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NASA NTRS2012

NASA OBPG Field Program and SeaBASS Updates

Calibrating ocean color satellite instruments and validating their data products requires temporal and spatial abundances of high quality in situ oceanographic data. To this end, the Ocean Ecology Laboratory (OEL) maintains two entities that are engaged in field data collection and archival. First, the OEL houses a Field Support Group to collect in situ oceanographic measurements, execute laboratory analyses, revise community-vetted protocols for conducting these exercises, and host community training events. Second, the OEL maintains the SeaWiFS Bio-optical Archive and Storage System (SeaBASS) as the permanent archive for all in situ data collected under the auspices of the NASA Ocean Biology and Biogeochemistry Program (OBB; Dr. Paula Bontempi, Program Manager). This talk provides the OBB community and interested researchers their annual update on both the Field Support Group and SeaBASS.

Wedell, P. Jeremy↗
NASA NTRS2013

Underway Sampling of Marine Inherent Optical Properties on the Tara Oceans Expedition as a Novel Resource for Ocean Color Satellite Data Product Validation

Developing and validating data records from operational ocean color satellite instruments requires substantial volumes of high quality in situ data. In the absence of broad, institutionally supported field programs, organizations such as the NASA Ocean Biology Processing Group seek opportunistic datasets for use in their operational satellite calibration and validation activities. The publicly available, global biogeochemical dataset collected as part of the two and a half year Tara Oceans expedition provides one such opportunity. We showed how the inline measurements of hyperspectral absorption and attenuation coefficients collected onboard the R/V Tara can be used to evaluate near-surface estimates of chlorophyll-a, spectral particulate backscattering coefficients, particulate organic carbon, and particle size classes derived from the NASA Moderate Resolution Imaging Spectroradiometer onboard Aqua (MODISA). The predominant strength of such flow-through measurements is their sampling rate-the 375 days of measurements resulted in 165 viable MODISA-to-in situ match-ups, compared to 13 from discrete water sampling. While the need to apply bio-optical models to estimate biogeochemical quantities of interest from spectroscopy remains a weakness, we demonstrated how discrete samples can be used in combination with flow-through measurements to create data records of sufficient quality to conduct first order evaluations of satellite-derived data products. Given an emerging agency desire to rapidly evaluate new satellite missions, our results have significant implications on how calibration and validation teams for these missions will be constructed.

remote sensing↗
NASA NTRS2003

Ocean Optics Protocols for Satellite Ocean Color Sensor Validation: Inherent Optical Properties: Instruments, Characterizations, Field Measurements and Data Analysis Protocols - Volume 4

This document stipulates protocols for measuring bio-optical and radiometric data for the Sensor Intercomparison and Merger for Biological and Interdisciplinary Oceanic Studies (SIMBIOS) Project activities and algorithm development. The document is organized into 6 separate volumes as Ocean Optics Protocols for Satellite Ocean Color Sensor Validation, Revision 4. Volume I: Introduction, Background and Conventions; Volume II: Instrument Specifications, Characterization and Calibration; Volume III: Radiometric Measurements and Data Analysis Methods; Volume IV: Inherent Optical Properties: Instruments, Characterization, Field Measurements and Data Analysis Protocols; Volume V: Biogeochemical and Bio-Optical Measurements and Data Analysis Methods; Volume VI: Special Topics in Ocean Optics Protocols and Appendices. The earlier version of Ocean Optics Protocols for Satellite Ocean Color Sensor Validation, Revision 3 (Mueller and Fargion 2002, Volumes 1 and 2) is entirely superseded by the six volumes of Revision 4 listed above.

Mueller, J. L.↗
NASA NTRS2022

Using Machine Learning for Timely Estimates of Ocean Color Information From Hyperspectral Satellite Measurements in the Presence of Clouds, Aerosols, and Sunglint

