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首页> 外文期刊>Revista Brasileira de Ciência do Solo >Digital Soil Mapping of Soil Properties in the “Mar de Morros” Environment Using Spectral Data
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Digital Soil Mapping of Soil Properties in the “Mar de Morros” Environment Using Spectral Data

机译:使用光谱数据在“ Mar de Morros”环境中对土壤性质进行数字土壤制图

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摘要

Quantification of soil properties is essential for better understanding of the environment and better soil management. The conventional techniques of laboratory analysis are sometimes costly and detrimental to the environment. Thus, development of new techniques for soil analysis that do not generate residues, such as spectroscopy, is increasingly necessary as a viable way to estimate a wide range of soil properties. The objective of this study was to predict the levels of organic carbon (OC), clay, and extractable phosphorus (P), from the spectral responses of soil samples in the visible and near infrared (Vis-NIR), medium infrared (MIR), and Vis-NIR-MIR using different preprocessing methods combined with five prediction models. Soil samples were collected in Iconha, Espírito Santo State, Brazil, in the Ribeir?o Inhaúma basin. A total of 184 samples were collected from 92 sites at two depths (0.00-0.10 and 0.10-0.30 m). Physical, chemical, and spectral analyses were performed according to routine soil laboratory methods. Random selection was made of 70 % of total samples for training and 30 % for validation of the models. The coefficient of determination (R 2 ) and root mean square error (RMSE) were calculated in order to assess model performance. The standardized indexes of prediction error RPD and RPIQ were also calculated. For clay and OC, the best R 2 was found in the MIR spectrum, at 0.69 and 0.65, respectively, and for P, it was 0.57 in Vis-NIR. The MSC (Multiplicative Scatter Correction), CR (Continuum removal), and SNV (Standard Normal Variate) preprocesses were most efficient for predicting clay, OC, and P, respectively, while the PLSR - Partial Least Squares Regression (OC and P) and SVM - Support Vector Machine (clay) gave the best predictions and are therefore recommended for modeling these properties in the study area. The models identified in this study can be used to discriminate soils according to a critical test value for clay, OC, and P.
机译:为了更好地了解环境和改善土壤管理,对土壤性质进行量化至关重要。实验室分析的常规技术有时成本高昂并且对环境有害。因此,越来越需要开发一种不会产生残留物的土壤分析新技术,例如光谱法,作为估算各种土壤特性的可行方法。这项研究的目的是从土壤样品在可见和近红外(Vis-NIR),中红外(MIR)的光谱响应中预测有机碳(OC),粘土和可提取磷(P)的水平,以及使用不同的预处理方法并结合了五个预测模型的Vis-NIR-MIR。在巴西Ribeir?oInhaúma盆地的圣埃斯皮里图州的Iconha收集了土壤样品。从两个深度(0.00-0.10和0.10-0.30 m)的92个站点总共采集了184个样品。根据常规土壤实验室方法进行了物理,化学和光谱分析。随机选择总样本的70%用于训练,30%用于模型验证。计算确定系数(R 2)和均方根误差(RMSE),以评估模型性能。还计算了预测误差RPD和RPIQ的标准化指标。对于粘土和OC,在MIR光谱中发现最佳的R 2,分别为0.69和0.65,对于P,在Vis-NIR中为0.57。 MSC(乘法散射校正),CR(连续峰去除)和SNV(标准正态变量)预处理分别对于预测粘土,OC和P最有效,而PLSR-偏最小二乘回归(OC和P)和SVM-支持向量机(粘土)给出了最佳预测,因此建议在研究区域中对这些属性进行建模。根据黏土,OC和P的临界测试值,本研究中确定的模型可用于区分土壤。

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