This PDF file contains the front matter associated with SPIE Proceedings Volume 7459, including the Title Page, Copyright information, Table of Contents, Introduction (if any), and the Conference Committee listing.

Scanning radiometers on earth-orbiting satellites are used to measure the chlorophyll content of the oceans via analysis of the water-leaving radiances. These radiances are very sensitive to the atmospheric correction process. In the standard atmospheric correction algorithms, two bands in the NIR wavelength region are used to determine the radiance contributions of aerosols to the top-of-atmosphere radiance. In the standard algorithms, thin cirrus clouds are treated as aerosols. The MODIS instruments on the Terra and Aqua satellites have a band at 1380nm that allows the detection of thin cirrus clouds. This paper shows that the presence of thin cirrus clouds causes a small bias in the water-leaving radiances derived with the traditional algorithms for the MODIS Aqua instrument. The bias is insignificant when averaging over large areas.

In this paper, we explore the performance of a bio-optical model used to estimate the water leaving radiance at 412nm and under what conditions this constraint can improve retrieval of water leaving radiances in the VIS and NIR channels. We first demonstrate that the bio-optical model performance is well modeled by Hydrolight simulations under coastal water conditions and is particularly suitable for coastal waters and that the 412nm estimator is well correlated to insitu measurements from SeaBASS. We then show that unlike prior uses, the bio-optical estimator can also be used not only to improve retrieval for absorbing aerosols but can be used to improve retrieval errors when regional aerosol models are not included. Furthermore, we explore the optimal a-posteri correction and show that the original n=6 coefficient used for aloft absorbing aerosols may need to be refined.

The performance of ocean color inversion algorithms is strongly impacted by the various sources of uncertainties, including measurement noise, calibration noise, pre-processing and radiation transfer modeling uncertainties. In this work, an attempt at assessing the overall departure of theory from measurements is conducted based on an in-situ matchup data set. The statistical properties of these differences are first estimated, and are next used to define a Bayesian solution to the inverse problem of atmospheric correction. It is found that there may exist multiple solutions to the inverse problem. The methodology also allows the construction of general confidence domains on the retrieved marine reflectance, without shape restrictions.

Ocean color imagery, when viewed from space, is degraded due to scattering by the atmosphere. The effect, also known as the adjacency effect, is especially important near the coast, sea-ice, and clouds, i.e., where the environment reflectance is much different from the target reflectance. The adjacency effect, however, may not be negligible in the open ocean. This is demonstrated by processing SeaWiFS imagery acquired over a typical upwelling system off the coast of Namibia, Africa. Ignoring the atmospheric point-spread function in the atmospheric correction algorithm or, equivalently, using a large-target formalism to describe the top-of-atmosphere reflectance, errors reaching over 10% are made on chlorophyll concentration retrievals. The structure of the spatial field of chlorophyll concentration is changed significantly after correction of the adjacency effect, with the influence of local processes comparatively decreased with increased distance. Correcting systematically (i.e., not only near the coast) Level 1b ocean color imagery for the adjacency effect is recommended. As a result, the accuracy, quality, and daily coverage of aerosol and ocean-color products would be improved substantially over water surfaces contiguous to land surfaces, sea-ice, clouds, and generally regions where spatial contrast is relatively large.

The efficient monitoring of coastal, or Case 2 waters by optical remote sensing has always been a challenging task. This study develops a neural network (NN) model to examine the possibility of accurate retrieval in waters where semianalytical and empirical algorithms do not perform satisfactory due to the large variability in the coexistence of particulates, dissolved matter and phytoplankton species. A multi-layer forward neural network was constructed to estimate the total absorption, phytoplankton absorption, total suspended matter and color dissolved organic matter absorption and total backscattering at the same time from in-situ measured water surface reflectance spectra. The neural network was trained using 60% of the 1000 reflectance spectra from a synthetic datasets that were generated using Hydrolight for water properties typical to coastal regions. Then, the neural network model was tested with the remaining 40% of the simulated reflectance spectra and applied to field data. Primarily the NN was trained and tested with the input of traditional visible channels. Thereafter one more channel was added from the UV region and the NN was again trained and tested. The retrievals with the addition of UV improve both in the simulated and field data..

