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Water quality is of great importance for humans and for the environment andhas to be monitored continuously. It is determinable through proxies such asthe chlorophyll a concentration, which can be monitored by remote sensingtechniques. This study focuses on the trade-off between the spatial and thespectral resolution of six simulated satellite-based data sets when estimatingthe chlorophyll a concentration with supervised machine learning models. Theinitial dataset for the spectral simulation of the satellite missions containsspectrometer data and measured chlorophyll a concentration of 13 differentinland waters. Focusing on the regression performance, it appears that themachine learning models achieve almost as good results with the simulatedSentinel data as with the simulated hyperspectral data. Regarding theapplicability, the Sentinel 2 mission is the best choice for small inlandwaters due to its high spatial and temporal resolution in combination with asuitable spectral resolution.

Application of Different Simulated Spectral Data and Machine Learning to Estimate the Chlorophyll a Concentration of Several Inland Waters