基于高光谱的土壤有机质快速检测模型构建  

Construction of Soil Organic Matter Rapid Detection Model Based on Hyperspectral

作　　者：李雪 范仲卿 高涵 张新宇 东野圣萍 洪丕征 王坤 柳平增[6] 杜昌文[7] 李新举[1] 丁方军 LI Xue;FAN Zhong-qing;GAO Han;ZHANG Xin-yu;DONGYE Sheng-ping;HONG Pi-zheng;WANG Kun;LIU Ping-zeng;DU Chang-wen;LI Xin-ju;DING Fang-jun(College of Resources and Environment/Shandong Agricultural University,Tai’an 271018,China;Shandong Agricultural University Fertilizer Science and Technology Co.Ltd.,Tai’an 271000,China;Key Laboratory of Humic Acid Fertilizer,Ministry of Agriculture,Tai’an 271018,China;Taishan Institute of Technology/Shandong University of Science and Technology,Tai’an 271000,China;Liaoning Normal University,Dalian 116000,China;Shandong Agricultural University,Tai’an 271018,China;Nanjing Institute of Soil Science,Chinese Academy of Sciences,Nanjing 210000,China)

机构地区：[1]山东农业大学资源与环境学院,山东泰安271018 [2]山东农大肥业科技有限公司,山东泰安271000 [3]农业部腐植酸类肥料重点实验室,山东泰安271018 [4]山东科技大学泰山科技学院,山东泰安271000 [5]辽宁师范大学,辽宁大连116000 [6]山东农业大学,山东泰安271018 [7]中国科学院南京土壤研究所,江苏南京210000

出　　处：《山东农业大学学报（自然科学版）》2021年第5期833-839,共7页Journal of Shandong Agricultural University：Natural Science Edition

基　　金：国家自然基金:高潜水位煤矿沉陷区土壤生态变化过程及碳循环机理研究(42077446);山东省重大科技创新工程项目:基于作物提质增效的农业种植精准管理智能服务平台开发与产业化应用(2019JZZY010713)。

摘　　要：土壤有机质作为土壤肥力的重要指标。为实现对农田土壤有机质含量的快速获取,以山东省青岛市平度地区农田116个土壤样本为试验材料,利用ASD Field4地物光谱仪获取土壤光谱反射率,分析农田土壤的光谱反射特征,研究光谱反射率与定量化学方法测定有机质含量的相关关系,构建土壤有机质快速检测模型。所得高光谱数据结合(Savitzky-golay,SG)平滑算法,原始光谱曲线的一阶微分、对数的倒数和对数的倒数一阶微分3种变换方式对光谱数据进行预处理,通过相关系数法选取土壤有机质含量的敏感波段,分别建立多元线性回归(MLR)、BP神经网络(BPNN)和偏最小二乘回归(PLSR)模型,并对模型精度进行验证。结果表明,建立的MLR、BPNN和PLSR回归模型中,以BPNN模型精度最优,其建模样本集R2为0.7362,RMSE为4.6005,RPD为1.8550;验证集模型的R2为0.8086,RMSE为3.7772,RPD为2.2630。Soil organic matter is an important index of soil fertility. In order to obtain farmland soil organic matter quickly,116 soil samples from Pingdu area of Qingdao City, Shandong Province were used as experimental materials. The spectral reflectance of soil was obtained by field4 spectrometer, and the spectral reflectance characteristics of farmland soil were analyzed. The correlation between spectral reflectance and quantitative chemical method to determine the content of organic matter was studied, and the rapid detection model of soil organic matter was constructed. The hyperspectral data were pretreated with savitzky Golay(SG) smoothing algorithm, first-order differential, reciprocal logarithm and reciprocal logarithm first-order differential of the original spectral curve. The sensitive bands of soil organic matter content were selected by correlation coefficient method, and multiple linear regression(MLR), BP neural network(BPNN) and partial least squares(PLS) were established respectively Regression(PLSR) model was used to verify the accuracy of the model.The results show that among the MLR, BPNN and PLSR regression models, BPNN model has the best accuracy, with R2 of0.7362, RMSE of 4.6005 and RPD of 1.8550 for the sample set, and R2 of 0.8086, RMSE of 3.7772 and RPD of 2.2630 for the validation set.

关 键 词：高光谱 土壤有机质 检测模型 

分 类 号：S151.95[农业科学—土壤学]