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首页> 外文期刊>Geoderma: An International Journal of Soil Science >Soil characterization across catenas via advanced proximal sensors
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Soil characterization across catenas via advanced proximal sensors

机译：通过先进的近端传感器在Catenas的土壤表征

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As countries of Eastern Europe look to advance their agricultural markets through large scale agronomic production, high resolution mapping of soil resources will be essential. Portable X-ray fluorescence (PXRF) spectrometry and diffuse reflectance spectroscopy (DRS) are non-invasive, proximal sensing techniques which provide quantitative data germane to physicochemical soil properties in seconds. While these techniques have been widely used to characterize individual soil samples, sample sets, or variability across individual fields, less work has been done at the catena scale (even less so in Eastern Europe), where variability due to topographic differences substantively affects a wide number of soil properties. The present study was conducted on three catenas of the Transylvanian Plain, Romania, each with 100 sampling points randomly established in ArcGIS. Laboratory analysis (particle size analysis, total carbon, total nitrogen, soil organic matter) was conducted at Texas Tech University, USA. Following Savitzky-Golay first derivative transformation, DRS spectra were used to predict soil physicochemical parameters of interest via support vector regression. The whole dataset was randomly divided into a 70% training (n = 210) and 30% test set (n = 90). Across all catenas, a combined PXRF + DRS approach showed better parameter prediction relative to either sensor independently as evidenced by higher R-2, lower RMSE, higher RPD, and higher RPIQ values. For each parameter, the 100 points per catena were used as input data to develop a PXRF + DRS predictive model, and the output data from each model was kriged using ArcGIS 10.3.1. Spatial analysis strongly reflected management and landscape dynamics across the catenas. Combined proximal sensor approaches show considerable advantages over traditional laboratory approaches, allowing for high sample throughput, greater analytical density, and less expensive data, with minimal fall off in data quality. The combined PXRF + DRS approach showed excellent potential for providing the data needed to support optimized soil resource mapping and land management decisions in Eastern Europe or worldwide. (C) 2017 Elsevier B.V. All rights reserved.
机译：随着东欧各国通过大规模农艺生产来推进其农业市场，土壤资源的高分辨率绘图将是必不可少的。便携式X射线荧光（PXRF）光谱法和漫射反射光谱（DRS）是非侵入性的，近端感测技术，其在几秒钟内为物理化学土壤性质提供定量数据缘锗。虽然这些技术已被广泛用于表征各个土壤样本，样品集或各个领域的可变性，但在Catena Scale（即使在东欧的变化）上取得更少的工作，其中由于地形差异的可变性实质性地影响着广泛的影响土壤性质数量。本研究在罗马尼亚的三丽象平原的三个Catenas进行，每次在ArcGIS中随机建立了100个采样点。美国德克萨斯科技大学进行实验室分析（粒度分析，总碳总，氮，土壤有机物）。在Savitzky-golay首次衍生改造之后，DRS Spectra用于通过支持向量回归预测利息的土壤物理化学参数。整个数据集随机分为70％训练（n = 210）和30％的测试集（n = 90）。在所有Catenas上，组合的PXRF + DRS方法显示相对于任何传感器的更好的参数预测，独立地，如较高的R-2，较低的RMSE，更高的RPD和更高的RPIQ值所证明。对于每个参数，每个Catena 100点被用作输入数据以开发PXRF + DRS预测模型，并且使用ArcGIS 10.3.1的每个型号的输出数据被克里格。空间分析强烈反映了Catenas的管理和景观动态。组合的近端传感器方法显示出与传统实验室方法相当大的优势，允许高样本吞吐量，更高的分析密度和更便宜的数据，以最小的数据质量下降。合并的PXRF + DRS方法表明，提供支持优化的土壤资源绘图和东欧或全球土地管理决策所需的数据的优异潜力。 （c）2017 Elsevier B.v.保留所有权利。

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