Machine Learning Using Hyperspectral Data Inaccurately Predicts Plant Traits Under Spatial Dependency

Rocha, Alby D.; Groen, Thomas A.; Skidmore, Andrew K.; Darvishzadeh, Roshanak; Willemen, Louise

doi:10.3390/rs10081263

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Machine Learning Using Hyperspectral Data Inaccurately Predicts Plant Traits Under Spatial Dependency

Alby D. Rocha

^1,*

Thomas A. Groen

Andrew K. Skidmore

^1,2

Roshanak Darvishzadeh

and

Louise Willemen

Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, 7500AE, Enschede, The Netherlands

Department of Environmental Science, Macquarie University, Sydney, NSW 2106, Australia

Author to whom correspondence should be addressed.

Remote Sens. 2018, 10(8), 1263; https://doi.org/10.3390/rs10081263

Received: 21 June 2018 / Revised: 21 July 2018 / Accepted: 8 August 2018 / Published: 11 August 2018

(This article belongs to the Special Issue Machine Learning Applications in Earth Science Big Data Analysis)

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Abstract

Spectral, temporal and spatial dimensions are difficult to model together when predicting in situ plant traits from remote sensing data. Therefore, machine learning algorithms solely based on spectral dimensions are often used as predictors, even when there is a strong effect of spatial or temporal autocorrelation in the data. A significant reduction in prediction accuracy is expected when algorithms are trained using a sequence in space or time that is unlikely to be observed again. The ensuing inability to generalise creates a necessity for ground-truth data for every new area or period, provoking the propagation of “single-use” models. This study assesses the impact of spatial autocorrelation on the generalisation of plant trait models predicted with hyperspectral data. Leaf Area Index (LAI) data generated at increasing levels of spatial dependency are used to simulate hyperspectral data using Radiative Transfer Models. Machine learning regressions to predict LAI at different levels of spatial dependency are then tuned (determining the optimum model complexity) using cross-validation as well as the NOIS method. The results show that cross-validated prediction accuracy tends to be overestimated when spatial structures present in the training data are fitted (or learned) by the model. View Full-Text

Keywords: remote sensing; radiative transfer models; spatial autocorrelation; data simulation; model accuracy

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

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Doi: 10.4121/uuid:2016d562-cf6e-4060-ac13-5db9477b6512

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MDPI and ACS Style

Rocha, A.D.; Groen, T.A.; Skidmore, A.K.; Darvishzadeh, R.; Willemen, L. Machine Learning Using Hyperspectral Data Inaccurately Predicts Plant Traits Under Spatial Dependency. Remote Sens. 2018, 10, 1263. https://doi.org/10.3390/rs10081263

AMA Style

Rocha AD, Groen TA, Skidmore AK, Darvishzadeh R, Willemen L. Machine Learning Using Hyperspectral Data Inaccurately Predicts Plant Traits Under Spatial Dependency. Remote Sensing. 2018; 10(8):1263. https://doi.org/10.3390/rs10081263

Chicago/Turabian Style

Rocha, Alby D., Thomas A. Groen, Andrew K. Skidmore, Roshanak Darvishzadeh, and Louise Willemen. 2018. "Machine Learning Using Hyperspectral Data Inaccurately Predicts Plant Traits Under Spatial Dependency" Remote Sensing 10, no. 8: 1263. https://doi.org/10.3390/rs10081263

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Machine Learning Using Hyperspectral Data Inaccurately Predicts Plant Traits Under Spatial Dependency

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