Quantification of Soil Organic Carbon in Biochar-Amended Soil Using Ground Penetrating Radar (GPR)

Shen, Xiaoqing; Foster, Tyler; Baldi, Heather; Dobreva, Iliyana; Burson, Byron; Hays, Dirk; Tabien, Rodante; Jessup, Russell

doi:10.3390/rs11232874

Quantification of Soil Organic Carbon in Biochar-Amended Soil Using Ground Penetrating Radar (GPR)

Journal Article · Mon Dec 02 23:00:00 EST 2019 · Remote Sensing

DOI:https://doi.org/10.3390/rs11232874· OSTI ID:1576839

; Foster, Tyler; ; ; Burson, Byron; Hays, Dirk; Tabien, Rodante;

The application of biochar amendments to soil has been proposed as a strategy for mitigating global carbon (C) emissions and soil organic carbon (SOC) loss. Biochar can provide additional agronomic benefits to cropping systems, including improved crop yield, soil water holding capacity, seed germination, cation exchange capacity (CEC), and soil pH. To maximize the beneficial effects of biochar amendments towards the inventory, increase, and management of SOC pools, nondestructive analytical methods such as ground penetrating radar (GPR) are needed to identify and quantify belowground C. The use of GPR has been well characterized across geological, archaeological, engineering, and military applications. While GPR has been predominantly utilized to detect relatively large objects such as rocks, tree roots, land mines, and peat soils, the objective of this study was to quantify comparatively smaller, particulate sources of SOC. This research used three materials as C sources: biochar, graphite, and activated C. The C sources were mixed with sand—12 treatments in total—and scanned under three moisture levels: 0%, 10%, and 20% to simulate different soil conditions. GPR attribute analyses and Naïve Bayes predictive models were utilized in lieu of visualization methods because of the minute size of the C particles. Significant correlations between GPR attributes and both C content and moisture levels were detected. The accuracy of two predictive models using a Naïve Bayes classifier for C content was trivial but the accuracy for C structure was 56%. The analyses confirmed the ability of GPR to identify differences in both C content and C structure. Beneficial future applications could focus on applying GPR across more diverse soil conditions.

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Sponsoring Organization:: USDOE Advanced Research Projects Agency - Energy (ARPA-E)

Grant/Contract Number:: AR0000662

OSTI ID:: 1576839

Alternate ID(s):: OSTI ID: 1799052

Journal Information:: Remote Sensing, Journal Name: Remote Sensing Journal Issue: 23 Vol. 11; ISSN RSBSBZ; ISSN 2072-4292

Publisher:: MDPI AGCopyright Statement

Country of Publication:: Switzerland

Language:: English

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