| 不同利用方式下土壤有机质和全磷的可见近红外高光谱反演 |
| |
| 引用本文： | 薛利红,周鼎浩,李 颖,杨林章.不同利用方式下土壤有机质和全磷的可见近红外高光谱反演[J].土壤学报,2014,51(5):993-1002. |
| |
| 作者姓名： | 薛利红 周鼎浩 李 颖 杨林章 |
| |
| 作者单位： | 中国科学院南京土壤研究所,江苏省农业科学院,中国科学院南京土壤研究所,江苏省农业科学院 |
| |
| 基金项目： | 国家自然科学基金(40901104和41171235)和中国科学院知识创新工程重要方向项目(KZCX2-YW-QN406)。 |
| |
| 摘 要： | 以太湖流域直湖港小流域稻田、桃园和菜地的土壤样本为研究对象,研究了不同光谱建模方法和土地利用方式对土壤有机质和全磷高光谱反演的影响。结果表明:(1)偏最小二乘回归分析(Partial least squarer egression,PLSR)模型的建模和预测精度较高且稳定;人工神经网络中广义回归神经网络(Generalized regression neural network,GRNN)网络预测精度较高但易出现过拟合现象,反向传播神经网络(Back propagation neural network,BPNN)网络比较稳健但精度略低;偏最小二乘与人工神经网络相结合则可综合两者优点,改善复杂样本下的预测精度。(2)土壤有机质的光谱反演结果优于全磷。3种土地利用方式中,稻田的预测效果总体优于桃园和菜地。在当前研究区域内土地利用方式对土壤有机质光谱反演影响不大,但对全磷反演影响较大。今后利用光谱对土壤全磷反演时需分土地利用方式对模型进行校准。
|
| 关 键 词： | 高光谱 偏最小二乘回归 人工神经网络 土壤有机质 土壤全磷 土地利用方式 |
| 收稿时间： | 2013/3/28 0:00:00 |
| 修稿时间： | 2013/12/31 0:00:00 |
Prediction of soil organic matter and total phosphorus with vis-nir hyperspectral inversion relative to land use |
| |
| Xue Lihong,Zhou Dinghao,Li Ying and Yang Linzhang.Prediction of soil organic matter and total phosphorus with vis-nir hyperspectral inversion relative to land use[J].Acta Pedologica Sinica,2014,51(5):993-1002. |
| |
| Authors: | Xue Lihong Zhou Dinghao Li Ying and Yang Linzhang |
| |
| Institution: | Institute of Soil Science,Chinese Academy of Sciences,Jiangsu Academy of Agricultural Sciences,Institute of Soil Science,Chinese Academy of Sciences,Jiangsu Academy of Agricultural Sciences |
| |
| Abstract: | Effects of spectral modeling methods and land use patterns were explored on hyperspectral inversion of soil organic matter (SOM) and total phosphorus(TP) in soil samples collected from paddy fields, peach orchards and vegetable gardens in the Zhihugang catchment, Taihu Lake Region. Results show that the PLSR (Partial Least Square Regression) model was quite high and stable in modeling and prediction precision; the GRNN (General Regression Neural Network) of ANN (Artificial Neural Network) was also quite high in prediction precision, but prone to overfitting; the BPNN (Back Propagaton Neural Network) was relatively stable, but slightly low in precision; and the combined PLSR-ANN model improved in prediction precision by combining the advantages of the two in handling complicated samples. The spectral inversion of SOM was better than that of TP, and among the three patterns of land use, paddy fields were fitter than the other two for use of the models in prediction of SOM and TP. In the current study zone, patterns of land use did not have much effect on spectral inversion of SOM, but did much on that of soil TP. It is, therefore, essential to calibrate the models in light of land use patterns in conducting spectral inversion of soil TP. |
| |
| Keywords: | Hyperspectrum PLSR(Partial least square regression) ANN(Artificial neural network) Soil organic matter (SOM) Total phosphorus (TP) Land uses |
| 本文献已被 CNKI 等数据库收录！ |
| 点击此处可从《土壤学报》浏览原始摘要信息 |
| 点击此处可从《土壤学报》下载免费的PDF全文 |
|
