干旱区绿洲农田土壤重金属含量高光谱反演

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Abstract Analysis of the relationship between the content of six heavy metals (As, Hg, Pb, Cr, Zn, and Cu) in soil samples from the Ugan-Kuqa River Oasis in Xinjiang and hyperspectral data was conducted. Feature bands were selected based on the stepwise regression method, and soil heavy metal hyperspectral inversion models were constructed using Geographically Weighted Regression (GWR) and Ordinary Least Squares (OLS). 1) There were differences in the content of the six heavy metal elements in the study area, with the average content of heavy metals in the soil being Zn > Pb > Cr > Cu > As > Hg, all of which did not exceed the national soil background values. The average Pb content in the study area was higher than the local (Xinjiang) soil background value, indicating significant enrichment of Pb in the surface soil layer of the study area; 2) Different spectral transformations enhanced the spectral characteristics of soil heavy metals, but with some differences in intensity, with soil spectra after second-order differential transformation (SD) and first-order cube root differential transformation (CRFD) showing the most significant enhancement compared to the original spectra; 3) From the perspective of model validation, when inverting the content of the six soil heavy metal elements, the R² of GWR was higher than that of OLS, with the R² of Zn approaching 0.8 and that of Cu approaching 0.6, indicating a certain predictive ability of the model, while the R² of As, Hg, Pb, and Cr were still below 0.5, indicating that the predictive ability of the model was not ideal; 4) The spatial distribution of the six soil heavy metal contents predicted by the two models exhibited some spatial differences. Among them, the spatial distribution difference of As was the largest in these two prediction models, while the distribution of the other five heavy metal elements, Hg, Pb, Cr, Zn, and Cu, was relatively uniform. By estimating soil heavy metal contents through hyperspectral reflectance, efficient and rapid inversion of soil heavy metal contents in oasis farmland in arid areas had been achieved, providing reliable technical support for dynamic monitoring of soil heavy metal contents in oasis farmland in arid areas.

Key words： GWR and OLS model heavy metals content in soils hyperspectral spatial distribution characteristics Ugan-Kuqa River oasis

Received: 12 September 2023

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X53

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	Mamat SAWUT
	Abudugheni ABLIZ
	HU Xin

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Mamat SAWUT,Abudugheni ABLIZ,HU Xin. Hyperspectral inversion study of heavy metals content in soils of oasis farmland in arid region[J]. CHINA ENVIRONMENTAL SCIENCECE, 2024, 44(4): 2208-2216.

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http://www.zghjkx.com.cn/EN/ OR http://www.zghjkx.com.cn/EN/Y2024/V44/I4/2208

