Akter Sonia, Huisman Johan Alexander, Bogena Heye Reemt
Agrosphere Institute (IBG-3), Forschungszentrum Jülich GmbH, 52428 Jülich, Germany.
Sensors (Basel). 2025 Jul 17;25(14):4453. doi: 10.3390/s25144453.
Monitoring soil moisture (SM) using permanently installed gamma radiation (GR) detectors is a promising non-invasive method based on the inverse relationship between SM and soil-emitted GR. In a previous study, we successfully estimated SM from environmental gamma radiation (EGR) measured by a low-cost counter-tube detector. Since this detector type provides a bulk GR response across a wide energy range, EGR signals are influenced by several confounding factors, e.g., soil radon emanation, biomass. To what extent these confounding factors deteriorate the accuracy of SM estimates obtained from EGR is not fully understood. Therefore, the aim of this study was to compare the accuracy of SM estimates from EGR with those from reference K GR (1460 keV) measurements which are much less influenced by these factors. For this, a Geiger-Mueller counter (G-M), which is commonly used for EGR monitoring, and a gamma spectrometer were installed side by side in an agricultural field equipped with in situ sensors to measure reference SM and a meteorological station. The EGR and spectrometry-based K measurements were related to reference SM using a functional relationship derived from theory. We found that daily SM can be predicted with an RMSE of 3.39 vol. % from K using the theoretical value of = 1.11 obtained from the effective ratio of GR mass attenuation coefficients for the water and solid phase. A lower accuracy was achieved for the EGR measurements (RMSE = 6.90 vol. %). Wavelet coherence analysis revealed that the EGR measurements were influenced by radon-induced noise in winter. Additionally, biomass shielding had a stronger impact on EGR than on K GR estimates of SM during summer. In summary, our study provides a better understanding on the lower prediction accuracy of EGR and suggests that correcting for biomass can improve SM estimation from the bulk EGR data of operational radioactivity monitoring networks.
使用永久安装的伽马辐射(GR)探测器监测土壤湿度(SM)是一种基于SM与土壤发射的GR之间反比关系的很有前景的非侵入性方法。在之前的一项研究中,我们成功地从低成本计数管探测器测量的环境伽马辐射(EGR)中估算出了SM。由于这种探测器类型在很宽的能量范围内提供总体GR响应,EGR信号会受到几个混杂因素的影响,例如土壤氡析出、生物量。这些混杂因素在多大程度上会降低从EGR获得的SM估算的准确性尚不完全清楚。因此,本研究的目的是比较从EGR估算SM的准确性与从参考K GR(1460 keV)测量估算SM的准确性,后者受这些因素的影响要小得多。为此,在一个配备了原位传感器以测量参考SM和气象站的农田里,将一个常用于EGR监测的盖革-米勒计数器(G-M)和一台伽马能谱仪并排安装。利用从理论推导出来的函数关系,将基于EGR和能谱法的K测量与参考SM联系起来。我们发现,使用从水相和固相的GR质量衰减系数的有效比率获得的 = 1.11的理论值,从K预测每日SM的均方根误差(RMSE)为3.39体积%。EGR测量的准确性较低(RMSE = 6.90体积%)。小波相干分析表明,EGR测量在冬季受到氡引起的噪声的影响。此外,在夏季,生物量屏蔽对EGR的影响比对SM的K GR估算的影响更大。总之,我们的研究更好地理解了EGR较低的预测准确性,并表明校正生物量可以提高从运行中的放射性监测网络的总体EGR数据估算SM的准确性。