Machine learning regression on hyperspectral data to estimate multiple water parameters

TL;DR

This study develops a machine learning regression framework to estimate multiple water quality parameters from hyperspectral data, demonstrating promising results on a benchmark dataset from the River Elbe.

Contribution

It introduces a novel regression framework that combines machine learning models with PCA preprocessing for water parameter estimation from hyperspectral data.

Findings

Successful estimation of water parameters using hyperspectral data

PCA preprocessing impacts regression performance

Framework shows potential for adaptation to different inland waters

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.