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Estimating Cadmium Concentration in Agricultural Soils with ZY1-02D Hyperspectral Data: A Comparative Analysis of Spectral Transformations and Machine Learning Models

文献类型：期刊论文

作者Lv, Junwei4; Geng, Jing1,4; Xu, Xuanhong4; Yu, Yong4; Fang, Huajun2,3; Guo, Yifan2; Cheng, Shulan5
刊名AGRICULTURE-BASEL
出版日期2024-09-01
卷号14期号:9页码:17
关键词soil cadmium concentration satellite hyperspectral data spectral transformations machine learning models
DOI10.3390/agriculture14091619
产权排序3
英文摘要The accumulation of cadmium (Cd) in agricultural soils presents a significant threat to crop safety, emphasizing the critical necessity for effective monitoring and management of soil Cd levels. Despite technological advancements, accurately monitoring soil Cd concentrations using satellite hyperspectral technology remains challenging, particularly in efficiently extracting spectral information. In this study, a total of 304 soil samples were collected from agricultural soils surrounding a tungsten mine located in the Xiancha River basin, Jiangxi Province, Southern China. Leveraging hyperspectral data from the ZY1-02D satellite, this research developed a comprehensive framework that evaluates the predictive accuracy of nine spectral transformations across four modeling approaches to estimate soil Cd concentrations. The spectral transformation methods included four logarithmic and reciprocal transformations, two derivative transformations, and three baseline correction and normalization transformations. The four models utilized for predicting soil Cd were partial least squares regression (PLSR), support vector machine (SVM), bidirectional recurrent neural networks (BRNN), and random forest (RF). The results indicated that these spectral transformations markedly enhanced the absorption and reflection features of the spectral curves, accentuating key peaks and troughs. Compared to the original spectral curves, the correlation analysis between the transformed spectra and soil Cd content showed a notable improvement, particularly with derivative transformations. The combination of the first derivative (FD) transformation with the RF model yielded the highest accuracy (R2 = 0.61, RMSE = 0.37 mg/kg, MAE = 0.21 mg/kg). Furthermore, the RF model in multiple spectral transformations exhibited higher suitability for modeling soil Cd content compared to other models. Overall, this research highlights the substantial applicative potential of the ZY1-02D satellite hyperspectral data for detecting soil heavy metals and provides a framework that integrates optimal spectral transformations and modeling techniques to estimate soil Cd contents.
WOS关键词ORGANIC-MATTER CONTENT ; REFLECTANCE SPECTROSCOPY ; HEAVY-METALS ; PREPROCESSING TECHNIQUES ; CONTAMINATION ; PREDICTIONS ; POLLUTION ; ELEMENTS ; RIVER ; RISK
资助项目National Natural Science Foundation of China ; List of Hanging Science and Technology Project of Jinggangshan Agricultural High-tech Industrial Demonstration Zone ; [32101301]
WOS研究方向Agriculture
语种英语
WOS记录号WOS:001323321800001
出版者MDPI
资助机构National Natural Science Foundation of China ; List of Hanging Science and Technology Project of Jinggangshan Agricultural High-tech Industrial Demonstration Zone
源URL[http://ir.igsnrr.ac.cn/handle/311030/210125]  
专题千烟洲站森林生态系统研究中心_外文论文
通讯作者Geng, Jing
作者单位1.Minist Nat Resources, Key Lab Nat Resources Monitoring Trop & Subtrop Ar, Zhuhai 519082, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China
3.Zhongke Jian Inst Ecoenvironm Sci, Jian 343000, Peoples R China
4.Sun Yat Sen Univ, Sch Geospatial Engn & Sci, Zhuhai 519082, Peoples R China
5.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Lv, Junwei,Geng, Jing,Xu, Xuanhong,et al. Estimating Cadmium Concentration in Agricultural Soils with ZY1-02D Hyperspectral Data: A Comparative Analysis of Spectral Transformations and Machine Learning Models[J]. AGRICULTURE-BASEL,2024,14(9):17.
APA Lv, Junwei.,Geng, Jing.,Xu, Xuanhong.,Yu, Yong.,Fang, Huajun.,...&Cheng, Shulan.(2024).Estimating Cadmium Concentration in Agricultural Soils with ZY1-02D Hyperspectral Data: A Comparative Analysis of Spectral Transformations and Machine Learning Models.AGRICULTURE-BASEL,14(9),17.
MLA Lv, Junwei,et al."Estimating Cadmium Concentration in Agricultural Soils with ZY1-02D Hyperspectral Data: A Comparative Analysis of Spectral Transformations and Machine Learning Models".AGRICULTURE-BASEL 14.9(2024):17.

入库方式： OAI收割

来源：地理科学与资源研究所

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