TIAN Anhong,FU Chengbiao,XIONG Heigang,et al.Hyperspectral Estimation of Na+ Ion in Saline Soils in Areas With Different Human Activities Using BPNN Model[J].Research of Soil and Water Conservation,2020,27(02):364-369.
BPNN对不同人为活动区域的盐渍土Na+高光谱估测
- Title:
- Hyperspectral Estimation of Na+ Ion in Saline Soils in Areas With Different Human Activities Using BPNN Model
- 文章编号:
- 1005-3409(2020)02-0364-06
- 分类号:
- S151.9
- 文献标志码:
- A
- 摘要:
- 土壤盐分阳离子Na+在盐渍土的形成过程中起着重要作用,以新疆无人为活动(A区)和有人为活动(B区)区域的土壤为研究对象,采集野外高光谱和土壤0—20 cm样本,化验Na+含量,利用BP神经网络(BPNN)、偏最小二乘(PLSR)和逐步多元回归(SMLR)模型对比分析Na+的高光谱估测,并力图解释Na+在不同人为活动区域的估算精度机理。结果表明:Na+在A区和B区的4种阳离子(Ca2+,Mg2+,K+,Na+)中,所占比例最高分别为48.4%和62.3%,均值最大分别为1.590,2.148。对原始(R)和倒数(1/R)两种光谱变换进行一阶与二阶微分预处理,提取出相关系数通过0.05检验的波段为特征波段,3种建模方法在两个研究区域中共有24种模型,且1/R在二阶微分处的BPNN模型均是A区和B区的最佳预测模型,分别迭代19次和9次时精度满足要求。相对分析误差RPD、决定系数R2和均方根误差RMSE在A区分别为2.461 6,0.860 9,0.350 1,在B区分别为2.169 8,0.800 6,0.803 5。BPNN对Na+离子的预测能力很好,PLSR的预测能力一般,SMLR的预测能力很差。研究成果可为改良干旱区的盐渍化土壤提供科学依据。
- Abstract:
- Cation Na+ of soil salinity plays an important role in the formation of saline soil. The soils in no human activity(Zone A)and human activity(Zone B)of Xinjiang were studied. Field hyperspectral data and 0—20 cm soil samples were collected, and Na+ contents were tested. BP neural network(BPNN), partial least squares(PLSR)and stepwise multiple regression(SMLR)models were used to compare and analyze the hyperspectral estimation of Na+, and to explain the mechanism of Na+ estimation accuracy in areas with different human activities. The results show that the Na+ has the highest proportion and the largest mean value among the four cations(Ca2+, Mg2+, K+, Na+), the proportion of Na+ in Zone A and B is 48.429% and 62.274%, respectively, and the mean value is 1.590 and 2.148, respectively. First-order and second-order differential processing were been performed on the Original(R)and reciprocal(1/R)spectral transforms, and the band whose correlation coefficient was checked by 0.05 was extracted as the characteristic band. There are 24 models in Zone A and B for the three modeling methods, and the BPNN models with 1/R at the second-order differential are the best prediction models for Zone A and B, which meet the accuracy requirements when they are iterated by 19 times and 9 times, respectively. The relative analysis error RPD, the decision coefficient R2, and the root mean square error RMSE are 2.461 6, 0.860 9, and 0.350 1 in Zone A, and 2.169 8, 0.800 6, and 0.803 5 in Zone B, respectively. The prediction ability of BPNN for Na+ ions is very good, the prediction ability of PLSR is general, and the prediction ability of SMLR is very poor. These research results can provide a scientific basis for improving salinized soil in arid regions.
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相似文献/References:
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备注/Memo
收稿日期:2019-09-22 修回日期:2019-10-12 资助项目:国家自然科学基金青年项目(41901065); 国家自然科学基金面上项目(41671198); 校级项目(2019JZ001) 第一作者:田安红(1984—),女(土家族),贵州安顺人,硕士,副教授,主要从事干旱区盐渍土的高光谱研究。E-mail:tianfucb@163.com 通信作者:付承彪(1982—),男,云南宣威人,硕士,讲师,主要从事高光谱遥感图像研究。E-mail:fucb305@163.com