Retrievals of ocean color from space are important for better understanding of the ocean ecosystem but can be limited under conditions such as clouds, aerosols, and sunglint. Many ocean color algorithms use a few selected spectral bands to perform an atmospheric correction and then derive the upwelling radiance from the ocean. The limitations in the atmospheric correction under certain conditions lead to many gaps in daily spatial coverage of ocean color retrievals. To address these limitations, we introduce a new approach that uses machine learning to estimate ocean color from top of atmosphere radiances or reflectance measurements. 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 and the underlying surface. The coefficients of the principal components are then used to train a neural network to predict ocean color properties derived from 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 the TROPOspheric Monitoring Instrument (TROPOMI), using measurements from 320–500 nm to show that it can be used to reproduce ocean color properties in less-than-ideal conditions. This machine learning approach complements the current atmospheric correction ocean color retrievals by filling in the gaps resulting from cloud, aerosol, and sunglint contamination. This method can be applied to the future hyperspectral Ocean Color Instrument (OCI), which will be onboard NASA’s Plankton, Aerosol Cloud, ocean Ecosystem (PACE) ocean color satellite set to launch in 2024.

Ocean color↗
NASA NTRS1992

The Airborne Ocean Color Imager - System description and image processing

The Airborne Ocean Color Imager was developed as an aircraft instrument to simulate the spectral and radiometric characteristics of the next generation of satellite ocean color instrumentation. Data processing programs have been developed as extensions of the Coastal Zone Color Scanner algorithms for atmospheric correction and bio-optical output products. The latter include several bio-optical algorithms for estimating phytoplankton pigment concentration, as well as one for the diffuse attenuation coefficient of the water. Additional programs have been developed to geolocate these products and remap them into a georeferenced data base, using data from the aircraft's inertial navigation system. Examples illustrate the sequential data products generated by the processing system, using data from flightlines near the mouth of the Mississippi River: from raw data to atmospherically corrected data, to bio-optical data, to geolocated data, and, finally, to georeferenced data.

Wrigley, Robert C.↗
NASA NTRS2001

Measurements of the Vertical Structure of Aerosols and Clouds Over the Ocean Using Micro-Pulse LIDAR Systems

The determination of the vertical distribution of aerosols and clouds over the ocean is needed for accurate retrievals of ocean color from satellites observations. The presence of absorbing aerosol layers, especially at altitudes above the boundary layer, has been shown to influence the calculation of ocean color. Also, satellite data must be correctly screened for the presence of clouds, particularly cirrus, in order to measure ocean color. One instrument capable of providing this information is a lidar, which uses pulses of laser light to profile the vertical distribution of aerosol and cloud layers in the atmosphere. However, lidar systems prior to the 1990s were large, expensive, and not eye-safe which made them unsuitable for cruise deployments. During the 1990s the first small, autonomous, and eye-safe lidar system became available: the micro-pulse lidar, or MPL. The MPL is a compact and eye-safe lidar system capable of determining the range of aerosols and clouds by firing a short pulse of laser light (523 nm) and measuring the time-of-flight from pulse transmission to reception of a returned signal. The returned signal is a function of time, converted into range using the speed of light, and is proportional to the amount of light backscattered by atmospheric molecules (Rayleigh scattering), aerosols, and clouds. The MPL achieves ANSI eye-safe standards by sending laser pulses at low energy (micro-J) and expanding the beam to 20.32 cm in diameter. A fast pulse-repetition-frequency (2500 Hz) is used to achieve a good signal-to-noise, despite the low output energy. The MPL has a small field-of-view (< 100 micro-rad) and signals received with the instrument do not contain multiple scattering effects. The MPL has been used successfully at a number of long-term sites and also in several field experiments around the world.

Welton, Ellsworth J.↗
NASA NTRS2022

Lake Champlain Water Resources: Using Earth Observations to Identify Spatial and Seasonal Trends of Harmful Algal Events in Lake Champlain

Lake Champlain provides clean drinking water for 35% of the surrounding watershed and offers recreational opportunities to millions of tourists. However, current levels of cyanobacteria and phosphorus created breeding grounds for harmful algal blooms (HABs). The excess of phosphorus runoff into Lake Champlain over the past decade encouraged toxic cyanobacterial formations, thereby increasing the severity of HABs towards local economy and ecology. In partnership with the Natural Resources Conservation Service (NRCS) Northeast Region, this project utilized Earth observations to identify risk factors associated with toxic algal blooms. The team detected historic algal bloom trends with Sentinel-2 Multispectral Instrument (MSI), Sentinel-3 Ocean and Land Color Instrument (OLCI), Landsat 8 Operational Land Imager (OLI) and Landsat 9 OLI-2. The team also used Sentinel-3 OLCI and the German Aerospace Center’s Earth Sensing Imagery Spectrometer (DESIS) to visualize algal bloom patterns and Landsat 8 OLI, Landsat 9 OLI-2, and Shuttle Radar Topography Mission (SRTM) to identify phosphorus sources within the watershed. The team’s analyses indicated an increase in cyanobacteria blooms during summer months from 2016-2022, with Missisquoi and St. Albans Bay exhibiting the greatest concentrations of toxic events. Furthermore, 16% of the watershed was identified as posing an immediate threat to the lake’s hydrology. The area of greatest concern was the Missisquoi Bay sub-watershed, with 229,044 acres of land prone to excessive phosphorus runoff. Providing this information to the NRCS Northeast Region enabled the organization to quantify risk factors associated with algal blooms and modify mitigation efforts to better target future bloom events.

