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NASA NTRS2020

High Spectral Resolution Datasets of in Situ Aquatic Inherent and Apparent Optical Properties

Light emerging from natural water bodies and measured by remote sensing radiometers contains information about the local type and concentrations of phytoplankton, non-algal particles and colored dissolved organic matter in the underlying waters. An increase in spectral resolution in forthcoming satellite and airborne remote sensing missions is expected to lead to new or improved capabilities to characterize aquatic ecosystems. Such upcoming missions include NASA’s Plankton, Aerosol, Cloud, ocean Ecosystem PACE Mission; the NASA Surface Biology and Geology observable mission; and NASA Airborne Visible / Infrared Imaging Spectrometer (AVIRIS) - Next Generation airborne missions. In anticipation of these missions, we present a dataset of geographically diverse, quality-controlled, high spectral resolution inherent and apparent optical property (IOP/AOP) aquatic data. The data are intended to be of use to increase our understanding of aquatic optical properties, to develop aquatic remote sensing data product algorithms, and to perform calibration and validation activities for forthcoming aquatic-focused imaging spectrometry missions. The dataset is comprised of contributions from several investigators and investigating teams collected over a range of geographic areas and water types, including inland water, estuaries and oceans. Specific in situ measurements include particulate absorption, particulate attenuation, non-algal particulate or detritus absorption, colored dissolved organic matter absorption, phytoplankton absorption, total absorption, total attenuation, particulate backscattering, total backscatter, remote sensing reflectance, and irradiance reflectance. The dataset can be downloaded from PANGAEA: (https://www.pangaea.de/TBD ).

K A Casey↗
NASA NTRS2019

Atmospheric Correction of Satellite Ocean-Color Imagery During the PACE Era

The Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission will carry into space the Ocean Color Instrument (OCI), a spectrometer measuring at 5 nm spectral resolution in the ultraviolet (UV) to near infrared (NIR) with additional spectral bands in the shortwave infrared (SWIR), and two multi-angle polarimeters that will overlap the OCI spectral range and spatial coverage, i. e., the Spectrometer for Planetary Exploration (SPEXone) and the Hyper-Angular Rainbow Polarimeter (HARP2). These instruments, especially when used in synergy, have great potential for improving estimates of water reflectance in the post Earth Observing System (EOS) era. Extending the top-of-atmosphere (TOA) observations to the UV, where aerosol absorption is effective, adding spectral bands in the SWIR, where even the most turbid waters are black and sensitivity to the aerosol coarse mode is higher than at shorter wavelengths, and measuring in the oxygen A-band to estimate aerosol altitude will enable greater accuracy in atmospheric correction for ocean color science. The multi-angular and polarized measurements, sensitive to aerosol properties (e.g., size distribution, index of refraction), can further help to identify or constrain the aerosol model, or to retrieve directly water reflectance. Algorithms that exploit the new capabilities are presented, and their ability to improve accuracy is discussed. They embrace a modern, adapted heritage two-step algorithm and alternative schemes (deterministic, statistical) that aim at inverting the TOA signal in a single step. These schemes, by the nature of their construction, their robustness, their generalization properties, and their ability to associate uncertainties, are expected to become the new standard in the future. A strategy for atmospheric correction is presented that ensures continuity and consistency with past and present ocean-color missions while enabling full exploitation of the new dimensions and possibilities. Despite the major improvements anticipated with the PACE instruments, gaps/issues remain to be filled/tackled. They include dealing properly with whitecaps, taking into account Earth-curvature effects, correcting for adjacency effects, accounting for the coupling between scattering and absorption, modeling accurately water reflectance, and acquiring a sufficiently representative dataset of water reflectance in the UV to SWIR. Dedicated efforts, experimental and theoretical, are in order to gather the necessary information and rectify inadequacies. Ideas and solutions are put forward to address the unresolved issues. Thanks to its design and characteristics, the PACE mission will mark the beginning of a new era of unprecedented accuracy in ocean-color radiometry from space.

ocean color↗
NASA NTRS2019

Simulating response versus scan angle characterization on OCI for the upcoming PACE mission

