Hyperspectral Data and Machine Learning for Estimating CDOM, Chlorophyll a, Diatoms, Green Algae and Turbidity

Sina Keller, Philipp M. Maier, Felix M. Riese, Stefan Norra, Andreas Holbach, Nicolas Börsig, Andre Wilhelms, Christian Moldaenke, André Zaake and Stefan Hinz
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Sina Keller: Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology, Kaiserstr. 12, 76131 Karlsruhe, Germany
Philipp M. Maier: Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology, Kaiserstr. 12, 76131 Karlsruhe, Germany
Felix M. Riese: Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology, Kaiserstr. 12, 76131 Karlsruhe, Germany
Stefan Norra: Institute of Applied Geoscience, Karlsruhe Institute of Technology, Kaiserstr. 12, 76131 Karlsruhe, Germany
Andreas Holbach: Department of Bioscience, Aarhus University, Frederiksborgvej 399, 4000 Roskilde, Denmark
Nicolas Börsig: Institute of Applied Geoscience, Karlsruhe Institute of Technology, Kaiserstr. 12, 76131 Karlsruhe, Germany
Andre Wilhelms: Institute of Applied Geoscience, Karlsruhe Institute of Technology, Kaiserstr. 12, 76131 Karlsruhe, Germany
Christian Moldaenke: bbe Moldaenke GmbH, Preetzer Chaussee 177, 24222 Schwentinental, Germany
André Zaake: bbe Moldaenke GmbH, Preetzer Chaussee 177, 24222 Schwentinental, Germany
Stefan Hinz: Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology, Kaiserstr. 12, 76131 Karlsruhe, Germany

IJERPH, 2018, vol. 15, issue 9, 1-15

Abstract: Inland waters are of great importance for scientists as well as authorities since they are essential ecosystems and well known for their biodiversity. When monitoring their respective water quality, in situ measurements of water quality parameters are spatially limited, costly and time-consuming. In this paper, we propose a combination of hyperspectral data and machine learning methods to estimate and therefore to monitor different parameters for water quality. In contrast to commonly-applied techniques such as band ratios, this approach is data-driven and does not rely on any domain knowledge. We focus on CDOM, chlorophyll a and turbidity as well as the concentrations of the two algae types, diatoms and green algae. In order to investigate the potential of our proposal, we rely on measured data, which we sampled with three different sensors on the river Elbe in Germany from 24 June–12 July 2017. The measurement setup with two probe sensors and a hyperspectral sensor is described in detail. To estimate the five mentioned variables, we present an appropriate regression framework involving ten machine learning models and two preprocessing methods. This allows the regression performance of each model and variable to be evaluated. The best performing model for each variable results in a coefficient of determination R 2 in the range of 89.9% to 94.6%. That clearly reveals the potential of the machine learning approaches with hyperspectral data. In further investigations, we focus on the generalization of the regression framework to prepare its application to different types of inland waters.

Keywords: machine learning; regression; water quality parameters; hyperspectral data; spectral features; algae; chlorophyll a; multi-sensor system; fluorometer; field campaign (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2018
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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