首页|基于高光谱的山区耕地土壤有机质含量估测

基于高光谱的山区耕地土壤有机质含量估测

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  • 万方数据
  • 维普
以贵州省典型山区耕地土壤高光谱数据为研究对象,基于光谱变换法和机器学习原理构建贵州省山区耕地土壤有机质(SOM)含量估算模型.于 2020 年8 月至2021 年3 月在贵州省13 个县(区、市)采集了120 个土壤样品,检测土壤可见光-近红外波段光谱信息,利用 5 种光谱数据变换(原始光谱、一阶微分、二阶微分、倒数对数的一阶微分、连续统去除)和 4 类模型(偏最小二乘回归、支持向量机、随机森林和BP 神经网络)组合出不同土壤有机质含量的预测模型,按照3 ∶ 1 选择训练样本和测试样本以估算山区SOM含量.结果表明,一阶微分数据变换与山区SOM含量的相关性较高,相关系数最高达到-0.635;反演模型中,基于一阶微分光谱变换构建的BP神经网络模型精度最高,训练集、测试集的决定系数(R2)分别为 0.845、0.838,测试集均方根误差(RMSE)为 3.452,相对分析误差(RPD)达到2.470,其次是RF、PLSR模型的RPD较高,SVM模型的RPD最低.光谱数据变换中一阶微分法能极大程度提取出山区耕地的SOM含量信息,BP神经网络模型是估算山区SOM含量的最优模型,本研究结果可为贵州省山区耕地土壤肥力的监测以及农业生产提供理论参考.
Estimation of soil organic matter content in mountain farmland based on hyperspectral data
Taking the hyperspectral data of cultivated land in typical mountainous areas of Guizhou province as the research object,a model for estimating soil organic matter(SOM)content in mountainous areas of Guizhou province was established by using spectral transformation method and machine learning.From August 2020 to March 2021,120 soil samples were collected from 13 counties and cities of Guizhou province,and the visible near-infrared spectral information of soil was detected.Five spectral data trans-formations(original spectra,first-order differential,sec-ond-order differential,first-order differential of reciprocal logarithm,continuum removal)and four types of models(partial least squares regression,support vector machine,random forest and BP neural network)were used to combine different soil organic matter content prediction models.The training samples and test samples were selected according to the ratio of 3 ∶ 1 to estimate the SOM content in mountain area.The correlation between the first-order differential data transformation and the SOM content in mountain area was high,and the highest correlation coefficient was-0.635.In the inversion model,the BP neural network model based on the first-order differential spectral transformation had the highest accuracy.The determination coefficients(R2)of the training set and the test set were 0.845 and 0.838,respectively.The root mean square error(RMSE)of the test set was 3.452.The relative a-nalysis error(RPD)reached 2.470,followed by RF,PLSR and SVM.The first-order differential method in spectral data transformation could greatly extract the SOM content information of mountain cultivated land.The BP neural network model was the optimal model for estimating the SOM content in mountain areas.The results of this study can provide theoretical reference for the monitoring of soil fertility and agricultural production in mountainous areas of Guizhou province.

soil organic matterhyperspectralmountainous farmlandfirst-order differentialBP neural network

张永亮、汪泓、肖玖军、李可相、王宇、邢丹

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贵州大学矿业学院,贵州 贵阳 550025

贵州省山地资源研究所,贵州 贵阳 550001

贵州省土地绿色整治工程研究中心,贵州 贵阳 550001

贵州省农业科学院辣椒研究所,贵州 贵阳 550009

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土壤有机质 高光谱 山区耕地 一阶微分 BP神经网络

贵州科学院青年基金项目贵州省科技支撑计划项目贵州省科技支撑计划项目国家重点研发计划项目贵州省基础研究计划项目贵州省基础研究计划项目

黔科院J字201825号黔科合支撑20201Y172号黔科合支撑2021一般496号2022YDF1100307黔科合基础-ZK2021一般100号黔科合基础-ZK2022一般276号

2024

10.3969/j.issn.1000-4440.2024.01.012

江苏农业学报
江苏省农业科学院

江苏农业学报

CSTPCD北大核心
影响因子：1.093
ISSN：1000-4440
年,卷(期)：2024.40(1)
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