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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 NTRS2020

The Effect of UV and Solar Wind Exposure on the Reflectance of Two Black Diffuse Materials

The Bidirectional Reflectance Distribution Function (BRDF) and Total Hemispherical Reflectance (THR) of two candidate black diffuse materials for the dim calibration targets of the NASA GSFC PACE Ocean Color Instrument (OCI)were reported in the SPIE conference last year. In this paper, we present new BRDF and THR results of the two black diffuse materials following additional UV exposure and solar wind tests. The BRDF measurements for five samples of each two black diffuse material were made at incident angles of 0° and 45° and at the wavelengths of 360 nm, 600 nm, and 1600 usi ng the Table-top Goniometer (TTG) located in the Diffuser Calibration Laboratory (DCL) at NASA GSFC. The THR of the samples, 15 mm in diameter, was measured using a commercial UV-VIS-NIR spectrophotometer from 200 nm to 2500 nm. The spectral THR results of the two black diffuse materials exposed to UV and solar wind show an approximate 10 % higher reflectivity than the unexposed samples. The spectral profiles of the THR of the exposed and unexposed samples are relatively similar. The BRDF results at the incident angle of 45° show different trends in the forward and backward scattering regions, while those at normal incident angle are consistent with the THR results. We will also present the details of the samples’ surface features and the comparison of the 0°/45° BRDF and THR results, demonstrate the significance of background subtraction in the THR measurements for small, low reflectance samples, and discuss validation of BRDF scale, measurement repeatability, and major contributions of uncertainty.

Jinan Zeng↗

NASA NTRSDate not supplied

Validation of Modis Cloud Liquid Water Path to Prepare for Pace Evaluation Efforts

The Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission will launch in January 2024, extending and improving NASA’s global satellite observations in its eponymous domains. PACE’s hyperspectral Ocean Color Instrument (OCI) will offer daily near-global spatial coverage with a 1.2 km horizontal pixel size at the subsatellite point. PACE will also have two multi-angle polarimeters (HARP2 and SPEXone) capable of advanced atmospheric characterization. Validation of cloud retrievals is challenging. Here we evaluate liquid water path (LWP) from MODIS from the standard (MOD06) product. The same cloud optical properties retrieval algorithm will be applied to OCI data. This will allow us to understand the expected performance of this algorithm and to develop the processing and analysis code needed to evaluate PACE data. Please tell us what you think and if we should be doing something differently!

MODIS↗

NASA NTRSDate not supplied

Pre-Launch Calibration Methods of OCI on the Pace Mission

Scheduled for launch in January 2024, the PACE mission represents NASA’s next investment in ocean biology, clouds, and aerosol data records. A key feature of PACE is the inclusion of an advanced satellite radiometer known as the Ocean Color Instrument (OCI), a global mapping radiometer that combines multispectral and hyperspectral remote sensing. This paper describes the methods used for pre-launch calibration of OCI and considerations to ensure the combination of Ground Support Equipment (GSE) and instrument effects meet uncertainty and performance requirements. General considerations when designing a calibration campaign are also discussed.

oci↗

NASA NTRSDate not supplied

Initial Look at the Results From the Prelaunch Characterization Campaign of OCI on the Pace Mission

Scheduled for launch in January 2024, the Phytoplankton, Aerosol, Cloud, and ocean Ecosystem (PACE) mission represents NASA’s next investment in ocean biology, clouds, and aerosol data records [1]. A key feature of PACE is the inclusion of an advanced satellite radiometer known as the Ocean Color Instrument (OCI), a global mapping radiometer that combines multispectral and hyperspectral remote sensing. This paper describes the results of the prelaunch test campaign of the OCI Flight Unit. The measured OCI flight unit performance exceeded requirement thresholds in all critical areas. Overall, the performance of the OCI is excellent, and will allow the PACE science team to meet its science objectives.

Calibration↗

NASA NTRSDate not supplied

PACE OCI Flight Unit Pre-launch Spectral Characterization

The Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission [1] will extend and improve the data record of NASA’s satellite observations of global ocean biology, aerosols, and clouds. The Ocean Color Instrument (OCI) is the primary sensor on-board the PACE platform [2]. The OCI is a scanning radiometer with hyperspectral coverage from the ultraviolet (UV) to the near infrared (NIR) wavelength range and a fiber-coupled multiband filter spectrograph in the short-wave infrared (SWIR) spectral region. The OCI Flight Unit completed system level testing in November 2022 at the Goddard Space Flight Center (GSFC). This paper presents the spectral characterization and performance of the OCI Flight Unit. The OCI Flight spectral performance was determined to be within design specifications and the characterization was measured within specified uncertainties.

