Traditional ocean color algorithms are based on a standard set of aerosol models and the assumption of negligible water-leaving radiance in the nearinfrared. Modern improvements have been developed to handle absorbing aerosols such as urban particulates in coastal areas and transported desert dust over the open ocean, where ocean fertilization can impact biological productivity at the base of the marine food chain. Even so, imperfect knowledge of the absorbing aerosol optical properties or height distribution results in well-documented sources of error. At short wavelengths, where PACE spectrometry intends to improve the separation of chlorophyll from CDOM as well as quantify different phytosynthetic pigments contributing to light absorption spectra, these problems are amplified due to the increased Rayleigh and aerosol optical depth, especially at off-nadir view angles. This proposal is to the Atmospheric Correction category of the PACE Science Team. Through sensitivity studies and simulated retrievals employing both Mie and nonspherical particle scattering codes in conjunction with a vector Markov Chain radiative transfer code, we will quantitatively evaluate the relative merits of various measurement modalities for meeting the PACE Science Definition Team uncertainty requirements of max (5%, 0.001) in water-leaving reflectance in the visible and max (10%, 0.002) in the near-UV. In particular we will quantify water leaving radiance measurement uncertainty in the presence of absorbing aerosols from ultraviolet observations at single view angles representative for the PACE ocean color spectrometer. Then we investigate the added value of observations from (a) multiangle UV radiometry, (b) multiangle visible photopolarimetry, and (c) oxygen A-band for simultaneous characterization of absorbing aerosol microphysical properties, effective altitude, and non-zero water-leaving radiance. Bio-optical models will be used to characterize surface bidirectional reflectances. Theoretical sensitivities will be then evaluated against AirMSPI observations at AERONET-OC UC SeaPrism site collected during PODEX, SEAC4RS, and HyspIRI campaigns. Measurements by TOMS, OMI, and JPL's airborne sensor AirMSPI demonstrate the importance of UV observations for detecting absorbing aerosols. Theoretically, multiangle UV radiometry, blue wavelength polarimetry, and narrowband (~5 nm) oxygen A-band measurements have the potential to estimate aerosol height. Our experience with MISR demonstrates the ability of multiangular radiances to distinguish dust from other airborne particles, and shows the value of such observations for separating aerosol and surface scattering over non-black ocean waters. Polarimetry offers additional constraints on aerosol size distribution and real refractive index. Drawing upon our expertise in aerosol remote sensing instrumentation and associated aerosol and surface retrieval algorithm development for MISR, AirMSPI, and AirMSPI- 2, we will refine the requirements for a PACE imager with multiangular, UV-shortwave infrared, A-band, and polarimetric sensing capability (the polarimeter), assess the practicality of the required observations, and quantify the added value of imaging polarimeter to the PACE ocean color spectrometer in compensating for the effects of absorbing aerosols.

Going Beyond Chlorophyll-A: Developing Phytoplankton Community Composition Algorithms from Hyperspectral Remote Sensing Reflectances

PI: Peter Gaube - Applied Physics Laboratory, University of Washington
Current remote sensing capabilities have allowed us to estimate the global biomass of plankton (Behrenfeld et al., 2005), how these stocks are changing (Behrenfeld et al., 2006), and the influ- ence that mesoscale eddies and meanders have on the distribution of phytoplankton (Chelton et al., 2011; Gaube et al., 2014) in the world's ocean, amongst a multitude of other valuable findings. Some efforts have been made to parse the reflectance signatures detected by existing satellite-based radiometers to try to identify the dominant phytoplankton groups present (e.g., Alvain et al., 2005, 2008; Hirata et al., 2011). These early estimates, however, are limited in the amount of information they can provide by the multispectral nature of the data used in analysis. The upcoming Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission will put into orbit a hyperspectral radiometer, the Ocean Color Instrument (OCI), that will span the ultraviolet to near- infrared region of the electromagnetic spectrum at 5 nm resolution, thus providing information on spectral features in reflectance signatures that result, in part, from the variable light absorption and scattering by different phytoplankton groups, which are often collectively referred to as phytoplankton community composition (PCC). We propose to further refine methodologies to derive the concentration of various phytoplankton pigments using hyperspectral observations and to link these to PCC, thus developing a validated and robust PCC algorithm to be applied to hyperspectral measurements from PACE OCI reflectances.

The proposed project will address the following objectives:
  1. Refine hyperspectral pigment identification algorithm for application to PACE OCI measurements
  2. Use high-throughput microscopy collected concurrently with hyperspectral optical measurements to develop algorithms for phytoplankton community composition (PCC) detection
  3. Define algorithm product uncertainties
The anticipated result of the work proposed here is a set of algorithms that will provide validated estimates of the phytoplankton pigments Chlorophyll-a, b, and c along with photoprotective and photosynthetic carotenoids. This pigment algorithm will be the base of the method by which we will estimate PCC using hyperspectral data from PACE along with physical parameters that can be estimated from satellite data and ocean data- assimilating models. Both the predictive capabilities and uncertainties for the estimation of several major phytoplankton groups along with pigments will be quantified in the proposed work. The resulting algorithms will have quantified uncertainties and will allow for a pixel-by-pixel analysis of PCC using PACE hyperspectral reflectance data.

How Useful Will the PACE UV Bands be for IOP Retrievals and Atmospheric Correction?

PI: Stephane Maritorena - Earth Research Institute, University of California, Santa Barbara
Several prospective ocean color sensors such as PACE will have spectral bands in the UV in addition to those in the visible and those designed for atmospheric correction in the NIR and SWIR regions. The expected usefulness of the UV bands for ocean color sensors is two-fold: 1) they should allow a better discrimination between phytoplankton and CDOM -through their inherent optical properties, IOPS-in the ocean and 2) they can help in the atmospheric correction when absorbing aerosols are present. They PACE UV bands are expected to help mostly in coastal and turbid waters where both high amounts of CDOM and the presence of absorbing aerosols are frequent. Because both CDOM and absorbing aerosol show increased absorption toward short wavelengths, confounding effects may limit the ability of the UV bands to discern the role of CDOM and aerosols in the remote sensing signal. Here, we propose to test the use of the PACE UV bands for both IOP retrievals and atmospheric correction. We will test the performance of a semi-analytic ocean color algorithm (an upgraded version of the GSM model) for the retrieval of IOPs using available in situ data that cover the UV and visible domains. Using simulated data, we will also test how perturbations in the NIR and SWIR atmospheric bands affect the spectral IOP retrievals (from UV to the green wavelengths). Last, we will test if the UV bands can be used to better constrain the aerosol path radiance and improve atmospheric correction. Some of these analyses will also be considered with the HICO data.

Hyperspectral Algorithms for PACE OCI Water Leaving Reflectances and UV Penetration Depths

PI: Nick Krotkov - Atmospheric Chemistry and Dynamics, Earth Sciences Division, NASA Goddard Space Flight Center
Co-I(s): Patricia Castellanos, NASA GSFC, Earth Sciences Division; Joanna Joiner, NASA GSFC, Earth Sciences Division; Omar Torres, NASA GSFC, Earth Sciences Division; Alexander Vasilkov, SSAI; Zachary Fasnacht, SSAI; Jungbin Mok, ESSIC/UMD
As compared with heritage and current ocean color imagers, the PACE OCI spectrometer will additionally measure hyperspectral reflectances in the ultraviolet (UV-A) to help characterize UV-absorbing aerosols (e.g., “Brown Carbon”), as well as phytoplankton composition and harmful algal blooms. Our team will apply multi-year experience producing operational aerosol, cloud and trace gas (NO2, ozone) products from the hyperspectral UV-Vis Ozone Monitoring Instrument (OMI) on EOS Aura satellite (2004- current) to develop and evaluate a novel OCI algorithms for the hyperspectral water leaving reflectances (WLRs) from 350 to 500nm and UV biological action spectra weighted penetration depths for different biological processes.

Our innovative hyperspectral WLR algorithm is based on the state-of-the-art atmospheric-ocean Vector Linearized Radiative Transfer Model (VLIDORT), which analytically calculates Jacobians with respect to the atmospheric (vertically resolved aerosols, trace gases) and ocean (wind speed, WLRs) parameters. To explicitly account for aerosols, we propose to use spatiotemporally resolved 3D aerosol optical properties (including newly developed BrC parameterization) from the NASA's Global Modeling and Assimilation Office (GMAO) analyses and forecasts combined with the on-line VLIDORT computations that account for polarization of multiple scattered radiation and anisotropic reflection of solar light from ocean water and rough surface accounting for wind speed and direction.

Quantifying the penetration of UV solar radiation into the ocean is important for the study of ocean biology, including evaluation of possible damage of UV radiation to the photosynthetic apparatus of phytoplankton, and biogeochemistry, e.g. evaluation of carbonyl sulfide production in the ocean. Our semi-analytical UV-visible spectral inherent optical property model previously developed for the heritage ozone (TOMS) and ocean color (SeaWiFS) measurements will be adapted for calculation of the biological action spectra-weighted UV penetration depths using VLIDORT calculation of the downwelling spectral irradiance at the ocean surface and GMAO stratospheric ozone fields.

We will demonstrate proposed algorithms and estimate uncertainties using satellite measured top-of-the-atmosphere reflectance spectra (Level 1B) from the current LEO UV-Vis spectrometers (EOS Aura/OMI and EU/ESA Copernicus Sentinel 5 Precursor /TROPOMI) an upcoming GEO spectrometer (GEMS).