Brianne Kendall↗
NASA NTRS2024

Big Cypress Water Resources: Using Earth Observations to Assess Water Quality in Big Cypress Reservation, FL

Upstream agricultural development and runoff are driving harmful algal blooms in the Big Cypress Reservation. The Seminole Tribe of Florida Environmental Resource Management Department monitors water quality in the Big Cypress Reservation, located on the north end of Big Cypress National Preserve. The Environmental Resource Management Department aims to incorporate Earth observations into its investigative approach, which consists of Geographic Information Systems and in situ water sampling. We assessed the feasibility of using Earth observations to measure harmful algal blooms over time and identify vulnerable areas. We analyzed data from multiple remote sensing platforms and sensors, including Landsat 8 Thermal Infrared Spectrometer(TIRS), Landsat 9 TIRS-2, Landsat 9 Operational Land Imager 2, Sentinel-2 MultiSpectral Instrument, and Sentinel-3 Ocean and Land Color Instrument. We determined that it was challenging to use Sentinel-3, Landsat 8, and Landsat 9 imagery for monitoring the canals within the Reservation, and that Sentinel-2 was the most capable platform for the study area. Using Sentinel-2, we created spectral indices for the detection of algal blooms. We used these indices to measure algal blooms at 8 stations across the Big Cypress Reservation. We then created time series and seasonal decompositions of in situ water sample data and indices to visualize relationships between datasets, along with correlation matrices. Our correlations were not conclusive. However, we found that the spectral indices have the potential to detect algal blooms. Lastly, we evaluated Maxar WorldView-3 imagery and found that using data from sensors with higher spatial resolutions would improve results.

Remote sensing↗
NASA NTRS2023

Lake Anna Water Resources: Using NASA Earth Observations to Identify Algal Event Risk Factors in Lake Anna and Help Inform Future Management Practices

Lake Anna is a man-made reservoir and popular recreation destination that spans over 13,000 acres-9,600 public and 3,400 private-in the Piedmont region of Virginia. The Lake has recently seen a rise in documented harmful algal blooms (HABs), which pose a variety of community and ecological concerns and are often exacerbated by anthropogenic factors, such as excess nutrient loads from agricultural runoff. NASA DEVELOP has partnered with the Virginia Department of Environmental Quality (DEQ) to help monitor cyanobacteria and nutrient pollution indicators across Lake Anna. The team utilized Earth observations (EO) and in situ ancillary data to identify and monitor algal bloom trends. The team used Landsat 8 Operational Land Imager (OLI), Landsat 9 OLI-2, Sentinel-2 Multispectral Instrument (MSI), and Sentinel-3 Ocean and Land Color Instrument (OLCI) to analyze water quality variables such as chlorophyll-a, turbidity, surface temperature, and cyanobacteria. Due to the lack of comprehensive in situ data and historic HAB event records, our ability to compare and validate EOs was limited. Additionally, deficient spatial resolutions, along with a geographically complicated shoreline, accentuated the spatial constraints we faced in our analysis. After examining EOs, our results indicated conditions conducive to the formation of HABs within the upper reaches of Lake Anna. Yet, spatially dependent limiting factors may have also influenced where these phenomena developed. When used in concert with existing in situ datasets, NASA EOs provide relevant stakeholders with more comprehensive analyses with which to engage in enhanced monitoring and watershed management.

cyanobacteria↗
NASA NTRS1993

Ocean Observation Instrument

The Airborne Ocean Color Imager (AOCI) was developed by Daedalus Enterprises, Inc. for Ames Research Center under a Small Business Innovation Research (SBIR) contract as a simulator for an advanced oceanographic satellite instrument. The instrument measures water temperature and detects water color in nine wavelengths. Water color indicates chlorophyll content or phytoplankton. After EOCAP assistance and technical improvements, the AOCI was successfully commercialized by Daedalus Enterprises, Inc. One version provides commercial fishing fleets with information about fish locations, and the other is used for oceanographic research.

