Title: Machine learning regression on hyperspectral data to estimate multiple water parameters,
Abstract: In this paper, we present a regression framework involving several machine
learning models to estimate water parameters based on hyperspectral data.
Measurements from a multi-sensor field campaign, conducted on the River Elbe,
Germany, represent the benchmark dataset. It contains hyperspectral data and
the five water parameters chlorophyll a, green algae, diatoms, CDOM and
turbidity. We apply a PCA for the high-dimensional data as a possible
preprocessing step. Then, we evaluate the performance of the regression
framework with and without this preprocessing step. The regression results of
the framework clearly reveal the potential of estimating water parameters based
on hyperspectral data with machine learning. The proposed framework provides
the basis for further investigations, such as adapting the framework to
estimate water parameters of different inland waters.