Quantifying moisture and roughness with Support Vector Machines improves spectroscopic soil organic carbon prediction

Roemer Christoph; Rodionov Andrei; Behmann Jan; Paetzold Stefan; Welp Gerhard; Pluemer Lutz

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Quantifying moisture and roughness with Support Vector Machines improves spectroscopic soil organic carbon prediction

机译：使用支持向量机量化水分和粗糙度，改善了光谱土壤有机碳的预测

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The challenges of Vis-NIR spectroscopy are permanent soil surface variations of moisture and roughness. Both disturbance factors reduce the prediction accuracy of soil organic carbon (SOC) significantly. For improved SOC prediction, both disturbance effects have to be determined from Vis-NIR spectra, which is especially challenging for roughness. Thus, an approach for roughness quantification under varying moisture and its impact on SOC assessment using Support Vector Machines is presented here.

机译：Vis-NIR光谱学的挑战是土壤表面水分和粗糙度的永久变化。这两个干扰因素都大大降低了土壤有机碳（SOC）的预测准确性。为了改善SOC预测，必须从Vis-NIR光谱确定两个干扰效应，这对于粗糙度尤其具有挑战性。因此，这里提出了一种在湿度变化下进行粗糙度定量的方法及其对使用支持向量机的SOC评估的影响。

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《Journal of plant nutrition and soil science》 |2014年第6期|共3页
作者
Roemer Christoph; Rodionov Andrei; Behmann Jan; Paetzold Stefan; Welp Gerhard; Pluemer Lutz;
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正文语种 eng
中图分类农业基础科学;
关键词
ordinal classification; moisture; roughness; soil organic carbon (SOC); Vis-NIR spectroscopy;

机译：序号分类;湿度;粗糙度;土壤有机碳（Vis-NIR）;

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1. Quantifying moisture and roughness with Support Vector Machines improves spectroscopic soil organic carbon prediction [J] . Roemer Christoph, Rodionov Andrei, Behmann Jan, Journal of plant nutrition and soil science . 2014,第6期

机译：使用支持向量机量化水分和粗糙度，改善了光谱土壤有机碳的预测
2. Use of VIS-NIRS for land management classification with a support vector machine and prediction of soil organic carbon and other soil properties [J] . Guillaume Debaene, Dorota Piku?a, Jacek Nied?wiecki Ciencia e Investigación Agraria . 2014,第1期

机译：利用支持向量机将VIS-NIRS用于土地管理分类，并预测土壤有机碳和其他土壤性质
3. Spectroscopic diagnosis of zinc contaminated soils based on competitive adaptive reweighted sampling algorithm and an improved support vector machine [J] . Xu Xibo, Ren Mengyan, Cao Jianfei, Spectroscopy Letters . 2020,第1a5期

机译：基于竞争自适应重载采样算法的锌污染土的光谱诊断与改进的支持向量机
4. Improving Soil Organic Carbon (SOC) prediction by field spectrometry in bare cropland by reducing the disturbing effect of soil roughness [C] . Denis Antoine, Tychon Bernard, Stevens Antoine, Geoscience and Remote Sensing Symposium, 2009 . 2009

机译：通过减少土壤粗糙度的干扰作用，改善田间光谱法对土壤裸碳的预测
5. Country to Global Prediction of Soil Organic Carbon and Soil Moisture Using Digital Soil Mapping [D] . ?Guevara, Mario 2020

机译：全国土壤有机碳和土壤水分的全球预测使用数字土壤制图
6. Improving the Spatial Prediction of Soil Organic Carbon Stocks in a Complex Tropical Mountain Landscape by Methodological Specifications in Machine Learning Approaches [O] . Mareike Ließ, Johannes Schmidt, Bruno Glaser -1

机译：通过机器学习方法中的方法规范改进复杂热带山区景观中土壤有机碳储量的空间预测
7. Total and Hot-Water Extractable Organic Carbon and Nitrogen in Organic Soil Amendments: Their Prediction Using Portable Mid-Infrared Spectroscopy with Support Vector Machines [O] . Ralf Wehrle, Gerhard Welp, Stefan Pätzold 2021

机译：有机土壤中的总和热水可提取的有机碳和氮气在有机土壤修正中：它们使用便携式中红外光谱与支持向量机的预测

Quantifying moisture and roughness with Support Vector Machines improves spectroscopic soil organic carbon prediction

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