We will compare OMI-retrieved WLRs with our semi-analytical spectral inherent optical property model in the UVA wavelengths and with the traditional ocean color retrievals from MODIS and SeaWiFS at the visible wavelengths.

Hyperspectral and Multispectral Atmospheric Correction Algorithms for Supporting the NASA PACE Mission

PI: Bo-Cai Gao - Remote Sensing Division, Naval Research Laboratory
At the core of the PACE mission is an advanced optical instrument, the Ocean Color Imager (OCI), designed to provide hyperspectral ultra violet (UV), to visible (VIS) and near-infrared (NIR) and multi-spectral short-wave infrared (SWIR 1.0 - 2.5 micron) observations of the earth ecosystems. We propose to join the PACE Atmospheric Correction team, to work together with other team members in developing hyperspectral and multispectral atmospheric correction algorithms to retrieve water leaving reflectances from OCI radiance measurements. We have extensive experience developing atmospheric correction algorithms. In early 1990s, we developed the first model-based land version of hyperspectral atmospheric correction algorithm (nicknamed ATREM) (Gao et al., RSE, 1993) to support the NASA HIRIS (High Resolution Imaging Spectrometer) Project. In late 1990s, we developed an ocean version of hyperspectral atmospheric correction algorithm for the Navy (Gao et al., Applied Optics, 2000), which was based on Robert Fraser's formulation (Fraser et al., JGR, 1997). In early 2000s, with funding support from the NASA SIMBIOS Project, we modified the ocean version of the hyperspectral atmospheric correction algorithm, and developed a MODIS version of multi-channel algorithm for remote sensing of water leaving reflectances over turbid coastal waters (Gao et al., IEEE TGRS, 2007) from a combination of MODIS land and ocean channels. A SWIR spectrum-matching technique using MODIS channels centered at 1.24, 1.64, and 2.13 micron was used to estimate aerosol models and optical depths. More recently we developed a VIIRS version of coastal water atmospheric correction algorithm. The VIIRS channels centered at 1.24, 1.61, and 2.25 micron with proper modeling of atmospheric CO2 and CH4 absorption effects and a SWIR spectrum-matching technique were used for atmospheric corrections. Over the past 6 years, we have supported the HICO (Hyperspectral Imager for Coastal Ocean) Project. We developed the L1B software for converting raw digital numbers to L1B radiances with proper consideration for instrument artifacts, such as spectral smear and second order light. We developed spectrum-matching algorithms for refining HICO wavelength calibrations and for monitoring the stability of the HICO instrument with time. We developed a functional version of atmospheric correction algorithm for processing HICO data. Here we propose to use our experience in hyperspectral and multi-channel algorithm development and in analysis of AVIRIS, MODIS, VIIRS, and HICO data, to help the development of atmospheric correction algorithms for processing PACE OCI data, and support the spectral and radiometric calibrations of the hyperspectral portion of the OCI instrument. We would work together with other PACE atmospheric correction team members for the design and implementation of a consensus OCI atmospheric correction algorithm.

Hyperspectral Retrieval of Stratification in Aquatic Systems (HyperStrata)

PI: Daniel Odermatt - Remote Sensing Group, Swiss Federal Institute of Aquatic Science and Technology
Co-I(s): Dr. Alexander Damm, University of Zurich; Dr. Camille Minaudo, Swiss Federal Institute of Technology
Over the last two decades, water quality remote sensing has matured to facilitate operational monitoring of anthropogenic impact on inland waters. In doing so, optical measurements of reflected sunlight are acquired and interpreted. This upwelling signal consists of contributions from different depths within the illuminated layer. But vertical variations in water quality add significantly to the complexity of water-leaving signals, making their interpretation based on today’s satellite observations an ill-posed task. The OCI sensor on-board the PACE satellite will sample the colors of reflected sunlight in more detail than current satellites, which opens new opportunities for signal interpretation. The main goal of the project HyperStrata is to account for vertical variations in water quality via data acquired by OCI and improved retrieval methods. The second goal is to support these retrieval methods by continuously measuring and modeling the physical and biogeochemical processes that cause vertical gradients in water quality.

Our test site is Lake Geneva on the border of France and Switzerland, which is about 580 km2 in area and the largest lake in Western Europe. It stratifies every year between May and October. Visibility in Lake Geneva varies between 5 and 20 meters, and is often larger than the stratification depth. It therefore constitutes an appropriate target for investigating vertical variations as seen by optical satellite sensors like OCI.

In order to prepare a novel retrieval method in time for the launch of PACE in 2022, we rely on the combined use of three independent data sources available for Lake Geneva:
  • OCI-analog data acquired by the airborne imaging spectrometer AVIRIS NG
  • Automated in situ measurements taken on the LéXPLORE research platform
  • Operational hydrodynamic simulations and forecasting on Meteolakes
In the scope of HyperStrata, we will link physical stratification obtained from hydrodynamic simulations to optical remote sensing signals, and identify sensitive wavelength domains that become accessible due to the improved spectral resolution of OCI. Different mathematical approaches will be evaluated to facilitate the inverse application of these principles, namely to obtain stratification properties from remotely sensed optical signals. The best performing approach will be implemented, validated and made accessible to other researchers.

Improved Satellite Ocean Color Retrievals of Ocean Inherent Optical Properties and Biogeochemical Properties Utilizing the Capabilities of PACE

PI: Greg Mitchell - Photobiology Group, Scripps Institution of Oceanography
To support algorithm development for PACE, we propose to use a globally diverse and detailed optical and biogeochemical data set to develop new parameterizations of absorption and scattering, to advance our understanding of the scattering phase function, and to assess closure of forward and inverse model predictions compared to measured optical and biogeochemical variables. Our data spans the range of spectral relevance for PACE for ocean retrievals (350-750 nm) and includes inherent and apparent optical properties (IOP, AOP) and the most fundamental biogeochemical parameters of interest for remote sensing of ocean ecology. Contemporary satellite retrievals of several IOP variables (phytoplankton absorption, the sum of detrital and correlated spectral channels on orbit. These limitations restrict retrievals of IOPs at few wavelengths and limit our ability to accurately estimate chlorophyll a, particulate organic carbon, and adg, defined as the sum of soluble absorption and detrital particle absorption (CHLA, POC and adg). Using our global data set, we evaluate in this proposal the performance of current empirical and inverse algorithms demonstrating important limitations that can be greatly improved by the work we propose. Combining our uniquely detailed and global data set with UV-Vis numerical modeling in Hydrolight we propose to develop new parameterizations of relationships between the most important ecological variables that govern upper ocean IOP and AOP over the extended spectral range of PACE (350-750 nm). Due to the improved spectral range and resolution of PACE, we envision an ability to broaden the number of biogeochemical constituents to also include UV-absorbing mycosporine amino acids (MAA), phycobiliproteins (PBP) and particle size distribution (PSD ) that are needed to specify phytoplankton functional groups and plankton ecosystem structure. PSD data will be based on our global observations of Coulter Counter size distributions and flow cytometer (FCYT) analysis. FCYT data will provide important details of phytoplankton functional groups and size distributions, 2 µm. We will analyze our liquid nitrogen archived samples for FCYT, MAA and PBP collected over the past 15 years and integrate these new analytical results to our uniquely detailed and global data base to allow us to pursue our proposed goals. Our goal will be to develop forward models of IOP and AOP as governed by CHLA, POC, adg, MAA, PBP and PSD. We will implement a system of optimization combining Hydrolight code extended to the UV, and our large, detailed and globally distributed measurements, to develop forward radiative transfer models dependent on the expanded set of biogeochemical variables. We will use our optimized data to create synthesized ocean spectral reflectance 350-750 nm that will be combined with our expanded set of biogeochemical observations to develop novel inverse algorithms for PACE (MAA, PBP, Nd), spectral values of IOPs, and more robust algorithms for the heritage retrievals (CHLA, POC, adg). We will also utilize our hyperspectral global ocean phytoplankton absorption, HPLC pigments, MAA, PBP and Nd data to develop improved estimates of phytoplankton functional groups. As we did for SeaWiFS (SeaBAM; O'Reilly et al. 1998), as participants in the PACE Science Team we will evaluate our data, models and satellite algorithms in a collaborative way to contribute to the community goal of robust consensus algorithms for a dramatically expanded set of biogeochemical variables that will be enabled by the capabilities of PACE.

