Eliminating the interference of soil moisture and particle size on predicting soil total nitrogen content using a NIRS-based portable detector
Eliminating the interference of soil moisture and particle size on predicting soil total nitrogen content using a NIRS-based portable detector
复制标题
基于近红外光谱的便携式检测仪消除土壤湿度和粒径对土壤全氮含量预测的干扰
DOI：
10.1016/j.compag.2014.11.003
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发表时间：
2015-03-01
影响因子：
8.3
通讯作者：
Sun Hong
中科院分区：
文献类型：
--
作者：
An Xiaofei;Li Minzan;Sun Hong
将近红外光谱技术应用于农田土壤，可以有效地在线检测土壤全氮含量。我们开发了一种基于近红外光谱的便携式土壤总氮含量检测仪，可测量940，1050，1100，1200，1300，1450和1550 nm的光谱数据。土壤光谱数据对外部环境条件，特别是土壤含水量和颗粒大小很敏感。使用便携式全氮测定仪时，必须排除这些因素对土壤全氮含量预测的干扰。首先，从中国北京的一个农场收集土壤样品，并使用检测器扫描，以获得不同土壤湿度和颗粒大小下的吸光度数据。其次，提出了吸光度校正法和混合校正集法，分别对原始光谱数据进行校正，消除土壤水分和粒径的干扰。土壤样品在1450 nm处的吸光度与土壤含水量呈高度相关。为此，提出了一种水分吸收校正方法（PMAI），将原始光谱数据归一化为标准光谱数据，从而消除土壤水分的干扰。为了消除土壤颗粒大小对便携式土壤全氮检测仪测量结果的干扰，利用近红外光谱的加和性，从土壤样品中分离出不同粒径的颗粒，建立了基于近红外光谱的混合校正集。基于校正后的940、1050、1100、1200、1300和1550 nm 6个波长的吸光度数据，采用BP神经网络算法建立了土壤全氮含量的估算模型。采用校正相关系数、验证相关系数、校正均方根误差、预测均方根误差和残差预测偏差对模型进行评价。与使用原始光谱数据的模型相比，新模型的准确性和稳定性都有显着提高。这些方法能有效地消除土壤含水量和颗粒大小对土壤全氮含量预测的干扰。(C)2014爱思唯尔有限公司版权所有。
Applying near infrared reflectance spectroscopy (NIRS) on farmlands can effectively estimate the total nitrogen (TN) content of soil online. We developed a NIRS-based portable detector of soil TN content that measures spectral data at 940, 1050, 1100, 1200, 1300, 1450, and 1550 nm. The soil spectral data are sensitive to external environmental conditions, particularly soil moisture content and particle size. The interference of these factors on predicting soil TN content must be eliminated when using the portable detector. First, soil samples were collected from a farm in Beijing, China, and scanned using the detector to obtain their absorbance data under varying soil moisture and particle size. Second, absorbance correction method and mixed calibration set method were proposed to correct the original spectral data and to eliminate the interference of soil moisture and particle size, respectively. The absorbance of the soil sample at 1450 nm exhibited a high correlation with soil moisture content. Thus, a moisture absorbance correction method (PMAI) was proposed to normalize the original spectral data into the standard spectral data and consequently eliminate the interference of soil moisture. A NIRS-based mixed calibration set based on the additivity of NIR spectra was produced with varying particle sizes, separated from the original soil samples, to eliminate the interference of soil particle size on the measurements of the portable soil TN detector. An estimation model of soil TN content was established based on the corrected absorbance data at six wavelengths (940, 1050, 1100, 1200, 1300, and 1550 nm) using an algorithm of the back propagation neural network. The correlation coefficient of calibration, correlation coefficient of validation, root mean square error of calibration, root mean square error of prediction, and residual prediction deviation were used to evaluate the model. Compared with the model used the original spectral data, the accuracy and stability of the new model were significantly improved. These methods could efficiently eliminate the interference of soil moisture and particle size on predicting soil TN content. (C) 2014 Elsevier B.V. All rights reserved.