PACE↗

NASA NTRSDate not supplied

Pace Oci Flight Unit Pre-Launch Spectral Characterization

PACE↗

NASA NTRSDate not supplied

OCI Geolocation Evaluation and Refinement Using Landsat Control Points

The Plankton, Aerosol, Cloud, and ocean Ecosystem (PACE) mission is NASA’s next investment in ocean biology, clouds, and aerosol data records. A key feature of PACE is the inclusion of an advanced satellite radiometer, Ocean Color Instrument (OCI), a global mapping radiometer that combines multispectral and hyperspectral remote sensing. The geolocation processing is performed for OCI using spacecraft navigation data and an instrument geometry model. To evaluate the geolocation accuracy for OCI and develop refinements to the processing methods, control point matching using Landsat data has been implemented as a step in the operational processing of OCI data at the Science Data Segment. This processing provides between 200 and 300 high-quality matchups per day with good global and geometric distribution, allowing rapid evaluation of the OCI geolocation accuracy. The results provided an early indication of the overall quality of the geolocation processing and of specific aspects needing improvement. A standard set of granules was identified to support rapid implementation and testing of geolocation refinements, and this approach has been highly successful in improving the geolocation processing accuracy to meet the science requirements. The evaluation will continue throughout the mission to ensure the ongoing accuracy of geolocation. This paper describes the control point matching methodology, the approach to development of the geolocation processing refinements, and the recent results.

PACE↗

NASA NTRSDate not supplied

OCI Geolocation Evaluation and Refinement Using Landsat Control Points

PACE↗

NASA NTRSDate not supplied

Use of TEMPO as a Proxy for Hyperspectral Geostationary Ocean Color Measurements from the GeoXO OCX Instrument: Harnessing Machine Learning and Principal Component Techniques for Atmospheric and Glint Correction

Retrievals of ocean color from space are important for better understanding the ocean ecosystem. The launch of atmospheric geostationary hyperspectral sensors such as TEMPO, provides a unique opportunity to examine the diurnal variability in ocean ecology. While TEMPO does not have as high spatial resolution or full spectral coverage as planned coastal ocean sensors such as the Geosynchronous Littoral Imaging and Monitoring Radiometer (GLIMR) or GeoXO Ocean Color instrument (OCX), its hourly measurements provide coverage of regions such as Lake Erie and the Gulf of Mexico at spatial scales of approximately 5 km. These data can be useful for testing new algorithms. We will apply our newly developed machine learning based atmospheric correction approach for ocean color retrievals to TEMPO data. Our approach begins by decomposing measured radiances from hyperspectral sensors into spectral features that describe the scattering and absorption of the atmosphere as well as the underlying surface reflectance. The coefficients of the principal components are then used to train a neural network to predict ocean color properties derived from collocated MODIS/VIIRS physically-based retrievals. This machine learning approach does not rely on radiative transfer modeling, and the use of MODIS/VIIRS data for training accounts for possible calibration b in hyperspectral data. Previously, we applied our approach using blue and UV wavelengths with the Ozone Monitoring Instrument (OMI) and TROPOspheric Monitoring Instrument (TROPOMI) to show that it can estimate ocean color properties in less-than-ideal conditions such as lightly to moderately clouded conditions as well as sun glint and thus improve the spatial coverage of ocean color measurements. TEMPO provides an opportunity to improve on this approach since it will provide collocated measurements at green and red wavelengths that were not available from OMI and TROPOMI and are important particularly for coastal waters. Additionally, our technique can be applied early in the mission and has potential to demonstrate the value of near real time ocean color products that are important for monitoring of harmful algae blooms and other oceanic phenomena.

