Abstract
To investigate the long-term trend of light conditions in Lake Kasumigaura (a shallow eutrophic lake with high turbidity) in Japan, 215 images of MERIS data from the period 2003–2012 were processed at four stations using a semi-analytical algorithm to retrieve their inherent optical properties after atmospheric correction. Previously obtained Secchi depths (SDs) were somewhat underestimated by the proposed algorithms, and the ratio of the predicted SD to the measured SD changed with the ratio of tripton to chlorophyll a. A submodel was then built describing the ratio of scattering to backscattering based on the ratio of tripton to chlorophyll a (a trend supported by a number of previous reports) and applied to the prediction of SD in this lake. The model showed a gradually increasing trend at all stations in the predicted SD over the period, which was validated by the observations. The relationship between the measured and predicted SDs within a 2-day period was scattered, but showed a positive correlation at a significant level. In addition, this proposed method with the submodel describing the ratio of scattering to backscattering was applied to in situ reflectance spectra, and a correlation at a significant level was confirmed between the measured and predicted SDs.
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Acknowledgements
This research was supported in part by Grants-in-Aid for Scientific Research from the Ministry of Education, Culture, Sport, Science and Technology (MEXT), Japan (Nos. 23404015 and 26281039), the Global Environment Research Fund (S9-4) of the Ministry of Environment, Japan, and the River Fund (27-1271-001) in charge of The River Foundation, Japan. Monitoring data on SD were provided by the National Institute for Environmental Studies (NIES). We express our appreciation to 2 anonymous reviewers for constructive criticisms on earlier versions of the manuscript.
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Fukushima, T., Matsushita, B., Yang, W. et al. Semi-analytical prediction of Secchi depth transparency in Lake Kasumigaura using MERIS data. Limnology 19, 89–100 (2018). https://doi.org/10.1007/s10201-017-0521-3
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DOI: https://doi.org/10.1007/s10201-017-0521-3