The Plankton, Aerosol, Cloud ocean Ecosystem (PACE) mission Ocean Color Instrument (OCI) is the next generation ocean color sensor following on from the SeaWiFS and VIIRS designs, to be launched into low Earth orbit in 2022. Like those heritage sensors, OCI has cross track scanning fore-optics, the reflectivity of which (referred to here as the response versus scan angle or RVS) must be characterized prior to launch. The expected test setup, component reflectivity measurements, instrument model, and heritage sensor testing were used to generate simulated RVS data. This simulated data set was then used to verify the functionality of the analysis software in preparation for the upcoming ground test campaign. The analysis software is designed to characterize the RVS for all OCI bands over the full range of scan angles to be measured on orbit as well as verify compliance with the sensor design requirements in near real-time.

PACE↗
NASA NTRS2019

Retrieving Aerosol Characteristics From the PACE Mission, Part 1: Ocean Color Instrument

NASA’s Plankton, Aerosol, Clouds, ocean Ecosystem (PACE) satellite mission is scheduled to launch in 2022, with the Ocean Color Instrument (OCI) on board. For the first time reflected sunlight from the Earth across a broad spectrum from the ultraviolet (UV: 350 nm) to the short wave infrared (SWIR: 2260 nm) will be measured from a single instrument at 1 km spatial resolution. While seven discrete bands will represent the SWIR, the spectrum from 350 to 890 nm will be continuously covered with a spectral resolution of 5 nm. OCI will thus combine in a single instrument (and at an enhanced spatial resolution for the UV) the heritage capabilities of the Moderate resolution Imaging Spectroradiometer (MODIS) and the Ozone Monitoring Instrument (OMI), while covering the oxygen A-band (O2A). Designed for ocean color and ocean biology retrievals, OCI also enables continuation of heritage satellite aerosol products and the development of new aerosol characterization from space. In particular the combination of MODIS and OMI characteristics allows deriving aerosol height, absorption and optical depth along with a measure of particle size distribution. This is achieved by using the traditional MODIS visible-to-SWIR wavelengths to constrain spectral aerosol optical depth and particle size. Extrapolating this information to the UV channels allows retrieval of aerosol absorption and layer height. A more direct method to derive aerosol layer height makes use of O2A absorption methods, despite the relative coarseness of the nominal 5 nm spectral resolution of OCI. Altogether the PACE mission with OCI will be an unprecedented opportunity for aerosol characterization that will continue climate data records from the past decades and propel aerosol science forward toward new opportunities.

PACE↗
NASA NTRS2021

An Operational Overview of the EXport Processes in the Ocean from RemoTe Sensing (EXPORTS) Northeast Pacific Field Deployment

The goal of the EXport Processes in the Ocean from RemoTe Sensing (EXPORTS) field campaign is to develop a predictive understanding of the export, fate, and carbon cycle impacts of global ocean net primary production. To accomplish this goal, observations of export flux pathways, plankton community composition, food web processes, and optical, physical, and biogeochemical (BGC) properties are needed over a range of ecosystem states. Here we introduce the first EXPORTS field deployment to Ocean Station Papa in the Northeast Pacific Ocean during summer of 2018, providing context for other papers in this special collection. The experiment was conducted with two ships: a Process Ship, focused on ecological rates, BGC fluxes, temporal changes in food web, and BGC and optical properties, that followed an instrumented Lagrangian float; and a Survey Ship that sampled BGC and optical properties in spatial patterns around the Process Ship. An array of autonomous underwater assets provided measurements over a range of spatial and temporal scales, and partnering programs and remote sensing observations provided additional observational context. The oceanographic setting was typical of late-summer conditions at Ocean Station Papa: a shallow mixed layer, strong vertical and weak horizontal gradients in hydrographic properties, sluggish sub-inertial currents, elevated macronutrient concentrations and low phytoplankton abundances. Although nutrient concentrations were consistent with previous observations, mixed layer chlorophyll was lower than typically observed, resulting in a deeper euphotic zone. Analyses of surface layer temperature and salinity found three distinct surface water types, allowing for diagnosis of whether observed changes were spatial or temporal.The 2018 EXPORTS field deployment is among the most comprehensive biological pump studies ever conducted. A second deployment to the North Atlantic Ocean occurred in spring 2021, which will be followed by focused work on data synthesis and modeling using the entire EXPORTS data set.

