To be commercially viable, soil carbon project developers need to be able to measure soil carbon stocks across large scales (e.g., 100,000 to 1,000,000 ha). These measurements need to be accurate, unbiased, inexpensive, and fast. One potential measurement modality for carbon markets is visible and near-infrared diffuse reflectance spectroscopy (VisNIR). VisNIR has been widely used to predict soil properties including soil organic carbon (SOC) concentration and stock under both lab settings and in situ soil conditions. Recent developments in low-cost spectrometers have enabled the creation of easy to operate, rapidly deployed, handheld VisNIR-equipped devices for in situ soil measurement. Our objective for this study is to 1) test one such handheld in situ VisNIR probe (handheld probe) to measure SOC stocks to 30 cm depth in Midwest US Mollisols, 2) to quantify the role of bulk density and SOC concentration in VisNIR probe calibration for probe-based estimation on SOC stock in Midwest US Mollisols, and 3) to quantify the effect of indirect (SOC + BD) vs direct calibration modeling (SOC stock directly) of SOC stocks using VisNIR data. We collected handheld probe measurements and soil core samples from six non-contiguous farms across the state of Illinois, USA. A one-farm hold out PLSR modeling approach was taken for SOC concentration, bulk density, 5-cm incremented SOC stocks down to 45 cm; and 0 to 30 cm SOC stocks using the in situ VisNIR spectra from the handheld probe. Models accurately predicted SOC concentration (R2 = 0.72, RMSE = 0.33 %, RPIQ = 2.39, bias = 0.0005 %), 5-cm increment SOC stocks (R2 = 0.68, RPIQ = 2.41 Mg/ha, bias = 0.05 Mg/ha) and 0 to 30 cm SOC stocks (R2 = 0.88, RMSEP = 7.8, bias = -0.49 Mg/ha, RPIQ = 4.19 Mg/ha). Models were not able to accurately predict bulk density (R2 = 0.28). Direct SOC stock modeling resulted in lower bias compared to indirect computation of SOC stock (bias = 0.05 and 0.15 Mg/ha for direct and indirect methods, respectively) and results demonstrated that, in this loess landscape, SOC stock prediction accuracy was driven by accurate prediction of SOC concentration, rather than accurate prediction of bulk density. The handheld probe shows promise as a rapid, low-cost tool for measuring SOC stocks in the midwestern Mollisols and can provide the data necessary to support large spatial scale soil carbon market development. These results justify continued investment in in situ spectral libraries for the handheld probes and eventually posit a modeling framework for measurement-based soil carbon accounting.

中文翻译：

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原位测量土壤碳储量：使用新型手持式 VisNIR 探头的研究


为了在商业上可行，土壤碳项目开发商需要能够测量大尺度（例如 100,000 至 1,000,000 公顷）的土壤碳储量。这些测量需要准确、公正、廉价且快速。碳市场的一种潜在测量方式是可见光和近红外漫反射光谱 （VisNIR）。VisNIR 已广泛用于预测实验室设置和原位土壤条件下的土壤特性，包括土壤有机碳 （SOC） 浓度和库存。低成本光谱仪的最新发展使易于操作、快速部署、配备 VisNIR 的手持式土壤测量设备成为可能。我们本研究的目标是 1） 测试一种这样的手持式原位 VisNIR 探头（手持式探头），以测量美国中西部 Mollisols 中 30 cm 深的 SOC 库存，2） 量化堆积密度和 SOC 浓度在 VisNIR 探针校准中的作用，用于基于探针的估计美国中西部 Mollisols 的 SOC 库存，以及 3） 量化间接（SOC + BD）与直接校准建模（SOC 库存直接）的影响SOC 库存使用 VisNIR 数据。我们从美国伊利诺伊州的六个非连续农场收集了手持式探针测量值和土壤核心样本。对 SOC 浓度、堆积密度、5 cm 递增的 SOC 库存至 45 cm 采用单一农场保持 PLSR 建模方法;和来自手持式探头的原位 VisNIR 光谱的 0 至 30 cm SOC 储备液。模型准确预测了 SOC 浓度（R2 = 0.72，RMSE = 0.33 %，RPIQ = 2.39，偏差 = 0.0005 %）、5 cm 增量的 SOC 储量（R2 = 0.68，RPIQ = 2.41 Mg/ha，偏差 = 0.05 Mg/ha）和 0 至 30 cm SOC 储量（R2 = 0.88，RMSEP = 7.8，偏差 = -0.49 Mg/ha，RPIQ = 4.19 Mg/ha）。 模型无法准确预测堆积密度 （R2 = 0.28）。与间接计算 SOC 存量相比，直接 SOC 存量建模导致较低的偏差（直接和间接方法的偏差分别为 0.05 和 0.15 Mg/ha），结果表明，在这种黄土景观中，SOC 存量预测的准确性是由 SOC 浓度的准确预测驱动的，而不是对堆积密度的准确预测。手持式探头有望成为测量中西部 Mollisols 土壤有机碳储量的快速、低成本工具，并且可以提供支持大规模空间规模土壤碳市场发展所需的数据。这些结果证明了对手持式探头原位光谱库的持续投资是合理的，并最终为基于测量的土壤碳核算提出了一个建模框架。