| Project/Area Number |
09440169
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Meteorology/Physical oceanography/Hydrology
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| Research Institution | Tokai University |
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Principal Investigator |
OISHI Tomohiko Tokai Univ., Natural Science, Asist. Prof., 理学部, 助教授 (20231730)
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| Co-Investigator(Kenkyū-buntansha) |
HAGIWARA Naoki Tokai Univ., Marine Science & Tech., Lecturer, 海洋学部, 講師 (50198652)
SAITO Horoshi Tokai Univ., Marine Science & Tech., Asist. Prof., 海洋学部, 助教授 (50235066)
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| Project Period (FY) |
1997 – 1999
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| Project Status |
Completed (Fiscal Year 1999)
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| Budget Amount *help |
¥7,500,000 (Direct Cost: ¥7,500,000)
Fiscal Year 1999: ¥1,000,000 (Direct Cost: ¥1,000,000)
Fiscal Year 1998: ¥2,400,000 (Direct Cost: ¥2,400,000)
Fiscal Year 1997: ¥4,100,000 (Direct Cost: ¥4,100,000)
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| Keywords | Inverse method / Algorithm / Ocean remote sensing / Neural Network / Marine Optics / Ocean colour / Radiative transfer / Phytoplankton / 植物プランクトン / アルゴリズム / リモートセンシング / 光吸収 / 光散乱 |
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| Research Abstract |
Ocean color remote sensing technology based on satellite provides us important information concerning spatial distribution and time variation of phytoplankton. However the present analyzing algorithm is based on the empirical relation, so that it is very difficult to apply large area of ocean. As the result, it is difficult to say that we can fully use the potential of satellite remote sensing technology. In order to solve this problem, we applied the inverse method based on radiative transfer theory, which obtains cause (Sea water optical properties) from result (Ocean colour). Further, it is essential to develop the under-water irradiance model, which express real irradiance fields with sufficient accuracy and the estimation method of backward scattering coefficients, which influence on under-water irradiance fields. These results are satisfactory for applying to the inversion of radiative transfer. Based on these results, we developed Neural Network algorithm to estimate the concentration of water constituents. The algorithm was applied to ADEOS/OCTS data and obtained relatively good results, which was confirmed by sea truth measurements. The present algorithm has the feature of non-dependent of remote sensor, seasons and sea area, so that it is quite efficient when we want to develop the algorithm to other sensor. Furthermore, it was found that the algorithm is quite useful for highly turbid sea area, where empirical algorithm can not work. However, the present algorithm is assumed that the inherent optical properties of water constituents are known, so that it is necessary to collect optical properties of water constituents.
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