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遥感技术与应用  2021, Vol. 36 Issue (6): 1321-1328    DOI: 10.11873/j.issn.1004-0323.2021.6.1321
遥感应用     
一种基于分段偏最小二乘模型的土壤重金属遥感反演方法
尹芳1(),封凯2,吴朦朦2,拜得珍3(),王蕊3,周园园3,尹春涛2,尹翠景2,刘磊2
1.长安大学土地工程学院,陕西 西安 710054
2.长安大学地球科学与资源学院,陕西 西安 710054
3.青海省环境科学研究设计院有限公司,青海 西宁 810000
A Remote Sensing Estimation Method for Heavy Metals in Soil based on Piecewise Partial Least Squares Model
Fang Yin1(),Kai Feng2,Mengmeng Wu2,Dezhen Bai3(),Rui Wang3,Yuanyuan Zhou3,Chuntao Yin2,Cuijing Yin2,Lei Liu2
1.School of Land Engineering,Chang’an University,Xi’an 710054,China
2.School of Earth Sciences and Resources,Chang’an University,Xi’an 710054,China
3.Qinghai Research and Design institute of Environmental Sciences,Xining 810000,China
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摘要:

土壤中重金属由于其毒性而成为最有害的环境污染物之一,利用遥感进行土壤重金属检测和分布制图是目前最为高效的手段。采用哨兵二号(Sentinel-2)多光谱影像与实测样品光谱数据,对山西省铜矿峪铜矿尾矿库及其周边农田土壤的铜(Cu)含量进行估算,利用68个土壤样品的反射光谱,优选出适合土壤铜含量预测的波段,结合分段偏最小二乘法(Piecewise Partial Least Squares Regression,P-PLSR),对土壤铜含量进行估算,将模型用于Sentinel-2影像获得了Cu含量的空间分布。通过P-PLSR对实测样品光谱建模反演Cu含量的决定系数(R2)为0.89,预测偏差比(RPD)为2.82;利用Sentinel-2多光谱影像获得了该区域Cu元素含量空间分布,其Cu含量的估算精度R2为0.74,RPD为1.73,Cu含量高值区空间分布与尾矿库关系密切。Sentinel-2多光谱数据具有高空间分辨率(10、20和60 m)、高时间分辨率和幅宽大(290 km)等优势,通过敏感波段选择并建立反演模型,可实现大范围土壤环境制图。

关键词: 土壤重金属多光谱遥感分段偏最小二乘定量反演    
Abstract:

Heavy metals in soil are among the most harmful environmental pollutants due to their toxicity. Detecting and mapping the distribution of heavy metal using remote sensing technique is inexpensive and efficient. In this study, Sentinel-2 multispectral data and field spectroscopy were adopted to estimate soil copper (Cu) concentrations of the tailing reservoir of Tongkuangyu Copper deposit, Shanxi Province, China and the surrounding farmland soil. Sixty-eight soil samples were collected and their reflectance spectra were used to estimate Cu concentration in soil. Spectral index applicable to the prediction of Cu contents in soil was derived, united with piecewise partial least square regression (P-PLSR), the soil Cu contents were estimated. The coefficient of determination (R2) and residual prediction deviation (RPD) for the model developed using lab-measured spectra were 0.89 and 2.81. The model was applied to the Sentinel-2 multispectral data and the spatial distribution map of Cu content was predicted with relatively high R2 (0.83) and RPD (1.56). The result could facilitate the development of remediation strategies in terms of environmental protection. Sentinel-2 multispectral data, due to its high spatial resolution (10 m, 20 m and 60 m), and large swath width (290 km), could provide an alternative method for large-scale soil environment monitoring through reasonable selection of sensitive bands.

Key words: Soil heavy metals    Multi-spectral remote sensing    Piecewise partial least squares    Quantitative inversion
收稿日期: 2020-10-10 出版日期: 2022-01-26
ZTFLH:  X833  
基金资助: 青海省重大科技专项“湟水流域水—气—土一体化环境管理体系及污染控制关键技术集成与示范”(2018-SF-A4);国家自然科学基金项目(42071258);中央高校基本科研业务费专项资金(300102270204)
通讯作者: 拜得珍     E-mail: yinf@chd.edu.cn;282785316@qq.com
作者简介: 尹芳(1983-),女,河北河间人,博士,副教授,主要从事遥感与GIS应用研究。E?mail:yinf@chd.edu.cn
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尹芳
封凯
吴朦朦
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王蕊
周园园
尹春涛
尹翠景
刘磊

引用本文:

尹芳,封凯,吴朦朦,拜得珍,王蕊,周园园,尹春涛,尹翠景,刘磊. 一种基于分段偏最小二乘模型的土壤重金属遥感反演方法[J]. 遥感技术与应用, 2021, 36(6): 1321-1328.

Fang Yin,Kai Feng,Mengmeng Wu,Dezhen Bai,Rui Wang,Yuanyuan Zhou,Chuntao Yin,Cuijing Yin,Lei Liu. A Remote Sensing Estimation Method for Heavy Metals in Soil based on Piecewise Partial Least Squares Model. Remote Sensing Technology and Application, 2021, 36(6): 1321-1328.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2021.6.1321        http://www.rsta.ac.cn/CN/Y2021/V36/I6/1321

图1  2018年研究区Sentinel-2自然彩色合成影像(RGB432)和土壤样品分布
重金属最小值最大值均值标准差变异系数样品个数
全部28.40413.6383.2374.640.9068
建模集28.40302.8179.5363.290.8048
验证集28.60413.6394.3298.871.0520
表1  68件土壤样品中Cu含量的描述性统计信息(mg/kg)
图2  68个土壤样品经平滑处理后的实验室光谱和土壤样品实测光谱重采样至Sentinel-2影像光谱范围

特征波段(nm)

\相关性

原始光谱均值规格化光谱
实测Cu含量Cu含量对数实测Cu含量Cu含量对数
B2~4900.450.480.680.65
B3~5600.360.390.670.63
B7~783-0.09-0.08-0.71-0.79
B8~865-0.10-0.10-0.73-0.70
B12~2 1900.340.360.580.59
表2  光谱变量与Cu含量相关性分析结果
图3  土壤Cu含量反演结果
图4  研究区Sentinel-2遥感数据土壤Cu浓度反演结果图
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