Soil erosion has been associated with various negative environmental impacts foremost of which is the potential pressure it could impose on global food security. The poor conditions of our agricultural soil can be attributed to years of unsustainable farming practices occurring throughout history that has placed significant pressure on the environment. Moreover, climate change scenarios indicate further intensification which is likely making prediction and assessment of erosion processes critical for long term agricultural sustainability. This study demonstrates the potential of mobile gamma-ray spectrometry with large volume NaI(Tl) detectors to identify, at high spatial resolution, changes in ¹³⁷Cs soil concentration within the ploughed layer of soil and enabling the soil erosion processes to be quantified. This technique represents a significant advantage over conventional spatially-isolated point measurements such as soil sampling as it offers real time mapping at the field scale. However, spectral signal derived from measurements in the field are highly dependent on the calibration procedure used and are particularly sensitive to source-detector changes such as the presence of a vehicle, ground curvature and soil moisture content. Conventional calibration procedures tend to not consider these potential sources of uncertainty potentially leaving the system vulnerable to systematic uncertainties, especially when 137Cs concentrations are low. This study used Monte Carlo simulations to investigate such changes utilising additional information including a high-resolution digital terrain model. The method was demonstrated on a ploughed site in Scotland, revealing a mixture of tillage and water erosion patterns supported by soil core data. Findings showed that the sites topography had relatively little effect (<10%) on calculated erosion rates, but moisture content could be the determining factor, albeit very difficult to measure reliably throughout a survey.

土壤侵蚀与各种负面环境影响有关，其中最主要的是它可能对全球粮食安全施加的潜在压力。我们农业土壤的贫瘠状况可以归因于在整个历史上发生的多年不可持续的耕作方式，这给环境带来了巨大压力。此外，气候变化情景表明进一步加剧，这可能使对侵蚀过程的预测和评估对于长期农业可持续性至关重要。这项研究表明，使用大体积NaI（Tl）探测器进行移动伽马射线光谱分析的潜力，可以在高空间分辨率下识别^137个变化Cs在土壤耕层中的土壤浓度，可以量化土壤侵蚀过程。与传统的空间隔离点测量（例如土壤采样）相比，该技术具有显着优势，因为它可以在现场进行实时制图。但是，从现场测量中得出的光谱信号高度依赖于所用的校准程序，并且对源探测器的变化（例如车辆的存在，地面曲率和土壤水分含量）特别敏感。传统的校准程序往往不会考虑这些潜在的不确定性来源，从而可能使系统容易受到系统性不确定性的影响，尤其是当137Cs浓度较低时。这项研究使用蒙特卡洛模拟，利用包括高分辨率数字地形模型在内的其他信息来研究此类变化。该方法在苏格兰的耕地上得到了证明，揭示了土壤核心数据支持的耕作和水蚀混合模式。结果表明，场地地形对计算出的侵蚀率影响相对较小（<10％），但水分含量可能是决定因素，尽管在整个调查过程中很难可靠地进行测量。