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NASA NTRS2019

Vicarious Calibration of GOCI for the SeaDAS Ocean Color Retrieval

A key on-orbit calibration step for satellite remote sensing of ocean color is the vicarious calibration. This establishes the final gains for each spectral band on the sensor that minimize bias in the retrieved ocean color signal. The vicarious calibration is specific to the instrument and the atmospheric correction algorithm. The vicarious calibration gains for the Geostationary Ocean Color Imager (GOCI) are presented here, which were derived to optimize the performance ofNASA's standard atmospheric correction algorithm as implemented in the l2gen code and distributed through the SeaDAS open-source software package. Following NASA's protocols, the near-infrared(NIR) bands were calibrated first, and the visible bands were then calibrated relative to this fixed NIR calibration. The gain for the 745-nm NIR band was derived using a fixed aerosol model, which waschosen based on the Angstrom Coefficients derived from MODIS onAqua (MODISA). For the vicarious gains of the visible bands, twosources for the target water-leaving radiances were tested: matchupsfrom MODISA and climatological data from SeaWiFS. A validation analysis using AERONET-OC data shows an improvement in sensor performance when compared with results using the current vicarious gains and results using no vicarious calibration. Good agreement was found in vicarious gains derived using both concurrent MODISA and climatological SeaWiFS as vicarious calibration data sources. These results support the use of a concurrent sensor for the vicarious calibration when in situ data are not available and demonstrate that using climatology from a well-calibrated sensor like SeaWiFSfor the vicarious calibration is a valid alternative when it is not possible to use a concurrent sensor or in situ data. We recommend using the gains derived from concurrent GOCI matchups with MODISA for GOCI processing in SeaDAS/l2gen.

Concha, Javier↗

NASA NTRS2019

Validation, Comparison, and Integration of GOCI, AHI, MODIS, MISR, and VIIRS Aerosol Optical Depth Over East Asia During the 2016 KORUS-AQ Campaign

Recently launched multichannel geostationary Earth orbit (GEO) satellite sensors, such as the Geostationary Ocean Color Imager (GOCI) and the Advanced Himawari Imager (AHI), provide aerosol products over East Asia with high accuracy, which enables the monitoring of rapid diurnal variations and the transboundary transport of aerosols. Most aerosol studies to date have used low Earth orbit (LEO) satellite sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Multi-angle Imaging Spectroradiometer (MISR), with a maximum of one or two overpass daylight times per day from midlatitudes to low latitudes. Thus, the demand for new GEO observations with high temporal resolution and improved accuracy has been significant. In this study the latest versions of aerosol optical depth (AOD) products from three LEO sensors – MODIS (Dark Target, Deep Blue, and MAIAC), MISR, and the Visible/Infrared Imager Radiometer Suite (VIIRS), along with two GEO sensors (GOCI and AHI), are validated, compared, and integrated for a period during the Korea–United States Air Quality Study (KORUS-AQ) field campaign from 1 May to 12 June 2016 over East Asia. The AOD products analyzed here generally have high accuracy with high R (0.84–0.93) and low RMSE (0.12–0.17), but their error characteristics differ according to the use of several different surface-reflectance estimation methods. High-accuracy near-real-time GOCI and AHI measurements facilitate the detection of rapid AOD changes, such as smoke aerosol transport from Russia to Japan on 18–21 May 2016, heavy pollution transport from China to the Korean Peninsula on 25 May 2016, and local emission transport from the Seoul Metropolitan Area to the Yellow Sea in South Korea on 5 June 2016. These high-temporal-resolution GEO measurements result in more representative daily AOD values and make a greater contribution to a combined daily AOD product assembled by median value selection with a 0.5∘×0.5∘ grid resolution. The combined AOD is spatially continuous and has a greater number of pixels with high accuracy (fraction within expected error range of 0.61) than individual products. This study characterizes aerosol measurements from LEO and GEO satellites currently in operation over East Asia, and the results presented here can be used to evaluate satellite measurement bias and air quality models.

Myungje Choi↗

NASA NTRS1970

Classifying forest and nonforest land on space photographs

Although the research reported is in its preliminary stages, results show that: (1) infrared color film is the best single multiband sensor available; (2) there is a good possibility that forest can be separated from all nonforest land uses by microimage evaluation techniques on IR color film coupled with B/W infrared and panchromatic films; and (3) discrimination of forest and nonforest classes is possible by either of two methods: interpreters with appropriate viewing and mapping instruments, or programmable automatic scanning microdensitometers and automatic data processing.