During the "Optic-Congo" oceanographic survey which took place in 2005 on board the "Beautemp-Beaupré" SHOM vessel, different optical measurements of the surface water were acquired using a TRIOS radiance sensor fixed onboard a mini-catamaran. Hydrological measurements (CTD, fluorescence, attenuation, scattering) and water samples were simultaneously collected in order to measure SPM, Chlorophyll-a and CDOM concentrations. Four types of surface water colours (blue, green-yellow, dark and brown) were identified. The main characteristics of these waters were the very low Chlorophyll-a concentrations for this period of the year (March), and the very high CDOM concentrations along the Congo coast, and particularly in the turbid plume of the Congo River. The attenuation and scattering measurements highlighted the predominance of organic matter at the water surface. These observations were documented using a beam electron microscope and by microanalysis. This data set was used to classify the water bodies along the Gabon and Congo coasts. We propose here to use the remote sensing reflectance (Rrs) measurements to invert the IOP (absorption (a) and backscattering (b_b)) using the WASI numerical bio-optical model. The model is iterative: the Rrs WASI simulations are computed given initial values of ocean constituents' concentrations and iteratively adjusted to the Rrs in-situ measurements. The IOP computations are satisfying when the correlations between simulated and measured Rrs are optimized. Then, the attenuation coefficients (K_d) are computed from the IOP coefficients. These results are compared with measurements of K_u carried out during the survey.

As a result of rich nutrient and terrestrial influence dissolved organic matter plays an important role in determining the optical properties of coastal water. Despite the fact that the features of its fluorescence spectroscopy depend on its complicated chemical components, which are source specific, estimation of solar stimulated CDOM fluorescence is usually based on a fixed Gaussian spectral shape or a modeled fluorescence transfer function obtained from extracted fuvic and humic acid. The excitation emission matrix (EEM) and absorption spectrum of CDOM extracted from seawater in various coastal zones are measured using Perkin Elmer fluorescence spectrometer. Unlike previous research, the obtained EEM spectroscopy data after calibration are then entered into the Hydrolight radiative transfer program together with the data of inherent optical properties such as absorption and attenuation simultaneously collected by Wetlabs in-situ instrument package to evaluate realistically the contribution of solar stimulated CDOM fluorescence to the total reflectance in the Hudson River and New York City area. In addition, the CDOM contribution to the total reflectance is evaluated with the presence of other water components based on a depth integrated fluorescence model and a semi-analytical reflectance model. The simulated remote sensing reflectance with the CDOM fluorescence on and off are compared with the field recorded spectra, through which its impact on the closure of ocean color data as well as on the accuracy of estimating backscattering ratio and chlorophyll fluorescence is also assessed.

A cross-calibrated, multi-satellite ocean surface wind data is described. It covers the global ocean for the twenty-one year period from 1987 to 2008 with 6-hour and 25-km resolution. This data set is produced using all ocean surface wind speed observations from SSM/I, AMSR-E, and TMI, and all ocean surface wind vector observations from QuikSCAT and SeaWinds. An enhanced variational analysis method (VAM) performs quality control and combines these data with available conventional ship and buoy data and ECMWF analyses. The VAM analyses fit the data used and withheld data very closely and contain small-scale structures not present in operational analyses. These data should be extremely useful to atmospheric and oceanic research, and to air-sea interaction studies.

Turbidity is one of the important factors that can be used for measuring water quality in the Chesapeake Bay. The Moderate Resolution Imaging Spectroradiometer (MODIS)-Aqua derived diffuse attenuation coefficient at the wavelength 490 nm (Kd(490)) can be used to relate the Chesapeake Bay water turbidity. In this presentation, we use the recently developed shortwave infrared (SWIR)-based atmospheric correction algorithm for deriving MODIS-Aqua ocean color products in the Chesapeake Bay. It has been demonstrated that the SWIR-based data processing produces better quality ocean color products over the turbid coastal waters. We use the Kd(490) data derived from MODIS-Aqua with SWIR-based algorithm to study the turbidity in the Chesapeake Bay. Spatial distribution and seasonal variations of turbidity are analyzed. In addition, simulations from the Regional Ocean Modeling System (ROMS) coupled with a sediment model have been carried out to investigate the mechanisms of sediment transport, deposition, and resuspension processes in the Chesapeake Bay. Factors that contribute to the turbidity variations, such as wind, current, tide, and sediment settling velocity are simulated in the model. The satellite observations combined with the model simulations are used to study and understand the turbidity variation and its impact on the water quality in the Chesapeake Bay.

