The Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission represents NASA’s next investment in satellite ocean color and the study of Earth’s ocean-atmosphere system, enabling new insights into oceanographic and atmospheric responses to Earth's changing climate. PACE objectives include extending systematic cloud, aerosol, ocean biological and biogeochemical data records, making essential ocean color measurements to further understand marine carbon cycles and ecosystem responses to a changing climate, as well as improving knowledge of how aerosols influence ocean ecosystems and, conversely, how ocean ecosystems and photochemical processes affect the atmosphere. PACE objectives also encompass management of fisheries, large freshwater bodies, and water quality and reducing uncertainties in climate and radiative forcing models of the Earth system. PACE observations will also provide information on radiative properties of land surfaces and characterization of the vegetation and soils that dominate their reflectance. The primary PACE instrument – the Ocean Color Instrument (OCI) – is a hyperspectral imaging radiometer that spans the ultraviolet to shortwave infrared, with a ground sample distance of 1-km at nadir. This includes continuous collection of spectra from 340 nm to 890 nm in 5 nm steps. The PACE payload is complemented by two multi-angle polarimeters with spectral ranges that span the visible to near-infrared region. Scheduled for launch in late 2022-to-early 2023, the PACE observatory will enable significant advances in the study of Earth’s biogeochemistry, carbon cycle, clouds, hydrosols, and aerosols in the ocean-atmosphere system. We present a brief overview of the PACE mission, followed by a discussion of the capabilities and design concept of OCI.
The Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission will launch no earlier than summer 2022. The primary payload is the Ocean Color Instrument (OCI). OCI is a hyperspectral imaging radiometer that will measure top-ofatmosphere radiances from 340nm to 2260nm at approximately 1km spatial resolution. The spectral resolution will be 5nm from 340nm to 890nm to enable the production of innovative ocean color products on a global scale (OCI will provide global coverage every 2 days). There are 7 different multispectral bands in the shortwave infrared to support atmospheric correction for ocean color and aerosol and cloud studies. Ocean color applications require state of the art radiometric accuracy (approximately 0.5%, excluding the absolute calibration uncertainty). Considerable effort has been invested in the planning of the prelaunch calibration campaign and the on-orbit calibration capabilities. This paper describes the current plans for the prelaunch calibration and characterization campaign of the OCI Engineering Test Unit (ETU), which is scheduled to begin towards the end of 2019. The prelaunch calibration campaign will characterize all sensor characteristics that are expected to influence radiometric sensitivity: absolute calibration (i.e. radiometric gains), signal to noise ratio, nonlinearity, response versus scan angle, dynamic range, signal to noise ratio, and sensitivities to polarization and temperature. In addition to these one-time characterization tests, two types of tests have been developed that monitor the evolution of several OCI radiometric characteristics: a Limited Performance Test (LPT, expected duration about 8 hours), and a Comprehensive Performance Test (CPT, expected duration about 2 days).
The Operational Land Imager (OLI) is a multispectral radiometer hosted on the recently launched Landsat8 satellite. OLI includes a suite of relatively narrow spectral bands at 30 m spatial resolution in the visible to shortwave infrared, which makes it a potential tool for ocean color radiometry: measurement of the reflected spectral radiance upwelling from beneath the ocean surface that carries information on the biogeochemical constituents of the upper ocean euphotic zone. To evaluate the potential of OLI to measure ocean color, processing support was implemented in Sea-viewing Wide Field-of-View Sensor (SeaWiFS) Data Analysis System (SeaDAS), which is an open-source software package distributed by NASA for processing, analysis, and display of ocean remote sensing measurements from a variety of spaceborne multispectral radiometers. Here we describe the implementation of OLI processing capabilities within SeaDAS, including support for various methods of atmospheric correction to remove the effects of atmospheric scattering and absorption and retrieve the spectral remote sensing reflectance (Rrs; sr−1). The quality of the retrieved Rrs imagery will be assessed, as will the derived water column constituents, such as the concentration of the phytoplankton pigment chlorophyll a.
