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      Imaging Spectrometry of Inland and Coastal Waters: State of the Art, Achievements and Perspectives

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          Absorption by dissolved organic matter of the sea (yellow substance) in the UV and visible domains1

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            Deriving inherent optical properties from water color: a multiband quasi-analytical algorithm for optically deep waters.

            For open ocean and coastal waters, a multiband quasi-analytical algorithm is developed to retrieve absorption and backscattering coefficients, as well as absorption coefficients of phytoplankton pigments and gelbstoff. This algorithm is based on remote-sensing reflectance models derived from the radiative transfer equation, and values of total absorption and backscattering coefficients are analytically calculated from values of remote-sensing reflectance. In the calculation of total absorption coefficient, no spectral models for pigment and gelbstoff absorption coefficients are used. Actually those absorption coefficients are spectrally decomposed from the derived total absorption coefficient in a separate calculation. The algorithm is easy to understand and simple to implement. It can be applied to data from past and current satellite sensors, as well as to data from hyperspectral sensors. There are only limited empirical relationships involved in the algorithm, and they are for less important properties, which implies that the concept and details of the algorithm could be applied to many data for oceanic observations. The algorithm is applied to simulated data and field data, both non-case1, to test its performance, and the results are quite promising. More independent tests with field-measured data are desired to validate and improve this algorithm.
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              Reflectance quantities in optical remote sensing—definitions and case studies

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                Author and article information

                Journal
                Surveys in Geophysics
                Surv Geophys
                Springer Science and Business Media LLC
                0169-3298
                1573-0956
                May 2019
                June 5 2018
                May 2019
                : 40
                : 3
                : 401-429
                Article
                10.1007/s10712-018-9476-0
                98d97e3f-865a-42e9-afc6-62eb5a746f8b
                © 2019

                https://creativecommons.org/licenses/by/4.0

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