Improving IOP Measurement Uncertainties for PACE Ocean Color Remote Sensing Applications

PI: James Sullivan - Harbor Branch Oceanographic Institute of Florida Atlantic University
A goal of the PACE Science Team is to achieve consensus and develop community-endorsed paths for measurement suites required for the PACE mission. This proposed work will specifically address the inherent optical properties of absorption and backscattering, better quantifying uncertainties using current and emerging methods, and improving uncertainties in both reprocessed historical data and future data collections for the PACE mission. Accurate values of absorption and backscattering, and estimates of their uncertainties, are critical for remote sensing validation and development/refinement of retrieval algorithms. However, one aspect of each property stands out as an enduring source of uncertainty. For absorption measurements in particle fields, this aspect is the scattering error associated with reflective tube absorption technologies (as in the widely used WET Labs ac devices). Recent testing in our labs has shown that the different schemes used to correct this error in virtually all of the ac device data submitted to SeaBASS over the last 20 years has significant errors (10% or >). At this time, these errors are acknowledged (e.g. Leymarie et al. 2010; McKee et al. 2013), but there is no consensus on a recommended protocol for correcting scattering errors with the best accuracy possible. For backscattering, the largest area of uncertainty in clear ocean waters is the backscattering contribution from the pure seawater itself, which can comprise 80-90% of total backscattering for great swaths of the ocean. Recently, Zhang et al. (2009) revised the theory to describe pure seawater scattering as a function of the physical properties of water (i.e., temperature, salinity, pressure), but a single critical physical constant of pure water used in these calculations remains poorly known: the depolarization ratio, which is the ratio of horizontally polarized light to vertically polarized light in the scattered beam at 90°. The value the in situ optics community is currently using comes from a single study conducted nearly 40 years ago (Farinato & Rowell 1976). In that work, 3 experimental values were actually derived: 0.051, 0.045, and 0.039, each with different viewing optics. Currently, the lowest one measured, 0.039 is usually recommended because of the difficulties with stray light contamination possibly elevating their other experimental values. For decades, the remote sensing community has typically used the pure seawater scattering values of Morel (1974), which followed a different theoretical approach, with a recommended depolarization value of 0.09 (more than 100% higher than our current best guess), based on the state of knowledge at the time. Thus, one is left with virtually no confidence in the accuracy of this parameter, only a gut feeling that we are in the ballpark. We should note here that even relatively modest uncertainties in the depolarization ratio (e.g. 10%) could translate to large uncertainties in open ocean particulate backscattering retrievals (e.g. 40%), due to the fact that the subtracted pure seawater component can be 80-90% of the entire water-leaving signal. We propose to conduct comprehensive historical data analyses to fully characterize uncertainties in the scattering error for reflective tube absorption meters, and to determine and validate optimal scattering correction methods through minor lab experimentation for different deployment configurations and suites of ancillary measurements. The impacts of using different depolarization ratio values for the determination of pure water backscattering on uncertainties in ocean color retrieval algorithms will also be investigated. We further propose to use modern, specialized, bench top volume scattering function equipment and water purification methods in our lab to determine and validate with rigorous uncertainty the precise value of the depolarization ratio to improve the accuracy of retrievals for historical data and the future PACE mission.

Improving Retrieval of IOPs from Ocean Color Remote Sensing Through Explicit Consideration of the Volume Scattering Function

PI: Michael Twardowski - Harbor Branch Oceanographic Institute / Florida Atlantic University
Radiative transfer (RT) approximations form the basis of semi-analytical (SA) inversion algorithms formulated to derive IOPs and subsequently biogeochemical parameters from ocean color remote sensing. Leading SA inversion algorithms, however, have not been rigorously assessed with respect to particulate volume scattering function (VSF) variability in the ocean, as, up until very recently, the comprehensive VSF data sets required for such an assessment have not been available. It is generally recognized by the community that there is likely no larger source of uncertainty in current SA algorithms to derive IOPs than the uncertainty associated with variability in VSF shapes, as the other important parameters in the inversions have been rigorously assessed (Loisel et al. 2001; Morel et al. 2002; Gordon 2002). Through field work since 2005 supported by NASA and other sources, we now have extensive in situ IOP data sets from 18 locations that contain fully resolved VSFs in a wide range of Case I and Case II waters throughout the world (Sullivan and Twardowski 2009; Czerski et al. 2011; You et al. 2011; Twardowski et al. 2012; Gilerson et al. 2013; Randolph et al., in press). Included in these data sets are NASA ocean color validation experiments with concurrently collected IOP (including VSF) and radiometric measurements, where the technological assets employed, scope of the measurements, and attention to accuracy make these data sets special and unique. Not only are they some of the few nominally complete data sets from an RT closure perspective that we are aware of (as they include full VSFs and full radiance distributions), but the data quality is the highest possible that can be achieved at this time. With such measurements in hand, we have an exciting opportunity to evaluate the effects of varying VSF shape on retrieval uncertainties for the leading SA inversions, and to assess performance for a variety of specific environmental conditions. There is also the opportunity to reevaluate RT approximations such as a Zaneveld (1995) model that explicitly includes VSF shape information, as these models have generally been avoided due to the historical lack of representative VSF data. The specific goals of this work are to 1) assess the full range of variability in VSF shapes in the ocean using an extensive data base of custom VSF measurements collected by our lab, 2) evaluate uncertainties in leading SA inversion approaches associated with this natural VSF variability through RT modeling, and 3) to resuscitate, rework, and evaluate uncertainties in native, analytical RT approximations developed by Zaneveld (1995) and Jerlov (1976) that have received little attention to date specifically because of the fact that they included explicit VSF formulations that could not be practically applied in the past. Our approach will first involve computing remote sensing reflectances from our extensive data sets using the Hydrolight RT solution. Reflectances will then be validated using concurrently measured radiometry data to assess convolved errors in the measurements (separate from uncertainties related to inversion approximations), and to ensure the data sets are sound from a theoretical standpoint. The SA inversion algorithms will then be applied, with the retrieved absorption and backscattering compared to the original measurements to quantify uncertainties. Our overall goal is to quantify and minimize errors in inversion results when applied to a representative range of observed VSFs in the ocean, leading to recommendations in SA algorithm applications for the future PACE mission.

MAIAC Processing of OCI Over Land: High Resolution Aerosol Retrievals and Atmospheric Correction

PI: Alexei Lyapustin - Climate and Radiation Laboratory, NASA Goddard Space Flight Center
Co-I(s): Yujie Wang, JCET UMBC; Sergey Korkin, USRA GESTAR; Sujung Go, JCET UMBC
The Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission, scheduled for launch in 2022, will carry three unique instruments which dramatically expand capabilities of the modern fleet of the polar-orbiting Earth observing satellites (MODIS, VIIRS). The Ocean Color Instrument (OCI), will, for the 1st time, provide well-calibrated, high signal-to- noise hyperspectral observations in 345-790nm interval with 5nm resolution and 5nm (possibly 2.5nm) step. With additional seven discrete bands in the NIR-SWIR region with strong heritage of operational MODIS and VIIRS use, OCI offers a unique potential for the high accuracy global aerosol retrievals and atmospheric correction over land. The hyperspectral surface reflectance has being used for numerous applications such as forest management, precision farming, detecting invasive species, local to global land cover change detection etc. Through advanced characterization of vegetation, including Light Use Efficiency (LUE), plants' nutrients and pigments etc., it will help improve global modeling of vegetation and of terrestrial carbon. To unlock the unique potential of OCI for land vegetation analysis and other hyperspectral applications, we propose to adapt and prototype MAIAC algorithm for OCI processing. Specifically, we propose to: 1) Develop the global over-land processing of OCI data for land analysis, based on MAIAC algorithm. It will provide advanced cloud and snow detection, water vapor retrieval, high spatial resolution aerosol retrievals and atmospheric correction for OCI bands. 2) Develop retrieval algorithm for spectral aerosol absorption and height from OCI. 3) Prototype and test developed algorithm using TROPOMI data spectrally aggregated to OCI bands which will help achieve high pre-launch readiness. 4) Contribute to the HARP algorithm development for detailed aerosol characterization.

Maximizing Utility of PACE in Coastal and Major Freshwater Ecosystems: Advancing Science for Societal Benefits

PI: Nima Pahlevan - Science Systems and Applications Inc. (SSAI)
With the increasing pressure imposed by the coupled effects of climate change and human-induced activities, harmful algal blooms (HABs) are becoming more frequent; posing a threat to global coastal and inland waters and the ecosystem services they provide. Ocean color (OC) remote sensing has long been harnessed to operationally or semi-operationally monitor spatio-temporal distribution of HABs across the United States coastal/inland waters (e.g., Chesapeake Bay, Lake Erie) or other regions like the Baltic Sea. Such HAB monitoring efforts, however, have primarily been limited only to "detection" using band-arithmetic indices. Such limitations are partially driven by limited accuracy in the retrievals of in-water optical properties in highly eutrophic/turbid waters as well as imperfect atmospheric corrections. Enhanced capabilities offered by the PACE suite of sensors will open pathways to go beyond generating bloom indices and enable identification (bloom type, dominant pigments) and accurate quantification (pigment concentration) of HABs empowering resource managers with more consistent, quantitative, and robust products for decision-making.

Considering the optical complexity of coastal/inland waters, this research proposes a novel machine-learning algorithm for enhanced retrievals of hyperspectral inherent optical properties (IOPs) from the Ocean Color Imager (OCI) observations. From the IOPs, algal bloom characteristics including types and pigment compositions are estimated. Further, concentrations of pigments like chlorophyll-a and phycocyanin as well as suspended particulate matter (SPM) are predicted, in order to enable a more complete assessment of water quality. Our proposed algorithm exploits the full spectral capabilities of OCI (345-890 nm) and learns to utilize the most relevant spectral regions for the desired products. We will train and validate our proposed machine-learning approach using high-fidelity data from the SeaWiFS Bio-optical Archive and Storage System (SeaBASS) representing a broad range of optical regimes in coastal waters. We will further enrich this dataset using high-quality data available in international databases. The algorithm will also be implemented on data from Sentinel-3 Ocean Land Color Imager (OLCI) imagery to allow for creating seamless IOP and water quality products for global coastal and inland waters.

