利用水平钻孔探地雷达资料估算玉米作物对介质介电常数时空分布的影响

Lena Lärm, F. Bauer, J. van der Kruk, J. Vanderborght, H. Vereecken, A. Schnepf, A. Klotzsche
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引用次数: 2

摘要

农业生态系统及其产量生产力受根系水分和养分吸收的影响。这种吸收取决于作物根系结构和土壤-根区的土壤含水量分布。调查该区域及其过程可以帮助优化农业实践,如灌溉和施肥,从而有助于实现可持续作物生产的目标。在整个作物生长季节,利用安装在地下不同深度的水平根管,微型根管对土壤根区进行非侵入性调查是有效的。在这项研究中,在德国塞尔豪森的两个微型根管设施中,收集了三个玉米作物生长季节的现场时距井探地雷达测量数据和根系图像。这些设备可以测量六个不同深度的数据,范围在0.1米至1.2米之间,并对三个不同的地块进行不同的处理。通过沿一对根管的标准射线分析,得到了水平井间探地雷达测量的介电常数。这种水平介电常数切片可以通过岩石物理关系与土壤含水量联系起来。根结构表示为根长度密度,并从图像中导出,使用结合最先进的软件工具、深度神经网络和自动特征提取的工作流程。介电常数的结果表明水平和垂直变化,取决于天气条件,土壤性质和根结构。为了量化根系对介电常数时空分布的影响,我们采用统计方法消除土壤异质性、管偏差和日蒸散发变化的影响。导致沿根管的介电常数变化受根的存在的影响。
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Using horizontal borehole GPR data to estimate the effect of maize plants on the spatial and temporal distribution of dielectric permittivity
Agro-ecosystems and their yield productivity are influenced by root water and nutrient uptake. This uptake depends on the crop root architecture and the soil water content distribution within the soil-root zone. Investigating this zone and its processes can help to optimize agricultural practices, like irrigation and fertilization and therefore helps to achieve the goal for sustainable crop production. Mini-rhizotrons have shown to be effective to non-invasively investigate the soil-root zone throughout crop growing seasons using horizontal rhizotubes installed at different depths in the subsurface. In this study, in-situ time-lapse crosshole ground penetrating radar measurements and root images were collected over three maize crop growing seasons at two mini-rhizotron facilities in Selhausen, Germany. These facilities allow to measure data at six different depths ranging between 0.1 m – 1.2 m and for three different plots with varying treatments. The dielectric permittivity was derived from the horizontal crosshole GPR measurements by using standard ray-based analysis along a pair of rhizotubes. Such horizontal permittivity slices can be linked to soil water content using petro-physical relationships. The root architecture is expressed as root length density and is derived from the images, using a workflow combining state-of-the-art software tools, deep neural networks and automated feature extraction. The results of the dielectric permittivity indicate horizontal and vertical variations, depending on weather conditions, soil properties, and root architecture. To quantify the impact of the roots on the spatial and temporal distribution of the dielectric permittivity, we used statistical methods to eliminate the effects of soil heterogeneity, tube deviations and daily evapotranspiration changes. Resulting in permittivity variation along the rhizotubes impacted by the presence of roots.
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