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Updated: Jul 3, 2025

Improving Infrared Spectroscopy Characterization of Soil Organic Matter with Spectral Subtractions

Improving Infrared Spectroscopy Characterization of Soil Organic Matter with Spectral Subtractions

Published on: January 10, 2019

Spectral Data Processing for Field-Scale Soil Organic Carbon Monitoring.

Javier Reyes¹, Mareike Ließ^1,2

¹Department of Soil System Science, Helmholtz Centre for Environmental Research-UFZ, 06120 Halle, Germany.

Sensors (Basel, Switzerland)

|February 10, 2024

Summary

This summary is machine-generated.

On-the-go soil spectroscopy shows promise for monitoring soil organic carbon (SOC) in agricultural soils, aiding climate change mitigation. While challenges exist, this method offers efficient spatial-temporal data acquisition for better soil management.

Keywords:

carbon sequestration long-term field experiment modeling pedometrics spectral correction

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Published on: October 16, 2018

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Last Updated: Jul 3, 2025

Improving Infrared Spectroscopy Characterization of Soil Organic Matter with Spectral Subtractions

Improving Infrared Spectroscopy Characterization of Soil Organic Matter with Spectral Subtractions

Published on: January 10, 2019

Monitoring Pedogenic Inorganic Carbon Accumulation Due to Weathering of Amended Silicate Minerals in Agricultural Soils.

Monitoring Pedogenic Inorganic Carbon Accumulation Due to Weathering of Amended Silicate Minerals in Agricultural Soils.

Published on: June 4, 2021

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

Published on: October 16, 2018

Area of Science:

Agricultural Science
Environmental Science
Spectroscopy

Background:

Soil organic carbon (SOC) sequestration is crucial for climate change mitigation in agricultural systems.
Efficient spatial-temporal monitoring of SOC is essential for effective land management.
Current methods for SOC monitoring can be labor-intensive and time-consuming.

Purpose of the Study:

To evaluate the potential of spectral on-the-go proximal measurements for soil organic carbon monitoring.
To assess the performance of partial least squares regression models using laboratory and field spectral data.
To investigate wavelength importance and data correction methods for improving spectroscopic SOC monitoring.

Main Methods:

Long-term field experiment with varying fertilization treatments.
Collection of spectral data using site-specific and on-the-go spectrometers.
Development and interpretation of partial least squares regression models, including data preprocessing and wavelength analysis.
Application of data correction techniques such as external parameter orthogonalization (EPO).

Main Results:

Soil organic carbon (SOC) values varied between 14 and 25 g kg⁻¹.
Partial least squares regression models were developed using both laboratory and field spectral data.
Wavelength importance analysis revealed challenges but indicated potential for SOC monitoring.
On-the-go spectroscopy demonstrated promising results for spatial-temporal SOC monitoring despite spectrometer and soil condition variations.

Conclusions:

On-the-go soil spectroscopy offers a promising approach for efficient spatial-temporal soil organic carbon monitoring.
Despite challenges related to varying conditions and spectral shifts, the technology holds potential for climate change mitigation strategies.
Further research and refinement of spectroscopic methods can enhance accuracy and reliability for agricultural soil management.