基于伽马能谱的田块尺度土壤快速检测技术初探

Study on rapid soil detection technology at the field scale based on gamma spectrometry

关键词:  土壤快速检测  伽马能谱  田块尺度  精准施肥  智慧农业

Key words:  soil rapid detection  gamma spectrometry  field scale  precision fertilization  smart agriculture

摘要:

【目的】 土壤肥力是反映土壤肥沃性的一个重要指标，对农业生产管理意义重大。文章旨在探索基于车载伽马能谱仪的田块尺度土壤肥力指标快速获取方法，为规模农场精准施肥与区域养分管理提供辅助决策支持。【方法】 基于土壤放射性物质含量与土壤属性间的相关性，利用车载CsI（碘化铯）伽马能谱仪采集土壤γ射线能谱数据并标定极值点，同时在典型点位采集土壤样品并进行实验室分析。通过SoilOptix软件解析伽马能谱数据，与实测指标建立拟合模型，生成田块尺度的土壤肥力属性空间分布图，并据此设计差异化施肥方案。【结果】 受部分田块蟹塘复垦还田的影响，研究区整体肥力水平偏低，车载伽马能谱反演技术能够在田块尺度有效表征土壤有机质（R²=0.76）、碱解氮（R²=0.87）和速效钾（R²=0.50）等肥力指标，并精细化解析其空间异质性，为精准施肥和农业绿色可持续发展提供了数据支撑。结合肥力指标分级，针对研究区水稻生产实际，该研究提出了“中低氮区补氮，普遍增加有机肥与磷肥，低钾区重点补钾”的差异化施肥方案，取得了较好成效。【结论】 车载伽马能谱快速检测土壤技术具备高时效性、高精度和高空间分辨率的优势，能够为农作物精准施肥提供支持，为土壤普查和智慧农业发展提供技术支撑，具有较好的推广应用潜力，但其模型稳定性、数据解译算法及成本问题仍需进一步优化和本地化改进。

Abstract:

[Purpose] Soil fertility is an important indicator that reflects the richness and productivity of the soil，and it holds great significance for agricultural production management. This study aims to explore a rapid field-scale method for obtaining soil fertility indicators based on a vehicle-mounted gamma spectrometer，providing auxiliary decision support for precision fertilization in large-scale farms and regional nutrient management.[Method] According to the correlation between the content of radioactive substances in the soil and its properties，soil γ-ray spectral data was collected using a vehicle-mounted cesium iodide （CsI） gamma spectrometer. The instrument was calibrated at key energy peaks to ensure accuracy. Soil samples were simultaneously collected from representative locations and analyzed in the laboratory for major fertility parameters. The SoilOptix software was employed to interpret the gamma spectra，and regression models were established between spectral data and measured soil indicators. These models were then used to generate high-resolution spatial distribution maps of soil fertility attributes across the experimental field. Based on the spatial patterns of nutrient variability，site-specific fertilization schemes were designed to optimize nutrient inputs.[Result] Due to the impact of reclamation and returning of some fields and crab ponds to farmland，the overall fertility level in the study area was relatively low. The vehicle-mounted γ-ray spectral inversion technology could effectively characterize soil organic matter （R²=0.76），alkali-hydrolyzable nitrogen （R²=0.87），and available potassium （R²=0.50） and other fertility indicators at the field scale，and precisely analyzed their spatial heterogeneity，providing data support for precision fertilization and sustainable agricultural development. Combining the classification of fertility indicators and considering the actual rice production situation in the study area，this study proposed a differentiated fertilization scheme of "supplementing nitrogen in medium and low nitrogen areas，generally increasing organic and phosphorus fertilizers，and focusing on potassium supplementation in low potassium areas"，which had achieved good results.[Conclusion] The vehicle-mounted gamma spectroscopy method for rapid soil detection demonstrates clear advantages in terms of timeliness，accuracy，and spatial resolution. It offers strong technical support for precision fertilization in crops，soil resource surveys，and the advancement of smart agriculture. The approach shows substantial potential for large-scale application and promotion. However，challenges remain in terms of model stability，spectral data interpretation algorithms，and cost-effectiveness. Further optimization and localized calibration are recommended to enhance the adaptability and robustness of this technology in diverse agricultural environments.