Biological pump↗
NASA NTRSDate not supplied

Progressive TDI Measurements with the PACE OCI ETU

The Plankton Aerosol Cloud ocean Ecosystem (PACE) Ocean Color Instrument (OCI) has completed the ground test program for its engineering unit (ETU) and testing of the flight unit will begin in the near future. OCIis a grating spectrometer with hyperspectral coverage from about 340 nm to 885 nm with 9 additional filtered channels in the SWIR. Two CCDs are used as detectors for the hyperspectral channels. One important operating mode of the CCDs on OCI is progressive time delay integration (or PTDI). In this mode, the charge in the CCD can be held for multiples of the nominal integration times. A series of these measurements can be made with progressively increasing multiples of the nominal integration time as the instrument scans across a uniform source. Ground testing with this operating mode on OCI ETU has shown promising results. This work will present measurements taken with the PTDI mode and the analysis of OCI ETU linearity and dynamic range

PACE↗
NASA NTRSDate not supplied

PACE OCI Pre-launch ETU Spectral Characterization and Performance

The Ocean Color Instrument (OCI) is the primary sensor on the upcoming Plankton, Aerosol, Cloud ocean Ecosystem (PACE) mission. OCI is a new type of sensor compared to NASA’s ocean color heritage sensors VIIRS, MODIS, and SeaWiFs. Unlike its heritage sensors, OCI has two slit grating hyperspectral spectrographs in addition to a fiber-coupled multiband filter spectrograph. The two hyperspectral spectrographs provide continuous coverage from 340nm to 885nm in the UV to NIR range. These spectrographs use programmable CCD detectors that aggregate multiple CCD pixels that can effectively provide various spectral resolutions. The fiber-coupled multiband filter spectrograph provides seven discrete spectral bands from 940nm to 2260nm. OCI completed system level testing of the Engineering Test Unit (ETU) in June 2021 at the Goddard Space Flight Center (GSFC). The ETU contains one of the slit grating spectrographs (600nm to 885nm) and the fiber-coupled spectrograph. The ETU was tested in thermal vacuum (TVAC) in February 2020 and January 2021 to assess characterization and performance compliance to design requirements. This paper presents an overview of the spectral performance of the OCI ETU for relative spectral response (RSR), integrated Out-of-Band response (IOOB), system gain, band centers, and bandwidths.

PACE↗
NASA NTRS2021

Adaptive Data Screening for Multi-Angle Polarimetric Aerosol and Ocean Color Remote Sensing Accelerated by Automatic Differentiation

Remote sensing measurements from multi-angle polarimeters (MAPs) contain rich aerosol microphysical property information, and these sensors have been used to perform retrievals in optically complex atmosphere and ocean systems. Previous studies have concluded that, generally, five moderately separated viewing angles in each spectral band provide sufficient accuracy for aerosol property retrievals, with performance gradually saturating as angles are added above that threshold. The Hyper-Angular Rainbow Polarimeter (HARP) instruments provide high angular sampling with a total of 90-120 unique angles across four bands, a capability developed mainly for liquid cloud retrievals. In practice, not all view angles are optimal for aerosol retrievals due to impacts of clouds, sun glint, and other impediments. The many viewing angles of HARP can provide resilience to these effects, if the impacted views are screened from the dataset, as the remaining views may be sufficient for successful analysis. In this study, we discuss how the number of available viewing angles impacts aerosol and ocean color retrieval uncertainties, as applied to two versions of the HARP instrument. AirHARP is an airborne prototype that was deployed in the ACEPOL field campaign, while HARP2 is an instrument in development for the upcoming NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission. Based on synthetic data, we find that a total of 20-30 angles across all bands (i.e. five to eight viewing angles per band) are sufficient to achieve good retrieval performance. Following from this result, we develop an adaptive multi-angle polarimetric data screening (MAPDS) approach to evaluate data quality by comparing measurements with their best-fitted forward model. The FastMAPOL retrieval algorithm is used to retrieve scene geophysical values, by matching an efficient, deep learning-based, radiative transfer emulator to observations. The data screening method effectively identifies and removes viewing angles affected by thin cirrus clouds and other anomalies, improving retrieval performance. This was tested with AirHARP data, and we found agreement with the High Spectral Resolution Lidar-2 (HSRL-2) aerosol data. The data screening approach can be applied to modern satellite remote sensing missions, such as PACE, where a large amount of multi-angle, hyperspectral, polarimetric measurements will be collected.

