The sensitivity of ocean color products to variations in vicarious calibration gains at Top of Atmosphere (TOA) shows varying impacts in different water types for Suomi- NPP VIIRS. Blue water vicarious gains from MOBY in situ data, which is used for global open waters, and green water gains derived from complex coastal WaveCIS AERONET waters, have a different impact on spectral normalized water leaving radiances and the derived ocean color products (inherent optical properties, chlorophyll). We evaluated the influence of gains from open and coastal waters by establishing a set of ensemble-processed products. The TOA gains show a non-linear impact on derived ocean color products, since gains affect multiple ocean color processing algorithms such as atmospheric correction, NIR iterations, etc. We show how the variations within the ensemble TOA gain members spatially impact derived products from different water types (high CDOM, high backscattering, etc). The difference in color products derived from the Blue and Green water gain show a spatial distribution to characterize the product uncertainty in coastal and open ocean water types. The results of the ensemble gain members are evaluated with in situ matchups. Results suggest the sensitivity of the ocean color processing for open ocean verses coastal waters.
Navy operational ocean color products of inherent optical properties and radiances are evaluated for the Suomi–NPP VIIRS and MODIS-Aqua sensors. Statistical comparisons with shipboard measurements were determined in a wide variety of coastal, shelf and offshore locations in the Northern Gulf of Mexico during two cruises in 2013. Product consistency between MODIS-Aqua, nearing its end-of-life expectancy, and Suomi-NPP VIIRS is being evaluated for the Navy to retrieve accurate ocean color properties operationally from VIIRS in a variety of water types. Currently, the existence, accuracy and consistency of multiple ocean color sensors (VIIRS, MODIS-Aqua) provides multiple looks per day for monitoring the temporal and spatial variability of coastal waters. Consistent processing methods and algorithms are used in the Navy’s Automated Processing System (APS) for both sensors for this evaluation. The inherent optical properties from both sensors are derived using a coupled ocean-atmosphere NIR correction extending well into the bays and estuaries where high sediment and CDOM absorption dominate the optical signature. Coastal optical properties are more complex and vary from chlorophyll-dominated waters offshore. The in-water optical properties were derived using vicariously calibrated remote sensing reflectances and the Quasi Analytical Algorithm (QAA) to derive the Inherent Optical Properties (IOP’s). The Naval Research Laboratory (NRL) and the JPSS program have been actively engaged in calibration/validation activities for Visible Infrared Imager Radiometer Suite (VIIRS) ocean color products.
As part of the Joint Polar Satellite System (JPSS) Ocean Cal/Val Team, Naval Research Lab - Stennis Space Center (NRL-SSC) has been working to facilitate calibration and validation of the Visible Infrared Imaging Radiometer Suite (VIIRS) ocean color products. By relaxing the constraints of the NASA Ocean Biology Processing Group (OBPG) methodology for vicarious calibration of ocean color satellites and utilizing the Aerosol Robotic Network Ocean Color (AERONET-OC) system to provide in situ data, we investigated differences between remotely sensed water leaving radiance and the expected in situ response in coastal areas and compare the results to traditional Marine Optical Buoy (MOBY) calibration/validation activities.
An evaluation of the Suomi National Polar-Orbiting Partnership (SNPP)-VIIRS ocean color products was performed in coastal waters using the time series data obtained from the Northern Gulf of Mexico AERONET-OC site, WaveCIS. The coastal site provides different water types with varying complexity of CDOM, sedimentary, and chlorophyll components. Time series data sets were used to develop a vicarious gain adjustment (VGA) at this site, which provides a regional top of the atmospheric (TOA) spectral offset to compare the standard MOBY spectral calibration gain in open ocean waters.
Same day ocean color products from the S-NPP and MODIS provide for a new capability to monitor changes in the bio-optical processes occurring in coastal waters. The combined use of multiple looks per day from several sensors can be used to follow the water mass changes of bio-optical properties. Observing the dynamic changes in coastal waters in response to tides, re-suspension and river plume dispersion, requires sequential ocean products per day to resolve bio-optical processes. We examine how these changes in bio-optical properties can be monitored using the NPP and MODIS ocean color products. Additionally, when linked to ocean circulation, we examine the changes resulting from current advection compared to bio-optical processes. The inter-comparison of NPP and MODIS ocean products are in agreement so that diurnal changes surface bio-optical processes can be characterized.
