Can in situ spectral measurements under disturbance-reduced environmental conditions help improve soil organic carbon estimation?

James Kobina Mensah Biney*, Johanna Ruth Blöcher, Stephen Mackenzie Bell, Luboš Borůvka, Radim Vašát

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

3 Citations (Scopus)

Abstract

In situ visible and near-infrared (Vis-NIR) spectroscopy has proven to be a reliable tool for determining soil organic carbon (SOC) content with a small loss of precision as compared to laboratory measurements. The loss of precision is a result of disturbing external environmental factors that disrupt spectral measurements. For example, roughness, changes in weather conditions, humidity, temperature, human factors, spectral noise and especially soil water. It has been assumed that, in situ predictive capability could be improved if some of these factors are either minimized or eliminated during the in situ measurement. For this study, the prediction of SOC was carried out under two different in situ measurement conditions; less favourable environmental conditions (with disturbances) and more favourable site-specific conditions (disturbance-reduced conditions). The primary goal is to determine whether the estimate of SOC can be improved under more favourable site-specific conditions, as well as the impact of pre-treatment algorithms on both less and more favourable disturbed conditions. The study employed a large range of pretreatment algorithms and their combinations. Three separate multivariate models were used to predict SOC, namely Cubist, support vector machine regression (SVMR), and partial least squares regression (PLSR). The result clearly shows that reduced disturbing factors (i.e., drier and unploughed soil as well as noise reduction) result in an improvement of SOC prediction with in situ Vis-NIR spectroscopy. The best overall result was achieved with SVMR (R2CV = 0.72, RMSEPcv = 0.21, RPIQ = 2.34). Although the combination of pre-treatment algorithms resulted in an improvement, overall, these pre-treatment algorithms could not compensate for the factors affecting the measured spectra with disturbance. Though the obtained result is promising, further study is still needed to disentangle the impacts and interactions of various disturbing factors for different soil types.

Original languageEnglish
Article number156304
Pages (from-to)156304
Number of pages6
JournalScience of the total environment
Volume838
Issue numberPt 3
DOIs
Publication statusPublished - 10 Sep 2022

Keywords

  • Agricultural soil
  • In situ spectroscopy
  • Machine learning algorithms
  • Pre-treatment algorithms
  • SOC
  • Spectroscopy, Near-Infrared/methods
  • Soil/chemistry
  • Humans
  • Least-Squares Analysis
  • Support Vector Machine
  • Carbon
  • AIRBORNE

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