| S/B和DS算法校正土壤水分对土壤有机质近红外光谱预测的影响 |
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| 引用本文： | 王世芳,韩平,宋海燕,梁刚,程旭.S/B和DS算法校正土壤水分对土壤有机质近红外光谱预测的影响[J].光谱学与光谱分析,2019,39(6):1986-1992. |
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| 作者姓名： | 王世芳 韩平 宋海燕 梁刚 程旭 |
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| 作者单位： | 北京农业质量标准与检测技术研究中心,北京 100097;山西农业大学工学院,山西 太谷 030801;农产品产地环境监测北京市重点实验室,北京 100097;北京农业质量标准与检测技术研究中心,北京 100097;农产品产地环境监测北京市重点实验室,北京 100097;山西农业大学工学院,山西 太谷 030801 |
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| 基金项目： | The National Key Research and Development Program of China(2016YFD0701804), the National Natural Science Foundation of China(41201294), the Collaborative Innovation Center Project of Beijing Academy of Agricultural and Forestry Sciences |
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| 摘 要： | 土壤水分对近红外光谱表现出强烈的吸收和对土壤有机质含量的预测造成干扰。研究选择41个样本作为校正集和9个样本作为预测集，所有样本做不同含水率（5%，10%，15%和17%）的处理。采用S/B和DS算法分别对预测结果和全光谱进行校正，消除土壤水分的影响。结果得出预测结果偏差减小和模型预测性能得到改善，Rp高于0.89和RMSEP低于0.885%。研究表明S/B和DS算法能有效消除土壤水分的影响和提高土壤有机质预测的准确性。
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| 关 键 词： | S/B算法 DS算法 土壤水分 土壤有机质 近红外光谱 |
| 收稿时间： | 2018-05-02 |
Application of Slope/Bias and Direct Standardization Algorithms to Correct the Effect of Soil Moisture for the Prediction of Soil Organic Matter Content Based on the Near Infrared Spectroscopy |
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| WANG Shi-fang,HAN Ping,SONG Hai-yan,LIANG Gang,CHENG Xu.Application of Slope/Bias and Direct Standardization Algorithms to Correct the Effect of Soil Moisture for the Prediction of Soil Organic Matter Content Based on the Near Infrared Spectroscopy[J].Spectroscopy and Spectral Analysis,2019,39(6):1986-1992. |
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| Authors: | WANG Shi-fang HAN Ping SONG Hai-yan LIANG Gang CHENG Xu |
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| Institution: | 1. Beijing Research Center for Agriculture Standards and Testing, Beijing 100097, China
2. College of Engineering, Shanxi Agricultural University, Taigu 030801, China
3. Beijing Municipal Key Laboratory of Agriculture Environment Monitoring, Beijing 100097, China |
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| Abstract: | Soil moisture has strong absorption in near infrared spectroscopy (NIRS) and causes interference in the prediction of the soil organic matter (SOM) content. In this paper, 41 dry soil samples were used to establish the SOM calibration model by PLSR, and 9 samples were used as the prediction set. All soil samples were rewetted to four different moisture contents (5%, 10%, 15% and 17%). The slope/bias (S/B) and direct standardization (DS) algorithms were used to correct SOM prediction results and whole-spectra obtained by different moisture content, eliminating the differences caused by soil moisture. The results showed that the bias reduced and prediction performances of the model were improved, with Rp higher than 0.89 and RMSEP lower than 0.885%. The study indicated that S/B and DS algorithm corrections could effectively remove the influence of soil moisture in NIRS and improve the accuracy of SOM predictions. |
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| Keywords: | Slope/bias algorithm Direct standardization algorithm Soil moisture Soil organic matter Near infrared spectroscopy |
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