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| Titel |
Do we really need large spectral libraries for the assessment of soil organic carbon at local scale? |
| VerfasserIn |
Cesar Guerrero, Johanna Wetterlind, Bo Stenberg, Raphael A. Viscarra Rossel, Raúl Zornoza, Fernando T. Maestre, Abdul M. Mouazen, Boyan Kuang, José Damián Ruiz-Sinoga, Miguel A. Gabarrón-Galeote |
| Konferenz |
EGU General Assembly 2014
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 16 (2014) |
| Datensatznummer |
250093413
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| Publikation (Nr.) |
 EGU/EGU2014-8107.pdf |
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| Zusammenfassung |
| 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-scale model, 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). After spiking, the predictions
obtained with the small-sized models were substantially improved. As example of the
changes due to spiking, the predictions obtained with the smallest-sized model
changed the R2 from 0.03 to 0.96, the RMSEP from 8.02% to 0.61% SOC, and
the RPD from 0.39 to 5.20. The spiking effects on the large-sized models were
clearly smaller than in small-sized models. The added samples (i.e., the spiking
subset) were more influential on the small-sized than on the larger-sized models.
We also obtained predictions when the spiking subset was extra-weighted. The
addition of several copies of the spiking subset increases the statistical weight of these
samples in the model, becoming more important than the other samples. Thus, the
calibrations are forced to fit preferentially to the extra-weighted samples. If the
extra-weighted samples are representative of the target site, then, an improvement of the
predictions is expected. Indeed, the predictions were better than those obtained with
the spiked model. In this case, the most important improvements of the prediction
quality were observed in large-sized models, but the best results were obtained using
small-sized models. When both approaches are used (spiking with extra-weight) the
results were very accurate (R2 >0.96; RMSEP |
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