Optical properties of oceanic and coastal waters are not only important for describing subsurface light field, but also
useful indexes of environmental status. To meet the demand of various users, optical data products of global waters are
now generated from ocean color satellite sensors (e.g. SeaWiFS, MODIS, MERIS). These products, due to imperfect
sensor technology and retrieval algorithms, inherently contain some degrees of uncertainties. Traditionally, an averaged
difference (or so-called error) for a dataset is usually provided via comparing retrieved values with in situ
measurements. This averaged "error" is good at providing an overall picture between the retrieved and measured
properties, but cannot indicate uncertainties for a specific product or a pixel, because that uncertainties in these products
are not spatially uniform. Here, using optical properties derived from the Quasi-Analytical Algorithm as an example, we
present an approach to quantify pixel-wise uncertainties of remote-sensing derived properties. Further, we quantitatively
evaluated the uncertainties of the derived inherent optical properties (IOPs) and water-clarity products with a simulated
dataset, and found that the relative uncertainty is generally within 10% for total absorption coefficients of oceanic
waters. This presentation shows the theoretical basis to evaluate and understand the impacts of the various components
on the analytically derived optical properties, and that a practical means to quantify the uncertainties of inverted
properties for each reflectance spectrum is now available. This effort lays the groundwork for generating quality maps
of optical properties derived from satellite ocean color images.
KEYWORDS: Satellites, Algorithm development, Global system for mobile communications, Ocean optics, Sensors, Atmospheric corrections, MODIS, Remote sensing, Water, Magnesium
Ocean color satellites provide a mechanism for studying the marine biosphere on temporal and spatial scales
otherwise unattainable via conventional in situ sampling methods. These satellites measure visible and infrared
radiances, which are used to estimate additional geophysical data products, such as the concentration of the
phytoplankton pigment chlorophyll a, Ca, via the application of secondary bio-optical algorithms. The operational
Ca algorithms for the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and Moderate Resolution
Imaging Spectroradiometer (MODIS), for example, perform well in the global open ocean, but often degrade
in more optically complex coastal environments where global parameterizations are less applicable. Organizations
such as the Chesapeake Bay Program, which have interest in using SeaWiFS and MODIS data products
to facilitate regional monitoring activities, must rely on locally parameterized algorithms to achieve requisite
accuracies. To facilitate algorithm selection, the NASA Ocean Biology Processing Group recently developed the
infrastructure to spatially and temporally evaluate a long-term regional time-series of satellite observations using
in situ measurements as ground-truth. Here, we present this approach using a case study in the Chesapeake Bay,
where a series of Ca algorithms and atmospheric correction schemes were evaluated for the full SeaWiFS and
MODIS-Aqua time-series. We demonstrate how the selection of the best algorithms and processing approaches
is driven by trade-offs in coverage needs and relative accuracy requirements. While our case study highlights Ca
in the Chesapeake Bay, our methodology is applicable to any geophysical data product and region of interest.
The Ocean Biology Processing Group (OBPG) at NASA's Goddard Space Flight Center is responsible for the processing and validation of oceanic optical property retrievals from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and the Moderate Resolution Imaging Spectroradiometer (MODIS). A major goal of this activity is the production of a continuous ocean color time-series spanning the mission life of these sensors from September 1997 to the present time. This paper presents an overview of the calibration and validation strategy employed to optimize and verify sensor performance for retrieval of upwelling radiances just above the sea surface. Substantial focus is given to the comparison of results over the common mission lifespan of SeaWiFS and the MODIS flying on the Aqua platform, covering the period from July 2002 through December 2004. It will be shown that, through consistent application of calibration and processing methodologies, a continuous ocean color time-series can be produced from two different spaceborne sensors.
The NASA Sensor Intercomparison and Merger for Biological and Interdisciplinary Oceanic Studies (SIMBIOS) Program had a worldwide, ongoing ocean color data collection program, as well as an operational data processing and analysis capability. SIMBIOS data collection takes place via the SIMBIOS Science Team. In addition, SIMBIOS had a calibration and product validation component (Project Office). The primary purpose of these calibration and product validation activities were to (1) reduce measurement error by identifying and characterizing true error sources, such as real changes in the satellite sensor or problems in the atmospheric correction algorithm, in order to differentiate these errors from natural variability in the marine light field; and (2) evaluate the various bio-optical and atmospheric correction algorithms being used by different ocean color missions. For each sensor, the SIMBIOS Project reviews the sensor design and processing algorithms being used by the particular ocean color project, compares the algorithms with alternate methods when possible, and provides the results to the appropriate project office.
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