multi-angle polarimeter↗
NASA NTRS2021

Application of Radon Transform to Multi-Angle Measurements Made by the Research Scanning Polarimeter: A New Approach to Cloud Tomography. Part I: Theory and Tests on Simulated Data.

The Research Scanning Polarimeter (RSP) is an airborne along-track scanner measuring the polarized and total reflectances in 9 spectral channels. The RSP was a prototype for the Aerosol Polarimetry Sensor (APS) launched on-board the NASA Glory satellite. Currently the retrieval algorithms developed for the RSP are being adopted for the measurements of the space-borne polarimeters on the upcoming NASA’s Plankton, Aerosol, Cloud Ocean Ecosystem (PACE)satellite mission. The RSP’s uniquely high angular resolution coupled with the high frequency of measurements allows for characterization of liquid water cloud droplet sizes using the polarized rainbow structure. It also provides geometric constraints on the cumulus cloud’s 2D cross section yielding the cloud’s geometric shape estimates. In this study we further build on the latter technique to develop a new tomographic approach to retrieval of cloud internal structure from remote sensing measurements. While tomography in the strict definition is a technique based on active measurements yielding a tomogram (directional optical thickness as a function of angle and offset of the view ray), we developed a “semi-tomographic” approach in which tomogram of the cloud is estimated from passive observations instead of being measured directly. This tomogram is then converted into 2D spatial distribution of the extinction coefficient using inverse Radon transform (filtered back projection) which is the standard tomographic procedure used e.g., in medical CT scans. This algorithm is computationally inexpensive compared to techniques relying on highly-multi-dimensional least-square fitting; it does not require iterative 3D RT simulations. The resulting extinction distribution is defined up to an unknown constant factor, so we discuss the ways to calibrate it using additional independent measurements. In the next step we use the profile of the droplet size distribution parameters from the cloud’s side (derived by fitting the polarized rainbows) to convert the 2D extinction distribution into that of the droplet number concentration. We illustrate and validate the proposed technique using 3D-RT-simulatedRSP observations of a LES-generated Cu cloud. Quantitative comparisons between the retrieved and the original optical and microphysical parameters are presented.

clouds↗
NASA NTRS2022

The Future: Nasa’s Surface Biology and Geology Mission and the Dark (Aquatic) Side

With the release of NASA’s Earth System Observatory (ESO) measurement targets that includes studying the Earth’s Surface Biology and Geology (SBG), a global visible to shortwave infrared imaging spectrometer and a multispectral thermal infrared imager have been planned to launch in the late 2020s. This mission will enable unprecedented interdisciplinary science and applications relevant to studying the biology and geology of the Earth's surface. Measurement targets are directly aligned for studying coastal and inland waters ecosystems and water quality, including coralreef ecosystems, snow and ice, mineralogy, volcanology, biology, ecology, biodiversity, and components of radiative forcing from the surface such as greenhouse gas emissions. The observations not only have scientific value in studying feedbacks and interactions of surface processes (e.g., harmful algal blooms, domoic acid, oil spills, and other hazardous events), but also societal benefit – with the capacity to support real-world decision-making such as response to theseevents and inland water conservation, protection of drinking water quality for public and environmental health, and habitat conservation. The work presented here outlines the conducted mission and architecture study and the initiation of science performance trades (signal sensitivity; spectral, spatial, and temporal resolution; and atmospheric effects) informing the design of the SBG segment of the ESO providing the most value to myriad science and applications as possible. Science and applications synergies and algorithms with the Plankton, Aerosol, Cloud, and ocean Ecosystem (PACE) and the Geostationary Littoral Imaging and Monitoring Radiometer (GLIMR) is opportunistic for advancing aquatics research and applications.

