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This article in SSSAJ

  1. Vol. 76 No. 6, p. 2174-2183
    Received: Feb 13, 2012
    Published: October 4, 2012

    * Corresponding author(s): antoine.stevens@uclouvain.be
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Soil Organic Carbon Predictions by Airborne Imaging Spectroscopy: Comparing Cross-Validation and Validation

  1. Antoine Stevens *a,
  2. Isabel Mirallesa and
  3. Bas van Wesemaela
  1. a Georges Lemaître Centre for Earth and Climate Research Earth and Life Institute UCLouvain Place Louis Pasteur, 3 1348 Louvain-La-Neuve, Belgium


Soil organic carbon (SOC) is considered to influence important processes affecting soil, air, and water quality. The management of this valuable resource could be assisted by remote sensing techniques able to provide high-resolution spatial estimates of SOC. Such estimations are usually based on empirical regressions that are likely to have poor extrapolation abilities and hence it is important to properly estimate their accuracy in unsampled fields. Based on an imaging spectroscopy image acquired over the Luxembourg (c. 420 km2), several multivariate calibration models (partial least square [PLSR], penalized-spline signal [PSR], and support vector machine [SVMR] regressions) were developed to predict SOC content of topsoil bare agricultural fields and compared. The performance of the models was evaluated by means of cross-validation (k-fold[KFO], leave-one-out [LOO], leave-one-group-out [LOGO], and leave-one-field-out [LOFO]) and these estimates were compared with model performance obtained by validation. The validation set excluded the fields used in the training set, to provide realistic measures of prediction error in unsampled fields. All cross-validation techniques, except LOFO, strongly underestimate validation error. In large areas, training samples are often not a representative subset of the soil and spectral variation. Leave-one-field-out cross-validation, by repeatedly leaving samples belonging to one field out of the calibration, better simulates model error at unknown locations than other cross-validation strategies. The root mean square error (RMSE) of the best models, obtained with a stringent validation procedure (leave-fields-out), was equal to 4.7 g C kg−1. This is higher than most of previous studies using imaging spectroscopy for SOC prediction, suggesting that measures of accuracy obtained by KFO, LOO, and LOGO are likely over-optimistic in large areas. Finally, a SOC content map for the topsoil of croplands was produced that may assist soil monitoring and/or management efforts in this region in the future.

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Copyright © 2012. Copyright © by the Soil Science Society of America, Inc.