Characterization of 3-D underwater light fields from above the sea surface requires passive and active remote sensing measurements. In this work, we suggest the use of passive ocean color sensors and lidar (Light Detection and Ranging) to examine the vertical structure of optical properties in marine waters of the Northern Part of the Gulf of Alaska (NGOA). We collected simultaneous airborne remote sensing reflectance (R_rs) in the spectral range 443-780 nm (MicroSAS, Satlantic) and lidar-derived volume backscattering (β) profiles (0-20 m depth, wavelength = 532 nm) during August 17 2002 in shelf waters situated south of Kodiak Island off Alaska (57.48°-58.04° N, 152.91°-151.67° W). We evaluated the spectral response of Rrs to perturbations on vertical distribution of β by comparing the spatial variability between aggregated (250 m horizontal resolution x 1 m vertical resolution) R_rs spectral ratios and different lidar statistics per bin (Maximum β per bin, mean β per bin, β_m, standard deviation of β per bin, β_std, integrated β per bin, β_int) or group of bins (lidar volume extinction coefficient of β between 0 and 5 m depth). Sub-surface changes of β_m, β_int, and β_std were mainly correlated with R_rs (490)/R_rs (555) variability along the flight-track (Semi-partial correlation coefficients = 0.12 to 0.21). Our results evidenced linkages between above and below-sea surface optical properties that can be used to derive water optical constituents as a function of depth based on combined passive-active data.

Inherent optical properties (IOPs) and remote sensing reflectance were measured in the southern part of the lagoon of New Caledonia during the VALHYBIO cruise (March-April 2008). The goal was to validate satellite chlorophyll data from MODIS and MERIS and to validate simulations of surface chlorophyll by a biogeochemical model. Physical parameters were collected from a Seabird CTD. Particulate and detritus absorption were measured with the filter pad technique. Backscattering was measured with a Hydroscat-6. Mapping of IOPs and Rrs were done for the whole southern lagoon area and compared with pigment maps. The cruise provided a description of the IOPs in different water types including bays, open ocean waters, mid-shelf lagoon, and above reefs. With respect to climatology, the heavy rainfall episode of March-April 2008 resulted in a large increase in chlorophyll-a concentration (by a factor of 3) attributed to increased nutrient availability from land drainage. Low backscattering ratios characterized the chlorophyll-rich plumes associated with the nutrient increase. The data are useful for the development of a specific algorithm for chlorophyll concentration retrieval by satellite in all oligotrophic lagoons during dry and wet seasons.

The World's first Ocean Color Observation Satellite, the GOCI (Geostationary Ocean Color Imager) equipped with is scheduled to be launched on Communication, Ocean and Meteorological Satellite (COMS) in November 2009. Korea Ocean Research & Development Institute (KORDI) has developed GOCI Data Processing System (GDPS) which produces ocean environment analysis data such as chlorophyll concentration, TSS, CDOM, Red-Tide, water current vector, etc. In order to retrieve water-leaving radiance more precisely, atmospheric and BRDF (Bi-Directional Reflectance Distribution Function) correction algorithms optimized for the environment of the GOCI coverage area and COMS satellite orbit characteristics have been developed and implemented into the GDPS. GOCI operational atmospheric correction algorithm has a capability to retrieve water-leaving radiance in the presence of aerosols with high optical thickness (i.e. Asian Dust). At-sensor radiance which is affected by relative change of the Sun and satellite position is corrected by the GOCI BRDF Correction algorithm. GOCI L2 data which is the product of the GDPS is provided with 8 VNIR band images with 4967 x 5185 pixel resolution on the GOCI coverage area. As GOCI main operation center, Korea Ocean Satellite Center (KOSC) has been established by KORDI. Main operational functions of KOSC are the acquisition, processing, and storage of the GOCI data and distribution service of ocean satellite standard products generated from the GOCI data. Operational systems of KOSC are GDAS(GOCI Data Acquisition System), IMPS(Image Pre-processing System), GDPS, DMS(Data Management System), and GDDS(GOCI Data Distribution System). After the launch, KOSC has a plan to provide the GOCI data for the real time ocean environment and marine bio-physical phenomena variability monitoring.