Our team of experts with a diverse range of skills and experiences in remote sensing, ocean color/optics, in situ and satellite-based HAB monitoring and identification, oceanography, and applied sciences in partnership with resource managers from regions with a long record of HAB episodes, impaired water quality, and applications to aquaculture and fisheries will work with the PACE Project Science Team to develop this suite of products in coordination with operational agencies (i.e., NOAA, EPA) and water authorities serving as end-users.

Next Generation Algorithms Based on PACE Capabilities to Obtain Inherent Optical Properties of Seawater Associated with Phytoplankton, Nonalgal Particles, and Colored Dissolved Organic Matter

PI: Dariusz Stramski - Marine Physical Laboratory, Scripps Institution of Oceanography, University of California San Diego
Co-I(s): Rick A. Reynolds, University of California San Diego, Scripps Institution of Oceanography
The spectral inherent optical properties (IOPs) of seawater are fundamental data products of the PACE Ocean Color Instrument (OCI) and critical to the interpretation of optical remote sensing. The total IOPs result from additive contributions of seawater constituents such as phytoplankton, nonalgal particles, colored dissolved organic matter (CDOM), and water. By carrying information about various biogeochemically important constituents, the IOPs are essential variables for advancing an understanding of the links and interactions between ocean biology, biogeochemistry, and Earth's climate. Current NASA IOP data products utilize semi-analytical algorithms that rely on spectral optimization and/or spectral deconvolution approaches to derive total and constituent IOPs from ocean color observations. These approaches are subject to significant limitations owing to the use of predefined spectral shapes for output IOPs, simultaneous solutions such that constituent IOPs are not independently obtained, and limited application to the UV spectral range. This proposal is motivated by a need for next-generation algorithms which will alleviate these limitations, take full advantage of PACE OCI capabilities, and achieve high level of scientific readiness before the mission launch.

Our goal is to develop and evaluate a three-step semi-analytical algorithm (3SAA) for deriving hyperspectral total and constituent IOPs from PACE OCI. The 3SAA will sequentially combine an inverse reflectance model with two absorption partitioning models. The reflectance model (LS2) will derive total spectral absorption and backscattering coefficients, and their corresponding nonwater coefficients, from OCI- derived remote sensing reflectance. The first partitioning model (ANW) will determine the phytoplankton and nonphytoplankton absorption components from the LS2-derived nonwater absorption coefficient, and the second model (ADG) will further partition the ANW-derived nonphytoplankton absorption coefficient to derive the nonalgal particulate and CDOM components. A major advantage of LS2 is that the key equations linking reflectance to IOPs were developed from radiative transfer simulations regardless of light wavelength and with no assumptions about the spectral shapes of IOPs, hence the model provides independent solutions at any arbitrary light wavelength. Similarly, the ANW and ADG models relax restrictive assumptions about spectral shapes of component absorption coefficients. The overall 3SAA approach provides a suitable framework to optimize performance of each model independently, and to quantify uncertainties associated with individual component models as well as the entire sequence of models.

We will improve the LS2 model by developing a refined neural network algorithm commensurate with OCI capabilities to obtain the hyperspectral diffuse attenuation coefficient of irradiance. We will also refine our previous and develop new absorption partitioning models to optimize their performance with hyperspectral input data including the UV spectral range. These tasks will yield the best-performing sequence of 3SAA component models (LS2, ANW, and ADG) for applications with PACE OCI measurements, including the UV range. The performance of the entire 3SAA and its component models will be evaluated with quantified uncertainties provided for all data products. We will provide an implementable algorithm pre-launch and a methodology for post-launch algorithm maintenance and data product validation activities. On these tasks we will collaborate with both the PACE Science and Applications Team and the NASA Ocean Biology Processing Group. Strong evidence from our earlier studies indicates that the proposed 3SAA approach will advance the capabilities for estimating hyperspectral IOPs from measurements with PACE OCI, thus reducing the risk of failing to achieve mission goals related to the generation of IOP products and advancing the science enabled by these products.

PACE Applications to Case II Waters: Quantifying the Uncertainty in Inherent Optical and Water Constituent Properties and the Impact On Remotely Sensed Ocean Color

PI: Steven Ackleson - Remote Sensing Division, Naval Research Laboratory
In response to solicitation NNH13ZDA001N-PACEST, Pre-Aerosol, Clouds, and Ocean Ecosystem (PACE) Science Team, we propose to investigate uncertainties in Case II inherent optical properties (IOPs) and associated water constituent properties (e.g., concentration, composition, and morphology) and to examine the impact of uncertainties on recommended PACE data products for coastal ocean, estuarine, and inland waters. These objectives strongly address ocean color science needs articulated within the PACE Mission Science Definition Team Report (October 2012) pertaining to marine biogeochemical cycles associated with land-ocean interactions in response to climate change. As a collaborative member of the IOP working group (ST/IOP), we will utilize existing in situ and remotely sensed data sets as well as new observations to be collected during the period of performance through existing and planned field campaigns. Uncertainties will be estimated through direct comparisons of optical and water constituent properties measured or derived through independent means, comparisons of functional relationships between associated parameters with relationships that have been published and vetted by the research community, and through tests of optical closure. In addition to the proposed research and collaborations with other members of the ST/IOP, the PI, Dr. Steven Ackleson, proposes to be the overall PACE Science Team leader. Working closely with NASA-appointed representatives, Dr. Ackleson will be responsible for organizing, planning, and chairing team meetings, coordinating focused working groups, integrating results, building consensus on PACE science objectives and requirements, representing science team activities at professional meetings and symposia, and preparing progress reports and consensus statements as directed by NASA.

PACE UV Retrieval of Ocean and Atmosphere Data-Products (PACE UV ROAD): CDM, BrC and BC Polarimetry

PI: Jacek Chowdhary - Columbia University and NASA Goddard Institute for Space Studies
Co-I(s): Li Liu, Columbia University; Kostas Tsigaridis, Columbia University and NASA/GISS; Matteo Ottaviani, Terra Research, Inc and NASA/GISS
PACE data sets will offer unprecedented opportunities to improve, and even provide new, aerosol and ocean products from inversions of space-borne observations. Of particular interest will be the synergy between UV radiometry (from the OCI sensor) and UV-VIS multi-angle polarimetry (from the SPEXone and HARP2 sensors). Enhanced multiple scattering (EMS) in the atmosphere, which results from an increase in molecular optical depth, causes space-borne UV radiance to be more sensitive to absorbing aerosols such as Black Carbon (BC) and Brown Carbon (BrC). At the same time, EMS increases the diffuse skylight illuminating the ocean, and therefore the radiative coupling between ocean and atmosphere. Hence, satellite retrievals of atmospheric and oceanic UV products must be carried out simultaneously; such inversions provide as an added bonus a rigorous evaluation of associated uncertainty budgets. The radiative coupling between atmosphere and ocean also highlights the importance of absorption by colored detrital matter (CDM) in the ocean. CDM exhibits similar absorption spectra as BrC aerosols and therefor has a similar impact on space-borne UV radiance. An advantage of polarized radiance is that it is less sensitive to multiple scattering, and thus to radiative coupling of atmosphere and ocean. Hence, polarimetric measurements constitute a crucial asset in simultaneous retrievals of BC/BrC and CDM UV products from space-borne observations.

The objective of our proposal is to perform simultaneous retrievals of CDM, BC and BrC products from (i) combined OCI-SPEXone data sets; and (ii) combined OCI-HARP2 data sets. OCI observations cover the UV down to 350 nm, but its radiance data sets are sensitive to variations in both atmospheric and oceanic properties. SPEXone observations complement OCI observations by also providing multi-angle polarized radiance observations down to 385 nm; however, the swath of SPEXone observations is significantly smaller than OCI's. HARP2 observations complement OCI observations by providing multi-angle polarized radiance observations for a swath similar to that of OCI observations; however, HARP2 observations capture the spectrum down to only 440 nm. Hence, for HARP2 we will focus on how the CDM/BC/BrC product retrievals from OCI-SPEXone data sets can be applied to analyses of OCI-HARP2 data sets obtained over a larger swath.

We will use the MAPP retrieval algorithm, which was developed to retrieve aerosol and ocean color properties from airborne multiangle polarimetry observations down to 410 nm by our RSP instrument. We will apply this algorithm to both simulated PACE data sets and available airborne data sets to develop and optimize the retrieval of CDM/BC/BrC UV products from PACE observations. MAPP will be extended to include (a) variations in phytoplankton absorption in the UV; (b) variations in spectral slopes of CDM absorption and in underwater light scattering coefficient; (c) variations in complex refractive indices for BrC aerosols. We will use MAPP inversions of (1) OCI radiance in the VIS to select appropriate phytoplankton absorption spectra for use in the UV; and (2) SPEXone/HARP2 VIS polarized radiance to mask dust-contaminated scenes.

We will use existing in-situ, airborne, and simulated space-borne data sets for our analyses of information content on, and retrieval uncertainties of, atmospheric and oceanic properties. For the in-situ and airborne data sets, we will focus on the SABOR and NAAMES campaigns where RSP observations were coordinated with ship measurements. For the NAAMES campaign, we will also use UV hyperspectral measurements from the airborne GCAS that were co-located with RSP observations. To simulate PACE observations, we will use SeaWiFS retrievals for extrapolation of UV ocean properties, and GISS ModelE results for global variations in aerosol UV properties.

