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Article

Exploring the Impact of Noise on Hybrid Inversion of PROSAIL RTM on Sentinel-2 Data

1
Institute of Environmental Sciences (CML), Leiden University, P.O. Box 9518, 2300 RA Leiden, The Netherlands
2
Leiden Institute of Advanced Computer Science (LIACS), Leiden University, P.O. Box 9512, 2300 RA Leiden, The Netherlands
*
Author to whom correspondence should be addressed.
Academic Editor: Katja Berger
Remote Sens. 2021, 13(4), 648; https://doi.org/10.3390/rs13040648
Received: 7 December 2020 / Revised: 6 February 2021 / Accepted: 8 February 2021 / Published: 11 February 2021
(This article belongs to the Special Issue Remote and Proximal Assessment of Plant Traits)
Remote sensing (RS) of biophysical variables plays a vital role in providing the information necessary for understanding spatio-temporal dynamics in ecosystems. The hybrid approach to retrieve biophysical variables from RS by combining Machine Learning (ML) algorithms with surrogate data generated by Radiative Transfer Models (RTM). The susceptibility of the ill-posed solutions to noise currently constrains further application of hybrid approaches. Here, we explored how noise affects the performance of ML algorithms for biophysical trait retrieval. We focused on synthetic Sentinel-2 (S2) data generated using the PROSAIL RTM and four commonly applied ML algorithms: Gaussian Processes (GPR), Random Forests (RFR), and Artificial Neural Networks (ANN) and Multi-task Neural Networks (MTN). After identifying which biophysical variables can be retrieved from S2 using a Global Sensitivity Analysis, we evaluated the performance loss of each algorithm using the Mean Absolute Percentage Error (MAPE) with increasing noise levels. We found that, for S2 data, Carotenoid concentrations are uniquely dependent on band 2, Chlorophyll is almost exclusively dependent on the visible ranges, and Leaf Area Index, water, and dry matter contents are mostly dependent on infrared bands. Without added noise, GPR was the best algorithm (<0.05%), followed by the MTN (<3%) and ANN (<5%), with the RFR performing very poorly (<50%). The addition of noise critically affected the performance of all algorithms (>20%) even at low levels of added noise (≈5%). Overall, both neural networks performed significantly better than GPR and RFR when noise was added with the MTN being slightly better when compared to the ANN. Our results imply that the performance of the commonly used algorithms in hybrid-RTM inversion are pervasively sensitive to noise. The implication is that more advanced models or approaches are necessary to minimize the impact of noise to improve near real-time and accurate RS monitoring of biophysical trait retrieval. View Full-Text
Keywords: radiative transfer models; PROSAIL; sensitivity analysis; inversion; biophysical variables; machine learning; multi-output; Bayesian optimization; Sentinel-2 radiative transfer models; PROSAIL; sensitivity analysis; inversion; biophysical variables; machine learning; multi-output; Bayesian optimization; Sentinel-2
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MDPI and ACS Style

de Sá, N.C.; Baratchi, M.; Hauser, L.T.; van Bodegom, P. Exploring the Impact of Noise on Hybrid Inversion of PROSAIL RTM on Sentinel-2 Data. Remote Sens. 2021, 13, 648. https://doi.org/10.3390/rs13040648

AMA Style

de Sá NC, Baratchi M, Hauser LT, van Bodegom P. Exploring the Impact of Noise on Hybrid Inversion of PROSAIL RTM on Sentinel-2 Data. Remote Sensing. 2021; 13(4):648. https://doi.org/10.3390/rs13040648

Chicago/Turabian Style

de Sá, Nuno C., Mitra Baratchi, Leon T. Hauser, and Peter van Bodegom. 2021. "Exploring the Impact of Noise on Hybrid Inversion of PROSAIL RTM on Sentinel-2 Data" Remote Sensing 13, no. 4: 648. https://doi.org/10.3390/rs13040648

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