Machine learning using hyperspectral data inaccurately predicts plant traits under spatial dependency

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

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    7 Citations (Scopus)
    8 Downloads (Pure)

    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.

    Original languageEnglish
    Article number1263
    Pages (from-to)1-20
    Number of pages20
    JournalRemote Sensing
    Volume10
    Issue number8
    DOIs
    Publication statusPublished - 1 Aug 2018

    Bibliographical note

    Copyright 2018 by the authors. Licensee MDPI, Basel, Switzerland. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

    Keywords

    • Data simulation
    • Model accuracy
    • Radiative transfer models
    • Remote sensing
    • Spatial autocorrelation

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