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Sun, L, Guo, MH, Wang, XM (2010). Ocean color products retrieval and validation around China coast with MODIS. ACTA OCEANOLOGICA SINICA, 29(4), 21-27.

Abstract
Waters along China coast are very turbid with high concentrations of suspended sediment nearly all the time, especially at the Hangzhou Bay, the Changjiang (Yangtze) River Estuary and the shoal along Jiangsu Province. In these turbid and optically complex waters, the standard MODIS ocean color products tend to have invalid values. Because the water-leaving radiances in the near-infrared (NIR) are significant resulting from the strong scattering of suspended particles, the standard MODIS atmospheric correction algorithm often gets no results or produces significant errors. And because of the complex water optical properties, the OC3 model used in the standard MODIS data processing tends to get extremely high chlorophyll-a (Chl-a) concentrations. In this paper, we present an atmospheric correction approach using MODIS short wave infrared (SWIR) bands based on the fact that water-leaving radiances are negligible in the SWIR region because of the extreme strong absorption of water even in turbid waters. A regional Chl-a concentration estimation model is also constructed for MODIS from in situ data. These algorithms are applied to MODIS Aqua data processing in the China coastal regions. In situ data collected in the Yellow Sea and the East China Sea in spring and autumn, 2003 are used to validate the performance. Reasonably good results have been obtained. It is noted that water-leaving reflectance in the NIR bands are significant in waters along the China coast with high sediment loadings. The satellite derived and in-situ reflectance spectra can match in the turbid waters along China coast, and there is relatively good linear relationship between satellite derived and in-situ reflectance. The RMSE value of Rrs(lambda) is 0.0031 sr(-1) for all the nine ocean color bands (412 to 869 nm). The satellite-derived Chl-a value is in the reasonable range and the root mean square percentage difference is 46.1%.

DOI:
10.1007/s13131-010-0047-6

ISSN:
0253-505X

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