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Evaluation of MLSR and PLSR for estimating soil element contents using visible/near-infrared spectroscopy in apple orchards on the Jiaodong peninsula

机译:可见/近红外光谱法对胶东半岛苹果园MLSR和PLSR评估土壤元素含量的评价

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摘要

Conventional methods for soil element content determination based on laboratory analyses are costly and time-consuming. A soil reflectance spectrum is an alternative approach for soil element content estimation with the advantage of being rapid, non-destructive, and cost effective. Visible/near-infrared spectra (350 nm to 2500 nm) were measured from 105 soil samples originating from 30 apple orchards on the jiaodong peninsula. The Savitzky-Golay (FD-SG) technique for spectral data was implemented to reduce the signal noise. Logarithm of the reciprocal of reflectance (logR(-1)) and the first derivative transformation (DR) were used to accentuate the features and to prepare the data for use in quantitative estimation models. The SI (sum index), DI (difference index), PI (product index), RI (ratio index), and NDI (normalized difference index) were calculated to extract sensitive waveband combinations that are significantly related to soil element contents. Soil element contents were retrieved based on sensitive waveband combinations by multiple linear stepwise regression (MLSR) and partial least square (PLSR) models. The results showed that DR performed better than logR-1 in eliminating the interfering factors of soil particle size and spectral noise. The MLSR and PLSR calibration models based on PI performed better than those based on SI or DI did. The MLSR performed better than PLSR in estimating soil elemental content. The contents of total nitrogen (TN), arsenic (As), and mercury (Hg) could be estimated well using MLSR and PLSR calibration models developed with PI. The MLSR calibration-model developed with PI performed well in estimating available potassium (A-K) content. However, the contents of available phosphorus (A-P), ammonium nitrogen (NH4+-N), nitric nitrogen (NO3--N), and soil organic matter (SOM) could not be estimated using MLSR or PISR calibration models. These outcomes will provide the theoretical basis and technical support for estimations of soil element content using visible/near-infrared spectra. Although they were shown to be useful in apple orchards of the jiaodong peninsula, these models and methods should be further tested in soil samples from other regions and countries to prove their validity. (C) 2015 Elsevier B.V. All rights reserved.
机译:基于实验室分析确定土壤元素含量的常规方法既昂贵又费时。土壤反射光谱是估计土壤元素含量的另一种方法,具有快速,无损且具有成本效益的优点。从胶东半岛30个苹果园中的105个土壤样品中测量了可见/近红外光谱(350 nm至2500 nm)。实施了用于频谱数据的Savitzky-Golay(FD-SG)技术以减少信号噪声。反射率倒数的对数(logR(-1))和一阶导数变换(DR)用于强调特征并准备用于定量估计模型的数据。计算SI(总指数),DI(差异指数),PI(产品指数),RI(比率指数)和NDI(归一化差异指数)以提取与土壤元素含量显着相关的敏感波段组合。通过多重线性逐步回归(MLSR)和偏最小二乘(PLSR)模型,基于敏感波段组合检索土壤元素含量。结果表明,DR在消除土壤粒径和光谱噪声的干扰因素方面优于logR-1。基于PI的MLSR和PLSR校准模型的性能优于基于SI或DI的模型。在估算土壤元素含量方面,MLSR的表现优于PLSR。使用由PI开发的MLSR和PLSR校准模型可以很好地估算总氮(TN),砷(As)和汞(Hg)的含量。用PI开发的MLSR校准模型在估算有效钾(A-K)含量方面表现良好。但是,无法使用MLSR或PISR校准模型估算可用磷(A-P),铵态氮(NH4 + -N),硝态氮(NO3--N)和土壤有机质(SOM)的含量。这些结果将为使用可见/近红外光谱估算土壤元素含量提供理论基础和技术支持。尽管它们在胶东半岛的苹果园中被证明是有用的,但这些模型和方法仍应在其他地区和国家的土壤样品中进行进一步测试,以证明其有效性。 (C)2015 Elsevier B.V.保留所有权利。

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