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Mr. Philipp M. Maier
Karlsruhe Institute of Technology (KIT)

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0 Algae
0 Ecology
0 Remote Sensing
0 environmental chemicals
0 machine learning

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Journal article
Published: 16 February 2021 in Remote Sensing
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Information about the chlorophyll a concentration of inland water bodies is essential for water monitoring. This study focuses on estimating chlorophyll a with remote sensing data, and machine learning (ML) approaches on the real-world SpecWa dataset. We adapt and apply a one-dimensional convolutional neural network (1D CNN) as a deep learning architecture for the first time to address this estimation. Since such a DL approach requires a large amount of data for its training, we rely on simulation data generated by the Water Color Simulator (WASI). This simulation is prepared accordingly and includes a knowledge-based water composition with two origins of the chlorophyll a concentration. Therefore, the training data is independent of the real-world SpecWa dataset, which is challenging for any ML approach. We define two spectral downsampling approaches as a pre-processing step, representing the hyperspectral EnMAP satellite mission (SR-EnMAP) and the multispectral Sentinel-2 mission (SR-Sentinel). Subsequently, we train a Random Forest, an artificial neural network, a band-ratio approach, and the 1D CNN on the WASI-generated simulation training dataset. Finally, all ML models are evaluated on the real SpecWa dataset. For both downsampled data, the 1D CNN outperforms the other ML models. On the finer resolved SR-EnMAP data it achieves an R 2 = 81.9 % , R M S E = 12.4 μg L−1, and M A E = 6.7 μg L−1. Besides, the 1D CNN’s performance decreases on the SR-Sentinel data to R 2 = 62.4 % . When focusing on the individual water bodies of the SpecWa dataset, the most significant differences exist between natural and artificial water bodies. We discover that the applied models estimate the chlorophyll a concentration of most natural water bodies satisfyingly. In sum, the newly DL approach can estimate the chlorophyll a values of unknown inland water bodies successfully, although it is trained on an entire simulation dataset.

ACS Style

Philipp Maier; Sina Keller; Stefan Hinz. Deep Learning with WASI Simulation Data for Estimating Chlorophyll a Concentration of Inland Water Bodies. Remote Sensing 2021, 13, 718 .

AMA Style

Philipp Maier, Sina Keller, Stefan Hinz. Deep Learning with WASI Simulation Data for Estimating Chlorophyll a Concentration of Inland Water Bodies. Remote Sensing. 2021; 13 (4):718.

Chicago/Turabian Style

Philipp Maier; Sina Keller; Stefan Hinz. 2021. "Deep Learning with WASI Simulation Data for Estimating Chlorophyll a Concentration of Inland Water Bodies." Remote Sensing 13, no. 4: 718.

Conference paper
Published: 01 September 2019 in 2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)
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ACS Style

Philipp M. Maier; Sina Keller. Application Of Different Simulated Spectral Data And Machine Learning To Estimate The Chlorophyll A Concentration Of Several Inland Waters. 2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS) 2019, 1 .

AMA Style

Philipp M. Maier, Sina Keller. Application Of Different Simulated Spectral Data And Machine Learning To Estimate The Chlorophyll A Concentration Of Several Inland Waters. 2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS). 2019; ():1.

Chicago/Turabian Style

Philipp M. Maier; Sina Keller. 2019. "Application Of Different Simulated Spectral Data And Machine Learning To Estimate The Chlorophyll A Concentration Of Several Inland Waters." 2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS) , no. : 1.

Journal article
Published: 29 May 2019 in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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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.

ACS Style

P. M. Maier; S. Keller. ESTIMATING CHLOROPHYLL A CONCENTRATIONS OF SEVERAL INLAND WATERS WITH HYPERSPECTRAL DATA AND MACHINE LEARNING MODELS. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2019, IV-2/W5, 609 -614.

AMA Style

P. M. Maier, S. Keller. ESTIMATING CHLOROPHYLL A CONCENTRATIONS OF SEVERAL INLAND WATERS WITH HYPERSPECTRAL DATA AND MACHINE LEARNING MODELS. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2019; IV-2/W5 ():609-614.

