Effect of sun glint reflectance to the measurement of ocean color sensors is generally observed, even some of the sensors
are equipped with tilt mechanisms to avoid sun glint. The traditional Cox-Munk model has been widely used to estimate
the sun glint reflectance together with objective analysis wind data. To reevaluate the sun glint model at the condition of
satellite viewing geometry, ADEOS-II/GLI data are analyzed jointly with SeaWinds microwave scatterometer data
which provides the concurrent wind data with the ocean color observation. The probability density of the wave slope is
then estimated using GLI data of 865nm band after carefully masking the cloud-contaminated pixels and removing the
aerosol effects, the latter being estimated from the SeaWiFS Level 3 daily aerosol data set. The satellite-retrieved
probability density functions are then analyzed as a function of wind speed and wave slope angle, by fitting the satellite
retrieved probability density of slope to the anisotropic model. Modified model parameters is given and applied into GLI
processing. Results are compared with the original anisotropic Cox-Munk model, as well as other published results.
Differences in the slope distributions are discussed, which is mostly found at very weak speed or very strong wind speed.
Ocean color products (aerosol, normalized water-leaving radiance, and chlorophyll-a concentration) were produced using 250-m resolution data (center wavelengths at 462, 543, 661, 824, 1645, and 2194nm) of Global Imager (GLI) on ADEOS-II by GLI standard ocean atmospheric correction algorithms (i.e., basically same as the 1-km algorithm). The 250-m ocean color products can show finer spatial structures than standard 1km products, but we found some problems; (a) there is large noise because GLI 250-m channels were designed originally for bright area (land vegetation), (b) we have to use channels which are not optimized for the atmospheric correction (wide bandwidth, large noise, and including slight water-vapor absorption), and (c) we need more consideration about cloud shadow, sea-surface reflectance (sunglint and white cap), and shallow bottom. Sea surface reflectance has fine spatial structures in the actual ocean due to fine structure of surface winds influenced by the coastal topography, and we often cannot distinguish the surface reflection from aerosol scattering above the sea surface. JAXA is planning a new mission, "Second generation GLI (SGLI) on GCOM satellites", which has visible and near-infrared channels with 250-m spatial resolution improving noise level and bandwidth applicable to the coastal ocean-color observations.
The study is to analyze changes in monthly-averaged aerosol parameters derived from the SeaWiFS observations over East Asia from January 1998 through December 2004. All the SeaWiFS GAC Level 1 data (4 by 4 km spatial resolution data) that cover the Northeast Asian area were collected and processed by the standard atmospheric correction algorithm released by the SeaWiFS Project to produce daily aerosol optical thickness (AOT) and Angstrom exponent imageries. Cloud screening was applied if AOT at 490 predicted from the aerosol look-up tables embedded in the algorithm exceeded 0.7. From the daily composite images, monthly average AOT and Angstrom exponent values were extracted for each one of the six study areas chosen from the surrounding waters of Japan. The results showed that, although annual mean of AOT did not show any trend, +0.01-0.015 increase in Angstrom exponent in almost all study areas was observed over the study period. This increase is interpreted as 4-5% increase in submicron fraction (SMF), or the ratio of contribution of submicron aerosol particles to the total AOT, and will be interpreted as an increase of submicron particles due to the enhanced anthropogenic activities in East Asia.
The paper compares GLI-derived estimates obtained under "version 2" GLI standard atmospheric correction algorithm with in situ measured data collected by SIMBADA handheld above-water radiometer, intending the evaluating the performance of the algorithm which includes empirical absorptive aerosol correction as well as sun glint correction. Over 395 match-up data, average estimation error (difference between GLI-derived and SIMBADA-measured data) in
normalized water-leaving radiance (nLW) is 0.3 μW/cm2/nm/sr in 412 and in 443 nm bands, showing improvement from version 1 GLI atmospheric correction by 10-30 %, whereas estimation bias is reduced significantly. The GLI-derived
aerosol optical thickness (AOT) in 865 nm band show 0.1 RMS error against SIMBADA measurement on average, whereas Angstrom exponent estimate shows significant bias, suggesting potential calibration offset among GLI near-infrared bands. Despite relatively large scattering in nLW match-up analysis, comparison between GLI chlorophyll a concentration estimates and SIMBADA-derived estimation show highly correlated and consistent relation. This will suggest that fluctuations in nLW estimate are systematic over GLI visible channels although the nature of the variability requires further investigation.