The Joint Polar Satellite System (JPSS) launched the Suomi National Polar-Orbiting Partnership (NPP) satellite
including the Visible Infrared Imager Radiometer Suite (VIIRS) on October 28, 2011 which has the capability to
monitor ocean color properties. Four months after launch, we present an initial assessment of the VIIRS ocean color
products including inter-comparisons with satellite and in situ observations. Satellite ocean color is used to
characterize water quality properties, however, this requires that the sensor is well characterized and calibrated, and
that processing addresses atmospheric correction to derive radiometric water leaving radiance (nLw ). These
radiometric properties are used to retrieve products such as chlorophyll, optical backscattering and absorption. The
JPSS ocean calibration and validation program for VIIRS establishes methods and procedures to insure the accuracy
of the retrieved ocean satellite products and to provide methods to improve algorithms and characterize the product
uncertainty. A global monitoring network was established to integrate in situ data collection with satellite retrieved
water leaving radiance values from ocean color satellites including Moderate Resolution Imaging Spectroradiometer
(MODIS), MEdium Resolution Imaging Spectrometer (MERIS) and VIIRS. The global network provides a
monitoring capability to evaluate the quality of the VIIRS nLw in different areas around the world and enables an
evaluation and validation of the products using in situ data and other satellites. Monitoring of ocean color satellite
retrievals is performed by tracking the "gain" at the Top of the Atmosphere (TOA) and then performing a vicarious
adjustment fo reach site. VIIRS ocean color products are compared with MODIS and MERIS retrieved nLw and
chlorophyll, and have been shown to provide similar quality. We believe that VIIRS can provide a follow-on to
MODIS and MERIS equivalent ocean color products for operational monitoring of water quality. Additional
research, including an assessment of stability, a full characterization of the sensor and algorithm comparisons is
underway. Weekly sensor calibration tables (look up tables) are produced by JPSS and an evaluation of their impact
on ocean color products is ongoing.
The objective of this work is to determine the location(s) in any given oceanic area during different temporal periods
where in situ sampling for Calibration/Validation (Cal/Val) provides the best capability to retrieve accurate radiometric
and derived product data (lowest uncertainties). We present a method to merge satellite imagery with in situ
measurements, to determine the best in situ sampling strategy suitable for satellite Cal/Val and to evaluate the present in
situ locations through uncertainty indices.
This analysis is required to determine if the present in situ sites are adequate for assessing uncertainty and where
additional sites and ship programs should be located to improve Calibration/Validation (Cal/Val) procedures.
Our methodology uses satellite acquisitions to build a covariance matrix encoding the spatial-temporal variability of the
area of interest. The covariance matrix is used in a Bayesian framework to merge satellite and in situ data providing a
product with lower uncertainty. The best in situ location for Cal/Val is then identified by using a design principle (A-optimum
design) that looks for minimizing the estimated variance of the merged products.
Satellite products investigated in this study include Ocean Color water leaving radiance, chlorophyll, and inherent and
apparent optical properties (retrieved from MODIS and VIIRS). In situ measurements are obtained from systems
operated on fixed deployment platforms (e.g., sites of the Ocean Color component of the AErosol RObotic NETwork-
AERONET-OC), moorings (e.g, Marine Optical Buoy-MOBY), ships or autonomous vehicles (such as Autonomous
Underwater Vehicles and/or Gliders).