Chicago/Turabian Style

P. M. Maier; S. Keller. 2019. "ESTIMATING CHLOROPHYLL A CONCENTRATIONS OF SEVERAL INLAND WATERS WITH HYPERSPECTRAL DATA AND MACHINE LEARNING MODELS." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences IV-2/W5, no. : 609-614.

Journal article
Published: 30 August 2018 in International Journal of Environmental Research and Public Health
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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.

ACS Style

Sina Keller; Philipp M. Maier; Felix M. Riese; Stefan Norra; Andreas Holbach; Nicolas Börsig; Andre Wilhelms; Christian Moldaenke; André Zaake; Stefan Hinz. Hyperspectral Data and Machine Learning for Estimating CDOM, Chlorophyll a, Diatoms, Green Algae and Turbidity. International Journal of Environmental Research and Public Health 2018, 15, 1881 .

AMA Style

Sina Keller, Philipp M. Maier, Felix M. Riese, Stefan Norra, Andreas Holbach, Nicolas Börsig, Andre Wilhelms, Christian Moldaenke, André Zaake, Stefan Hinz. Hyperspectral Data and Machine Learning for Estimating CDOM, Chlorophyll a, Diatoms, Green Algae and Turbidity. International Journal of Environmental Research and Public Health. 2018; 15 (9):1881.

Chicago/Turabian Style

Sina Keller; Philipp M. Maier; Felix M. Riese; Stefan Norra; Andreas Holbach; Nicolas Börsig; Andre Wilhelms; Christian Moldaenke; André Zaake; Stefan Hinz. 2018. "Hyperspectral Data and Machine Learning for Estimating CDOM, Chlorophyll a, Diatoms, Green Algae and Turbidity." International Journal of Environmental Research and Public Health 15, no. 9: 1881.

Preprint
Published: 03 May 2018
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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.

ACS Style

Philipp M. Maier; Sina Keller. Machine learning regression on hyperspectral data to estimate multiple water parameters. 2018, 1 .

AMA Style

Philipp M. Maier, Sina Keller. Machine learning regression on hyperspectral data to estimate multiple water parameters. . 2018; ():1.

Chicago/Turabian Style

Philipp M. Maier; Sina Keller. 2018. "Machine learning regression on hyperspectral data to estimate multiple water parameters." , no. : 1.

Preprint
Published: 24 April 2018
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In this paper, we investigate the potential of estimating the soil-moisture content based on VNIR hyperspectral data combined with IR data. Measurements from a multi-sensor field campaign represent the benchmark dataset which contains measured hyperspectral, IR, and soil-moisture data. We introduce a regression framework with three steps consisting of feature selection, preprocessing, and well-chosen regression models. The latter are mainly supervised machine learning models. An exception are the self-organizing maps which are a combination of unsupervised and supervised learning. We analyze the impact of the distinct preprocessing methods on the regression results. Of all regression models, the extremely randomized trees model without preprocessing provides the best estimation performance. Our results reveal the potential of the respective regression framework combined with the VNIR hyperspectral data to estimate soil moisture. In conclusion, the results of this paper provide a basis for further improvements in different research directions.

ACS Style

Sina Keller; Felix M. Riese; Johanna Stötzer; Philipp M. Maier; Stefan Hinz. Is it possible to retrieve soil-moisture content from measured VNIR hyperspectral data? 2018, 1 .

AMA Style

Sina Keller, Felix M. Riese, Johanna Stötzer, Philipp M. Maier, Stefan Hinz. Is it possible to retrieve soil-moisture content from measured VNIR hyperspectral data? . 2018; ():1.

Chicago/Turabian Style

Sina Keller; Felix M. Riese; Johanna Stötzer; Philipp M. Maier; Stefan Hinz. 2018. "Is it possible to retrieve soil-moisture content from measured VNIR hyperspectral data?" , no. : 1.