Spectral Data Processing for Field-Scale Soil Organic Carbon Monitoring

Carbon sequestration in soils under agricultural use can contribute to climate change mitigation.
Spatial–temporal soil organic carbon (SOC) monitoring requires more efficient data acquisition.
This study aims to evaluate the potential of spectral on-the-go proximal measurements to
serve these needs. The study was conducted as a long-term field experiment. SOC values ranged
between 14 and 25 g kg−1 due to different fertilization treatments. Partial least squares regression
models were built based on the spectral laboratory and field data collected with two spectrometers
(site-specific and on-the-go). Correction of the field data based on the laboratory data was done by
testing linear transformation, piecewise direct standardization, and external parameter orthogonalization
(EPO). Different preprocessing methods were applied to extract the best possible information
content from the sensor signal. The models were then thoroughly interpreted concerning spectral
wavelength importance using regression coefficients and variable importance in projection scores.
The detailed wavelength importance analysis disclosed the challenge of using soil spectroscopy for
SOC monitoring. The use of different spectrometers under varying soil conditions revealed shifts in
wavelength importance. Still, our findings on the use of on-the-go spectroscopy for spatial–temporal
SOC monitoring are promising.

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