Aldrich, R. C.↗

NASA NTRS2012

Satellite Observation Systems for Polar Climate Change Studies

The key observational tools for detecting large scale changes of various parameters in the polar regions have been satellite sensors. The sensors include passive and active satellite systems in the visible, infrared and microwave frequencies. The monitoring started with Tiros and Nimbus research satellites series in the 1970s but during the period, not much data was stored digitally because of limitations and cost of the needed storage systems. Continuous global data came about starting with the launch of ocean color, passive microwave, and thermal infrared sensors on board Nimbus-7 and Synthetic Aperture Radar, Radar Altimeter and Scatterometer on board SeaSat satellite both launched in 1978. The Nimbus-7 lasted longer than expected and provided about 9 years of useful data while SeaSat quit working after 3 months but provided very useful data that became the baseline for follow-up systems with similar capabilities. Over the years, many new sensors were launched, some from Japan Aeronautics and Space Agency (JAXA), some from the European Space Agency (ESA) and more recently, from RuSSia, China, Korea, Canada and India. For polar studies, among the most useful sensors has been the passive microwave sensor which provides day/night and almost all weather observation of the surface. The sensor provide sea surface temperature, precipitation, wind, water vapor and sea ice concentration data that have been very useful in monitoring the climate of the region. More than 30 years of such data are now available, starting with the Scanning Multichannel Microwave Radiometer (SMMR) on board the Nimbus-7, the Special Scanning Microwave/Imager (SSM/I) on board a Defense Meteorological Satellite Program (DMSP) and the Advanced Microwave Scanning Radiometer on board the EOS/ Aqua satellite. The techniques that have been developed to derive geophysical parameters from data provided by these and other sensors and associated instrumental and algorithm errors and validation techniques will be discussed. An important issue is the organization and storage of hundreds of terabytes of data collected by even just a few of these satellite sensors. Advances in mass storage and computer technology have made it possible to overcome many of the collection and archival problems and the availability of comprehensive satellite data sets put together by NASA's Earth Observing System project will be discussed.

Comiso, Josefino C.↗

NASA NTRS1999

Robotic Rock Classification

This report describes a three-month research program undertook jointly by the Robotics Institute at Carnegie Mellon University and Ames Research Center as part of the Ames' Joint Research Initiative (JRI.) The work was conducted at the Ames Research Center by Mr. Liam Pedersen, a graduate student in the CMU Ph.D. program in Robotics under the supervision Dr. Ted Roush at the Space Science Division of the Ames Research Center from May 15 1999 to August 15, 1999. Dr. Martial Hebert is Mr. Pedersen's research adviser at CMU and is Principal Investigator of this Grant. The goal of this project is to investigate and implement methods suitable for a robotic rover to autonomously identify rocks and minerals in its vicinity, and to statistically characterize the local geological environment. Although primary sensors for these tasks are a reflection spectrometer and color camera, the goal is to create a framework under which data from multiple sensors, and multiple readings on the same object, can be combined in a principled manner. Furthermore, it is envisioned that knowledge of the local area, either a priori or gathered by the robot, will be used to improve classification accuracy. The key results obtained during this project are: The continuation of the development of a rock classifier; development of theoretical statistical methods; development of methods for evaluating and selecting sensors; and experimentation with data mining techniques on the Ames spectral library. The results of this work are being applied at CMU, in particular in the context of the Winter 99 Antarctica expedition in which the classification techniques will be used on the Nomad robot. Conversely, the software developed based on those techniques will continue to be made available to NASA Ames and the data collected from the Nomad experiments will also be made available.

Hebert, Martial↗

NASA NTRS2017

Atmospheric Correction for Hyperspectral Ocean Color Retrieval with Application to the Hyperspectral Imager for the Coastal Ocean (HICO)

The classical multi-spectral Atmospheric Correction (AC) algorithm is inadequate for the new generation of spaceborne hyperspectral sensors such as NASA's first hyperspectral Ocean Color Instrument (OCI) onboard the anticipated Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) satellite mission. The AC process must estimate and remove the atmospheric path radiance contribution due to the Rayleigh scattering by air molecules and scattering by aerosols from the measured top-of-atmosphere (TOA) radiance, compensate for the absorption by atmospheric gases, and correct for reflection and refraction of the air-sea interface. In this work, we present and evaluate an improved AC for hyperspectral sensors developed within NASA's Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Data Analysis System software package (SeaDAS). The improvement is based on combining the classical AC approach of multi-spectral capabilities to correct for the atmospheric path radiance, extended to hyperspectral, with a gas correction algorithm to compensate for absorbing gases in the atmosphere, including water vapor. The SeaDAS-hyperspectral version is capable of operationally processing the AC of any hyperspectral airborne or spaceborne sensor. The new algorithm development was evaluated and assessed using the Hyperspectral Imager for Coastal Ocean (HICO) scenes collected at the Marine Optical BuoY (MOBY) site, and other SeaWiFS Bio-optical Archive and Storage System (SeaBASS) and AERosol Robotic NETwork - Ocean Color (AERONET-OC) coastal sites. A hyperspectral vicarious calibration was applied to HICO, showing the validity and consistency of HICO's ocean color products. The hyperspectral AC capability is currently available in SeaDAS to the scientific community at https://oceancolor.gsfc.nasa.gov/.

