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• W2899369595 abstract "Occurrence of cloud cover over remotely sensed area is a significant limitation in the ocean colour and infra-red remote sensing applications, especially when operational use of such a data is considered. A method for the reconstruction of missing data in remote sensing images has been proposed. It is based on complementing satellite data with the corresponding information from other sources of data, in our tested case it was the ecohydrodynamic model. The method solves the problem the presence of a cloud cover also during an extended period. Unlike in many other similar methods, emphasis has been put on retaining remotely sensed information to a high degree and preserving local phenomena that are usually difficult to capture by other methods than satellite remote sensing. The method has been tested on the Baltic Sea. Sea surface temperature and chlorophyll a concentration estimated from satellite data, ecohydrodynamic models and merged product were compared with in situ data. The algorithm was optimized for the two parameters that are crucial for e.g. creating algae bloom forecasts. The root mean square error (RMSE) of the final product of sea surface temperature was 0.73 °C, whereas of the input satellite images 1.26 °C or 1.33 °C and of model maps 0.89 °C. The error factor of chlorophyll a concentration product was 1.8 mg m−3, in comparison to 2.55 mg m−3 for satellite input source and 2.28 mg m−3 for the model one. The results show that the proposed method well utilizes advantages of both satellite and numerical simulation data sources, at the same time reducing the errors of estimation of merged parameters compared to similar errors for both primary sources. It would be a valuable component of fuzzy logic and rule-based HABs prediction." @default.
• W2899369595 created "2018-11-09" @default.
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• W2899369595 date "2019-03-01" @default.
• W2899369595 modified "2023-10-02" @default.
• W2899369595 title "The operational method of filling information gaps in satellite imagery using numerical models" @default.
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• W2899369595 doi "https://doi.org/10.1016/j.jag.2018.09.002" @default.
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