| 基于高光谱的不同人类干扰程度下荒漠土壤有机质含量估算模型 |
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| 引用本文： | 郑曼迪,熊黑钢,乔娟峰,刘靖朝.基于高光谱的不同人类干扰程度下荒漠土壤有机质含量估算模型[J].干旱区地理,2018,41(2):384-392. |
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| 作者姓名： | 郑曼迪 熊黑钢 乔娟峰 刘靖朝 |
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| 作者单位： | 1. 新疆大学资源与环境科学学院, 教育部绿洲生态重点实验室, 新疆 乌鲁木齐 830046;
2. 北京联合大学应用文理学院城市系, 北京 100083 |
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| 基金项目： | 国家自然科学基金（41671198） |
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| 摘 要： | 为对比同一背景下不同人类干扰程度的荒漠土壤有机质含量的预测模型，以天山北麓阜康市的土壤为研究对象，通过对无人干扰区、人为干扰区全样本和剔除有机质质量分数大于2%的样点的原始光谱反射率进行6种光谱变换，分析不同变换形式与有机质含量的相关性，以相关系数通过P=0.01和0.05水平上显著性检验的敏感波段为自变量，运用多元逐步回归、偏最小二乘回归以及主成分回归法分别建立了无人干扰区、人为干扰区土壤有机质高光谱的预测模型，并选择精度最高的为最优模型。结果表明：（1）无人干扰区与人为干扰区的原始光谱所有波段与有机质含量的相关性都没有通过0.01水平的显著性检验。将有机质质量分数大于2%的样点剔除后，有机质含量与原始光谱反射率的相关系数都大于全样本且有部分波段通过了0.01水平的显著性检验。（2）不论采用何种方法建立的全样本无人干扰区和人为干扰区的预测有机质模型的RPD均小于1.4，不具有预测有机质含量的能力。其中全样本无人干扰区一阶微分、人为干扰区倒数一阶微分多元逐步回归模型是其所有模型中，建模精度最高的，R2分别为0.652、0.512，但是其RPD仅分别是0.662、0.655，表明模型的预测能力很差。（3）剔除有机质质量分数大于2%的样点之后，预测效果最好的是无人干扰区一阶微分多元逐步回归模型，R2达到0.776，RMSE为1.408，RPD为2.136；而人为干扰区的二阶微分模型预测效果最优，R2为0.542，RMSE为2.261，RPD为2.087。
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| 关 键 词： | 干扰程度 荒漠土 有机质 估算模型 |
| 收稿时间： | 2017-11-28 |
Hyperspectral based estimation model about organic matter in desert soil at different levels of human disturbance |
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| ZHENG Man-di,XIONG Hei-gang,QIAO Juan-feng,LIU Jing-chao.Hyperspectral based estimation model about organic matter in desert soil at different levels of human disturbance[J].Arid Land Geography,2018,41(2):384-392. |
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| Authors: | ZHENG Man-di XIONG Hei-gang QIAO Juan-feng LIU Jing-chao |
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| Affiliation: | 1. College of Resource and Environment Science, Xinjiang University, Urumqi 830046, Xinjiang, China;
2. College of Art and Science, Beijing Union University, Beijing 100083, China |
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| Abstract: | In order to compare the estimation models about the organic matter in desert soil at different levels of human disturbance under the same background,this study took the soil in Fukang City,which is at the north of Tianshan Mountains,as the study object and established the hyperspectral-based soil organic matter (SOM) estimation models for soils in the undisturbed area and in the human-disturbed area respectively using the multiple stepwise regression,partial least squares regression and principal component regression method after 6 spectral transformations of the original spectral reflectance.The correlation between the transformation and the SOM was analyzed using the sensitive bands as the independent variables which had passed the P=0.01 and 0.05 level of significance test.The results indicated as follows:(1) All the correlation coefficients between the original spectral bands and the SOM in both the undisturbed area and human-disturbed area did not pass the 0.01 level of significance test.After removing the samples with the mass fraction being greater than 2%,the correlation coefficients were bigger than those concerning all samples and a portion of the bands had passed the 0.01 level of significance test. (2) Regardless of the method in establishing the SOM estimation model for the full sample size in undisturbed area or in human-disturbed area,the RPD of the models were less than 1.4 which indicated they were not capable of predicting organic content.The first order differential model for all samples in the undisturbed area,and the first order differential multiple stepwise regression model of the reciprocals in the human-disturbed area had the highest precision among all the models with R2 being 0.652 and 0.512 respectively.(3) After removing the samples whose mass fraction were greater than 2%,for the undisturbed area the best estimation model was the first order differential multiple stepwise regression model with R2 being 0.776,the RMSE being 1.408,and RPD,2.136; for the human-disturbed area,the second-order differential model was optimal with R2 being 0.542,the RMSE being 2.261 and RPD,2.087. |
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| Keywords: | level of human disturbance desert soil organic matter estimation models |
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