Source record↗
NASA NTRS2023

Use of Machine Learning and Principal Component Analysis to Retrieve Nitrogen Dioxide (NO 2 ) With Hyperspectral Imagers and Reduce Noise in Spectral Fitting

Nitrogen dioxide (NO 2 ) is an important trace-gas pollutant and climate agent whose presence also leads to spectral interference in ocean color retrievals. NO 2 column densities have been retrieved with satellite UV–Vis spectrometers such as the Ozone Monitoring Instrument (OMI) and the Tropospheric Monitoring Instrument (TROPOMI) that typically have spectral resolutions of the order of 0.5 nm or better and spatial footprints as small as 3.6 km × 5.6 km. These NO 2 observations are used to estimate emissions, monitor pollution trends, and study effects on human health. Here, we investigate whether it is possible to retrieve NO 2 amounts with lower-spectral-resolution hyperspectral imagers such as the Ocean Color Instrument (OCI) that will fly on the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) satellite set for launch in early 2024. OCI will have a spectral resolution of 5 nm and a spatial resolution of ∼ 1 km with global coverage in 1–2 d. At this spectral resolution, small-scale spectral structure from NO 2 absorption is still present. We use real spectra from the OMI to simulate OCI spectra that are in turn used to estimate NO 2 slant column densities (SCDs) with an artificial neural network (NN) trained on target OMI retrievals. While we obtain good results with no noise added to the OCI simulated spectra, we find that the expected instrumental noise substantially degrades the OCI NO 2 retrievals. Nevertheless, the NO 2 information from OCI may be of value for ocean color retrievals. OCI retrievals can also be temporally averaged over timescales of the order of months to reduce noise and provide higher-spatial-resolution maps that may be useful for downscaling lower-spatial-resolution data provided by instruments such as OMI and TROPOMI; this downscaling could potentially enable higher-resolution emissions estimates and be useful for other applications. In addition, we show that NNs that use coefficients of leading modes of a principal component analysis of radiance spectra as inputs appear to enable noise reduction in NO 2 retrievals. Once trained, NNs can also substantially speed up NO 2 spectral fitting algorithms as applied to OMI, TROPOMI, and similar instruments that are flying or will soon fly in geostationary orbit.

NO2↗
NASA NTRS1988

The Multichannel Ocean Color Sensor (MOCS)

The MOCS is an imaging spectroradiometer with a sensitivity that matches the light upwelling from open bodies of water. Twenty contiguous spectral channels in the visible are recorded in a line scanning mode from an aircraft. The system also supports a thermal infrared radiometer and will accept input from a position location system. The sensor in the MOCS system is an image dissector tube. A spectral curvature algorithm was developed. No atmospheric correction to the data is needed for low flying aircraft gathering chlorophyll data using this algorithm. Although the MOCS is now about 15 years old, because its response has been so stable, its data recording system was upgraded to the system used by the Aircraft Oceanographic Lidar where possible. The MOCS has been a reliable instrument for recording ocean color from low flying aircraft. As NASA enters the next period of ocean color investigations from space, such a device can play an important role in calibration of the space instrument, as well as provide support for programs developing the next generation of spaceborne devices.

Oberholtzer, J. D.↗
NASA NTRS2003

Ocean Optics Protocols for Satellite Ocean Color Sensor Validation, Revision 4, Volume IV: Inherent Optical Properties: Instruments, Characterizations, Field Measurements and Data Analysis Protocols

This document stipulates protocols for measuring bio-optical and radiometric data for the Sensor Intercomparision and Merger for Biological and Interdisciplinary Oceanic Studies (SIMBIOS) Project activities and algorithm development. The document is organized into 6 separate volumes as Ocean Optics Protocols for Satellite Ocean Color Sensor Validation, Revision 4. Volume I: Introduction, Background, and Conventions; Volume II: Instrument Specifications, Characterization and Calibration; Volume III: Radiometric Measurements and Data Analysis Methods; Volume IV: Inherent Optical Properties: Instruments, Characterization, Field Measurements and Data Analysis Protocols; Volume V: Biogeochemical and Bio-Optical Measurements and Data Analysis Methods; Volume VI: Special Topics in Ocean Optics Protocols and Appendices. The earlier version of Ocean Optics Protocols for Satellite Ocean Color Sensor Validation, Revision 3 is entirely superseded by the six volumes of Revision 4 listed above.