The instrument level ground test of the Geostationary Ocean Color Imager(GOCI) has been completed and integrated onto the Communication, Ocean and Meteorological Satellite(COMS) which is scheduled for launch in late 2009. In order to monitor the short-term biophysical phenomena with better temporal and spatial resolution, The GOCI has developed with eight VNIR bands, 500m GSD, and 2500km×2500km coverage centered at 36°N and 130°E. The GOCI planned to observe the full coverage region by every hour in daytime, and provide 8 images in daytime during single day. The GOCI ground test campaign for characterization and calibration has been performed by Korea Aerospace Research Institute(KARI), Korea and EADS Astrium, France. Korea Ocean Research & Development Institute(KORDI) has verified that test results satisfy all the GOCI performance requirements(Ex. MTF, SNR, Polarization, etc.) requested by KORDI. The GOCI has been sufficiently characterized under both of ambient and thermal-vacuum environments in order to develop the on-orbit radiometric calibration algorithm. GOCI radiometric model has been finalized with 3rd order polynomial. Because solar calibration is the on-orbit radiometric calibration method of the GOCI, Solar Diffuser made of fused silica and Diffuser Aging Monitoring Device(DAMD) are implemented as on-board calibration system. Diffusion factor of the Solar Diffuser and DAMD with respect to the solar incident angle, wavelength, and pixel location has been successfully characterized. Diffuser aging factor has been calculated for the compensation of the diffuser degradation by space environment. Diffusion factor of Solar Diffuser and DAMD, and diffuser aging factor characterized during prelaunch ground test are implemented into the GOCI radiometric calibration S/W developed by KORDI.

Optical properties derived from ocean color imagery represent vertically-integrated values from roughly the first attenuation length in the water column, thereby providing no information on the vertical structure. Robotic, in situ gliders, on the other hand, are not as synoptic, but provide the vertical structure. By linking measurements from these two platforms we can obtain a more complete environmental picture. We merged optical measurements derived from gliders with ocean color satellite imagery to reconstruct vertical structure of particle size spectra (PSD) in Antarctic shelf waters during January 2007. Satellite-derived PSD was estimated from reflectance ratios using the spectral slope of particulate backscattering (γ_bbp). Average surface values (0-20 m depth) of γ_bbp were spatially coherent (1 to 50 km resolution) between space and in-water remote sensing estimates. This agreement was confirmed with shipboard vertical profiles of spectral backscattering (HydroScat-6). It is suggested the complimentary use of glider-satellite optical relationships, ancillary data (e.g., wind speed) and ecological interpretation of spatial changes on particle dynamics (e.g., phytoplankton growth) to model underwater light fields based on cloud-free ocean color imagery.

The deep water chlorophyll concentration fluctuation from 2003 to 2007 has been studied using fractal analysis. The SeaWiFS global daily mean chlorophyll concentration time series were used. The Higuchi fractal algorithm was used to calculate fractal dimension, which is given by the slope of an associated length versus the lag. Short range fluctuation investigation using a six point slope gives fractal dimensions from 1.80 to 1.85, suggesting the presence of correlation, which was confirmed by computer simulations. The gradual increase of fractal dimension to 1.9 in about 15 lag-days suggests that a long-range de-correlation mechanism favoring random fluctuation is present. The 2007 times series shows a relatively low overall fractal dimension and exhibits a peculiar multi-fractal behavior. This phenomenon and the observed low accumulated cyclone energy in 2007 support the interpretation that cyclone energy can promote deep-water chlorophyll concentration fluctuation. A regression of fractal dimension at 10 lag-days versus the log of cyclone energy gives an R² value of 0.75 (N = 5)., which suggests the presence of additional or related de-correlation mechanisms.