The SIMBIOS (Sensor Intercomparison and Merger for Biological and Interdisciplinary Oceanic Studies) Program
was conceived as a result of a NASA management review of the agency's strategy for monitoring the bio-optical
properties of the global ocean through space-based ocean color remote sensing. SIMBIOS Radiometric
Intercomparisons (SIMRICs) were carried out in 2001 and 2002. The purpose of the SIMRICs was to ensure
a common radiometric scale among the calibration facilities that are engaged in calibrating in-situ radiometers
used for ocean color related research and to document the calibration procedures and protocols. The SeaWiFS
Transfer Radiometer (SXR-II) measured the calibration radiances at six wavelengths from 411nm to 777nm
in the participating laboratories. The measured radiances were compared with the radiances expected by the
laboratories. NIST calibrations of the SXR-II were obtained in December 2000, December 2001 and January
2003. Two independent light sources (SQMs, SeaWiFS Quality Monitors) were used to monitor changes in the
SXR-II responsivity between the NIST calibrations and after, with monthly measurements until the end of 2003,
and less frequent measurements thereafter. This paper discusses the calibration and trending history of the
SXR-II from December 2000 to June 2008.
Sun photometers are used to characterize the radiative properties of the atmosphere. They measure both the incident solar irradiance as well as the sky radiance (from scattered incident flux). Global networks of sun photometers provide data products such as aerosol optical thickness derived from these measurements. Instruments are typically calibrated for irradiance responsivity by cross-calibration against a primary reference sun photometer and for radiance responsivity using a lamp-illuminated integrating sphere source. A laser-based facility for Spectral Irradiance and Radiance Responsivity Calibrations using Uniform Sources (SIRCUS) has been developed at the National Institute of Standards and Technology. Sensors can be calibrated in this facility for absolute spectral irradiance and radiance responsivity with combined expanded (k = 2) uncertainties ranging from 0.15% to 0.25%. Two multi-channel filter radiometers used in the Sensor Intercomparison and Merger for Biological and Interdisciplinary Oceanic Studies (SIMBIOS) program of the National Aeronautics and Space Administration (NASA) at the Goddard Space Flight Center (GSFC) were calibrated for radiance and irradiance responsivity using conventional approaches and using laser-illuminated integrating spheres on SIRCUS. The different calibration methods are compared, the uncertainties are evaluated, and the impact on remote sensing applications is discussed.
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.
Satellite ocean-color algorithms generally use aerosol-mixture models to estimate and remove the atmospheric contribution to the measured signal. These models, based on aerosol samples, may or may not be realistic. In atmospheric correction, we are more interested in the optical behavior of the aerosols through the entire atmosphere. Comparisons of SeaWiFS-derived and measured aerosol optical thickness have revealed a systematic underestimation of the Angstrom coefficient, suggesting that the reference models may not be representative of actual conditions. To investigate the adequacy of the models and ultimately to improve atmospheric correction, we analyze atmospheric optics data collected by the AERONET project under a wide range of aerosol conditions at coastal and island sites. Using non-supervised classification techniques (self-organized mapping, hierarchical clustering), we determine the natural distribution of retreived aerosol properties of the total atmospheric column, i.e., the volume size distribution function and the refractive index, and more importantly identify clusters in this distribution. These clusters may be used as new aerosols mixtures in radiative transfer algorithms. We compare the clusters with the SeaWiFS reference models and, through application examples, conclude about their potential to improve atmospheric correction of satellite ocean color.
The purpose of data merger activities undertaken by the National Aeronautic and Space Administration's (NASA) Sensor Intercomparison and Merger for Biological and Interdisciplinary Studies (SIMBIOS) Project is to create scientific quality ocean color data encompassing measurements from multiple satellite missions. The fusion of data from multiple satellites will improve the quality of ocean color products over single-mission data sets by expanding spatial and temporal coverage of the world's oceans and increasing
statistical confidence in generated parameters. The merger will also support a variety of new applications by taking advantage of sensor-varying calibration, spectral, spatial, temporal, and ground coverage
characteristics. Leading to the data merger goals, the SIMBIOS Project has established a thorough ocean color validation program and has been cross-comparing and cross-calibrating sensor data with in situ measurements and data among the missions. The SIMBIOS Science Team has been studying data merger algorithms based on spectral data assimilation and spatial interpolation. The SIMBIOS Project Office has implemented statistical objective analysis and regression techniques based on artificial neural networks and
support vector machines. The accuracy of the merger methods will be evaluated using in situ data, statistical analyses, and simple chlorophyll means -- the method already implemented within the SIMBIOS Project. This paper defines challenges and suggests solutions for data merger based on the example of daily chlorophyll concentration products from Moderate Resolution Imaging Spectroradiometer (MODIS) and Sea-viewing Wide Field-of-view Sensor (SeaWiFS).
