English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Diffuse reflectance spectroscopy is a powerful technique to predict soilproperties. It can be used in situ to provide data inexpensively and rapidlycompared to the standard laboratory measurements. Because most spectral databases contain air-dried samples scanned in the laboratory, field spectraacquired in situ are either absent or rare in calibration data sets. However,when models are calibrated on air-dried spectra, prediction using field spectraare often inaccurate. We propose a framework to calibrate partial least squaresmodels when field spectra are rare using synthetic minority oversamplingtechnique (SMOTE). We calibrated a model to predict soil organic carbon contentusing air-dried spectra spiked with synthetic field spectra. The rootmean-squared error of prediction decreased from 6.18 to 2.12 mg g$^{-1}$ and$R^2$ increased from $-$0.53 to 0.82 compared to the model calibrated onair-dried spectra only.

Predicting with limited data - Increasing the accuracy in VIS-NIR diffuse reflectance spectroscopy by SMOTE