Preprints
https://doi.org/10.5194/egusphere-2022-273
https://doi.org/10.5194/egusphere-2022-273
31 May 2022
 | 31 May 2022

Can soil spectroscopy contribute to soil organic carbon monitoring on agricultural soils?

Javier Reyes and Mareike Ließ

Abstract. Carbon sequestration in soils under agricultural use can contribute to climate change mitigation. However, the spatial-temporal monitoring of soil organic carbon (SOC) requires more efficient data acquisition. The use of soil Vis-NIR spectroscopy is a promising research field in this context. However, the interpretation of the recorded spectral signal with regards to SOC is not trivial due to the complexity of the soil matrix, and factors affecting the measurements under field conditions. A model-building process is required to relate the spectral signal to the SOC content. For this study, spectral on-the-go proximal measurements and soil sampling were conducted on a long-term field experiment (LTE) located in the state of Saxony-Anhalt, Germany. SOC values ranged between 14–25 g kg−1 due to different fertilization treatments. Partial least squares regression (PLSR) models were built on behalf of spectral laboratory and field measurements conducted with two spectrometers and preprocessed by various methods. A data correction of the field data was done with three different approaches: linear transformation, piecewise direct standardization (PDS), and external parameter orthogonalization (EPO). The models were then thoroughly interpreted with regards to spectral wavelength importance using regression coefficients (RC) and variable importance in projection scores (VIP). The detailed wavelength importance analysis disclosed the challenge of using soil spectroscopy for SOC monitoring. The use of spectrometers with a differing spectral resolution for soil Vis-NIR measurements under varying soil conditions revealed shifts in wavelength importance. Still, some wavelengths related to SOC were extracted (560 nm, 1330 nm, 1400 nm, 1720 nm, and 1900 nm) by various preprocessing methods and were highly important in models trained on both, laboratory, and field measurements. Furthermore, we showed, that the correction of spectral field data with spectral laboratory measurements improved the predictive performance of the models built on behalf of the proximal on-the-go sensing measurements.

Javier Reyes and Mareike Ließ

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-273', Anonymous Referee #1, 10 Jul 2022
    • AC1: 'Reply on RC1', Javier Reyes, 11 Nov 2022
  • RC2: 'Comment on egusphere-2022-273', Anonymous Referee #2, 02 Oct 2022
    • AC2: 'Reply on RC2', Javier Reyes, 11 Nov 2022

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-273', Anonymous Referee #1, 10 Jul 2022
    • AC1: 'Reply on RC1', Javier Reyes, 11 Nov 2022
  • RC2: 'Comment on egusphere-2022-273', Anonymous Referee #2, 02 Oct 2022
    • AC2: 'Reply on RC2', Javier Reyes, 11 Nov 2022
Javier Reyes and Mareike Ließ
Javier Reyes and Mareike Ließ

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Latest update: 29 Feb 2024
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Short summary
The use of soil Vis-NIR spectroscopy is a promising research field in the context of SOC monitoring. Predictive models were built on behalf of spectral laboratory and field measurements and thoroughly interpreted concerning spectral wavelength importance. The analysis disclosed the challenge of using soil spectroscopy for SOC monitoring. Shifts in wavelength importance were observed between spectrometers. Correction of spectral field data improved the predictive performance of the models.