基于Munsell颜色的土壤游离铁预测研究
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中图分类号:

S151.9+5

基金项目:

国家自然科学基金项目(41671218)和国家科技基础性工作专项项目(2014FY110200A12)资助


Prediction of Soil Free Iron Oxide Content Based on Soils Munsell Color
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Fund Project:

National Natural Science Foundation of China(No. 41671218)and Basic Work of the Ministry of Science and Technology of China(No. 2014FY110200A12)

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    摘要:

    土壤颜色和游离铁均是土壤系统分类中的指标,两者之间具有一定的关系,但迄今关于两者之间定量关系的报道甚少。以川中丘陵区典型土系为研究对象,通过定量分析其Munsell颜色与游离铁含量间的关系,尝试利用Munsell颜色建立的BP神经网络模型反演土壤游离铁含量,并与利用反射光谱模型反演的土壤游离铁含量进行比较。结果表明,Munsell色调、明度、彩度值与游离铁含量呈显著正相关,据此建立的游离铁含量Munsell颜色预测模型在单隐含层神经元个数为4时,R2为0.94,RMSE为4.20,RPD为4.37;光谱模型的R2为0.98,RMSE为3.35,RPD为5.99,两者的模型拟合度、精度均呈较高水平,表明利用Munsell颜色可以有效地对土壤游离铁含量进行预测。

    Abstract:

    [Objective] Soil iron oxides are found in almost all the types of soils and are good indicators reflecting variation of the environment thanks to their high variability in concentration. Soil iron oxides, mostly in the form of free iron, function as important mineral binders in the soil and have a significant effect on color of the soil. Soil color is an important soil property, described by Munsell color space, in the soil taxonomy. Both soil color and free iron are indicators in the soil taxonomy, and fairly related to each other, but so far few papers have been reported on quantitative relationships between them. [Method] Since soil color is an indicator reflecting genesis and evolution of a soil, it is often used to invert and predict soil properties via modeling. Therefore, in this paper, the typical soil series in the hilly region of Central Sichuan were taken as the research object for analysis of relationships between soil Munsell color and free iron content in the soils. On this basis, a BP neural network model is established to explore differences between the Munsell color based model and the traditional spectral model in predicting soil free iron content. [Result] Results show significantly positive relationships of free iron content with hue, value and chroma of soil Munsell color. When the Munsell color prediction model had 4 neurons in the single hidden layer, the determination coefficient R2 of its test set was 0.94, its standard deviation RMSE 4.20, and its relative analysis error RPD 4.37; When the spectral prediction model had 6 neurons in the single hidden layer, its R2 was 0.98, RMSE 3.35, and RPD 5.99. Both models have demonstrated a high level of goodness of fit and accuracy, though the spectral model is slightly higher than the color model. [Conclusion] Munsell color can be used to predict soil free iron content effectively, but by comparison, the spectral model is a bit higher in goodness of fit and prediction accuracy, which may be attributed to the fewer neurons in the input layer of the color model and the dispersion degree of free iron oxides dispersion degree. Color information is easy to obtain, for some historical soil literature, which do have color data, but lack the data of free iron contents, the Munsell color-based prediction model can be used to figure out an approximate content of free iron in the soil.

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余星兴,袁大刚,陈剑科,翁倩,付宏阳,黄宇潇.基于Munsell颜色的土壤游离铁预测研究[J].土壤学报,2021,58(5):1322-1329. DOI:10.11766/trxb202004130048 YU Xingxing, YUAN Dagang, CHEN Jianke, WENG Qian, FU Hongyang, HUANG Yuxiao. Prediction of Soil Free Iron Oxide Content Based on Soils Munsell Color[J]. Acta Pedologica Sinica,2021,58(5):1322-1329.

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  • 收稿日期:2020-04-13
  • 最后修改日期:2020-09-04
  • 录用日期:2020-10-18
  • 在线发布日期: 2020-12-08
  • 出版日期: 2021-09-11
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