  Water is a key component of life, the natural environment and human health.
For monitoring the conditions of a water body, the chlorophyll a concentration
can serve as a proxy for nutrients and oxygen supply. In situ measurements of
water quality parameters are often time-consuming, expensive and limited in
areal validity. Therefore, we apply remote sensing techniques. During field
campaigns, we collected hyperspectral data with a spectrometer and in situ
measured chlorophyll a concentrations of 13 inland water bodies with different
spectral characteristics. One objective of this study is to estimate
chlorophyll a concentrations of these inland waters by applying three machine
learning regression models: Random Forest, Support Vector Machine and an
Artificial Neural Network. Additionally, we simulate four different
hyperspectral resolutions of the spectrometer data to investigate the effects
on the estimation performance. Furthermore, the application of first order
derivatives of the spectra is evaluated in turn to the regression performance.
This study reveals the potential of combining machine learning approaches and
remote sensing data for inland waters. Each machine learning model achieves an
R2-score between 80 % to 90 % for the regression on chlorophyll a
concentrations. The random forest model benefits clearly from the applied
derivatives of the spectra. In further studies, we will focus on the
application of machine learning models on spectral satellite data to enhance
the area-wide estimation of chlorophyll a concentration for inland waters.
