排灌机械工程学报
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排灌机械工程学报  2018, Vol. 36 Issue (11): 1175-1179    DOI: 10.3969/j.issn.1674-8530.18.1160
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基于高光谱的区域土壤颗粒组成及有机质预测模型尺度转换
张娜1,2, 张红玲2, 张栋良1, 屈忠义1*
1.内蒙古农业大学水利与土木建筑工程学院, 内蒙古 呼和浩特 010018; 2.宁夏回族自治区水利科学研究院, 宁夏 银川 750021
Scale transformation of regional soil particle composition and organic matter prediction models based on hyper-spectrum
ZHANG Na1,2, ZHANG Hongling2, ZHANG Dongliang1,QU Zhongyi1*
1.Water conservancy and Civil Engineering College, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia 010018, China; 2.Ningxia Institute of Water Resources Research, Yinchuan, Ningxia 750021, China
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摘要 以内蒙古河套灌区3个尺度下1 024个样本的高光谱为模型输入,黏粒、粉粒、砂粒及有机质质量百分数为模型输出,通过多元回归、支持向量机及BP神经网络方法建立基于中尺度的反演模型,将其尺度上推至大尺度及下推至小尺度,并对其尺度转换的适用性进行评价.结果表明,基于中尺度建立的高光谱与土壤颗粒组成及有机质的反演模型均可以较好地应用于其他2个尺度:多元回归方法在其他2个尺度上的相关性为0.33~0.60,支持向量机方法为0.41~0.52,BP神经网络方法为0.52~0.72,其中BP神经网络方法建立的模型在其他2个尺度上具有更好的适用性;不同参数中,黏粒、粉粒、砂粒及有机质的相关系数分别为0.44~0.62,0.37~0.72,0.42~0.72及0.33~0.56,即颗粒组成的效果整体好于有机质质量百分数.
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张娜
张红玲
张栋良
屈忠义*
关键词河套灌区   颗粒组成   有机质   高光谱   支持向量机   BP神经网络   尺度转换     
Abstract: Mesoscale inversion models of mass percentage of clay, power, sand and organic matter were established by using multiple regression, support vector machine and BP neural network based on the hyper-spectrum of 1 024 samples in 3 scales in Hetao Irrigation District of Inner Mongolia. The mesoscale scale was shifted to large and small scales, and the applicability of scale transformation was assessed for these models. Results showed that the mesoscale inversion models of soil particle composition and organic matter were well applicable at the other two scales with correlation coefficient of 0.33-0.60 in multiple regression, 0.41-0.52 in support vector machine and 0.52-0.72 in BP neural network. Clearly, the models based on BP neural network method showed even better applicability at the other two scales. The correlation coefficients of clay, power, sand and organic matter were 0.44-0.62, 0.37-0.72, 0.42-0.72 and 0.33-0.56, respectively, suggesting the fitting effect of particle compositions was better than that that of organic matter as a whole.
Key wordsHetao Irrigation District   soil particles   organic matter   hyper-spectrum   support vector   BP neural network   scale transformation   
收稿日期: 2018-05-10;
基金资助:

国家自然科学基金资助项目(51069006)

引用本文:   
张娜,,张红玲等. 基于高光谱的区域土壤颗粒组成及有机质预测模型尺度转换[J]. 排灌机械工程学报, 2018, 36(11): 1175-1179.
ZHANG Na-,,ZHANG Hong-Ling- et al. Scale transformation of regional soil particle composition and organic matter prediction models based on hyper-spectrum[J]. Journal of Drainage and Irrigation Machinery Engin, 2018, 36(11): 1175-1179.
 
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