Zachary Fasnacht↗

NASA NTRS2012

Instrument Characterization for Ocean Color Remote Sensing

No abstract available

ocean color↗

NASA NTRS1984

NIMBUS-7 CZCS. Coastal Zone Color Scanner Imagery for Selected Coastal Regions. North America - Europe. South America - Africa - Antarctica. Level 2 Photographic Product

The Nimbus-7 Coastal Zone Color Scanner (CZCS) is the first spacecraft instrument devoted to the measurement of ocean color. Although instruments on other satellites have sensed ocean color, their spectral bands, spatial resolution, and dynamic range were optimized for geographical or meteorological use. In the CZCS, every parameter is optimized for use over water to the exclusion of any other type of sensing. The signal-to-noise ratios in the spectral channels sensing reflected solar radiance are higher than those required in the past. These ratios need to be high because the ocean is such a poor reflecting surface that the majority of the signal seen by the reflected energy channels at spacecraft altitudes is backscattered solar radiation from the atmosphere rather than reflected solar energy from the ocean. The CZCS is a conventional multichannel scanning radiometer utilizing a rotating plane mirror at a 45 deg angle to the optic axis of a Cassegrain telescope. The mirror scans 360 deg; however, only 80 deg of data centered on the spacecraft nadir is collected for ocean color measurements. Spatial resolution at spacecraft nadir is 825x825 m with some degradation at the edges of the scan swath. The useful swath width from a spacecraft altitude of 955 km is 1600 km.

Source record↗

NASA NTRS1982

LIDAR and acoustics applications to ocean productivity

The requirements for the submersible, the instrumentation necessary to perform these measurements, and the optical and acoustical technology required to develop the ocean color scanner instrumentation are described. The development of a second generation ocean color scanner produced the need for coincident in situ scientific measurements which examine the primary productivity of the upper ocean on time and space scales which are large compared to the environmental scales. The vertical and horizontal variability of the biota, including the relationship between chlorophyll and primary productivity, the productivity of zooplankton, and the dynamic interaction between phytoplankton and zooplankton, and between these populations and the physical environment are investigated. A towed submersible will be constructed which accommodates both an underwater LIDAR instrument and a multifrequency sonar.

Collins, D. J.↗

NASA NTRS2013

Retrieving Marine Inherent Optical Properties from Satellites Using Temperature and Salinity-dependent Backscattering by Seawater

Time-series of marine inherent optical properties (IOPs) from ocean color satellite instruments provide valuable data records for studying long-term time changes in ocean ecosystems. Semi-analytical algorithms (SAAs) provide a common method for estimating IOPs from radiometric measurements of the marine light field. Most SAAs assign constant spectral values for seawater absorption and backscattering, assume spectral shape functions of the remaining constituent absorption and scattering components (e.g., phytoplankton, non-algal particles, and colored dissolved organic matter), and retrieve the magnitudes of each remaining constituent required to match the spectral distribution of measured radiances. Here, we explore the use of temperature- and salinity-dependent values for seawater backscattering in lieu of the constant spectrum currently employed by most SAAs. Our results suggest that use of temperature- and salinity-dependent seawater spectra elevate the SAA-derived particle backscattering, reduce the non-algal particles plus colored dissolved organic matter absorption, and leave the derived absorption by phytoplankton unchanged.

Passive Remote Sensing↗

NASA NTRS1972

The tongue of the ocean as a remote sensing ocean color calibration range

In general, terrestrial scenes remain stable in content from both temporal and spacial considerations. Ocean scenes, on the other hand, are constantly changing in content and position. The solar energy that enters the ocean waters undergoes a process of scattering and selective spectral absorption. Ocean scenes are thus characterized as low level radiance with the major portion of the energy in the blue region of the spectrum. Terrestrial scenes are typically of high level radiance with their spectral energies concentrated in the green-red regions of the visible spectrum. It appears that for the evaluation and calibration of ocean color remote sensing instrumentation, an ocean area whose optical ocean and atmospheric properties are known and remain seasonably stable over extended time periods is needed. The Tongue of the Ocean, a major submarine channel in the Bahama Banks, is one ocean are for which a large data base of oceanographic information and a limited amount of ocean optical data are available.

Strees, L. V.↗

NASA NTRSDate not supplied

A Principal Component and Machine Learning Approach to Spatially Gap Fill Hyperspectral Ocean Color Satellite Retrievals