Ocean Color↗

NASA NTRS1981

Remote sensing of the diffuse attenuation coefficient of ocean water

A technique was devised which uses remotely sensed spectral radiances from the sea to assess the optical diffuse attenuation coefficient, K (lambda) of near-surface ocean water. With spectral image data from a sensor such as the coastal zone color scanner (CZCS) carried on NIMBUS-7, it is possible to rapidly compute the K (lambda) fields for large ocean areas and obtain K "images" which show synoptic, spatial distribution of this attenuation coefficient. The technique utilizes a relationship that has been determined between the value of K and the ratio of the upwelling radiances leaving the sea surface at two wavelengths. The relationship was developed to provide an algorithm for inferring K from the radiance images obtained by the CZCS, thus the wavelengths were selected from those used by this sensor, viz., 443, 520, 550 and 670 nm. The majority of the radiance arriving at the spacecraft is the result of scattering in the atmospheric and is unrelated to the radiance signal generated by the water. A necessary step in the processing of the data received by the sensor is, therefore, the effective removal of these atmospheric path radiance signals before the K algorithm is applied. Examples of the efficacy of these removal techniques are given together with examples of the spatial distributions of K in several ocean areas.

Austin, R. W.↗

NASA NTRS2010

Achieving Global Ocean Color Climate Data Records

Ocean color, or the spectral distribution of visible light upwelling from beneath the ocean surface, carries information on the composition and concentration of biological constituents within the water column. The CZCS mission in 1978 demonstrated that quantitative ocean color measurements could be. made from spaceborne sensors, given sufficient corrections for atmospheric effects and a rigorous calibration and validation program. The launch of SeaWiFS in 1997 represents the beginning of NASA's ongoing efforts to develop a continuous ocean color data record with sufficient coverage and fidelity for global change research. Achievements in establishing and maintaining the consistency of the time-series through multiple missions and varying instrument designs will be highlighted in this talk, including measurements from NASA'S MODIS instruments currently flying on the Terra and Aqua platforms, as well as the MERIS sensor flown by ESA and the OCM-2 sensor recently launched by ISRO.

Franz, Bryan↗

NASA NTRS1972

Measurements of ocean color

An airborne instrument for determining ocean color and measurements made with the instrument are discussed. It was concluded that a clear relationship exists between the chlorophyll concentration and the color of the water. High altitude measurements from 50,000 feet are described and the effects of atmospheric scattering on the energy reaching the sensor are examined. The measured spectrum of ocean color at high and low altitudes is plotted.

Hovis, W. A.↗

NASA NTRS1976

Application of remote sensor data to geologic analysis of the Bonanza test site, Colorado

The author has identified the following significant results. The Hayden Pass (Orient mine area) includes 60 sq miles of the northern Sangre de Cristo Mountains and San Luis Valley in south-central Colorado. Based on interpretation of the remote sensor data, a geologic map was prepared and compared with a second geologic map, prepared from interpretation of both remote sensor data and field data. Comparison of the two maps gives an indication of the usefulness and reliability of the remote sensor data. The relative utility of color and color infrared photography was tested. The photography was used successfully to locate 75% of all faults in a portion of the geologically complex Bonanza volcanic center and to map and correctly identify 93% of all quaternary deposits and 62% of all areas of tertiary volcanic outcrop. Using a filter wheel photometer, more than 8,600 measurements of band reflectance of several sedimentary rocks were performed. The following conclusions were drawn: (1) the typical spectral reflectance curve shows a gradual increase with increasing wavelength; (2) the average band reflectance is about 0.20; and (3) within a formation, the minimum natural variation is about 0.04, or about 20% of the mean band reflectance.

