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Open AccessArticle

Deep Learning with WASI Simulation Data for Estimating Chlorophyll a Concentration of Inland Water Bodies

Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Assefa M. Melesse
Remote Sens. 2021, 13(4), 718; https://doi.org/10.3390/rs13040718
Received: 30 December 2020 / Revised: 8 February 2021 / Accepted: 9 February 2021 / Published: 16 February 2021
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 R2=81.9%, RMSE=12.4 μg L−1, and MAE=6.7 μg L−1. Besides, the 1D CNN’s performance decreases on the SR-Sentinel data to R2=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. View Full-Text
Keywords: machine learning; regression; CNN; artificial neural network; radiative transfer model; WASI; hyperspectral data; algae; chlorophyll a; downsampling machine learning; regression; CNN; artificial neural network; radiative transfer model; WASI; hyperspectral data; algae; chlorophyll a; downsampling
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MDPI and ACS Style

Maier, P.M.; Keller, S.; Hinz, S. Deep Learning with WASI Simulation Data for Estimating Chlorophyll a Concentration of Inland Water Bodies. Remote Sens. 2021, 13, 718. https://doi.org/10.3390/rs13040718

AMA Style

Maier PM, Keller S, Hinz S. Deep Learning with WASI Simulation Data for Estimating Chlorophyll a Concentration of Inland Water Bodies. Remote Sensing. 2021; 13(4):718. https://doi.org/10.3390/rs13040718

Chicago/Turabian Style

Maier, Philipp M.; Keller, Sina; Hinz, Stefan. 2021. "Deep Learning with WASI Simulation Data for Estimating Chlorophyll a Concentration of Inland Water Bodies" Remote Sens. 13, no. 4: 718. https://doi.org/10.3390/rs13040718

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