Paul Martinolich

Spectral dependence of the scattering coefficient in case 1 and case 2 waters

An approximate linear relationship between the scattering coefficient and the wavelength of light in the visible is found in case 1 and case 2 waters. From this relationship, we estimate scattering at an unknown wavelength from scattering at a single measured wavelength. This approximation is based on measurements in a 1.5-m-thick surface layer collected with an AC9 instrument at 63 stations in the Arabian Sea, northern Gulf of Mexico, and coastal North Carolina. The light-scattering coefficient at 412 nm ranged from 0.2 to 15.1 m(-1) in these waters, and the absorption coefficient at 412 nm ranged from 0.2 to 4.0 m(-1). A separate data set for 100 stations from Oceanside, California, and Chesapeake Bay, Virginia, was used to validate the relationship. Although the Oceanside waters were considerably different from the developmental data set (based on absorption-to-scattering ratios and single-scattering albedos), the average error between modeled and measured scattering values was 6.0% for the entire test data set over all wavelengths (without regard to sign). The slope of the spectral scattering relationship decreases progressively from high-scattering, turbid waters dominated by suspended sediments to lower-scattering, clear waters dominated by phytoplankton.

Development of finer spatial resolution optical properties from MODIS

Typical MODIS ocean color products are at 1 kilometer (km) spatial resolution, although two 250 meter (m) and five 500 m bands are also available on the sensor. We couple these higher resolution bands with the 1km bands to produce pseudo-250m resolution MODIS bio-optical properties. Finer resolution bio-optical products from space significantly improve our capability for monitoring coastal ocean and estuarine processes. Additionally, increased resolution is required for validation of ocean color products in coastal regions due to the shorter spatial scales of coastal processes and greater variability compared to open-ocean regions. Using the 250m bands coupled with the 1km and 500m bands (which are bi-linearly interpolated to 250m resolution), we estimate remote sensing reflectances (Rrs) at 250m resolution following atmospheric correction. The aerosol correction makes use of the 1km near infrared (NIR) bands at 748 nanometers (nm) and 869 nm to determine aerosol type and concentration. The water leaving radiances in the NIR bands are modeled from retrieved water leaving radiances in the visible bands using the short wave infrared (SWIR) channels at 1240 nm and 2130 nm. The increased resolution spectral Rrs channels are input into bio-optical algorithms (Quasi-Analytical Algorithm (QAA), Water Mass Classification, OC2, etc.) that have traditionally used the 1 km reflectances resulting in finer resolution products. Finer resolution bio-optical properties are demonstrated in bays, estuaries, and coastal regions providing new capabilities for MODIS applications in coastal areas. The finer resolution products of total absorption (at), phytoplankton absorption (aph), Color-Dissolved Organic Matter (CDOM) absorption (ag) and backscattering (bb) are compared with the 1km products and in situ observations. We demonstrate that finer resolution is required for validation of coastal products in order to improve match ups of in situ data with the high spatial variability of satellite properties in coastal regions.

Near real time calibration of the Ocean Land Colour Imager

The success of current ocean color satellite missions relies on the spectral quality, consistency, accuracy and precision of products (water leaving radiances, aerosols and clouds) derived from the satellite sensors. We propose leveraging available in situ data from various autonomous ocean color data collection sites to provide a near real time (NRT) spectral calibration for the Ocean Land Colour Imager (OLCI) by tuning the top of atmosphere (TOA) spectral radiances. Using the Naval Research Laboratory – Stennis Space Center (NRL-SSC) Automated Processing System (APS) software, NRT calibration of OLCI is demonstrated using in situ data from the MOBY and AERONET-OC collection sites. This calibration procedure has been used with other multi-spectral satellites to rapidly improve the data quality of emergent sensors so that they can be used to support marine spectrometric applications, track the satellite sensor stability, and enable continuity and consistency of ocean color products between several satellites.

Comparative Analysis of GOCI Ocean Color Products

The Geostationary Ocean Color Imager (GOCI) is the first geostationary ocean color sensor in orbit that provides bio-optical properties from coastal and open waters around the Korean Peninsula at unprecedented temporal resolution. In this study, we compare the normalized water-leaving radiance (nLw) products generated by the Naval Research Laboratory Automated Processing System (APS) with those produced by the stand-alone software package, the GOCI Data Processing System (GDPS), developed by the Korean Ocean Research & Development Institute (KORDI). Both results are then compared to the nLw measured by the above water radiometer at the Ieodo site. This above-water radiometer is part of the Aerosol Robotic NETwork (AeroNET). The results indicate that the APS and GDPS processed nLw correlates well within the same image slot where the coefficient of determination (r2) is higher than 0.84 for all the bands from 412 nm to 745 nm. The agreement between APS and the AeroNET data is higher when compared to the GDPS results. The Root-Mean-Squared-Error (RMSE) between AeroNET and APS data ranges from 0.24 [mW/(cm2srμm)] at 555 nm to 0.52 [mW/(cm2srμm)] at 412 nm while RMSE between AeroNET and GDPS data ranges from 0.47 [mW/(cm2srμm)] at 443 nm to 0.69 [mW/(cm2srμm)] at 490 nm.

Automated Validation of Satellite Derived Coastal Optical Products

Automated validation methods and a suite of tools have been developed in a Quality Control Center to analyze the stability and uncertainty of satellite ocean products. The automatic procedures analyze match-ups of near real time coastal bio-optical observations from Marthas Vineyard Coastal Observatory (MVCO) with satellite-derived ocean color products from MODIS Aqua and Terra, SeaWIFS, Ocean Color Monitor, and MERIS. These tools will be used to compare MVCO in situ data sets (absorption, backscattering, and attenuation coefficients), co-located SeaPRISM-derived water leaving radiances, and the Aerosol Robotic Network (AeroNet) derived aerosol properties with daily satellite bio-optical products and atmospheric correction parameters (aerosol model types, epsilon, angstrom coefficient), to track the long term stability of the bio-optical products and aerosol patterns. The automated procedures will be used to compare the in situ and satellite-derived values, assess seasonal trends, estimate uncertainty of coastal products, and determine the influence and uncertainty of the atmospheric correction procedures. Additionally we will examine the increased resolution of 250m, 500m, and 1 km satellite data from multiple satellite borne sensors to examine the spatial variability and how this variability affects assessing the product uncertainty of coastal match-ups of both bio-optical algorithms and atmospheric correction methods. This report describes the status of the QCC tool development and potential applications of the QCC tool suite.