How Spectrally Nearby Samples Influence the Inversion of Soil Heavy Metal Copper

Yi Liu, Tiezhu Shi (), Yiyun Chen, Wenyi Zhang, Chao Yang, Yuzhi Tang, Lichao Yuan, Chuang Wang and Wenling Cui
Additional contact information
Yi Liu: School of Public Administration, Guangdong University of Finance & Economics, Guangzhou 510320, China
Tiezhu Shi: School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China
Yiyun Chen: School of Resource and Environmental Science, Wuhan University, Wuhan 430079, China
Wenyi Zhang: School of Public Administration, Guangdong University of Finance & Economics, Guangzhou 510320, China
Chao Yang: School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China
Yuzhi Tang: Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen 518107, China
Lichao Yuan: School of Public Administration, Guangdong University of Finance & Economics, Guangzhou 510320, China
Chuang Wang: Sociology Department, Lakehead University, Thunder Bay, ON P7B 5E1, Canada
Wenling Cui: School of Public Administration, Guangdong University of Finance & Economics, Guangzhou 510320, China

Land, 2025, vol. 14, issue 9, 1-17

Abstract: Monitoring soil heavy metal contamination in urban land to protect human health requires rapid and low-cost methods. Visible and infrared (vis-NIR) spectroscopy shows strong promise for monitoring metals such as copper (Cu). However, an important question is how “spectrally nearby” samples influence Cu estimation models. This study investigates that issue in depth. We collected 250 soil samples from Shenzhen City, China (the world’s tenth-largest city). During building the model, we selected spectrally nearby samples for each validation sample, varying the number of neighbors from 20 to 200 by adding one sample at a time. Results show that, compared with the traditional method, incorporating nearby samples substantially improved Cu prediction: the coefficient of determination in prediction ( R p 2 ) increased from 0.75 to 0.92, and the root mean square error of prediction (RMSEP) decreased from 8.56 to 4.50 mg·kg −1 . The optimal number of nearby samples was 125, representing 62.25% of the dataset. And the performance followed an L-shape curve as the number of neighbors increased—rapid improvement at first, then stabilization. We conclude that using spectrally nearby samples is an effective way to improve vis-NIR Cu estimation models. The optimal number of neighbors should balance model accuracy, robustness, and complexity.

Keywords: copper contamination; soil heavy metal; urban environments; environment monitoring (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2073-445X/14/9/1830/pdf (application/pdf)
https://www.mdpi.com/2073-445X/14/9/1830/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:14:y:2025:i:9:p:1830-:d:1744516

Access Statistics for this article

Land is currently edited by Ms. Carol Ma

More articles in Land from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().