THE FUTURE↗
NASA NTRS2022

A Radiative Transfer Simulator for PACE: Theory and Applications

A radiative transfer simulator was developed to compute the synthetic data of all three instruments onboard NASA’s Plankton Aerosol, Cloud, ocean Ecosystem (PACE) observatory, at the top of the atmosphere (TOA). The instrument suite includes the ocean color instrument (OCI), the HyperAngular Rainbow Polarimeter 2 (HARP2), and the Spectro-Polarimeter for Planetary Exploration 1 (SPEXone). The PACE simulator is wrapped around a monochromatic radiative transfer model based on the successive order of scattering (RTSOS), which accounts for atmosphere and ocean coupling, polarization, and gas absorption. Inelastic scattering, including Raman scattering from pure ocean water, fluorescence due to chlorophyll, and colored dissolved organic matter (CDOM), is also simulated. This PACE simulator can be used to explore the sensitivity of the hyperspectral and polarized reflectance of the Earth system with tunable atmosphere and ocean parameters, which include aerosol and cloud number concentration, refractive indices, and size distribution, ocean particle microphysical parameters, and solar and sensor-viewing geometry. The PACE simulator is used to study two important case studies. One is the impact of the significant uncertainty in pure ocean water absorption coefficient to the radiance field in the ultraviolet (UV) spectral region, which can be as much as 6%. The other is the influence of different amounts of brown carbon aerosols and CDOM on the polarized radiance field at TOA. The percentage variation of the radiance field due to CDOM is mostly for wavelengths smaller than 600 nm, while brown aerosols affect the whole spectrum from 350 to 890 nm, primarily due to covaried soot aerosols. Both case studies are important for aerosol and ocean color remote sensing and have not been previously reported in the literature.

PACE↗
NASA NTRSDate not supplied

NASA’s Evolving Ka-band Network Capabilities to Meet Mission Demand

Space missions are increasingly demanding higher data rates to support the growth in information-intensive mission operations. This growth is reflected in planned and operational missions from low Earth orbit, such as the upcoming NASA-Indian Space Research Organization (ISRO) Synthetic Aperture Radar (NISAR) and Plankton, Aerosol, Cloud and Ocean Ecosystem (PACE) missions, to the future Artemis lunar campaign, and the recently launched James Webb Space Telescope orbiting at the Sun-Earth L2 Lagrange point. JWST was the first L2 mission to be defined as a high data rate mission transmitting at 8 Mbps, or 270 gigabits of science data per day. ISRO and PACE anticipate achieving data throughputs of 5-40 terabits per day. These data rates exceed the capabilities of S-band and X-band frequency allocations and are a key driver for migrating to the 26 GHz Ka-band frequency allocation. The NASA Space Communications and Navigation (SCaN) program has been preparing the networks to support this demand by pursuing critical Ka-band infrastructure. The status of current and evolving network capability, including the Near Space Network’s Initiative for Ka-band Advancement (NIKA), and the Deep Space Network’s Lunar Exploration Upgrades (DLEU), as well as profiling mission usage of Ka-band services, are discussed in detail. The push toward Ka-band, is not only an opportunity for increased performance, but alleviates current challenges with contentious and cluttered spectrum access in S- and X-band. The paper provides an overview of these advantages and advanced techniques that optimize its use before the transition to optical communications becomes an imperative. The challenges and potential mitigations for missions considering selection of Ka-band network services are also discussed.

space communications↗
NASA NTRSDate not supplied

Simultaneous Aerosol and Ocean Polarimeter Products Using Coupled Atmosphere-Ocean Vector Radiative Transfer and Neural Networks: The PACE-MAPP Algorithm