Phytoplankton Algorithms and Data Assimilation: Preparing a Pre-launch Path to Exploit PACE Spectral Data

PI: Cecile Rousseaux - Global Modelling and Assimilation Office, NASA Goddard Space Flight Center
The lack of previous global ocean color satellite mission with spectral capabilities similar to those from PACE calls for a framework to assess and develop the best approaches and anticipate potential problems. This proposal directly applies to the first area of basic and applied research called out in the solicitation: "Theoretical and analytical studies associated with the use of OCI-analog hyperspectral data for the development of an algorithm or approach for one or more ocean ... products from OCI". We will exploit potential PACE capabilities by (1) developing algorithms to derive phytoplankton composition and (2) producing global, complete, hyperspectral water leaving radiances by assimilating the satellite radiances in an Earth System Model. The algorithms will be developed by deriving hyperspectral water leaving radiance spectra for various phytoplankton concentration and composition using a radiation model, the Ocean- Atmosphere Spectral Irradiance Model (OASIM). The changes in spectral shape and magnitude of these simulations will be used as the basis to derive the critical bands that are representative of the various phytoplankton groups and begin to develop the algorithms in a controlled environment (where the concentration and composition of phytoplankton is pre-defined). We will then test and refine the algorithms in a more 'natural' settings (all phytoplankton groups included and allowed to vary depending on the physical and biogeochemical conditions) using global hyperspectral simulated water leaving radiances developed during the first PACE Science Team (Figure 1). Prior to the algorithm development, this simulated water leaving radiances dataset will be calibrated using existing airborne (i.e. AVIRIS, PRISM) and spaceborne (i.e. HICO) instruments. Once the algorithms have been refined we will validate these using an in situ database of phytoplankton composition, also developed during the first PACE Science Team. We will further exploit the capabilities of PACE by setting up a framework to assimilate hyperspectral water leaving radiances, as well as other products that can be derived from the model, producing Level-4 data products that are temporally and spatially complete. Comprehensive quantitative error and uncertainty analysis will be integral in each of the stages of the proposal. This project builds on the efforts by the PI and Co-Is during the first PACE Science Team and on ongoing collaboration with the PACE Project Team. Our approach can provide valuable information in preparation for PACE and also enhance the usefulness of PACE data as soon as they become available after launch.

Phytoplankton Composition Algorithms for PACE

PI: Cecile Rousseaux - Global Modelling and Assimilation Office, NASA Goddard Space Flight Center
We propose to develop an algorithm to derive phytoplankton composition using a radiation model that provides hyperspectral data similar to what we expect from the PACE mission. Because there has not been any previous global mission with similar capabilities, there is a need for assessing the best approaches and anticipate potential problems if we are to get the most information out of this mission. Here we propose to use the Ocean-Atmosphere Spectral Irradiance Model (OASIM) to derive the total water-leaving radiance of single and mixed phytoplankton functional groups. We will then use an extensive dataset to develop an algorithm or algorithms to derive phytoplankton composition from these hyperspectral data. We will test this algorithm against in situ data that were withheld from the algorithm development. Finally we will apply this algorithm to a state-of-the-art biogeochemical model (NASA Ocean Biogeochemical Model) that has been shown to represent reasonably well the global distribution of phytoplankton composition. Using this model, we will assess how well this newly developed algorithm does in representing the natural global distribution of phytoplankton groups. Comprehensive quantitative error and uncertainty analysis will be integral in each of the stages of the proposal. Additionally, we propose simulations to test different configurations of the sensor to understand the capabilities and limitations associated with engineering options. Although we acknowledge that there may be formidable challenges throughout these steps, we believe that we can learn some valuable information from these challenges.

Quantifying the Spectral Absorption Coefficients of Phytoplankton and NonPhytoplankton Components of Seawater from In Situ and Remote-Sensing Measurements

PI: Dariusz Stramski - Marine Physical Laboratory, Scripps Institution of Oceanography, University of California San Diego
As members of the PACE Science Team we propose to pursue analytical and theoretical studies as part of the measurement suite area "Inherent Optical Properties (IOPs) of the Ocean". Our goal is to improve field measurements of particulate absorption coefficients and remote sensing estimates of absorption coefficients of phytoplankton and non-phytoplankton components and particulate carbon pools associated with these components. The main objectives are to: (1) Develop a protocol and quantify the uncertainties of a new filter-pad approach and the existing filter-pad methods to measuring the particulate absorption coefficient; (2) Develop a model to partition the absorption coefficient of seawater into phytoplankton, non-algal particulate (NAP), and colored dissolved organic matter (CDOM) components with a key novel aspect of separating NAP from CDOM; and (3) Conduct a pilot study of the relationship between NAP absorption and NAP organic carbon to enable a capability for remote sensing of carbon pools associated with separate phytoplankton and non-phytoplankton components. The overall approach to address these objectives will be based primarily on the analysis of existing laboratory and field data, but will also encompass a combination of limited number of laboratory and field measurements to collect new data, and the application of remote sensing data from high spectral resolution sensor HICO. With regard to Objective 1 we will examine the filter-pad methods for determining the absorption coefficient of particles with high spectral resolution(~ 1 nm) over a broad spectral range from UV through NIR; specifically the traditional transmittance (T) and transmittance-reflectance (T-R) methods as well as the inside-sphere (IS) method which is the most recent refinement with the filter placed inside an integrating sphere. The IS method offers several advantages over the T and T-R methods, leading to improved accuracy and precision of absorption measurements. We will determine complete protocols including new optimal correction algorithms for pathlength amplification factor (which is the main source of uncertainty) and will quantify uncertainties for all three methods, benefiting the interpretation of historical data and acquisition of future data of particulate absorption. We anticipate that the IS approach will serve as the new recommended (preferable) method for measurements of particle absorption. With regard to Objective 2 we will develop a model to provide for the first time a capability for estimating the three major absorption components separately (phytoplankton, NAP, and CDOM) from the total absorption coefficient of seawater, which is derivable from remote sensing. In this development, we will use the existing quality-verified field data of absorption coefficients from various regions of the world's ocean and will expand the approach that has been successful for partitioning the absorption coefficient into phytoplankton and non-phytoplankton (NAP+CDOM combined) components (Zheng and Stramski 2013). The significance of the proposed partitioning model is associated with relationships between the component absorption coefficients and biogeochemical stocks, such as DOC, particulate non-algal and phytoplankton carbon, chlorophyll-a, as well as phytoplankton community structure and primary productivity. As a prototyping activity (Objective 3) we will examine one such unexplored link, specifically the relationship between the NAP absorption and NAP organic carbon, which will provide a basis for estimating separate pools of phytoplankton and non-algal organic carbon from remote sensing. This project will create new and advance existing algorithms for deriving ocean color data products, in particular IOPs and carbon stocks associated with separate phytoplankton and non-phytoplankton components, which will contribute to scientific goals of PACE mission to understand ocean carbon cycling and ecology.

Quantifying Uncertainties in Phytoplankton Absorption Coefficients forAccurate Validation of the PACE Ocean Color Sensor: Moving Towards Satellite Retrieved Phytoplankton Functional Types (PFTs)

PI: Collin Roesler - Earth and Oceanographic Science, Bowdoin College
NASA's fleet of ocean color satellites have provided an enduring times series of phytoplankton chlorophyll concentration of a quality sufficient for a climate data record. Following on from SeaWiFS and MODIS missions, the Pre-Aerosol, Cloud, ocean Ecosystem (PACE) mission is designed to fulfill the climate continuity requirements, with a launch readiness in the time frame of 7 years. Beyond the estimation of phytoplankton chlorophyll concentration, the goals of PACE are to provide climate-quality global ocean color measurements that are essential for understanding the carbon cycle and global ocean ecology and determining how the ocean's role in global biogeochemical (carbon) cycling and ocean ecology both affects and is affected by climate change. Among the improvements for the ocean color specifications are increased spectral and spatial resolution.

One of the specific objectives of the PACE mission is the retrieval of the inherent optical properties (IOPs), absorption and backscattering, via ocean color inversion algorithms. The capability for retrieving spectral phytoplankton absorption coefficients is key to addressing questions of carbon cycling and ocean ecology as these coefficients provide not only an estimate of algal concentration (that can be linked by proxy to algal carbon) but also to algal composition via pigment-based taxonomic discrimination. Pigment-based taxonomic composition provides a key approach to defining phytoplankton functional types (PFTs) as many of the pigment-based lineages coincide with biogeochemical niches, calcifiers, silicifiers, nitrogen fixers, etc. One challenge for the PACE mission is to define robust protocols with quantified uncertainty terms for constructing validation data sets for phytoplankton absorption. While in situ optical technologies exist to measure hyperspectral absorption on the spatial and temporal scales approaching those required for satellite validation, extracting the signature associated solely with phytoplankton cannot currently be performed analytically and thus we rely on model estimates.

