4.7 Article

Can Agricultural Management Induced Changes in Soil Organic Carbon Be Detected Using Mid-Infrared Spectroscopy?

Journal

REMOTE SENSING
Volume 13, Issue 12, Pages -

Publisher

MDPI
DOI: 10.3390/rs13122265

Keywords

diffuse reflectance spectroscopy; soil spectroscopy; long term agricultural trials

Funding

  1. Ida and Robert Gordon Family Foundation
  2. USDA-ARS, USDA-ARS-Greenhouse gas Reduction through Agricultural Carbon Enhancement Network (GRACEnet)
  3. USDA-ARS-Resilient Economic Agricultural Practices (REAP) project
  4. North Central Regional Sun-Grant Center at South Dakota State University through US Department of Energy-Office of Biomass Programs [DE-FC36-05GO85041]
  5. NSF Long-term Ecological Research Program [DEB 1832042]
  6. United States Department of Agriculture
  7. USDA NIFA awards [2017-67003-26481, 2020-67021-32467]
  8. Michigan State University AgBio Research

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Diffuse reflectance spectroscopy (DRS) is considered a low-cost alternative to traditional laboratory analysis for measuring soil organic carbon (SOC) changes. Utilizing archived soil samples from long-term research trials in the U.S., it was found that mid-infrared (MIR) spectroscopy coupled with the Kellogg Soil Survey Laboratory MIR spectral library can provide accurate estimates of SOC across landscapes. Despite some additional uncertainty, the results suggest that large existing MIR spectral libraries can be used successfully for carbon monitoring in other laboratories.
A major limitation to building credible soil carbon sequestration programs is the cost of measuring soil carbon change. Diffuse reflectance spectroscopy (DRS) is considered a viable low-cost alternative to traditional laboratory analysis of soil organic carbon (SOC). While numerous studies have shown that DRS can produce accurate and precise estimates of SOC across landscapes, whether DRS can detect subtle management induced changes in SOC at a given site has not been resolved. Here, we leverage archived soil samples from seven long-term research trials in the U.S. to test this question using mid infrared (MIR) spectroscopy coupled with the USDA-NRCS Kellogg Soil Survey Laboratory MIR spectral library. Overall, MIR-based estimates of SOC%, with samples scanned on a secondary instrument, were excellent with the root mean square error ranging from 0.10 to 0.33% across the seven sites. In all but two instances, the same statistically significant (p < 0.10) management effect was found using both the lab-based SOC% and MIR estimated SOC% data. Despite some additional uncertainty, primarily in the form of bias, these results suggest that large existing MIR spectral libraries can be operationalized in other laboratories for successful carbon monitoring.

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