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

Journal Article · 03 December 2019 · Remote Sensing

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

Shen, Xiaoqing  ^[1]; Foster, Tyler ^[1]; Baldi, Heather  ^[1]; Dobreva, Iliyana  ^[1]; Burson, Byron ^[2]; Hays, Dirk ^[1]; Tabien, Rodante ^[3]; Jessup, Russell  ^[1]

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1. Texas A & M Univ., College Station, TX (United States). Dept. of Soil and Crop Science
2. US Dept. of Agriculture (USDA), College Station, TX (United States). ARS-Southern Plains Agricultural Research Center
3. Texas A&M AgriLife Research Center, Beaumont, TX (United States)

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.

• Research Organization: Texas A & M Univ., College Station, TX (United States). Texas A & M AgriLife Research
• 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, Vol. 11, Issue 23; ISSN 2072-4292
• Publisher: MDPICopyright Statement
• Country of Publication: United States
• Language: English