农业工程学报2024，Vol.40Issue(4)：121-128,8.DOI:10.11975/j.issn.1002-6819.202308082
基于波谱响应特征的雄安新区农田土壤重金属含量反演
Inversing heavy metal contents in farmland soil in Xiong'an New Area of China using spectral response characteristics
摘要
Abstract
Accurate monitoring of farmland environment and soil pollution is of great significance for food security and ecologically sustainable development in recent years.Taking Xiong'an New Area as the research area,this study aims to inverse the heavy metal content in farmland.Spectral response data of heavy metal content was extracted from multi-source remote sensing data Sentinel-2(L2A),Zhuhai-1(OHS),and field measurement.Pollution levels of heavy metal were evaluated in farmland soil using the one-factor and Nemero indexes.The heavy metal elements were screened to cause soil pollution in farmland,such as copper(Cu),zinc(Zn)and plumbago(Pb).Partial least squares regression(PLSR)was integrated with principal component and multiple linear regression.The quadratic inversion model was then established for the contents of the three heavy metal elements.The quantitative inversion of the heavy metal contents was realized in a wide range of soil.Sentinel-2 was used to extract seven vegetation indices,while Zhuhai-1 was to extract the original spectral reflectance of the sample points,as well as four transformed spectral reflectance of sample points.Pearson's correlation coefficient was used to analyze the correlation between spectral reflectance and vegetation index with the content of three heavy metal elements.A comparison was also made on the correlation coefficients,sensitive bands and vegetation indices.The spectral response indicators were modeled as the independent variables,while the measured parameters of the soil heavy metal contents were the dependent variables.The models were finally evaluated using the coefficient of determination(R2),mean absolute error(MAE),and root mean squared error(RMSE).The results showed that excellent performance was achieved in the overall inversion accuracy of the three models.The R2,RMSE,and MAE values of the Pb content inversion model were 0.490,4.66,and 1.92 mg/kg,respectively.The inversion model was obtained for Cu with the R2 of 0.491,the RMSE of 16.85 mg/kg,and the MAE of 3.69 mg/kg.While,the inversion model of Zn was achieved with the R2 of 0.664,the RMSE of 20.63 mg/kg,and the MAE of 9.36 mg/kg.The spatial distribution of soil heavy metal content in farmland was mapped,according to the constructed inversion model of soil heavy metal content.The visualization of the inversion was conducive to the distribution of regional soil heavy metals from a more intuitive perspective.Among them,the Pb element content in soil in most areas was within the risk screening standard;Cu element exceeded the risk screening value of soil pollution in the southwestern and western parts of the test area;The western and southwestern parts of the test area were more seriously contaminated by Zn element,indicating the sporadic distributions in some farmlands in the southeastern part.The Cu and Zn contents in other areas were within the national risk control value of soil pollution.Therefore,the multi-source remote sensing data and spectral response can be expected to synergistically invert the Cu,Zn and Pb content of soil heavy metals.High feasibility and accuracy can also be obtained to investigate soil heavy metal pollution.At the same time,the findings can provide a theoretical basis for monitoring the heavy metal content of soil in large areas.A new idea can also be offered to construct the inverse model of heavy metal content in farmland soil.关键词
土壤/重金属/反演/多源遥感/波谱响应/农田Key words
soils/heavy metals/inversion/multi-source remote sensing/spectral response/farmland分类
农业科技引用本文复制引用
李旭青,顾会涛,丁雪瑶,张文龙,李凌飞,唐瑞尹,陈旭颖,吴艳萍..基于波谱响应特征的雄安新区农田土壤重金属含量反演[J].农业工程学报,2024,40(4):121-128,8.基金项目
河北省"三三三人才工程"资助项目(C20221032) （C20221032）
遥感科学国家重点实验室开放基金项目(OFSLRSS202303) （OFSLRSS202303）
国家科技重大专项雄安新区生态环境高分遥感监测平台应用与示范项目(67-Y50G04-9001-22/23) （67-Y50G04-9001-22/23）
河北省高等学校科学技术研究青年拔尖人才项目(BJ2020056) （BJ2020056）
北华航天工业学院研究生创新资助项目(YKY-2023-66) （YKY-2023-66）