The NASA Sensor Intercomparison and Merger for Biological and Interdisciplinary Oceanic Studies (SIMBIOS) Project has a worldwide, ongoing ocean color data collection program, as well as an operational data processing and analysis capability. SIMBIOS dat collection takes place via the SIMBIOS Science Team and the National Aeronautics and Space Administration (NASA) Aerosol Robotic Network. In addition, SIMBIOS has a calibration and product validation component. The primary purpose of these calibration and product validation activities are 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 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.
The NASA Sensor Intercomparison and Merger for Biological and Interdisciplinary Oceanic Studies (SIMBIOS) Project has 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 and the NASA Aerosol Robotic Network (AERONET). In addition, SIMBIOS has a calibration and product validation component. The primary purpose of these calibration and product validation activities are 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 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 alternative methods when possible, and provides the results to the appropriate project office.
The Sensor Intercomparison and Merger for Biological and Interdisciplinary Oceanic Studies (SIMBIOS) Project has a worldwide, ongoing ocean color data collection program, plus an operational data processing and analysis capability, SIMBIOS data collection takes place via the SIMBIOS Science Team and the Aerosol Robotic Network (AERONET). In addition, SIMBIOS has a calibration and product validation component. The primary purpose of these calibration and product validation activities are 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 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 alternative methods when possible, and provides the results to the appropriate project office, e.g., Centre National D'Etudes Spatialle (CNES) and National Space Development Agency of Japan (NASDA) for Polarization and Directionality of the Earth's Reflectance (POLDER) and Ocean Color and Temperature Sensor (OCTS), respectively. In the near future the Project is looking forward to collaborate with Global Imager (GLI), Ocean Color Imager (OCI) and international entities such as the International Ocean-Colour Coordinating Group (IOCCG) and Space Application Institute (Joint Research Center).
KEYWORDS: Analytical research, Temporal resolution, Internet, Inspection, Systems modeling, Optical inspection, Data acquisition, Product engineering, Satellites, Data archive systems
The EOSDIS Product Survey was designed to understand the need for, and use of, data products that will become available through EOSDIS in the years 1998 - 2000. This information is needed by the EOSDIS Core System (ECS) performance modelers and system developers for determining adequate processing-, storage-, and network-size specifications to accommodate the expected user-pull load. The survey was tailored to science users. A message inviting potential EOSDIS users to complete the survey was e-mailed in late spring of 1995. The survey was administered electronically via the World Wide Web (WWW) and responses were received from 595 potential users. Quantitative results and analyses are presented on the user demographics, product levels, time-space research requirements and relative product access frequencies. The results indicate that the pull of a product is related to the dynamic nature of the phenomena studied, which, in turn, is closely related to the temporal resolution of the product.
During the last decade, we have seen an explosive growth in our ability to collect and generate data. When implemented, NASA's Earth observing system data information system (EOSDIS) will receive about 50 gigabytes of remotely sensed image data per hour. This will generate an urgent need for new techniques and tools that can automatically and intelligently assist in transforming this abundance of data into useful knowledge. Some emerging technologies that address these challenges include data mining and knowledge discovery in databases (KDD). The most basic data mining application is a content-based search (examples include finding images of particular meteorological phenomena or identifying data that have been previously mined or interpreted). In order that these technologies be effectively exploited for EOSDIS development, a better understanding of data mining and the requirements for using this technology is necessary. The authors are currently undertaking a project exploring the requirements and options of content-based search and data mining for use on EOSDIS. The scope of the project is to develop a prototype with which to investigate user interface concepts, requirements, and designs relevant for EOSDIS core system (ECS) subsystem utilizing these techniques. The goal is to identify a generic handling of these functions. This prototype will help identify opportunities which the earth science community and EOSDIS can use to meet the challenges of collecting, searching, retrieving, and interacting with abundant data resources in highly productive ways.
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