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

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.