We describe the PACE-MAPP algorithm that simultaneously retrieves aerosol and ocean optical parameters using multiangle and multi-channel polarimeter measurements from the SPEXone, Hyper-Angular Rainbow Polarimeter 2 (HARP2), and Ocean Color Instrument (OCI) instruments onboard the NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) observing system PACE-MAPP is adapted from the Research Scanning Polarimeter (RSP) Microphysical Aerosol Properties from Polarimetry (RSP-MAPP) algorithm. A key feature of the MAPP family of algorithms is the use of a coupled vector radiative transfer model such that the atmosphere and ocean are always considered together as one system. Consequently, conservation of energy ensures that negative water-leaving radiances do not occur. PACE-MAPP uses optimal estimation to simultaneously characterize the optical and microphysical properties of aerosol and ocean constituents, find the optimal solution, and reliably account for the uncertainties of each parameter. This coupled approach, together with multiangle, multi-channel polarimeter measurements, will enable retrievals of aerosol and water properties across the Earth’s oceans. The PACE-MAPP algorithm provides aerosol and ocean products for both the open ocean and coastal areas and is designed to be accurate, modular, and efficient by using fast neural networks that replace the time-consuming vector radiative transfer calculations. We provide an overview of the PACE-MAPP framework and also describe its modular components including its aerosol and hydrosol models, ocean bio-optical models, and thin cirrus model.

Snorre Stamnes↗
NASA NTRSDate not supplied

NASA's Implementation of Cloud Services for Human Space Flight

Cloud is a tried-and-true technology used throughout United States government agencies, including the National Aeronautics and Space Administration (NASA). With reliable results and infrequent downtimes, cloud allows for secure remote access, customizability, and streamlined monitoring options, creating an environment for better data integrity and availability. As NASA increasingly migrates functions to the cloud, the Space Communications and Navigation Program (SCaN) program has been investigating how this capability can be leveraged to provide communication services to its users and customers. Currently, missions such as NASA-ISRO Synthetic Aperture Radar (NISAR), Plankton, Aerosol, Cloud, ocean Ecosystem (PACE), and Roman Space Telescope (RST) are planned to incorporate cloud into their data delivery architecture. However, SCaN is looking to expand further. This conversion to using cloud services allows for greater availability of mission data for both robotic and human space flight (HSF)missions. The SCaN program and the Near Space Network (NSN) are working to consolidate resources and create a cloud environment suitable for the entirety of the SCaN program network architecture. SCaN is in the process of finalizing its cloud architecture and soon will be implementing cloud services. The new services used will adhere to federal regulations including Federal Risk and Authorization Management Program (FedRAMP), which is built upon National Institute of Standards and Technology (NIST)documentation. While keeping in mind these security requirements, an auxiliary objective of the cloud integration is to ensure the most cost-efficient solution; providing a scalable, robust and resilient system. Using cloud services, NASA will gain access to better centralized monitoring and management features, along with customizable services on a pay-per-use plan. With the ever-growing NASA mission data volume needs, maintaining ample storage space is another major constraint. Processing and storing such large amounts of data, on the order of terabytes a day, requires dynamic processing capability which is inherently a strength of cloud computing. By routing this data from ground stations through the cloud, there will be greater ease of access for both SCaN and the user community. Artificial intelligence and other built-in cloud functions can also enhance efficiency, improving data processing time. Thereby also allowing for better data availability. As we look to the future of cloud services, NASA will continue to leverage capabilities that will benefit NASA’s ability to provide cost-effective communication services. This paper further outlines the evolution of cloud use by SCaN in the context of Human Space Flight.

cloud storage↗
NASA NTRSDate not supplied

PACE OCI Short-Wave Infrared Detection Assembly Optical System Design, Alignment, and Environmental Test