Mueller, J. L.↗
NASA NTRS2011

Cross Calibration of SeaWiFS and MODIS Using On-Orbit Observations of the Moon

Observations of the Moon provide a primary technique for the on-orbit cross calibration of Earth remote sensing instruments. Monthly lunar observations are major components of the on-orbit calibration strategies of SeaWiFS and MODIS. SeaWiFS has collected more than 132 low phase angle and 59 high phase angle lunar observations over 12 years, Terra MODIS has collected more than 82 scheduled and 297 unscheduled lunar observations over 9 years, and Aqua MODIS has collected more than 61 scheduled and 171 unscheduled lunar observations over 7 years. The NASA Ocean Biology Processing Group s Calibration and Validation Team and the NASA MODIS Characterization Support Team use the USGS RObotic Lunar Observatory (ROLO) photometric model of the Moon to compare these time series of lunar observations over time and varying observing geometries. The cross calibration results show that Terra MODIS and Aqua MODIS agree, band-to-band, at the 1-3% level, while SeaWiFS and either MODIS instrument agree at the 3-8% level. The combined uncertainties of these comparisons are 1.3% for Terra and Aqua MODIS, 1.4% for SeaWiFS and Terra MODIS, and 1.3% for SeaWiFS and Aqua MODIS. Any residual phase dependence in the ROLO model, based on these observations, is less than 1.7% over the phase angle range of -80deg to -6deg and +5deg to +82deg . The lunar cross calibration of SeaWiFS, Terra MODIS, and Aqua MODIS is consistent with the vicarious calibration of ocean color products for these instruments, with the vicarious gains mitigating the calibration biases for the ocean color bands.

Eplee, Robert E., Jr.↗
NASA NTRS1991

Instrumentation for optical ocean remote sensing

Instruments used in ocean color remote sensing algorithm development, validation, and data acquisition which have the potential for further commercial development and marketing are discussed. The Ocean Data Acquisition System (ODAS) is an aircraft-borne radiometer system suitable for light aircraft, which has applications for rapid measurement of chlorophyll pigment concentrations along the flight line. The instrument package includes a three channel radiometer system for upwelling radiance, an infrared temperature sensor, a three-channel downwelling irradiance sensor, and Loran-C navigation. Data are stored on a PC and processed to transects or interpolated 'images' on the ground. The instrument has been in operational use for two and one half years. The accuracy of pigment concentrations from the instrument is quite good, even in complex Chesapeake Bay waters. To help meet the requirement for validation of future satellite missions, a prototype air-deployable drifting buoy for measurement of near-surface upwelled radiance in multiple channnels is undergoing test deployment. The optical drifter burst samples radiance, stores and processes the data, and uses the Argos system as a data link. Studies are underway to explore the limits to useful lifetime with respect to power and fouling.

Esaias, W. E.↗
NASA NTRS2002

Seasonal and Global Variations of Water Vapor and High Clouds Observed with MODIS near-IR Channels

The NASA Moderate Resolution Imaging Spectrometer (MODIS) on the Terra Spacecraft has been collecting scientific data since February of 2000. MODIS is a major facility instrument for remote sensing of the atmosphere, land surfaces, and ocean color. On the MODIS instruments, there are five channels located within and around the .0.94 micron water vapor band absorption region for remote sensing of atmospheric water vapor. There is also a channel located at 1.375 micron for detecting thin cirrus clouds. We will describe the basic principles for using these near-IR channels for remote sensing of water vapor and high clouds. Based on our analysis of two years# measurements with these channels, we have found that reliable observations of water vapor and high clouds on regional and global scales can be made. We will present results on daily, seasonal and annual variations of water vapor and high clouds.

Gao, Bo-Cai↗