Lee, K.↗

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 NTRS1979

Proton-induced coloring of multicomponent glasses

The paper describes the coloring induced by 85-MeV protons in three Schott glasses representative of glass types used as focusing elements in the International Ultraviolet Explorer Fine Error Sensor. It is shown that the absorption (i.e., coloring) induced in each glass by the radiation can be resolved into three optical absorption bands in the near UV-visible range. It is also discussed how that absorption grows with increasing dosages of protons, thus providing the means to predict degradation in any of the three glasses for a particular fluence of proton radiation.

Bartusiak, M. F.↗

NASA NTRS1975

Ocean color imagery: Coastal zone color scanner

Investigations into the feasibility of sensing ocean color from high altitude for determination of chlorophyll and sediment distributions were carried out using sensors on NASA aircraft, coordinated with surface measurements carried out by oceanographic vessels. Spectrometer measurements in 1971 and 1972 led to development of an imaging sensor now flying on a NASA U-2 and the Coastal Zone Color Scanner to fly on Nimbus G in 1978. Results of the U-2 effort show the imaging sensor to be of great value in sensing pollutants in the ocean.

Hovis, W. A.↗

NASA NTRS1975

Ocean color measurement from high altitude

Investigations into the feasibility of sensing ocean color from high altitude for determination of chlorophyll and sediment distributions have been carried out using sensors on NASA aircraft, coordinated with surface measurements carried out by oceanographic vessels. Spectrometer measurements in 1971 and 1972 led to development of an imaging sensor now flying on a NASA U-2 and the Coastal Zone Color Scanner to fly on Nimbus G in 1978. Results of the U-2 effort have shown the imaging sensor to also be of great value in sensing pollutants in the ocean.

Hovis, W. A., Jr.↗

NASA NTRS2004

Real Data and Rapid Results: Ocean Color Data Analysis with Giovanni (GES DISC Interactive Online Visualization and ANalysis Infrastructure)

The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) has taken a major step addressing the challenge of using archived Earth Observing System (EOS) data for regional or global studies by developing an infrastructure with a World Wide Web interface which allows online, interactive, data analysis: the GES DISC Interactive Online Visualization and ANalysis Infrastructure, or "Giovanni." Giovanni provides a data analysis environment that is largely independent of underlying data file format. The Ocean Color Time-Series Project has created an initial implementation of Giovanni using monthly Standard Mapped Image (SMI) data products from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) mission. Giovanni users select geophysical parameters, and the geographical region and time period of interest. The system rapidly generates a graphical or ASCII numerical data output. Currently available output options are: Area plot (averaged or accumulated over any available data period for any rectangular area); Time plot (time series averaged over any rectangular area); Hovmeller plots (image view of any longitude-time and latitude-time cross sections); ASCII output for all plot types; and area plot animations. Future plans include correlation plots, output formats compatible with Geographical Information Systems (GIs), and higher temporal resolution data. The Ocean Color Time-Series Project will produce sensor-independent ocean color data beginning with the Coastal Zone Color Scanner (CZCS) mission and extending through SeaWiFS and Moderate Resolution Imaging Spectroradiometer (MODIS) data sets, and will enable incorporation of Visible/lnfrared Imaging Radiometer Suite (VIIRS) data, which will be added to Giovanni. The first phase of Giovanni will also include tutorials demonstrating the use of Giovanni and collaborative assistance in the development of research projects using the SeaWiFS and Ocean Color Time-Series Project data in the online Laboratory for Ocean Color Users (LOCUS). The synergy of Giovanni with high-quality ocean color data provides users with the ability to investigate a variety of important oceanic phenomena, such as coastal primary productivity related to pelagic fisheries, seasonal patterns and interannual variability, interdependence of atmospheric dust aerosols and harmful algal blooms, and the potential effects of climate change on oceanic productivity.

Acker, J. G.↗

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 NTRS1987

Reflectance- and radiance-based methods for the in-flight absolute calibration of multispectral sensors

Variations reported in the in-flight absolute radiometric calibration of the Coastal Zone Color Scanner (CZCS) and the Thematic Mapper (TM) on Landsat 4 are reviewed. At short wavelengths these sensors exhibited a gradual reduction in response, while in the midinfrared the TM showed oscillatory variations, according to the results of TM internal calibration. The methodology and results are presented for five reflectance-based calibrations of the Landsat 5 TM at White Sands, NM, in the period July 1984 to November 1985. These show a + or - 2.8 percent standard deviation for the six solar-reflective bands. Analysis and preliminary results of a second, independent calibration method, based on radiance measurements from a helicopter at White Sands, indicate that this is potentially an accurate method for corroborating the results from the reflectance-based method.

Slater, P. N.↗