| 基于高光谱的土壤养分含量反演模型研究 |
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| 引用本文： | 陶培峰,王建华,李志忠,周萍,杨佳佳,高樊琦.基于高光谱的土壤养分含量反演模型研究[J].地质与资源,2020,29(1):68-75,84. |
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| 作者姓名： | 陶培峰 王建华 李志忠 周萍 杨佳佳 高樊琦 |
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| 作者单位： | 1. 中国地质大学 地球科学与资源学院, 北京 100083;2. 国际黑土地协会, 辽宁 沈阳 110034;3. 中国地质调查局 沈阳地质调查中心, 辽宁 沈阳 110034;4. 中国科学院遥感与数字地球研究所, 北京 100094;5. 华中师范大学 城市与环境科学学院, 湖北 武汉 430079 |
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| 基金项目： | 国际地球科学计划项目“全球黑土地关键带土地资源演化与可持续利用”（IGCP 665）;省部级科研项目“兴凯湖平原及松辽平原西部土地质量地球化学调查”（DD20190520) |
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| 摘 要： | 为实现土壤养分（有机质SOM、全氮TN、全磷TP、全硫TS）含量的快速测定，以建三江创业农场为例，对土壤原始反射率进行了一阶微分（FD）、倒数对数（RL）、倒数一阶微分（FDR）、多元散射校正（MSC）和连续统去除（CR）变换，分析6种光谱变量与土壤养分的相关性，将在α=0.01水平上显著相关的波段作为特征波段，运用多元逐步回归（SMLR）、偏最小二乘回归（PLSR）和BP神经网络（BPNN）三种分析方法分别建立有机质、全氮、全磷和全硫的高光谱预测模型，并利用决定系数（R2）、均方根误差（RMSE）和相对分析误差（RPD）对预测模型进行评价.结果显示，PLSR和BPNN建立的土壤养分含量预测模型均优于SMLR，能极好地预测有机质和全氮含量，同时具有粗略估算全硫含量的能力.三种方法中仅有CR-BPNN能对全磷含量进行粗略估算.对有机质、全氮、全磷和全硫预测效果最佳的模型及其验证集决定系数分别为：MSC-PLSR （0.86）、MSC-PLSR （0.75）、CR-BPNN （0.56）、FDR-BPNN （0.67）.
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| 关 键 词： | 土壤养分 高光谱 多元逐步回归 偏最小二乘 BP神经网络 反演模型 |
| 收稿时间： | 2019-08-06 |
RESEARCH OF SOIL NUTRIENT CONTENT INVERSION MODEL BASED ON HYPERSPECTRAL DATA |
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| TAO Pei-feng,WANG Jian-hua,LI Zhi-zhong,ZHOU Ping,YANG Jia-jia,GAO Fan-qi.RESEARCH OF SOIL NUTRIENT CONTENT INVERSION MODEL BASED ON HYPERSPECTRAL DATA[J].Geology and Resources,2020,29(1):68-75,84. |
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| Authors: | TAO Pei-feng WANG Jian-hua LI Zhi-zhong ZHOU Ping YANG Jia-jia GAO Fan-qi |
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| Institution: | 1. School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, China;2. International Black Soils Society, Shenyang 110034, China;3. Shenyang Center of Geological Survey, CGS, Shenyang 110034, China;4. Institute of Remote Sensing and Digital Earth, CAS, Beijing 100094;5. School of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China |
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| Abstract: | In order to quickly test the soil nutrient contents(SOM,TN,TP and TS),the authors collect 117 soil samples at 0-20 cm depth from Chuangye Farm in Jiansanjiang reclamation area as research objects.First derivative(FD),logarithmic reciprocal(RL),first derivative of reciprocal(FDR),multivariate scattering correction(MSC)and continuum removal(CR)transformations are performed on the raw spectral reflectance(R).By analyzing the correlation between the six spectral variables and soil nutrient content,the bands that are significantly correlated at theα=0.01 level are adopted as characteristic bands,and the methods of stepwise multiple linear regression(SMLR),partial least squares regression(PLSR)and back propagation neural network(BPNN)are used respectively to establish hyperspectral prediction model of SOM,TN,TP and TS.The model is evaluated by R2,RMSE and RPD.The results show that the soil nutrient content prediction models established by PLSR and BPNN are superior to that by SMLR.The PLSR and BPNN methods can well predict the organic matter and total nitrogen content,and roughly estimate the total sulfur content.Only the CR-BPNN method can roughly estimate the total phosphorus content.The models with the best prediction effect on SOM,TN,TP and TS are,respectively,MSC-PLSR,MSC-PLSR,CR-BPNN and FDR-BPNN,with the validation set determination coefficients of 0.86,0.75,0.56 and 0.67 respectively. |
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| Keywords: | soil nutrient hyperspectral stepwise multiple linear regression (SMLR) partial least squares regression (PLSR) back propagation neural network (BPNN) inversion model |
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