| 基于EPO算法去除水分影响的土壤有机质高光谱估算 |
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| 引用本文： | 洪永胜,于 雷,朱亚星,吴红霞,聂 艳,周 勇,Feng QI,夏 天.基于EPO算法去除水分影响的土壤有机质高光谱估算[J].土壤学报,2017,54(5):1068-1078. |
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| 作者姓名： | 洪永胜 于 雷 朱亚星 吴红霞 聂 艳 周 勇 Feng QI 夏 天 |
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| 作者单位： | 1. 华中师范大学地理过程分析与模拟湖北省重点实验室,武汉 430079;华中师范大学城市与环境科学学院,武汉 430079;2. School of Environmental and Sustainability Sciences,Kean University,NJ 07083,USA |
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| 基金项目： | 国家自然科学基金项目（41401232）、中央高校基本科研业务费专项资金项目（CCNU15A05006）、湖北省自然科学基金面上项目（2016CFB558）、华中师范大学研究生教育创新资助项目（2016CXZZ15）共同资助 |
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| 摘 要： | 野外进行土壤有机质的光谱快速预测时需考虑土壤含水量的影响。在室内设计人工加湿实验分别获取9个土壤含水量梯度(0~32%,间隔4%)的土壤光谱数据,分析土壤含水量变化对光谱的影响,再利用外部参数正交化法(external parameter orthogonalization,EPO)进行湿土光谱校正,并结合偏最小二乘回归和支持向量机回归分别建立土壤有机质预测模型。结果表明,土壤光谱反射率随着土壤含水量的增加呈非线性降低趋势,偏最小二乘回归模型的预测偏差比为1.16,模型不可用;经EPO算法校正后,各土壤含水量梯度之间的光谱差异性降低,能实现土壤有机质在不同土壤含水量梯度的有效估算,偏最小二乘回归和支持向量机回归模型的预测偏差比分别提高至1.76和2.15。研究结果可为田间快速预测土壤有机质提供必要参考。
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| 关 键 词： | 土壤光谱 有机质 含水量 外部参数正交化法 支持向量机回归 江汉平原 |
| 收稿时间： | 2016/8/11 0:00:00 |
| 修稿时间： | 2017/4/24 0:00:00 |
Removing the Effect of Soil Moisture on Prediction of Soil Organic Matter with Hyperspectral Reflectance Using External Parameter Orthogonalization |
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| HONG Yongsheng,YU Lei,ZHU Yaxing,WU Hongxi,NIE Yan,ZHOU Yong,Feng QI and XIA Tian.Removing the Effect of Soil Moisture on Prediction of Soil Organic Matter with Hyperspectral Reflectance Using External Parameter Orthogonalization[J].Acta Pedologica Sinica,2017,54(5):1068-1078. |
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| Authors: | HONG Yongsheng YU Lei ZHU Yaxing WU Hongxi NIE Yan ZHOU Yong Feng QI and XIA Tian |
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| Abstract: | Objective]Soil organic matter is an important index of soil properties,because it is vital to crop growth and soil quality. The technology of hyperspectral analysis is a rapid,convenient,low-cost and alternative method and exhibits an increasingly remarkable development potential in estimation of soil organic matter. However,when hyperspectral reflectance is used in the field,there are several external environmental factors,including soil moisture content,temperature,and surface of the soil that may affect soil spectra. Especially soil moisture content,a major limit to field hyperspectral survey,might mask the absorption features of soil organic matter,and hence dramatically lower accuracy of the prediction of soil organic matter. Therefore,it is essential to find a method capable of removing the impact of soil moisture content on spectral reflectance,so as to improve the accuracy of quantitative prediction of soil organic matter. In this paper,the EPO(external parameter orthogonalization)algorithm was introduced for that purpose.Method]A total of 217 soil samples were collected from the 0~20 cm soil layer in the Jianghan Plain. In the laboratory,the soil samples were air-dried and ground to pass a sieve with mesh < 2 mm. Then the soil samples were analyzed separately for soil organic matter content with the potassium dichromate external heating method.The total of 217 soil samples were further divided into three non-overlapping subsets:a model calibration set(S0),consisting of 122 samples and dedicated to development of a multivariate model for soil organic matter;an EPO development subset(S1)consisting of 60 samples for EPO development;and a validation subset(S2)consisting of 35 samples for independentEPO validation. Then,the samples in S1 and S2 were rewetted in line with the following procedure:from each soil sample 150 g oven-dried soil was weighed out,put in a black cylindrical box and rewetted along the gradient of soil moisture content increment with interval being 4% each,making up a total of 9 treatments in soil moisture content along the gradient i.e. 0,4%,8%,12%,16%,20%,24%,28% and 32%. An spectrometer was used to acquire hyperspectral reflectances of the samples of three subsets(S0,S1 andS2,including the rewetting samples)on 350 to 2500 nm. And then influences of the soil moisture content on the soil spectra were analyzed,and the scores of the first two principal components in the principal component analysis were used for comparison to determine performance of EPO algorithmin removing the effects of soil moisture content on spectral reflectance of the wet samples. In the end,modeling for the S0 subset was done using the partial least squares regression and support vector machine regression,and the S2 subset of wet samples were used as external validation set before and after calibration with EPO. The coefficient of determination(R2),root mean squared error (RMSE)and the ratio of prediction to deviation(RPD)between the predicted and measured values of soil organic matter were used to compare the 3 models in performance:High R2,RPD and low RMSE were indicators of optimal models for partial least squares regression(before EPO calibration),EPO-partial least squares regression and EPO-support vector machine regression. Result]Results show that(1) Soil moisture content does have obvious influence on spectral reflectance,and the reflectance decreases in value across the entire wavelength domain with increasing soil moisture content,making it more challenging to identify useful features of soil organic matter with spectra;(2)For Subset S2 before EPO calibration, no spectral overlaps are observed between the wet and dry samples,and spectra of the wet sample cluster in spaces free from those of the dry sample(mutual independent space). However,after EPO calibration of Subset S2 set,the spectra of the wet sample appear almost in the same positions as those of the dry sample do within the eigen space,demonstrating that the two groups of spectra are highly similar;(3)Before EPO calibration,the partial least squares regression model is the poorest in prediction accuracy(the validation RPD=1.16). EPO calibration has improved prediction accuracy of the model up to an acceptable level(the validation RPD=1.76). And EPO-support vector machine regression model performs better than the other two with validation R2 reaching 0.78,and RPD = 2.15,which indicates that the effects of soil moisture content on spectra are successfully eliminated.Conclusion]In the future,this approach will facilitate rapid measurement of soil organic matter for this study area. |
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| Keywords: | Soil spectra Soil organic matter Moisture content External parameter orthogonalization Support vector machine regression Jianghan Plain |
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