The ocean color remote sensing is used to retrieve the water constituents such as phytoplankton generally from satellites observations. However, it is still difficult to apply this technology for the coastal regions, due to the low spatial resolution of the sensor, the limited observation periods. To address these problems, we newly developed a compact multi-band spectral imaging sensor, which is suitable for coastal ocean color remote sensing by a drone. In this paper, we experimentally evaluated our coastal ocean color observation system in Suruga Bay in Japan. The result showed the spectral shape of observed ocean color exhibited the typical color of case II water. Finally, we briefly introduce the latest-version of our radiometer.
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.
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).
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.).
An inverse modeling has a possibility to retrieve the concentration of water constituents, such as chlorophyll (alpha) (Chl-(alpha) ), suspended matter (SS) and yellow substance (CDOM), in case 2 water from remotely sensed data. It turned out to be useful for mass processing of satellite data. Standard approach for inversion of radiative transfer needs long computation time, because iteration procedure is essential. On the other hand, Neural Network (NN) method is able to overcome this problem since it is consider to be a kind of non-linear multiple regression method, so that it is possible to retrieve the concentration of multiple water constituents by NN. The NN Method was applied to OCTS (Ocean Color and Temperature Scanner) data in the Yellow Sea and East China Sea to retrieve of chlorophyll a concentration, in organic suspended solids and yellow substances. The large mount of suspended Solids the Yellow Sea and the East China Sea were supplied from the Yellow River (Wei He) and the Yangtze River (Chang Jiang). The temporal and spatial distributions of chlorophyll (alpha) , inorganic mineral suspension and yellow substance were analyzed.
Concerning with the ocean color remote sensing, the algorithm based on the empirical method is hard to apply to coastal regions, so-called case II water, since the constitution of water is optically complex. On the other hand, an inverse modeling using a neural network has a potential to retrieve the concentration of water constituents, such as chlorophyll a, inorganic suspended matter and colored dissolved organic matter, in case Ii water from remotely sensed data. As a representative area of Asian Case II water, Yellow Sea, which is known as a region with high SS and CDOM supplied form the Yellow River, was analyzed using ADEOS/OCTS data observed on 31.5.1997. Three different types of algorithm were examined: Alg. 1) Retrieval from nLw using 670 and 865nm for the atmospheric correction bands; Alg. 2) Retrieval from nLw using 765 and 865nm for the atmospheric correction bands; Alg. 3) Retrieval from atmosphere-ocean coupled simple radiative transfer model using all bands, where, nLw is the normalized water leaving radiance. Although the best result was obtained by Alg. 2), Alg. 3) seems to be promising for case II water since reliable atmospheric correction can be carried out.
The underwater irradiance measurements are usually carried out from a ships, so that it is easy to imagine hat the data is affected by the shadow due to a ship. The simple algorithm to compute the 3D underwater irradiance fields was developed based on the forward Monte Carlo method, by assuming that the infinitely thin and totally absorbing disk exists at the air-sea interface. The model was confirmed by the field experiment, and found the good agreement with observed irradiance fields. According to the computations, it was found that the influence of the shadow on downward and upward irradiances appear in different manner. That is, the downward irradiance affected by the disk appears in downward with respect to the direction of the direct of the sunlight. On the other hand, it appears in upward in the case of upward irradiance. As the result, it is predicted that the overestimate and underestimate regions of irradiance reflectance are formed.
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