Satellite mapping of Baltic Sea Secchi depth with multiple regression models |
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| Institution: | 1. University of Zagreb, Faculty of Electrical Engineering and Computing, Department of Applied Mathematics, Unska 3, 10000 Zagreb, Croatia;2. Hasselt University, Campus Diepenbeek, Agoralaan Gebouw D, 3590 Diepenbeek, Belgium;1. National Research Institute of Fisheries Science, Fisheries Research Agency, 2-12-4 Fukuura, Kanazawa, Yokohama, Kanagawa 236-8648, Japan;2. National Research Institute of Fisheries and Environment of Inland Sea, Fisheries Research Agency, 2-17-5 Maruishi, Hatsukaichi, Hiroshima 739-0452, Japan;3. Nagasaki Prefectural Institute of Fisheries, 1551-4 Taira, Nagasaki, Nagasaki 851-2213, Japan;4. Nagasaki Prefectural Tsushima District Fisheries Extension Advisory Center, 668 Kusubo, Mitsushima, Tsushima, Nagasaki 817-0324, Japan;5. Nagasaki Prefectural Nagasaki District Fisheries Extension Advisory Center, 1551-4 Taira, Nagasaki, Nagasaki 851-2213, Japan;6. Fisheries Department, Nagasaki Prefectural Government, 2-13 Edomachi, Nagasaki, Nagasaki 850-8570, Japan;1. Department of Mathematics and Computer Sciences, 06123 Perugia, Italy;2. Department of Mathematics, University of Palermo, Via Archirafi 34, 90123 Palermo, Italy;3. Institut of Mathematics, Wroc?aw University, Pl. Grunwaldzki 2/4, 50-384 Wroc?aw, Poland;1. Biology Department, Alma College, 614 West Superior Street, Alma, MI 48801, USA;2. Illinois Natural History Survey, 1816 S. Oak Street, Champaign, IL 61820, USA;3. Kibbe Field Station, Department of Biological Sciences, Western Illinois University, Macomb, IL 61455, USA;1. Department of Mathematical and Statistical Methods, Poznan University of Life Sciences, Wojska Polskiego 28, 60-625 Poznań, Poland;2. Department of Agricultural Chemistry and Environmental Biogeochemistry, Poznan University of Life Sciences, Wojska Polskiego 71F, 60-625 Poznań, Poland;3. Institute of Plant Protection – National Research Institute, W?adys?awa W?gorka 20, 60-318 Poznań, Poland |
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| Abstract: | Secchi depth is a measure of water transparency. In the Baltic Sea region, Secchi depth maps are used to assess eutrophication and as input for habitat models. Due to their spatial and temporal coverage, satellite data would be the most suitable data source for such maps. But the Baltic Sea’s optical properties are so different from the open ocean that globally calibrated standard models suffer from large errors. Regional predictive models that take the Baltic Sea’s special optical properties into account are thus needed. This paper tests how accurately generalized linear models (GLMs) and generalized additive models (GAMs) with MODIS/Aqua and auxiliary data as inputs can predict Secchi depth at a regional scale. It uses cross-validation to test the prediction accuracy of hundreds of GAMs and GLMs with up to 5 input variables. A GAM with 3 input variables (chlorophyll a, remote sensing reflectance at 678 nm, and long-term mean salinity) made the most accurate predictions. Tested against field observations not used for model selection and calibration, the best model’s mean absolute error (MAE) for daily predictions was 1.07 m (22%), more than 50% lower than for other publicly available Baltic Sea Secchi depth maps. The MAE for predicting monthly averages was 0.86 m (15%). Thus, the proposed model selection process was able to find a regional model with good prediction accuracy. It could be useful to find predictive models for environmental variables other than Secchi depth, using data from other satellite sensors, and for other regions where non-standard remote sensing models are needed for prediction and mapping. Annual and monthly mean Secchi depth maps for 2003–2012 come with this paper as Supplementary materials. |
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| Keywords: | Baltic Sea Secchi depth MODIS Mapping Regression GAM |
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