Hyperspectral Inversion of Soil Heavy Metal Content in Anshan-style Iron Tailings Area
-
摘要:
土壤中的铜浓度占比过高会对人体和环境造成一定的危害，因此探讨重金属铜含量反演具有重要意义。本研究以唐山典型铁尾矿区43个土壤样品为例，同时测定土壤中铜的反射光谱和含量信息，经多种波谱变换后，通过相关性分析法（CA）和连续投影法（SPA）进行土壤铜含量的特征波段选取，然后利用多元线性回归(MLR)和偏最小二乘回归(PLSR)算法建立了铜含量的反演模型，得到多种光谱数据的反演成效。结果显示：二阶微分处理后的光谱数据，其反演效果较好，CA-PLSR和SPA-MLR两种反演模型中，SPA-MLR的反演精度相对较准确；二阶微分光谱变换后的SPA-MLR模型在估算土壤铜含量方面更有优势。
Abstract:Excessive copper concentration in the soil will cause certain harm to human body and the environment, so it is of great significance to explore the inversion of heavy metal copper content. In this study, 43 soil samples in the typical iron tailings area of Tangshan were taken as examples, and the reflectance spectrum and content information of copper in the soil were measured at the same time. After a variety of spectral transformations, the correlation analysis method (CA) and the continuous projection method (SPA) were carried out. The characteristic wavebands of soil copper content were selected, and then the inversion model of copper content was established using multiple linear regression (MLR) and partial least square regression (PLSR) algorithms, and the inversion results of various spectral data were obtained. The results show that the spectrum data after the second-order differential processing has the best inversion effect. Among the two inversion models of CA-PLSR and SPA-MLR, the inversion accuracy of SPA-MLR is relatively accurate; after the second-order differential spectrum transformation te SPA-MLR model has more advantages in estimating soil copper content.
-
Key words:
- Hyperspectral /
- Copper /
- Characteristic band /
- Inversion
-
-
表 1 铁尾矿区尾砂土壤重金属铜含量分析/（mg·kg-1）
Table 1. Analysis of heavy metal copper content in tailing soil of iron tailings area
样本 均值 最大值 最小值 标准差 变异系数 Cu 11.80 25.05 4.49 4.07 34% 
下载: 导出CSV
表 2 土壤变异系数等级分类
Table 2. Classification of soil coefficient of variation
系数区间 等级 0 ~15% 小 16% ~ 35% 中 ＞36% 高
下载: 导出CSV
表 3 土壤铜含量CA-PLSR模型结果
Table 3. Results of CA-PLSA model for soil copper content
反射率类型 RC RMSEC RP RMSEP RPD R 0.17 2.94 0.13 5.66 1.07 FD 0.61 2.02 0.33 4.98 1.22 SD 0.94 0.75 0.68 3.41 1.78 CR 0.45 2.39 0.13 5.65 1.07
下载: 导出CSV
表 4 土壤铜含量SPA-MLR模型结果
Table 4. Results of SPA-MLR model for soil copper content
反射率类型 RC RMSEC RP RMSEP RPD R 0.09 3.18 0.01 5.98 1.01 FD 0.98 1.04 0.68 3.32 1.83 SD 0.99 0.56 0.85 2.30 2.63 CR 0.99 0.93 0.75 2.93 2.07
下载: 导出CSV
-
[1] 宋文, 成少平, 迟晓杰, 等. 重金属污染土壤修复遥感监测研究进展[J]. 矿产综合利用, 2021(4):21-28. SONG W, CHENG S P, CHI X J, et al. Research progress on remediation of heavy metal contaminated soil monitored by remote sensing[J]. Multipurpose Utilization of Mineral Resources, 2021(4):21-28. doi: 10.3969/j.issn.1000-6532.2021.04.004
[2] 杨帆, 郝志红, 张舜尧, 等. 长江武汉段冲积土壤中重金属的环境地球化学特征[J]. 地质学报, 2016, 90(8):1955-1964. YANG F, HAO Z H, ZHANG S Y, et al. Environmental geochemistry of heavy metals in alluvial soils of Yangtze River in Wuhan City[J]. Acta Geologica Sinica, 2016, 90(8):1955-1964. doi: 10.3969/j.issn.0001-5717.2016.08.023
[3] 黄善洛, 金妍, 郭慧, 等. 基于Android平台的便携式痕量重金属离子检测仪的研制[J]. 分析化学, 2015, 43(7):1098-1103. HUANG S L, JIN Y, GUO H, et al. Development of portable trace heavy metal ion detector based on Android platform[J]. Chinese Journal of Analytical Chemistry, 2015, 43(7):1098-1103. doi: 10.11895/j.issn.0253-3820.150211
[4] 余克强, 赵艳茹, 刘飞, 等. 激光诱导击穿光谱技术检测土壤中的铅和镉含量[J]. 农业工程学报, 2016, 32(15):197-203. YU K Q, ZHAO Y R, LIU F, et al. Determination of lead and cadmium in soil by laser induced breakdown spectroscopy[J]. Journal of Agricultural Engineering, 2016, 32(15):197-203. doi: 10.11975/j.issn.1002-6819.2016.15.027
[5] 徐国栋, 葛建华, 杜谷, 等. 成都市中心城区地表沉积物中重金属分布及矿物学特征[J]. 岩矿测试, 2019, 38(4):418-428. XU G D, GE J H, DU G, et al. The heavy metal distribution and mineralogical characteristics of surface sediments in Chengdu[J]. Rock and Mineral Analysis, 2019, 38(4):418-428. doi: 10.15898/j.cnki.11-2131/td.201811100120
[6] 汪金花, 曹兰杰, 白洋, 等. 铁尾矿粒径和湿度因子对高光谱特征参量影响[J]. 矿产综合利用, 2019(2):128-133. WANG J H, CAO L J, BAI Y, et al. Influence of iron tailings' particle size and humidity factor on hyperspectral characteristic parameters[J]. Multipurpose Utilization of Mineral Resources, 2019(2):128-133. doi: 10.3969/j.issn.1000-6532.2019.02.027
[7] 杨晓峰, 苏兴强, 张廷东. 鞍山铁尾矿特性及综合利用前景[J]. 矿业工程, 2008(5):47-49. YANG X F, SU X Q, ZHANG T D. Features of Anshan iron ore tailings and future of their comprehensive utilization[J]. Modern Mining, 2008(5):47-49. doi: 10.3969/j.issn.1671-8550.2008.05.019
[8] ROSSEL R A V, MCGLYNN R N, MCBRATNEY A B. Determining the composition of mineral-organic mixes using UV-vis-NIR diffuse reflectance spectroscopy[J]. Geoderma, 2006, 137:70-82. doi: 10.1016/j.geoderma.2006.07.004
[9] Eunyoung Choe, Freek van der Meer, Frank van Ruitenbeek, Harald van der Werff, Boudewijn de Smeth, Kyoung-Woong Kim. Mapping of heavy metal pollution in stream sediments using combined geochemistry, field spectroscopy, and hyperspectral remote sensing: A case study of the Rodalquilar mining area, SE Spain[J]. Remote Sensing of Environment, 2008, 112(7):3222-3233.
-
图(3)
表(4)
计量
- 文章访问数: 652
- PDF下载数: 14
- 施引文献: 0