Retrievals of ocean color properties from space are important for monitoring the health of the ocean ecosystem but such retrievals tend to be limited in spatial coverage due to conditions such as clouds, aerosols, and sun glint. Gap filling of ocean color retrievals is typically performed by combining retrievals from multiple satellites or temporally averaging multiple days of retrievals but despite these techniques large gaps still exist posing challenges for near real time monitoring of events like harmful algae blooms. To address these limitations, we propose a spatial gap filling approach using machine learning to learn how to perform an atmospheric correction under challenging retrieval conditions. In this approach a principal component analysis is used to decompose the hyperspectral measurements into spectral features that describe the scattering and absorption of the atmosphere as well as the underlying surface. The coefficients of the principal components are then used to train a neural network to predict ocean color properties derived from a standard ocean color algorithm such as the MODIS atmospheric correction algorithm. This machine learning approach is independent of a priori information and does not rely on any radiative transfer modeling. We apply the approach to two hyperspectral UV/VIS instruments, the Ozone Monitoring Instrument (OMI) and TROPOspheric Monitoring Instrument (TROPOMI) to show that it can be used to estimate ocean color properties such as chlorophyll, remote sensing reflectance, and fluorescence line height. This method could be used as a gap-filling technique for the future Ocean Color Instrument (OCI) which will be onboard NASA's Plankton, Aerosol Cloud, ocean Ecosystem (PACE) ocean color satellite to provide additional information for monitoring the health of our global oceans. Additionally, it could be applied to the geostationary satellite Tropospheric Emissions: Monitoring of Pollution (TEMPO) to better understand diurnal variability in ocean ecology.

MODIS atmospheric correction algorithm↗

NASA NTRS2012

NASA In Situ Data Needs to Support the Operational Calibration and Validation of Ocean Color Satellite Data Products

Calibrating ocean color satellite instruments and validating their data products requires temporal and spatial abundances of high quality in situ oceanographic data. The Consortium for Ocean Leadership Ocean Observing Initiative (OOl) is currently implementing a distributed array of in-water sensors that could provide a significant contribution to future ocean color activities. This workshop will scope the optimal way to use and possibly supplement the planned OOl infrastructure to maximize its utility and relevance for calibration and validation activities that support existing and planned NASA ocean color missions. Here, I present the current state of the art of NASA validation of ocean color data products, with attention to autonomous time-series (e.g., the AERONET -OC network of above-water radiometers), and outline NASA needs for data quality assurance metrics and adherence to community-vetted data collection protocols

Werdel, P. Jeremy↗

NASA NTRS2022

Simultaneous Retrieval of Selected Optical Water Quality Indicators From Landsat-8, Sentinel-2, and Sentinel-3

Constructing multi-source satellite-derived water quality (WQ) products in inland and nearshore coastal waters from the past, present, and future missions is a long-standing challenge. Despite inherent differences in sensors’ spectral capability, spatial sampling, and radiometric performance, research efforts focused on formulating, implementing, and validating universal WQ algorithms continue to evolve. This research extends a recently developed machine-learning (ML) model, i.e., Mixture Density Networks (MDNs) (Pahlevan et al., 2020; Smith et al., 2021), to the inverse problem of simultaneously retrieving WQ indicators, including chlorophyll-a (Chla), Total Suspended Solids (TSS), and the absorption by Colored Dissolved Organic Matter at 440 nm (a cdom (440)), across a wide array of aquatic ecosystems. We use a database of in situ measurements to train and optimize MDN models developed for the relevant spectral measurements (400–800 nm) of the Operational Land Imager (OLI), MultiSpectral Instrument (MSI), and Ocean and Land Color Instrument (OLCI) aboard the Landsat-8, Sentinel-2, and Sentinel-3 missions, respectively. Our two performance assessment approaches, namely hold-out and leave-one-out, suggest significant, albeit varying degrees of improvements with respect to second-best algorithms, depending on the sensor and WQ indicator (e.g., 68%, 75%, 117% improvements based on the hold-out method for Chla, TSS, and a cdom (440), respectively from MSI-like spectra). Using these two assessment methods, we provide theoretical upper and lower bounds on model performance when evaluating similar and/or out-of-sample datasets. To evaluate multi-mission product consistency across broad spatial scales, map products are demonstrated for three near-concurrent OLI, MSI, and OLCI acquisitions. Overall, estimated TSS and a cdom (440) from these three missions are consistent within the uncertainty of the model, but Chla maps from MSI and OLCI achieve greater accuracy than those from OLI. By applying two different atmospheric correction processors to OLI and MSI images, we also conduct matchup analyses to quantify the sensitivity of the MDN model and best-practice algorithms to uncertainties in reflectance products. Our model is less or equally sensitive to these uncertainties compared to other algorithms. Recognizing their uncertainties, MDN models can be applied as a global algorithm to enable harmonized retrievals of Chla, TSS, and a cdom (440) in various aquatic ecosystems from multi-source satellite imagery. Local and/or regional ML models tuned with an apt data distribution (e.g., a subset of our dataset) should nevertheless be expected to outperform our global model.

Machine learning↗