Measuring phytoplankton absorption requires collection of discrete water samples, collecting the particles on glass fiber filters to remove the optical contribution by colored dissolved organic matter (CDOM), and measuring the absorption spectrophotometrically before and after pigment extraction. The absorption by the phytoplankton pigments in vivo is calculated by difference. However, the filter pad contaminates the signal due to its strong scattering properties and additionally amplifies the optical pathlength of transmitted photons in the spectrophotometer. These two error sources are inadvertently combined into a single correction factor, beta, called the pathlength amplification factor. Many researchers over the years have investigated this factor using a variety of strategies and technologies and yet it remains the largest source of uncertainty in the quantification of phytoplankton absorption. Unfortunately, models for extracting the phytoplankton absorption from in situ observations of whole water or particulate absorption are based upon laboratory investigations in which beta was poorly constrained at worst, or at best for which the uncertainties were not quantified. Thus in order to address the need for ocean color validation of phytoplankton absorption coefficients, a unified approach linking the quantitative filter pad technique to continuous in situ absorption observations is required and is the primary focus of this proposal. The secondary focus of this proposal is to investigate whether multispectral chlorophyll fluorescence can provide a quantitative proxy for phytoplankton absorption at the excitation wavelengths. This approach would expand the opportunities for in situ phytoplankton absorption validation by making use of a simple, economical, easily deployed technology that does not require the same level of optical expertise as absorption technologies.

Radiative Products for the PACE Era

PI: Emmanuel Boss - School of Marine Sciences, University of Maine
Co-I(s): Robert Frouin, Scripps Institution of Oceanography, UCSD
Hyperspectral radiative measurements spanning from UV to NIR such as will be available with PACE's OCI will improve our ability to estimate radiative products compared to the current ones by constraining atmospheric transmission at many more wavelengths, narrower bands and into a wider range of wavelengths than current satellites. Together with PACE's expanded suite of biogeochemical products, such as absorption by phytoplankton and dissolved organic materials, the improved radiative products will allow for improved constraints of aquatic biogeochemical rate process, for example, near-surface primary production and photo-oxidation.

It has recently become apparent that current radiative products are not optimal for users. In particular, to compute rates of photochemical and photobiological processes the necessary input is the sub-surface scalar irradiance and its attenuation with depth spanning from UV and across the visible. However, current ocean color radiative products are currently limited to above surface downwelling spectral irradiance and PAR.

Here we propose to expand the radiative products suite distributed by NASA's Ocean Biology and biogeochemistry Processing Group (OBPG) to include both daily and instantaneous (at the time of satellite passage) spectral downwelling and scalar irradiance below the surface, ocean albedo and the appropriate attenuation function to propagate them to different depths. We will provide OBPG the algorithms for these novel products and work closely with them on their implementation. In addition, we will generate software tools (subroutines) for users interested in obtaining band integrated PAR and UVA radiative values at a given depth or integrated over a depth range (e.g., mixed- layer). Prior to PACE, we will create and test demonstration algorithms using data from HICO (spectral and PAR) and MOBY (UVA) and validated using radiometry data available on SeaBASS, PANGAEA, profiling floats and from different mooring sites.

We will reach out to the relevant communities (e.g., ecosystem and biogeochemical modelers) early in this project to insure that the algorithms we produce are fit-for-purpose and that these communities are ready to ingest the relevant products as soon as they become available online.

Remote Sensing of Cloud Properties using PACE SPEXone and HARP-2

PI: Bastiaan van Diedenhoven - Columbia University
Co-I(s): Mikhail Alexandrov, Igor Geogdzhayev, Zhibo Zhang, Daniel Miller
In addition to the Ocean Color Instrument (OCI), the PACE mission will also carry two polarimeters, namely the Spectro-polarimeter for Planetary Exploration (SPEXone) and The Hyper Angular Research Polarimeter (HARP-2). Both of these polarimeters provide multi-angle polarimetric measurements that contain unique information on cloud properties that compliment OCI-based retrievals, especially in challenging conditions such as broken cloud cover and mixed-phase tops. Here we propose to develop and evaluate cloud products inferred from the measurements of SPEXone and HARP-2.

Cloud properties that we propose to infer from SPEXone and HARP-2 measurements are cloud top thermodynamic phase, cloud droplet size distribution, ice crystal shape and ice scattering properties, cloud top height, cloud fraction and cloud physical thickness. Methods for droplet size retrievals have substantial heritage from their operational applications to, e.g., POLDER and the airborne Research Scanning Polarimeter (RSP). These approaches are expected to be readily applicable to HARP-2, which includes one channel with sufficiently high angular sampling. For their application to SPEXone, we propose using its broad and continuous wavelength range to partly compensate for its lower angular resolution. Existing ice cloud retrieval approaches that can be readily adapted to both HARP-2 and SPEXone and yield unique and relevant microphysical information on cloud top ice crystals, in addition to allowing OCI retrievals of ice cloud optical thickness and effective radius to be corrected for biases in assumed asymmetry parameter. In addition to applying these heritage approaches to PACE, we propose to explore other retrieval approaches of cloud properties utilizing the strengths of the distinct measurements of SPEXone. Specifically, we propose to infer cloud top height and layer physical thickness from polarimetric measurements in the Oxygen A band made by SPEXone. Furthermore, we propose to explore the potential to infer cloud fraction from the polarimetric measurements in the UV provided by SPEXone. We aim to develop a flexible suite of algorithms to infer consistent cloud properties from either SPEXone or HARP-2, or both. For the evaluation of the proposed methods, we will use available 1-D and 3-D radiative transfer simulators and measurements of airborne versions of SPEXone and HARP-2, in addition to RSP observations. Such simulations will allow quantification of effects caused by, e.g., sub-pixel inhomogeneity, mixed- phase conditions and errors associate with the collation of angular measurements with respect to the surface rather than cloud top. Given the specific sampling of scattering angle ranges that are required for each of the proposed products, their availability will depend on solar and viewing geometries and thus on season and location. We propose to map the expected availability of each product from SPEXone and HARP-2 given, e.g., day of year, latitude and orbital specifics. We aim for delivering implementable approaches to the PACE science team before launch. In this project, we will leverage many algorithms, tools and knowledge acquired over past years using NASA funding. Our proposed work yields to potential for continuity products using not only the PACE polarimeters, but also past POLDER measurements, and other future polarimeters, such as 3MI and potential polarimeters on NASA's A&CCP mission concept.

Remote Sensing of the Ocean Surface Refractive Index and Oil Spill Detection for the PACE Mission

PI: Matteo Ottaviani - Terra Research Inc / NASA Goddard Institute for Space Studies
Co-I(s): Jacek Chowdhary, Columbia University / NASA GISS
We have recently demonstrated in peer-reviewed publications how polarimetry can deliver measurements of the ocean surface refractive index. The method exploited observations from the NASA GISS Research Scanning Polarimeter (RSP) within the sunglint region, where the degree of linear polarization is determined by the fundamental Fresnel laws of specular reflection, regardless of the windspeed. At short-wave infrared wavelength, essentially unaffected by atmospheric scattering, the degree of polarization is therefore a direct function of the surface refractive index.

We intend to extend the method to PACE observations from the HARP-2 and SPEXone sensors. Although such instruments lack polarized channels in the SWIR similar to those of RSP, the method can be applied to the NIR wavelengths provided corrections to the aerosol contributions are applied. We will therefore (ii) perform advanced inversions of RSP polarimetric data using the NIR wavelengths in place of the SWIR; (ii) simulate PACE observations of the ocean surface under a range of conditions, and analyze their information content via a rigorous assessment of the uncertainties; and (iii) create a novel "ocean surface refractive index"-type of product for every HARP2 pixel within the sunglint region. We anticipate the results to be useful for investigations of processes involving the ocean surface, and for the detection of oil spills (specifically mentioned in the solicitation) and other contaminants. The study will also help recovering the significant portion of pixels otherwise discarded from PACE imagery as “sunglint- contaminated”, by turning them into a useful resource for new products associated with the retrieval of the ocean surface refractive index.

Retrieval Studies In Support of Cloud Property Products from the PACE Ocean Color Imager

PI: Steve Platnick - Atmospheres, Earth Sciences Division , NASA Goddard Space Flight Center
Obtaining cloud climate data records from the current generation of global imagers (MODIS, VIIRS) is challenging due to the need for exacting reflectance stability over multiple decades. Imager stability requirements for ocean color applications have been demonstrated to the sub-percent level for SeaWiFS using lunar observations; similar capabilities for the PACE Ocean Color Instrument (OCI) are defined in the PACE Science Definition Team (SDT) report, in addition to stringent requirements in other radiometric/spectral specifications that are essential for the establishing climate records. Understanding the extent to which the PACE imager can be used to produce relevant and stable cloud products is of strategic importance. We propose to support PACE ocean color imager instrument requirements related to cloud property retrievals as part of the Atmospheric Correction measurement suite. Initial studies on the use of OCI for initiating cloud records were studied by the SDT for instrument options that included three additional spectral channels (OCI+) in addition to higher spatial resolution in selected channels (OCI/A). The team will participate in instrument specification, trade studies, and mission/instrument development laboratory studies as they pertain to cloud retrievals and information content from OCI+/A. Studies will include use of the instrument for cloud detection, thermodynamic phase detection, optical/microphysical retrievals, and cloud-top information from the O2 A-band and water vapor bands. More broadly, the team will study the retrieval capability of an OCI+/A instrument relative to MODIS and VIIRS cloud data records, with the goal of understanding the ability of a PACE imager to continue and/or compliment the existing imager products using a combination of theoretical and empirical retrieval studies. The SDT report has already provided nominal retrieval capabilities and associated science for a 3M polarimeter. Though our team has theoretical and practical experience with polarimetric cloud retrievals, we have not explicitly budgeted for additional studies due to continued uncertainty in a PACE polarimeter. The PACE SDT report provided details on imager options, measurement specifications, and derived science. However, as acknowledged in the solicitation, the PACE mission instrument suite and measurement requirements have yet to be determined, especially with regard to cloud retrieval capabilities. Further, the team is being solicited before the scope of an Announcement of Opportunity (AO) for the PACE instrument(s) is known. Therefore, it is critically important to have comprehensive cloud retrieval expertise on the PACE Science Team. The proposed multi-institution team collectively provides broad research, observational, and measurement experience in cloud remote sensing, and is well-suited for carrying out retrieval studies as PACE mission instruments and objectives evolve. Several team members contributed to the PACE SDT report and associated Instrument and Mission Design Lab studies.