The Global Imager (GLI) was launched on board the Advanced Earth Observing Satellite II (ADEOS-II) on December 14, 2002. We conducted vicarious calibration of the GLI ocean color channels in visible to near-infrared channels. For the calibration we used the normalized water-leaving radiance derived from the Marine Optical Buoy (MOBY), and the aerosol optical properties (aerosol optical depth, size distribution, and refractive index) released in the Aerosol Robotic Network (AERONET).
The following GLI characteristics are recognized from the calibration results. First, GLI underestimates the radiance in channels 1, 2, 4, and 5. Next, in near-infrared channels, it is suggested that GLI overestimates the radiance on the order of 15% in channels 18 and 19. Furthermore, the comparison of the result with other vicarious calibration results suggests the possibility that the GLI observed radiance has offset radiance versus the simulated radiance. The estimated offset is about 0.4 W/m2/um/sr in channel 19, which is considered appropriate by the adaptation test to the GLI standard atmospheric correction algorithm.
This paper proposes scheme of absorptive aerosol correction for ocean color data of Global Imager (GLI). 380 nm band (GLI band 1) is used to detect absorptive aerosol, and to correct absorption. Spectral dependency of absorption is given empirically from GLI scenes with absorptive aerosol. It is built in GLI atmospheric correction. Satellite-derived water-leaving radiance is compared with in-situ data over East China Sea under presence of absorptive aerosol. Estimation error of water-leaving radiance is decreased from 79 to 49% at 380nm, though error still remained. The scheme applied to GLI data with absorptive aerosol, and it was confirmed that this atmospheric correction was effective.
Widespread boreal forest fires persisted in Eastern Asia for several months from the beginning of April until September 2003. This resulted in enhanced concentrations of smoke aerosol in a very large region, ranging from the source area of the fires in eastern Siberia to northern and eastern China, Korea, and Japan. The smoke was also detected over large areas of the Pacific Ocean, and was even observed in Alaska. E.g., during mid-May aerosol optical thickness values higher than 4 at mid-visible wavelengths were observed on the ground at Anmyon, Korea, due to transport of forest fire aerosol to this region. Satellite remote sensing provides a very useful tool to observe the temporal evolution and the spatial distribution of the aerosol over large areas. In this work, we employ a newly developed algorithm for the ADEOS-2/GLI sensor, that was launched onboard the ADEOS-2 sensor in December 2002. The algorithm employs two channels in the near-UV to retrieve the aerosol optical thickness and single-scattering albedo of aerosols. Although GLI had only a 7-month lifetime due to the early power failure of the ADEOS-2 satellite in October 2003, it was able to observe the whole period of large-scale forest fire smoke, that heavily impacted Eastern Asia. We also analyze ground based skyradiometer measurements at Sapporo, Japan, which was frequently influenced by forest fire aerosols during spring 2003.
Global Imager (GLI) is the visible to infrared imager aboard ADEOS-II satellite with 30 and 6 channels for 1 km and 250m resolutions, respectively. The sensor was successfully captured the first image on January 25, 2003. Sea surface temperature (SST) will be retrieved in combination with simultaneous SST observation by low-resolution microwave sensor, AMSR-E. Distribution of chlorophyll and other constituents will be obtained from ocean color channels. Frequent observations with 250 m visible channels will be also available, and combination with 1 km ocean color and SST will be useful for coastal applications. Early scientific results of GLI ocean group will be presented in this presentation.
The paper presents initial results of atmospherically corrected ocean color data from the Global Imager (GLI), a moderate resolution spectrometer launched in December 2002 aboard ADEOS-II satellite. The standard GLI atmospheric correction algorithm, which includes an iterative procedure based on in-water optical modeling is first described, followed by brief description of standard in-water algorithms for output geophysical parameters. Ship/buoy-observed and satellite-derived marine reflectances, or normalized water-leaving radiance, are then compared, under vicarious calibration correction factors based on global GLI-SeaWiFS data comparison. The results, over 15 water-leaving radiance match-up data collected mostly off California and off Baja California, show standard errors in GLI estimate of 0.1 to 0.36 μW/cm2/nm/sr for 412, 443, 490, and 565 nm bands, with improved standard errors of 0.09 to 0.14 μW/cm2/nm/sr if in situ data set is limited to those obtained by in-water radiance measurement. Under provisional de-striping procedure, satellite-derived chlorophyll a estimates compares well with 35 ship-measured data collected off California within one day difference from the satellite observation, showing standard error factor of 1.73 (+73% or -43% error).
