| 土壤有机质高光谱特征与波长变量优选方法 |
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| 引用本文： | 朱亚星,于雷,洪永胜,章涛,朱强,李思缔,郭力,刘家胜. 土壤有机质高光谱特征与波长变量优选方法[J]. 中国农业科学, 2017, 50(22): 4325-4337. DOI: 10.3864/j.issn.0578-1752.2017.22.009 |
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| 作者姓名： | 朱亚星 于雷 洪永胜 章涛 朱强 李思缔 郭力 刘家胜 |
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| 基金项目： | 国家自然科学基金（41401232）、中央高校基本科研业务费专项资金（CCNU15A05006）、华中师范大学研究生教育创新资助项目（2017CXZZ007） |
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| 摘 要： | 【目的】探究土壤有机质的高光谱特征及响应规律,优选土壤有机质的敏感波长,降低土壤有机质高光谱估测模型复杂度,提高模型稳健性,为利用高光谱技术对农田土壤肥力的定量监测提供理论支撑。【方法】采集江汉平原潮土土样130个,将其中40个样本作为训练集,测量其去有机质前、后的土壤有机质含量及光谱数据,计算差值及变化率,分析土壤有机质含量变化对光谱特征的影响,结合无信息变量消除(uninformative variables elimination,UVE)、竞争适应重加权采样(competitive adaptive reweighted sampling,CARS)变量优选方法确定土壤有机质敏感波长;采用45个建模集样本,基于偏最小二乘回归(partial Least Squares Regression,PLSR)和反向传播神经网络(back propagation neural network,BPNN)建立土壤有机质含量的估算模型;利用45个验证集样本检验敏感波长对同类土壤的适用性。【结果】通过有机质去除试验,供试土壤的平均光谱反射率在全波段均有所增加,在可见光波段变化率高于近红外波段;比较UVE、CARS、UVE-CARS、CARS-UVE这4种变量优选方法,得到最佳变量优选方法为UVE-CARS,该方法从2001个波长变量中优选得到84个变量作为土壤有机质的敏感波长,分布于561—721、1 920—2 280 nm波段覆盖范围;基于敏感波长的PLSR、BPNN模型性能均优于全波段模型,其中,基于敏感波长的BPNN模型的估测能力高于PLSR,模型验证集R~2、RMSE、RPD、MAE、MRE值分别为0.74、1.33 g·kg~(-1)、2.02、1.04 g·kg~(-1)、6.2%,可实现土壤有机质含量的有效估测。【结论】通过训练集获得的土壤有机质敏感波长,能够实现对该试验区同种土壤类型样本土壤有机质含量的有效估测;利用去有机质试验结合变量优选方法确定的敏感波长建模,不仅将输入波长压缩至全波段波长数目的 4.2%,而且提升了模型估测精度,降低了变量维度和模型复杂度,为快速准确评估农田土壤有机质含量提供了新途径。
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| 关 键 词： | 土壤有机质 高光谱 变量优选 偏最小二乘回归 反向传播神经网络 潮土 |
| 收稿时间： | 2017-05-07 |
Hyperspectral Features and Wavelength Variables Selection Methods of Soil Organic Matter |
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| ZHU YaXing,YU Lei,HONG YongSheng,ZHANG Tao,ZHU Qiang,LI SiDi,GUO Li,LIU JiaSheng. Hyperspectral Features and Wavelength Variables Selection Methods of Soil Organic Matter[J]. Scientia Agricultura Sinica, 2017, 50(22): 4325-4337. DOI: 10.3864/j.issn.0578-1752.2017.22.009 |
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| Authors: | ZHU YaXing YU Lei HONG YongSheng ZHANG Tao ZHU Qiang LI SiDi GUO Li LIU JiaSheng |
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| Affiliation: | Key Laboratory for Geographical Process Analysis and Simulation, Hubei Province, Central China Normal University/ College of Urban & Environmental Science, Central China Normal University, Wuhan 430079 |
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| Abstract: | 【Objective】The objective of this study is to explore the hyperspectral features and response regularity of the soil organic matter, and to select the sensitive wavelengths of soil organic matter, so as to reduce complexity of hyperspectral estimation model of soil organic matter and improve robustness of the model, which is to provide theoretical support to quantitatively monitor the soil fertility of farmland by using the hyperspectral technology. 【Method】 A total of 130 fluvo-aquic soil samples were collected from Jianghan plain, of which 40 were the training set samples. The soil organic matter content (SOMCraw) and spectral reflectance (SRraw) were measured from total samples, and an experiment of removal of organic matter was performed using the training set samples, and then we measured the soil organic matter content (SOMCrem) and spectral reflectance (SRrem) from samples of removal of organic matter. By calculating the difference and rate of change between SRraw and SRrem from training set samples, we could analyze how the content changes of soil organic matter itself influence the spectral features. The soil organic matter sensitive wavelengths were determined by the methods of uninformative variables elimination (UVE) and competitive adaptive reweighted sampling (CARS). The calibration set with 45 samples was utilized to build the soil organic matter estimation models base on partial least squares regression (PLSR) and back propagation neural network (BPNN), and the validation set of 45 samples was utilized to test whether sensitive wavelengths were suitable for the same type soil. 【Result】 The experiment of removal of organic matter showed that the average spectral reflectance of test soil samples increased at full-spectrum with removing organic matter content, especially at the visible spectrum; after the comparison of UVE, CARS, UVE-CARS, and CARS-UVE, the optimal method of variables selection was UVE-CARS. The method of UVE-CARS provided 84 selected variables which were the soil organic matter sensitive wavelengths with coverage area of 561-721, 1 920-2 280 nm. Based on soil organic matter sensitive wavelengths, the PLSR and BPNN had better performance than full-spectrum model, and BPNN was better than PLSR in predictive ability with its value of R2, RMSE, RPD, MAE, MRE were 0.74, 1.33 g·kg-1, 2.02, 1.04 g·kg-1, 6.2%, respectively, so it could effectively estimate soil organic matter. 【Conclusion】 The soil organic matter sensitive wavelengths from training set could effectively estimate soil organic matter content in this test area with the same type samples. In addition, modeling of sensitive wavelengths by obtaining from the experiment of removal of organic matter and variables selection method could not only compress input wavelengths down into 4.2% of full-spectrum, but also enhance the estimation accuracy and reduce the model complexity. In this study, it provided a new approach to quickly and accurately evaluate soil organic matter content in the farmland. |
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| Keywords: | soil organic matter hyperspectra variables selection partial least squares regression back propagation neural network fluvo-aquic |
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