The Ocean Color Instrument (OCI), which will be integrated with the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) satellite, will collect science data that will be used to monitor the health of Earth’s oceans and atmosphere. The Short-Wave Infrared (SWIR) Detection Assembly (SDA), built and characterized by Utah State University Space Dynamics Laboratory (SDL), is a subsystem of OCI consisting of 32 channels covering seven discrete optical bands of interest. A total of 16 SWIR Detection Subassemblies (SDSs) compose the SDA and house the cold optical system. The science data optical input for each SDS is supplied by a 0.22 NA multimode fiber interfacing with a fiber adapter. The diverging light from the fiber is collimated, split by a dichroic beamsplitter to two separate channels, filtered by the science filter, and then reimaged onto the single-element detectors with a final 0.76 NA. Aspheric, diamond-turned powered elements are used throughout the optical design. Fabrication and alignment tolerance analysis/budgets are balanced to ensure the optical system meets throughput requirements. All systems are aligned at ambient temperature using an InSb camera and an in-line illumination microscope system to directly image the active detector area through the science filters. Compensators used during alignment are detector focus and decenter, which are adjusted via photoetched shims in increments of 25 µm. Average focus and centering errors were less than 8 µm among all 32 flight and 10 flight spare detectors. Each SDS spectral response and conversion gain was verified at operational temperature of -65°C in vacuum.

OCI↗
NASA NTRS2022

Infrared Spectral Responses of the Ocean Color Instrument (OCI) Pre-assembly and Integration

The Ocean Color Instrument (OCI) to go on the Plankton, Aerosol, Cloud, ocean Ecology (PACE) Earth-observing satellite has a Short-wave infrared (SWIR) Detection Assembly (SDA). This SDA is used to measure upwelling radiation in seven discrete bands from 940 to 2260 nm. There are redundant measurements of each band for a total of 32 physical channels, which includes optical components through to detection. The relative spectral response (RSR) is measured for each channel, which is needed when accounting for the spectral distribution of sensed radiance. From the RSR, single-value performance metrics are computed including the center wavelength, the full width at half of the maximum (FWHM), and the full width at 1% of the maximum (FW1P). Besides in-band responses, the out-of-band rejection ratio (OOBRR) is also calculated for each of the channels, which is a measure of the sensitivity outside the band of interest. We find that all 32 SDA detection channels meet the spectral response requirements at the qualification temperatures at which tests were conducted.

PACE↗
NASA NTRSDate not supplied

Global Ocean Studies from ICESat-2 Mission

The primary purpose of ICESat-2 mission is to monitor changes in the cryosphere. Fortunately, additional, and unrealized information from the penetration of laser light below ocean surface offers a new and exciting opportunity to study the ocean biology globally. The objective of this study is to provide the global ocean subsurface results (e.g., depolarization ratio and particulate backscattering coefficient) from ATLAS/ICESat-2 lidar measurements. The seasonal maps of ATLAS retrieved subsurface results exhibit all the major ocean plankton features anticipated from the earlier passive ocean color and CALIOP/CALIPSO lidar measurements. The ICESat-2 ATLAS lidar can continue to monitor global ocean phytoplankton properties after CALIOP/CALIPSO mission. Moreover, the ICESat-2 ocean subsurface results provide unique information to augment existing ocean color measurements by adding nighttime observations and the depth dimension with high horizontal and vertical resolutions.

ICESat2↗
NASA NTRS2022

Spatial Characterization of PACE OCI ETU Using Time-Delay Mode

The OCI (Ocean Color Instrument) is the main sensor on the upcoming PACE (Plankton Aerosol Cloud ocean Ecosystem) mission. OCI has two hyperspectral CCD sensors covering 340nm to 885nm and 9 SWIR (Short Wave IR) bands from 940nm to 2260nm. SWIR bands have nominal 1km ground pixel size and CCD bands have native 1/8 km ground pixel size in diagnostic mode that will be aggregated into 1km pixels to improve SNR and meet the data rate constraints. OCI has a rotating telescope that is synchronized to the readout of the CCD and SWIR detectors. Full pre-launch system level testing for the OCI ETU (Engineering Test Unit) was completed in June 2021.With time-delayed scan mode, a sub-pixel level time-delay step is applied to the detector readout. This sub-pixel level time-delay step causes a sub-pixel level shift in the start of the data collection. After collecting time-delay step scans with different step sizes, a scan profile with sub-pixel resolution can be constructed. 1/8 and 1/4 of CCD pixel resolutions were achieved using this mode. In this paper, the OCI time-delayed scan mode will be described as well as how it was used to calculate OCI’s high spatial resolution PSF (Point Spread Function), IFOV (instantaneous Field of View), MTF (Modulation Transfer Function), and BBR (Band to Band Registration).

PACE↗