MDPI and ACS Style
Keller, S.; Maier, P.M.; Riese, F.M.; Norra, S.; Holbach, A.; Börsig, N.; Wilhelms, A.; Moldaenke, C.; Zaake, A.; Hinz, S.
Hyperspectral Data and Machine Learning for Estimating CDOM, Chlorophyll a, Diatoms, Green Algae and Turbidity. Int. J. Environ. Res. Public Health 2018, 15, 1881.
https://doi.org/10.3390/ijerph15091881
AMA Style
Keller S, Maier PM, Riese FM, Norra S, Holbach A, Börsig N, Wilhelms A, Moldaenke C, Zaake A, Hinz S.
Hyperspectral Data and Machine Learning for Estimating CDOM, Chlorophyll a, Diatoms, Green Algae and Turbidity. International Journal of Environmental Research and Public Health. 2018; 15(9):1881.
https://doi.org/10.3390/ijerph15091881
Chicago/Turabian Style
Keller, Sina, Philipp M. Maier, Felix M. Riese, Stefan Norra, Andreas Holbach, Nicolas Börsig, Andre Wilhelms, Christian Moldaenke, André Zaake, and Stefan Hinz.
2018. "Hyperspectral Data and Machine Learning for Estimating CDOM, Chlorophyll a, Diatoms, Green Algae and Turbidity" International Journal of Environmental Research and Public Health 15, no. 9: 1881.
https://doi.org/10.3390/ijerph15091881