Asian dust aerosol layer of 4-6 km altitude accompanied by low clouds was observed with a LIDAR in Tokyo urban area on April 10 2001. In addition, column-integrated size distribution of aerosol was measured with a SkyRadiometer. To synthesize the top of atmosphere (TOA) reflectance, radiative transfer simulation was conducted assuming aerosol/cloud vertical structure and aerosol size distribution that were estimated after the ground observations. The refractive index of Asian dust was derived from a laboratory measurement of sampled Chinese soil particles. The synthesized TOA reflectance was compared to the SeaWiFS-derived one sampled at the low cloud pixels whose airmass is the same as the one passed at the observation site. TOA reflectance of the one of Asian dust models compare generally well with few percent difference in reflectance. We estimated an affect of Asian dust aerosol to ocean color remote sensing. Simulated TOA radiance absorbed by Asian dust was 20.0 W/m2/μm/sr in 443 nm. It is suggest that the existence of Asian dust occurs to dervie negative water-leaving radiance.
SeaWiFS observation of the East-Asian seas during the Aerosol Characterization Experiment-Asia shows large areas of under-estimated or even negative water-leaving radiance in the blue. To investigate it, three match-up stations were analyzed. An iterative radiative transfer simulation was carried out, in an attempt to reproduce the satellite measured top-of-atmosphere reflectance. The resulting water reflectance and aerosol optical thickness (AOT) agreed well with field measurements when the effect of sub-micron absorbing particles was considered in the simulation. The error in the retrieved water reflectance was much decreased, with average values of about 6% at 412nm and 443nm for the three stations. The effect of the Asian dust was also simulated in comparison with that of small absorptive aerosols. The under-estimation could not be solely attributed to Asian dust. It was also found that at one of the station, where the presence of dust aerosols was anticipated, an aerosol model mixed with both dust and soot improved the accuracy of the estimated AOT compared with the case of soot as the only absorptive aerosol. Sub-micron absorbing particles, in addition of the Asian dust, should be considered in the optical remote sensing of East-Asian waters.
The atmospheric correction in ocean color remote sensing ins the most significant technique to retrieve the water leaving radiances, which are less than 10 percent of the satellite radiances, and its main errors are occurred by the estimation errors of the aerosol property and quantity which are highly variable in both space and time. In this study, our main purpose is to develop the advanced atmospheric correction method by using multi-viewing satellite data. POLDER onboard ADEOS can acquire multi-direction reflectance up to 14 viewing angles. In order to evaluate the validity of the multi-angle algorithm, we tired to test the algorithm with POLDER data. Results of comparisons with OCTS algorithm show that this algorithm with 3 or 4 angles POLDER data is available to estimate the aerosol properties because it is not affected form the errors depend on the band ratio.
Two types of algorithm selected for ADEOS-II/GLI atmospheric correction for ocean applications were summarized. The standard algorithm, OTSK1a, is an extension from the OCTS algorithm (and also similar to current SeaWiFS/MODIS algorithm). The research algorithm, OTSK1b, is selected to validate OTSK1a. OTSK1b is a new approach for atmospheric correction by using multi-layered perceptrons (i.e. neural network) to model the transfer function between top-of- atmosphere GLI reflectances and above-surface marine reflectances. The performance of these two algorithms was tested with GLI synthetic dataset and MODIS data.
The presentation focuses on the peculiarity of Asian waters with respect to the atmospheric correction of the satellite ocean color data such as of Ocean Color and Temperature Scanner (OCTS). We first demonstrate the effect of highly turbid case 2 waters on the atmospheric correction via non- zero water reflectance in the near infrared region. The results of applying the OCTS standard correction scheme to typical Chinese coastal OCTS scenes reveal that a significant portion of the area is masked due to the negative water reflectance retrieved by the scheme, even using 765 nm and 865 nm bands instead of 670 and 865 nm pair to determine aerosol contribution. An optical model that relates suspended solid (SS) and chlorophyll-a (Chl-a) concentrations to the near infrared water reflectances was implemented into the atmospheric correction, together with a neural network that estimates Chl-a and SS concentrations. The new iterative scheme is applied to the Chinese coastal scenes and the results are assessed to be favorable. The paper then discuss the modeling of Asian dust aerosol in hope of establishing aerosol models that can be used for atmospheric correction. A set of models are designed with varying controlling parameters such as size distribution, vertical profile, and imaginary part of the refractive index. A series of radiative transfer simulation is conducted and the spectrum of the top-of- atmosphere radiance is compared to that of a Sea Wide Filed- of-view Scanner (SeaWiFS) data obtained under Asian dust event. The results of the comparison suggest that the Asian dust aerosol has unique spectral absorption feature at the blue region (in 412 nm band, i.e.).
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).
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