Spectral Matching Inversion Algorithms for PACE Application in Optically Shallow Waters: An Assessment Using HICO and PRISM Data

PI: Brian Barnes - University of South Florida
Co-I(s): Chuanmin Hu, University of South Florida
Optically shallow waters (particularly coral reef and seagrass environments) have long been tantalizing targets for satellite ocean color scientists. These environments include habitats that draw tourism, provide shoreline stabilization, and serve as nurseries for commercially and recreationally harvested fishes, among numerous other benefits. Despite their importance, quantitative assessment of these targets using ocean color data is difficult, primarily due to the challenges in separating the benthic signal from that of the water column. Toward that end, a number of analytical models have been developed to deconstruct reflectance spectra into relevant water column and benthic properties (including depth). Using in situ hyperspectral reflectance data, these models can be "solved" for a given spectra through optimization. Recent efforts to apply spectral matching optimization approaches to multispectral satellite data have proven quite fruitful. Unfortunately, however, such applications suffer from the intractable problem of needing to derive too many parameters from too few wavebands. To overcome this, various multi-band approaches either require exogenous inputs (which can be quite rare), or make simplifying assumptions that (1) create uncertainties and (2) reduce the utility of the final results.

Given the hyperspectral capacity of the PACE sensor, the objective of the proposed study is to develop and validate a spectral matching optimization approach for application to the hyperspectral reflectance dataset to be delivered through the upcoming mission. Beyond the traditional products possible from multispectral satellite datasets (i.e., concentrations of major in-water constituents, water depth, and benthic albedo), the products derived using this approach will also include (1) classification of benthic habitats at sub-pixel scale, (2) assessment of condition (i.e., ecosystem health) change over time, and (3) assessment of uncertainties associated with each of these parameters. This work will not start from scratch, but will benefit from previous and ongoing works from the PI and collaborators. The approach will be developed primarily using HICO and PRISM data covering coral and seagrass environments throughout the globe (focus locations offshore Hawaii, Florida, and Queensland, Australia), with validation dataset sources from NASA's CORAL dataset, bathymetric lidar, and previously collected measurements of discrete water samples. In addition to these data, simulated data (through radiative transfer and addition of realistic spectral noise) will also be used to understand algorithm uncertainties. Deliverables will include technical reports and publications as well as algorithms and computer codes available to the NASA Science Data Segment (SDS) team (led by Bryan Franz) to implement and test upon the launch of the PACE mission.

Through development of this approach and eventual implementation to the PACE datastream and processing regiment, numerous benefits are expected to be realized. First, raster bathymetries and benthic classification maps for optically shallow waters will provide critical information to data poor regions throughout the globe, and will also elevate capabilities of heritage and ongoing multispectral satellite ocean color missions. Assessment of benthic condition changes over time will allow for identification of the spatiotemporal extent of impacts from local disturbances (e.g., hurricanes, eutrophication), as well as global environmental responses to climate changes (e.g., coral bleaching). Accurate mapping and monitoring of benthic habitat changes are also one of the key components under the "surface biology and geology" theme recommended by the most recent decadal survey.

Terrestrial Ecology Products from PACE

PI: K. Fred Huemmrich - University of Maryland Baltimore County
PACE, with its frequently collected continuous high spectral resolution imagery, provides an opportunity to describe dynamics of key terrestrial vegetation biochemical and functional characteristics. Frequent observations of leaf pigment contents provide information on vegetation seasonal activity and stress responses that are related to photosynthetic rates and ecosystem primary productivity. Spectral reflectance can also identify vegetation functional type coverage, related to biodiversity, which further constrains physiological responses. Thus, taken together, PACE measurements can describe factors determining plant productivity, stress responses, and resource allocations, providing new insights into global patterns of the function of terrestrial ecosystems and their response to environmental conditions.

The potential uses of PACE data for terrestrial applications are demonstrated in a variety of studies using data from combinations of MODIS ocean and land bands, HICO and Hyperion imaging spectrometers, AVIRIS imagery from aircraft, ground measurements, and vegetation canopy models.

The PACE-MAPP Algorithm: Coupled Aerosol and Ocean Products from Combined Polarimeter and OCI SWIR Measurements

PI: Snorre Stamnes - NASA Langley Research Center
Co-I(s): Sharon Burton, NASA Langley Research Center; Xu Liu, NASA Langley Research Center; Jacek Chowdhary, Columbia University; Bastiaan van Diedenhoven, Columbia University
We propose to apply our automated and operational RSP-MAPP retrieval algorithm (Stamnes et al., 2018), which was developed for the airborne NASA GISS Research Scanning Polarimeter (RSP) (Cairns et al., 1999), to the PACE observing system to accurately retrieve aerosol and ocean products for PACE. A key feature of this proposed PACE-MAPP algorithm is that it is coupled; the atmosphere and ocean are solved together as one system from a radiative transfer perspective. Conservation of energy thus ensures that negative water-leaving radiances are impossible; and by simultaneously solving for the aerosol and ocean products, we can determine the optimal solution, together with a full accounting of the uncertainties of each parameter. A missing piece in the puzzle for spaceborne sensing of estuarine and coastal zones is provided by PACE with its introduction of multi-angle, multi-channel polarimeter measurements capable of accurately retrieving the complex aerosol scenes that arise in coastal areas, and which will now be available thanks to PACE. The coupled approach, using polarimeter measurements, is the only reliable and accurate way to retrieve aerosol and ocean products in the Earth's complex coastal zones. The PACE-MAPP algorithm will thus be capable of providing aerosol and ocean products for both the global ocean and coastal areas.

The emphasis of the PACE-MAPP algorithm products will be two-fold: i) accurate retrieval of aerosol microphysical properties including aerosol absorption (single- scattering albedo), aerosol location, aerosol effective radius, and the separation of aerosol optical depth into a fine mode and two coarse modes (sea salt and dust), and ii) simultaneous retrieval of ocean products using a polarized bio-optical model that parameterizes phytoplankton and nonalgal particles across multiple water types including the open ocean, phytoplankton blooms, and coastal zones. The PACE-MAPP retrieval algorithm will be tested using simulated datasets, and with airborne PACE-like data collected during the SABOR, NAAMES and ACEPOL campaigns, and the future ACTIVATE dataset.

All aerosol and ocean data products will include uncertainties produced by the PACE- MAPP optimal estimation retrieval, considering the instrument measurement error uncertainty and a priori information. Algorithm validation will be accomplished by performing retrievals upon simulated PACE SPEXone/HARP2/OCI-SWIR data for the full range of realistic aerosol and ocean parameters. Additionally, validation of the ocean retrieval products from airborne RSP/SPEXair/AirHARP will be accomplished using ship-based in situ measurements and collocated HSRL (High Spectral Resolution Lidar) ocean measurements. Validation of the aerosol retrieval products will be accomplished via HSRL aerosol measurements and AERONET station overpass comparisons.

The proposed PACE-MAPP retrieval algorithm will be robust so that it will have the capability to provide improved results for aerosol and ocean products data if either SPEXone or HARP2 is operational, by combining data from a single polarimeter with the OCI SWIR channels, or using only data from both the polarimeters without the OCI SWIR channels.

Theoretical Support for Developing the PACE Atmospheric Correction Algorithm: Radiative Transfer and Polarimetric Retrieval of Aerosol Properties

PI: Pengwang Zhai - Department of Physics, University of Maryland Baltimore County
Co-I(s): Yongxiang Hu, NASA Langley Research Center
We propose to contribute to the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) Science and Applications Team (SAT) with the expertise in the theories of radiative transfer in coupled atmosphere and ocean systems, aerosol and ocean color remote sensing, and climatology of aerosol properties from space lidar missions. Specifically, we proposed to achieve the following objectives for a three-year performance period: Objective I. Develop a radiative transfer simulator for the PACE instruments.

We will develop a radiative transfer package that can simulate synthetic datasets for the PACE instruments with flexible atmospheric and oceanic conditions. All major light- matter interaction mechanisms will be accounted for, including the polarization nature of light, atmosphere-ocean coupling, gas absorption, fluorescence of phytoplankton, Fluorescence of Dissolved Organic Matter (FDOM), Raman scattering of pure ocean water, and spherical shell effects. All three instruments onboard the PACE satellite, i.e., the Ocean Color Instrument (OCI) and two Multi-Angle Polarimeters (MAP): Hyper Angular Research Polarimeter (HARP-2) and Spectro-polarimeter for Planetary Exploration (SPEXone), will be emulated by considering their spectral coverages, instrument line shape functions, and viewing geometries. Sensors can be placed at arbitrary locations. This work is important for validating the PACE's Level 2 science algorithms and testing the streamlines of PACE's data production systems.