[1] Liu K, Zhao D, Fang J Y, et al. Estimation of heavy-metal contamination in soil using remote sensing spectroscopy and a statistical approach[J/OL]. Journal of the Indian Society of Remote Sensing, 2017,45(5):805-813.
[2] 张明月,张奇栎,王璐,等.东北黑土区土壤铬含量高光谱反演研究[J]. 遥感技术与应用, 2019,34(2):313-322. Zhang M Y, Zhang Q L, Wang L, et al. Research on chromium retrieval of black soil with hyperspectral imagery in northeast of China[J]. Remote Sensing Technology and Application, 2010,25(2):169- 177.
[3] 江振蓝,杨玉盛,沙晋明.福州市土壤铬含量高光谱预测的GWR模型研究[J]. 生态学报, 2017,37(23):8117-8127. Jiang Z L, Yang Y S, Sha J M. Study on GWR model applied for hyperspectral prediction of soil chromium in Fuzhou City[J]. Acta Ecologica Sinica, 2017,37(23):8117-8127.
[4] 赵玉玲,杨楠楠,张海霞,等.基于高光谱的邯郸市土壤重金属统计估算模型研究[J/OL]. 生态环境学报, 2020,29(4):819-826. Zhao Y L, Yang N N, Zhang H X, et al. Study on the statistical estimation model of soil heavy metals in Handan city based on hyper-spectral[J]. Ecology and Environmental Sciences, 29(4):819- 826.
[5] 滕靖,何政伟,倪忠云,等.西范坪矿区土壤铜元素的高光谱响应与反演模型研究[J]. 光谱学与光谱分析, 2016,36(11):3637-3642. Teng J, He Z W, Ni Z Y, et al. Spectral response and inversion models for prediction of total copper content in soil of Xifanping mining area[J]. Spectroscopy and Spectral Analysis, 2016,36(11):3637-3642.
[6] Yu X, Liu Q, Wang Y, et al. Evaluation of MLSR and PLSR for estimating soil element contents using visible/near-infrared spectroscopy in apple orchards on the Jiaodong peninsula[J/OL]. CATENA, 2016,137:340-349.
[7] Wang F, Gao J, Zha Y. Hyperspectral sensing of heavy metals in soil and vegetation:Feasibility and challenges[J/OL]. ISPRS Journal of Photogrammetry and Remote Sensing, 2018,136:73-84.
[8] LAMINE S, PETROPOULOS G P, BREWER P A, et al.. Heavy metal soil contamination detection using combined geochemistry and field spectroradiometry in the United Kingdom[J/OL]. Sensors, 2019,19(4):762.
[9] KEMPER T, SOMMER S. Estimate of heavy metal contamination in soils after a mining accident using reflectance spectroscopy[J/OL]. Environmental Science & Technology, 2002,36(12):2742-2747.
[10] WANG J, CUI L, GAO W, et al. Prediction of low heavy metal concentrations in agricultural soils using visible and near-infrared reflectance spectroscopy[J/OL]. Geoderma, 2014,216:1-9.
[11] 林楠,刘翰霖,孟祥发,等.基于高光谱的黑土区土壤重金属含量估测[J]. 农业机械学报, 2021,52(3):218-225. Lin N, Liu H L, Meng X F, et al. Hyperspectral estimation of heavy metal contents in black soil region[J]. Transactions of the Chinese Society for Agricultural Machinery, 2021,52(3):218-225.
[12] 陈元鹏,张世文,罗明,等.基于高光谱反演的复垦区土壤重金属含量经验模型优选[J]. 农业机械学报, 2019,50(1):170-179. Chen Y P, Zhang S W, Luo Ming, et al. Empirical model optimization of hyperspectral inversion of heavy metal content in reclamation area[J]. Transactions of the Chinese Society for Agricultural Machinery, 2019,50(1):170-179.
[13] 张秋霞.新郑市高标准基本农田建设区域土壤属性高光谱反演研究[D/OL]. 河南理工大学, 2017. Zhang Q X. Inversion of soil properties with hyperspectral reflectance in well-facilitied capital farmland construction areas of Xinzheng City[D/OL]. Henan Polytechnic University, 2017.
[14] 郭学飞,曹颖,焦润成,等.基于高光谱的北京铁矿区土壤重金属镍元素含量反演研究[J/OL]. 土壤通报, 2021,52(4):960-967. Guo X F, Cao Y, Jiao R C, et al. An inversion of soil nickel contents with hyperspectral in iron mine area of Beijing[J/OL]. Chinese Journal of Soil Science, 2021,52(4):960-967.
[15] 麦尔耶姆·亚森,买买提·沙吾提,尼格拉·塔什甫拉提,等.渭干河-库车河绿洲土壤重金属分布特征与生态风险评价[J]. 农业工程学报, 2017,33(20):226-233. Maieryemu Yasen, Mamat Sawut, Nigela Taxipulati, Yikiliman Abudoumijiti, Ma Chunyue, Ruzimaimaiti Mijiti, Mayila Rehiman, Wang Jingzhe. Distribution of heavy metal pollution and assessment of its potential ecological risks in Ugan-Kuqa River Delta of Xinjiang[J]. Transactions of the Chinese Society of Agricultural Engineering, 2017,33(20):226-233.
[16] 吴明珠,李小梅,沙晋明.亚热带土壤铬元素的高光谱响应和反演模型[J]. 光谱学与光谱分析, 2014,34(6):1660-1666. Wu M Z, Li X M, Sha J M, et al. Spectral inversion models for prediction of total Chromium content in subtropical soil[J]. Spectroscopy and Spectral Analysis, 2014,34(6):1660-1666.
[17] 戴彬,吕建树,战金成,等.山东省典型工业城市土壤重金属来源、空间分布及潜在生态风险评价[J/OL]. 环境科学, 2015,36(2):507- 515. Dai B, Lv J S, Zhan J C, et al. Assessment of sources, spatial distribution and ecological risk of heavy metals in soils in a typical industry-based city of Shandong Province,Eastern China[J/OL]. Environment Science, 2015,36(2):507-515.
[18] 王库.基于地理权重回归模型的土壤有机质空间预测[J/OL]. 土壤通报, 2013,44(1):21-28. Wang K. Spatial estimation of soil organic matter by using geographically weighted regression model[J/OL]. Chinese Journal of Soil Science, 2013,44(1):21-28.
[19] 陈玉真,王峰,王果,等.土壤锌污染及其修复技术研究进展[J/OL]. 福建农业学报, 2012,27(8):901-908. Chen Y Z, Wang F, Wang G, et al. Research advances on Zinc pollution and remediation of soil system[J/OL]. Fujian Journal of Agricultural Sciences, 2012,27(8):901-908.
[20] 李志远,邓帆,贺军亮,等.土壤重金属砷的高光谱估算模型[J]. 光谱学与光谱分析, 2021,41(9):2872-2878. Li Z Y, Deng F, He J L, et al. Hyperspectral estimation model of heavy metal Arsenic in soil[J]. Spectroscopy and Spectral Analysis, 2021,41(9):2872-2878.