  Water quality is of great importance for humans and for the environment and
has to be monitored continuously. It is determinable through proxies such as
the chlorophyll a concentration, which can be monitored by remote sensing
techniques. This study focuses on the trade-off between the spatial and the
spectral resolution of six simulated satellite-based data sets when estimating
the chlorophyll a concentration with supervised machine learning models. The
initial dataset for the spectral simulation of the satellite missions contains
spectrometer data and measured chlorophyll a concentration of 13 different
inland waters. Focusing on the regression performance, it appears that the
machine learning models achieve almost as good results with the simulated
Sentinel data as with the simulated hyperspectral data. Regarding the
applicability, the Sentinel 2 mission is the best choice for small inland
waters due to its high spatial and temporal resolution in combination with a
suitable spectral resolution.
