Do we really need large spectral libraries for the assessment of soil organic carbon at local scale?

  1. César Guerrero Maestre 3
  2. Johanna Wetterlind 2
  3. Bo Stenberg 2
  4. Raphael A. Viscarra Rossel 4
  5. Raúl Zornoza Belmonte 3
  6. Fernando T. Maestre 6
  7. Abdul M. Mouazen 1
  8. Boyan Kuang 1
  9. José Damián Ruiz Sinoga 5
  10. Miguel Ángel Galeote Moreno 5
  1. 1 Cranfield University
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    Cranfield University

    Cranfield, Reino Unido

    ROR https://ror.org/05cncd958

    Geographic location of the organization Cranfield University
  2. 2 Swedish University of Agricultural Sciences (SLU)
  3. 3 Universidad Miguel Hernández de Elche
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    Universidad Miguel Hernández de Elche

    Elche, España

    ROR https://ror.org/01azzms13

    Geographic location of the organization Universidad Miguel Hernández de Elche
  4. 4 CSIRO Land and Water, Canberra, Australia
  5. 5 Universidad de Málaga
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    Universidad de Málaga

    Málaga, España

    ROR https://ror.org/036b2ww28

    Geographic location of the organization Universidad de Málaga
  6. 6 Universidad Rey Juan Carlos
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    Universidad Rey Juan Carlos

    Madrid, España

    ROR https://ror.org/01v5cv687

    Geographic location of the organization Universidad Rey Juan Carlos
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Proceedings:
European Geosciences Union General Assembly 2014

Year of publication: 2014

Type: Conference paper

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Abstract

Spiking is an approach to improve the accuracy of large-scale spectroscopic models when they are used to predict at local scale. But, if models are to be spiked, do we really need large-sized spectral libraries? Different calibrations relating the SOC and NIR spectra were obtained using PLS as regression method: i) model #1: local-scale model (n=40); ii) model #2: local-scale model (n=88); iii) model #3: provincial-scale model (n=147); iv) model #4: provincial-scale model, constructed with 50% of samples used in model #3 (n=73); v) model #5: provincial-scalemodel, constructed with 25% of samples used in model #3 (n=36); vi) model #6: national-scale model (n=1096); vii) model #7: national-scale model, constructed with 33% of samples used in model #6 (n=362). Each of these models was used to predict the SOC contents in target site samples. In this work, nine target sites were evaluated. Each target site is a relatively small area (from several hectares to a few square kilometers), where a dense sampling was made. The coefficient of the determination (R2 ), root mean square error of prediction (RMSEP), bias, standard error of prediction (SEP) and the ratio of performance to deviance (RPD) were calculated pooling the predictions of the nine target sites. In overall, more than 900 local samples were predicted. The highest R2 values were obtained with the national-scale models (R2 >0.85), and the lowest R2 values were obtained with the models of small size.In general, the RMSEP tended to decrease with the increase of the models size. However, the predictions obtained with the large-sized models were clearly biased, and despite the high R2 values, the RPD values were below 1.2. We also obtained predictions when these models were spiked with eight local samples (i.e., from the target site)