Objective II. Joint retrieval of the aerosol and ocean color properties using the MAP data We have developed a joint retrieval algorithm for aerosol and ocean color properties using the MAP data for both open and coastal ocean waters. The algorithm can handle both absorbing and non-absorbing aerosols with help from the rich information content of the MAP measurements. The retrieved ocean parameters are the spectral water leaving radiances. The retrieved aerosol properties include the particle size distribution, the optical depth, single scattering albedo, and phase matrices. The joint retrieval algorithm has been demonstrated by both the radiative transfer synthetic dataset and the Research Scanning Polarimeter (RSP) measurements. We propose to further extend the algorithm's capabilities to process both the HARP-2 and SPEXone dataset, which have different spectral and spatial coverages rendering different information content for aerosol and ocean color properties. The sensitivity of aerosol and ocean color properties to the different characteristic of the two MAPs will be studied and the retrieval parameters will be adjusted accordingly. We will validate the algorithm for a large variety of ocean scenes.

Objective III. Atmospheric correction for OCI with the aerosol information retrieved from the MAP data.

Atmospheric correction for scenes involving coastal waters and absorbing aerosols is a challenging task for single-viewing spectrometers. We will use the retrieved aerosol properties from the MAP data to aid the atmospheric correction for the OCI data. The PACE platform will provide a plethora of co-located OCI and MAP data for this research. The resultant water leaving radiance from the atmospheric correction algorithm will be evaluated by in-situ measurements and/or co-located Aerosol Robotic Network (AERONET) data products.

Four important research areas are listed in the PACE SAT solicitation, covering theoretical and analytical studies using the precursors to OCI, HARP-2, and SPEXone. Our proposed research objectives address the first three out of the four areas outlined. The radiative transfer simulator and retrieval algorithm in this effort will be delivered to the Ocean Biology Process Group (OBPG) for implementing in their production system.

Understanding Natural Variability of VSFs and Its Impact on Biogeochemical Retrieval from Ocean Color

PI: Xiaodong Zhang - University of Southern Mississippi
A key challenge in applying ocean color remote sensing for assessing biogeochemical stocks in the ocean is to link the signal seen by satellite or airborne sensors with the optically active water constituents and their biogeochemical origin. The linkage between ocean color and biogeochemical stocks is established via the inherent optical properties of water, most importantly the volume scattering function (Î2, m-1 sr-1) and the total absorption coefficient (a, m-1). While our ability to understand and separate the various components of absorption has improved over the last decades, the major challenge remains in the understanding of the sources of variability in the volume scattering function, and particularly the backscattering that are directly relevant to ocean color observation. This has hindered our ability to derive IOPs accurately and to interpret their variability biogeochemically. Of IOPs, the VSF is most difficult to measure with only few data available. The scarcity of data has led to unrealistic assumptions, such as that the phase function of particles can be represented by the average of Petzold's data. This in turn has led to uncertainties in understanding the roles played by particles of different type in generating remote sensing reflectance and the color of the ocean. Recent technological and theoretical advances have allowed us 1) to measure the full angular scattering over a diverse aquatic environments and 2) to interpret the measurements in terms of particle size distribution and composition. We have greatly improved our understanding of natural variability of the VSF and the biogeochemical origin of the variability. However, these improved knowledge has yet to applied to ocean color. The objective of this study is to understand the natural variability of the VSF and its impact on biogeochemical interpretation of ocean color. The answers to this question will help to constrain two major uncertainties affecting both the current and future PACE ocean color missions: bidirectional effect and sources of backscattering. Our approach is centered on in-depth analysis of the field measurements of complete sets of IOPs (including full range VSFs) and biogeochemical stocks covering various aquatic environments through both forward and inverse modeling. The information of biogeochemical stocks is also contained in the detailed angular pattern of the VSF and can be retrieved by VSF-inversion. Applying forward modeling to simulate spectral backscattering from the inversion results will tell what biogeochemical information about particles (such as the size or the type) is retained in, and hence can be possibly retrieved from, the backscattering coefficient derived from ocean color. Comparisons of the modeled and measured biogeochemical stocks and comparisons of the modeled and measured spectral backscattering will aid in the interpretation of the modeling results and will also provide a basis for validating, and possibly refining, the overall modeling approach. The proposed study addresses a fundamental, yet poorly known, linkage between the optical scattering and biogeochemical properties of natural waters. The potential outcome of the study can not only advance our understanding of the VSF as an key IOP parameter but also improve the performance of existing ocean color algorithms by further constraining the uncertainty associated with angular scattering as well as to guide the development of new approaches for ocean color algorithms.

Unified Algorithm for Aerosol Characterization from OCI on PACE 19-PACESAT19-0014

PI: Lorraine Remer - Joint Center for Earth Systems Technology, University of Maryland Baltimore County
Co-I(s): Omar Torres, NASA GSFC; N. Christina Hsu, NASA GSFC; Robert C. Levy, NASA GSFC
For decades, NASA has flown a fleet of single-view radiometers that have been used for characterizing the global aerosol system. These have included the Total Ozone Mapping Spectrometer (TOMS), the MODerate resolution Imaging Spectroradiometer (MODIS) on both Terra and Aqua satellites, the Ozone Monitoring Instrument (OMI) on Aura, the Ozone Mapping Profiler Suite (OMPS) and the VIsible InfraRed Sensor (VIIRS) on Suomi-National Polar orbiting Partnership (S-NPP) satellite.

During this era, multiple algorithms have been developed to extract the specific information offered by each sensor and retrieve properties of global aerosol. Algorithms derived for TOMS, OMI and OMPS make use the ultraviolet (UV) portion of the reflected solar spectrum. Algorithms developed for MODIS and VIIRS rely on the visible (VIS) through the shortwave infrared (SWIR). Each spectral range provides information that has been exploited for aerosol characterization, some of which is unique to the specific spectral range and some of which overlaps between ranges. For example, the UV is particularly sensitive to aerosol absorption characteristics, the SWIR sensitive to size distribution and all wavelengths useful to derive aerosol loading in the form of aerosol optical depth (AOD).

With the Ocean Color Instrument (OCI) on PACE, there is opportunity to exploit the full reflected shortwave spectrum for retrieving global aerosol properties. Although we expect there will be at-launch algorithms applied to OCI, these will be based on the heritage set, which was tuned to either UV or VIS/NIR/SWIR. Although these products would include AOD in the UV and VIS over land and ocean, and the fine mode fraction of the AOD over oceans, they would NOT include any information on aerosol absorption, aerosol layer height, nor aerosol above clouds. To retrieve these important characteristics, and narrow uncertainties in estimating aerosol effects on climate change and air quality, we must mine PACE-OCI's full potential. Using OCI's broad spectrum from the UV to the SWIR we can bring home spectral AOD, size parameter, absorption, layer height over ocean and land, and aerosol above clouds. Furthermore, we can use OCI's hyperspectral capability through the oxygen absorption bands to provide an independent measure of aerosol layer height.

We propose a comprehensive aerosol characterization algorithm for OCI-alone, rooted in heritage, but yet completely innovative. By joining forces, we are cutting out the redundancies (and inconsistencies) of multiple aerosol algorithm groups working on separate algorithms on separate UV and VIS/NIR/SWIR sensors. We are not ignoring the added benefit that polarimeters can bring to aerosol characterization, but will not rely on this information for real-time aerosol retrievals.

We intend to provide a unified aerosol algorithm for OCI-unified in its spectral approach from UV to SWIR and unified in bringing multiple heritage groups together into one team. Together spectrally and together in experience, we can produce the best aerosol characterization possible in an operational environment.

Using Multi-Angle Polarimetry to Derive Χ Factor and Improve BRDF Correction for PACE's OCI

PI: Xiaodong Zhang - University of Southern Mississippi
Co-I(s): Deric Gray, Naval Research Lab
Water leaving reflectance that an ocean color mission (such as PACE) measures is inherently bidirectional, i.e., it varies with the position of sun and the viewing direction. This variation, expressed as bidirectional reflectance distribution function (BRDF) is ultimately determined by the volume scattering function (VSF), and in particular its backward shape (called Χ factor). Previous studies investigating BRDF have used the VSFs that evidently underestimate the natural variability of Χ factor and could lead to errors that are greater than the 30% target of retrieving remote sensing reflectance from the PACE mission. On the other hand, the two multi-angle polarimeters (HARP2 and SPEXone) that the PACE will deploy along with the hyperspectral OCI sensor offer an opportunity to measure Χ factor. Taking advantage of this opportunity, we propose to develop a method using multi-angle polarimetry data to derive Χ factor and improve the BRDF correction for PACE's OCI remote sensing reflectance (rrs). The proposed work is based on insight and knowledge that we have gained during past decade in studying the natural variability of the volume scattering function and its impact on ocean color. Specifically, we propose to achieve three objectives. (1) Improve our understanding of natural variability of the Χ factor and its impact on bidirectional remote sensing reflectance. Because of the essential role played by the Χ factor in determining the BRDF effect, addressing this objective will allows us to develop a mechanistic understanding on the source of variability in BRDF. (2) Utilize Zaneveld's theoretical derivation that explicitly includes VSF in rrs formula to retrieve Χ factor from the multi-angle rrs observation. (3) Explore a multi-sensor capability to constrain the BRDF correction for the hyperspectral OCI using Χ factor retrieved from the multi-angle HARP2 and/or SPEXone. In additional to improving BRDF correction, our method will also retrieve Χ factor, which as an important inherent optical property, contains critical information on the composition and structure of the particle assemblages in the oceans. Therefore, it also has potential to derive additional biogeochemical products from the PACE mission.